huggingface-course/codeparrot-ds-train · Datasets at Hugging Face

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xray/xray	xarray/plot/facetgrid.py	1	21774	import functools import itertools import warnings import numpy as np from ..core.formatting import format_item from .utils import ( _infer_xy_labels, _process_cmap_cbar_kwargs, import_matplotlib_pyplot, label_from_attrs, ) # Overrides axes.labelsize, xtick.major.size, ytick.major.size # from mpl.rcParams _FONTSIZE = "small" # For major ticks on x, y axes _NTICKS = 5 def _nicetitle(coord, value, maxchar, template): """ Put coord, value in template and truncate at maxchar """ prettyvalue = format_item(value, quote_strings=False) title = template.format(coord=coord, value=prettyvalue) if len(title) > maxchar: title = title[: (maxchar - 3)] + "..." return title class FacetGrid: """ Initialize the matplotlib figure and FacetGrid object. The :class:`FacetGrid` is an object that links a xarray DataArray to a matplotlib figure with a particular structure. In particular, :class:`FacetGrid` is used to draw plots with multiple Axes where each Axes shows the same relationship conditioned on different levels of some dimension. It's possible to condition on up to two variables by assigning variables to the rows and columns of the grid. The general approach to plotting here is called "small multiples", where the same kind of plot is repeated multiple times, and the specific use of small multiples to display the same relationship conditioned on one ore more other variables is often called a "trellis plot". The basic workflow is to initialize the :class:`FacetGrid` object with the DataArray and the variable names that are used to structure the grid. Then plotting functions can be applied to each subset by calling :meth:`FacetGrid.map_dataarray` or :meth:`FacetGrid.map`. Attributes ---------- axes : numpy object array Contains axes in corresponding position, as returned from plt.subplots col_labels : list list of :class:`matplotlib.text.Text` instances corresponding to column titles. row_labels : list list of :class:`matplotlib.text.Text` instances corresponding to row titles. fig : matplotlib.Figure The figure containing all the axes name_dicts : numpy object array Contains dictionaries mapping coordinate names to values. None is used as a sentinel value for axes which should remain empty, ie. sometimes the bottom right grid """ def __init__( self, data, col=None, row=None, col_wrap=None, sharex=True, sharey=True, figsize=None, aspect=1, size=3, subplot_kws=None, ): """ Parameters ---------- data : DataArray xarray DataArray to be plotted row, col : strings Dimesion names that define subsets of the data, which will be drawn on separate facets in the grid. col_wrap : int, optional "Wrap" the column variable at this width, so that the column facets sharex : bool, optional If true, the facets will share x axes sharey : bool, optional If true, the facets will share y axes figsize : tuple, optional A tuple (width, height) of the figure in inches. If set, overrides ``size`` and ``aspect``. aspect : scalar, optional Aspect ratio of each facet, so that ``aspect * size`` gives the width of each facet in inches size : scalar, optional Height (in inches) of each facet. See also: ``aspect`` subplot_kws : dict, optional Dictionary of keyword arguments for matplotlib subplots """ plt = import_matplotlib_pyplot() # Handle corner case of nonunique coordinates rep_col = col is not None and not data[col].to_index().is_unique rep_row = row is not None and not data[row].to_index().is_unique if rep_col or rep_row: raise ValueError( "Coordinates used for faceting cannot " "contain repeated (nonunique) values." ) # single_group is the grouping variable, if there is exactly one if col and row: single_group = False nrow = len(data[row]) ncol = len(data[col]) nfacet = nrow * ncol if col_wrap is not None: warnings.warn("Ignoring col_wrap since both col and row " "were passed") elif row and not col: single_group = row elif not row and col: single_group = col else: raise ValueError("Pass a coordinate name as an argument for row or col") # Compute grid shape if single_group: nfacet = len(data[single_group]) if col: # idea - could add heuristic for nice shapes like 3x4 ncol = nfacet if row: ncol = 1 if col_wrap is not None: # Overrides previous settings ncol = col_wrap nrow = int(np.ceil(nfacet / ncol)) # Set the subplot kwargs subplot_kws = {} if subplot_kws is None else subplot_kws if figsize is None: # Calculate the base figure size with extra horizontal space for a # colorbar cbar_space = 1 figsize = (ncol * size * aspect + cbar_space, nrow * size) fig, axes = plt.subplots( nrow, ncol, sharex=sharex, sharey=sharey, squeeze=False, figsize=figsize, subplot_kw=subplot_kws, ) # Set up the lists of names for the row and column facet variables col_names = list(data[col].values) if col else [] row_names = list(data[row].values) if row else [] if single_group: full = [{single_group: x} for x in data[single_group].values] empty = [None for x in range(nrow * ncol - len(full))] name_dicts = full + empty else: rowcols = itertools.product(row_names, col_names) name_dicts = [{row: r, col: c} for r, c in rowcols] name_dicts = np.array(name_dicts).reshape(nrow, ncol) # Set up the class attributes # --------------------------- # First the public API self.data = data self.name_dicts = name_dicts self.fig = fig self.axes = axes self.row_names = row_names self.col_names = col_names self.figlegend = None # Next the private variables self._single_group = single_group self._nrow = nrow self._row_var = row self._ncol = ncol self._col_var = col self._col_wrap = col_wrap self.row_labels = [None] * nrow self.col_labels = [None] * ncol self._x_var = None self._y_var = None self._cmap_extend = None self._mappables = [] self._finalized = False @property def _left_axes(self): return self.axes[:, 0] @property def _bottom_axes(self): return self.axes[-1, :] def map_dataarray(self, func, x, y, kwargs): """ Apply a plotting function to a 2d facet's subset of the data. This is more convenient and less general than ``FacetGrid.map`` Parameters ---------- func : callable A plotting function with the same signature as a 2d xarray plotting method such as `xarray.plot.imshow` x, y : string Names of the coordinates to plot on x, y axes kwargs : additional keyword arguments to func Returns ------- self : FacetGrid object """ if kwargs.get("cbar_ax", None) is not None: raise ValueError("cbar_ax not supported by FacetGrid.") cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs( func, self.data.values, kwargs ) self._cmap_extend = cmap_params.get("extend") # Order is important func_kwargs = { k: v for k, v in kwargs.items() if k not in {"cmap", "colors", "cbar_kwargs", "levels"} } func_kwargs.update(cmap_params) func_kwargs.update({"add_colorbar": False, "add_labels": False}) # Get x, y labels for the first subplot x, y = _infer_xy_labels( darray=self.data.loc[self.name_dicts.flat[0]], x=x, y=y, imshow=func.__name__ == "imshow", rgb=kwargs.get("rgb", None), ) for d, ax in zip(self.name_dicts.flat, self.axes.flat): # None is the sentinel value if d is not None: subset = self.data.loc[d] mappable = func( subset, x=x, y=y, ax=ax, func_kwargs, _is_facetgrid=True ) self._mappables.append(mappable) self._finalize_grid(x, y) if kwargs.get("add_colorbar", True): self.add_colorbar(cbar_kwargs) return self def map_dataarray_line( self, func, x, y, hue, add_legend=True, _labels=None, kwargs ): from .plot import _infer_line_data for d, ax in zip(self.name_dicts.flat, self.axes.flat): # None is the sentinel value if d is not None: subset = self.data.loc[d] mappable = func( subset, x=x, y=y, ax=ax, hue=hue, add_legend=False, _labels=False, kwargs, ) self._mappables.append(mappable) _, _, hueplt, xlabel, ylabel, huelabel = _infer_line_data( darray=self.data.loc[self.name_dicts.flat[0]], x=x, y=y, hue=hue ) self._hue_var = hueplt self._hue_label = huelabel self._finalize_grid(xlabel, ylabel) if add_legend and hueplt is not None and huelabel is not None: self.add_legend() return self def map_dataset( self, func, x=None, y=None, hue=None, hue_style=None, add_guide=None, kwargs ): from .dataset_plot import _infer_meta_data, _parse_size kwargs["add_guide"] = False kwargs["_is_facetgrid"] = True if kwargs.get("markersize", None): kwargs["size_mapping"] = _parse_size( self.data[kwargs["markersize"]], kwargs.pop("size_norm", None) ) meta_data = _infer_meta_data(self.data, x, y, hue, hue_style, add_guide) kwargs["meta_data"] = meta_data if hue and meta_data["hue_style"] == "continuous": cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs( func, self.data[hue].values, kwargs ) kwargs["meta_data"]["cmap_params"] = cmap_params kwargs["meta_data"]["cbar_kwargs"] = cbar_kwargs for d, ax in zip(self.name_dicts.flat, self.axes.flat): # None is the sentinel value if d is not None: subset = self.data.loc[d] maybe_mappable = func( ds=subset, x=x, y=y, hue=hue, hue_style=hue_style, ax=ax, kwargs ) # TODO: this is needed to get legends to work. # but maybe_mappable is a list in that case :/ self._mappables.append(maybe_mappable) self._finalize_grid(meta_data["xlabel"], meta_data["ylabel"]) if hue: self._hue_label = meta_data.pop("hue_label", None) if meta_data["add_legend"]: self._hue_var = meta_data["hue"] self.add_legend() elif meta_data["add_colorbar"]: self.add_colorbar(label=self._hue_label, cbar_kwargs) return self def _finalize_grid(self, axlabels): """Finalize the annotations and layout.""" if not self._finalized: self.set_axis_labels(axlabels) self.set_titles() self.fig.tight_layout() for ax, namedict in zip(self.axes.flat, self.name_dicts.flat): if namedict is None: ax.set_visible(False) self._finalized = True def add_legend(self, kwargs): figlegend = self.fig.legend( handles=self._mappables[-1], labels=list(self._hue_var.values), title=self._hue_label, loc="center right", kwargs, ) self.figlegend = figlegend # Draw the plot to set the bounding boxes correctly self.fig.draw(self.fig.canvas.get_renderer()) # Calculate and set the new width of the figure so the legend fits legend_width = figlegend.get_window_extent().width / self.fig.dpi figure_width = self.fig.get_figwidth() self.fig.set_figwidth(figure_width + legend_width) # Draw the plot again to get the new transformations self.fig.draw(self.fig.canvas.get_renderer()) # Now calculate how much space we need on the right side legend_width = figlegend.get_window_extent().width / self.fig.dpi space_needed = legend_width / (figure_width + legend_width) + 0.02 # margin = .01 # _space_needed = margin + space_needed right = 1 - space_needed # Place the subplot axes to give space for the legend self.fig.subplots_adjust(right=right) def add_colorbar(self, kwargs): """Draw a colorbar""" kwargs = kwargs.copy() if self._cmap_extend is not None: kwargs.setdefault("extend", self._cmap_extend) # dont pass extend as kwarg if it is in the mappable if hasattr(self._mappables[-1], "extend"): kwargs.pop("extend", None) if "label" not in kwargs: kwargs.setdefault("label", label_from_attrs(self.data)) self.cbar = self.fig.colorbar( self._mappables[-1], ax=list(self.axes.flat), kwargs ) return self def set_axis_labels(self, x_var=None, y_var=None): """Set axis labels on the left column and bottom row of the grid.""" if x_var is not None: if x_var in self.data.coords: self._x_var = x_var self.set_xlabels(label_from_attrs(self.data[x_var])) else: # x_var is a string self.set_xlabels(x_var) if y_var is not None: if y_var in self.data.coords: self._y_var = y_var self.set_ylabels(label_from_attrs(self.data[y_var])) else: self.set_ylabels(y_var) return self def set_xlabels(self, label=None, kwargs): """Label the x axis on the bottom row of the grid.""" if label is None: label = label_from_attrs(self.data[self._x_var]) for ax in self._bottom_axes: ax.set_xlabel(label, kwargs) return self def set_ylabels(self, label=None, kwargs): """Label the y axis on the left column of the grid.""" if label is None: label = label_from_attrs(self.data[self._y_var]) for ax in self._left_axes: ax.set_ylabel(label, kwargs) return self def set_titles(self, template="{coord} = {value}", maxchar=30, size=None, kwargs): """ Draw titles either above each facet or on the grid margins. Parameters ---------- template : string Template for plot titles containing {coord} and {value} maxchar : int Truncate titles at maxchar kwargs : keyword args additional arguments to matplotlib.text Returns ------- self: FacetGrid object """ import matplotlib as mpl if size is None: size = mpl.rcParams["axes.labelsize"] nicetitle = functools.partial(_nicetitle, maxchar=maxchar, template=template) if self._single_group: for d, ax in zip(self.name_dicts.flat, self.axes.flat): # Only label the ones with data if d is not None: coord, value = list(d.items()).pop() title = nicetitle(coord, value, maxchar=maxchar) ax.set_title(title, size=size, kwargs) else: # The row titles on the right edge of the grid for index, (ax, row_name, handle) in enumerate( zip(self.axes[:, -1], self.row_names, self.row_labels) ): title = nicetitle(coord=self._row_var, value=row_name, maxchar=maxchar) if not handle: self.row_labels[index] = ax.annotate( title, xy=(1.02, 0.5), xycoords="axes fraction", rotation=270, ha="left", va="center", kwargs, ) else: handle.set_text(title) # The column titles on the top row for index, (ax, col_name, handle) in enumerate( zip(self.axes[0, :], self.col_names, self.col_labels) ): title = nicetitle(coord=self._col_var, value=col_name, maxchar=maxchar) if not handle: self.col_labels[index] = ax.set_title(title, size=size, kwargs) else: handle.set_text(title) return self def set_ticks(self, max_xticks=_NTICKS, max_yticks=_NTICKS, fontsize=_FONTSIZE): """ Set and control tick behavior Parameters ---------- max_xticks, max_yticks : int, optional Maximum number of labeled ticks to plot on x, y axes fontsize : string or int Font size as used by matplotlib text Returns ------- self : FacetGrid object """ from matplotlib.ticker import MaxNLocator # Both are necessary x_major_locator = MaxNLocator(nbins=max_xticks) y_major_locator = MaxNLocator(nbins=max_yticks) for ax in self.axes.flat: ax.xaxis.set_major_locator(x_major_locator) ax.yaxis.set_major_locator(y_major_locator) for tick in itertools.chain( ax.xaxis.get_major_ticks(), ax.yaxis.get_major_ticks() ): tick.label1.set_fontsize(fontsize) return self def map(self, func, args, kwargs): """ Apply a plotting function to each facet's subset of the data. Parameters ---------- func : callable A plotting function that takes data and keyword arguments. It must plot to the currently active matplotlib Axes and take a `color` keyword argument. If faceting on the `hue` dimension, it must also take a `label` keyword argument. args : strings Column names in self.data that identify variables with data to plot. The data for each variable is passed to `func` in the order the variables are specified in the call. kwargs : keyword arguments All keyword arguments are passed to the plotting function. Returns ------- self : FacetGrid object """ plt = import_matplotlib_pyplot() for ax, namedict in zip(self.axes.flat, self.name_dicts.flat): if namedict is not None: data = self.data.loc[namedict] plt.sca(ax) innerargs = [data[a].values for a in args] maybe_mappable = func(innerargs, *kwargs) # TODO: better way to verify that an artist is mappable? # https://stackoverflow.com/questions/33023036/is-it-possible-to-detect-if-a-matplotlib-artist-is-a-mappable-suitable-for-use-w#33023522 if maybe_mappable and hasattr(maybe_mappable, "autoscale_None"): self._mappables.append(maybe_mappable) self._finalize_grid(args[:2]) return self def _easy_facetgrid( data, plotfunc, kind, x=None, y=None, row=None, col=None, col_wrap=None, sharex=True, sharey=True, aspect=None, size=None, subplot_kws=None, ax=None, figsize=None, kwargs, ): """ Convenience method to call xarray.plot.FacetGrid from 2d plotting methods kwargs are the arguments to 2d plotting method """ if ax is not None: raise ValueError("Can't use axes when making faceted plots.") if aspect is None: aspect = 1 if size is None: size = 3 elif figsize is not None: raise ValueError("cannot provide both `figsize` and `size` arguments") g = FacetGrid( data=data, col=col, row=row, col_wrap=col_wrap, sharex=sharex, sharey=sharey, figsize=figsize, aspect=aspect, size=size, subplot_kws=subplot_kws, ) if kind == "line": return g.map_dataarray_line(plotfunc, x, y, kwargs) if kind == "dataarray": return g.map_dataarray(plotfunc, x, y, kwargs) if kind == "dataset": return g.map_dataset(plotfunc, x, y, kwargs)	apache-2.0
yunfeilu/scikit-learn	sklearn/cluster/tests/test_k_means.py	63	26190	"""Testing for K-means""" import sys import numpy as np from scipy import sparse as sp from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import SkipTest from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_raises_regexp from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_less from sklearn.utils.testing import assert_warns from sklearn.utils.testing import if_safe_multiprocessing_with_blas from sklearn.utils.validation import DataConversionWarning from sklearn.utils.extmath import row_norms from sklearn.metrics.cluster import v_measure_score from sklearn.cluster import KMeans, k_means from sklearn.cluster import MiniBatchKMeans from sklearn.cluster.k_means_ import _labels_inertia from sklearn.cluster.k_means_ import _mini_batch_step from sklearn.datasets.samples_generator import make_blobs from sklearn.externals.six.moves import cStringIO as StringIO # non centered, sparse centers to check the centers = np.array([ [0.0, 5.0, 0.0, 0.0, 0.0], [1.0, 1.0, 4.0, 0.0, 0.0], [1.0, 0.0, 0.0, 5.0, 1.0], ]) n_samples = 100 n_clusters, n_features = centers.shape X, true_labels = make_blobs(n_samples=n_samples, centers=centers, cluster_std=1., random_state=42) X_csr = sp.csr_matrix(X) def test_kmeans_dtype(): rnd = np.random.RandomState(0) X = rnd.normal(size=(40, 2)) X = (X * 10).astype(np.uint8) km = KMeans(n_init=1).fit(X) pred_x = assert_warns(DataConversionWarning, km.predict, X) assert_array_equal(km.labels_, pred_x) def test_labels_assignment_and_inertia(): # pure numpy implementation as easily auditable reference gold # implementation rng = np.random.RandomState(42) noisy_centers = centers + rng.normal(size=centers.shape) labels_gold = - np.ones(n_samples, dtype=np.int) mindist = np.empty(n_samples) mindist.fill(np.infty) for center_id in range(n_clusters): dist = np.sum((X - noisy_centers[center_id]) 2, axis=1) labels_gold[dist < mindist] = center_id mindist = np.minimum(dist, mindist) inertia_gold = mindist.sum() assert_true((mindist >= 0.0).all()) assert_true((labels_gold != -1).all()) # perform label assignment using the dense array input x_squared_norms = (X 2).sum(axis=1) labels_array, inertia_array = _labels_inertia( X, x_squared_norms, noisy_centers) assert_array_almost_equal(inertia_array, inertia_gold) assert_array_equal(labels_array, labels_gold) # perform label assignment using the sparse CSR input x_squared_norms_from_csr = row_norms(X_csr, squared=True) labels_csr, inertia_csr = _labels_inertia( X_csr, x_squared_norms_from_csr, noisy_centers) assert_array_almost_equal(inertia_csr, inertia_gold) assert_array_equal(labels_csr, labels_gold) def test_minibatch_update_consistency(): # Check that dense and sparse minibatch update give the same results rng = np.random.RandomState(42) old_centers = centers + rng.normal(size=centers.shape) new_centers = old_centers.copy() new_centers_csr = old_centers.copy() counts = np.zeros(new_centers.shape[0], dtype=np.int32) counts_csr = np.zeros(new_centers.shape[0], dtype=np.int32) x_squared_norms = (X 2).sum(axis=1) x_squared_norms_csr = row_norms(X_csr, squared=True) buffer = np.zeros(centers.shape[1], dtype=np.double) buffer_csr = np.zeros(centers.shape[1], dtype=np.double) # extract a small minibatch X_mb = X[:10] X_mb_csr = X_csr[:10] x_mb_squared_norms = x_squared_norms[:10] x_mb_squared_norms_csr = x_squared_norms_csr[:10] # step 1: compute the dense minibatch update old_inertia, incremental_diff = _mini_batch_step( X_mb, x_mb_squared_norms, new_centers, counts, buffer, 1, None, random_reassign=False) assert_greater(old_inertia, 0.0) # compute the new inertia on the same batch to check that it decreased labels, new_inertia = _labels_inertia( X_mb, x_mb_squared_norms, new_centers) assert_greater(new_inertia, 0.0) assert_less(new_inertia, old_inertia) # check that the incremental difference computation is matching the # final observed value effective_diff = np.sum((new_centers - old_centers) 2) assert_almost_equal(incremental_diff, effective_diff) # step 2: compute the sparse minibatch update old_inertia_csr, incremental_diff_csr = _mini_batch_step( X_mb_csr, x_mb_squared_norms_csr, new_centers_csr, counts_csr, buffer_csr, 1, None, random_reassign=False) assert_greater(old_inertia_csr, 0.0) # compute the new inertia on the same batch to check that it decreased labels_csr, new_inertia_csr = _labels_inertia( X_mb_csr, x_mb_squared_norms_csr, new_centers_csr) assert_greater(new_inertia_csr, 0.0) assert_less(new_inertia_csr, old_inertia_csr) # check that the incremental difference computation is matching the # final observed value effective_diff = np.sum((new_centers_csr - old_centers) 2) assert_almost_equal(incremental_diff_csr, effective_diff) # step 3: check that sparse and dense updates lead to the same results assert_array_equal(labels, labels_csr) assert_array_almost_equal(new_centers, new_centers_csr) assert_almost_equal(incremental_diff, incremental_diff_csr) assert_almost_equal(old_inertia, old_inertia_csr) assert_almost_equal(new_inertia, new_inertia_csr) def _check_fitted_model(km): # check that the number of clusters centers and distinct labels match # the expectation centers = km.cluster_centers_ assert_equal(centers.shape, (n_clusters, n_features)) labels = km.labels_ assert_equal(np.unique(labels).shape[0], n_clusters) # check that the labels assignment are perfect (up to a permutation) assert_equal(v_measure_score(true_labels, labels), 1.0) assert_greater(km.inertia_, 0.0) # check error on dataset being too small assert_raises(ValueError, km.fit, [[0., 1.]]) def test_k_means_plus_plus_init(): km = KMeans(init="k-means++", n_clusters=n_clusters, random_state=42).fit(X) _check_fitted_model(km) def test_k_means_new_centers(): # Explore the part of the code where a new center is reassigned X = np.array([[0, 0, 1, 1], [0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 0, 0]]) labels = [0, 1, 2, 1, 1, 2] bad_centers = np.array([[+0, 1, 0, 0], [.2, 0, .2, .2], [+0, 0, 0, 0]]) km = KMeans(n_clusters=3, init=bad_centers, n_init=1, max_iter=10, random_state=1) for this_X in (X, sp.coo_matrix(X)): km.fit(this_X) this_labels = km.labels_ # Reorder the labels so that the first instance is in cluster 0, # the second in cluster 1, ... this_labels = np.unique(this_labels, return_index=True)[1][this_labels] np.testing.assert_array_equal(this_labels, labels) @if_safe_multiprocessing_with_blas def test_k_means_plus_plus_init_2_jobs(): if sys.version_info[:2] < (3, 4): raise SkipTest( "Possible multi-process bug with some BLAS under Python < 3.4") km = KMeans(init="k-means++", n_clusters=n_clusters, n_jobs=2, random_state=42).fit(X) _check_fitted_model(km) def test_k_means_precompute_distances_flag(): # check that a warning is raised if the precompute_distances flag is not # supported km = KMeans(precompute_distances="wrong") assert_raises(ValueError, km.fit, X) def test_k_means_plus_plus_init_sparse(): km = KMeans(init="k-means++", n_clusters=n_clusters, random_state=42) km.fit(X_csr) _check_fitted_model(km) def test_k_means_random_init(): km = KMeans(init="random", n_clusters=n_clusters, random_state=42) km.fit(X) _check_fitted_model(km) def test_k_means_random_init_sparse(): km = KMeans(init="random", n_clusters=n_clusters, random_state=42) km.fit(X_csr) _check_fitted_model(km) def test_k_means_plus_plus_init_not_precomputed(): km = KMeans(init="k-means++", n_clusters=n_clusters, random_state=42, precompute_distances=False).fit(X) _check_fitted_model(km) def test_k_means_random_init_not_precomputed(): km = KMeans(init="random", n_clusters=n_clusters, random_state=42, precompute_distances=False).fit(X) _check_fitted_model(km) def test_k_means_perfect_init(): km = KMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42, n_init=1) km.fit(X) _check_fitted_model(km) def test_k_means_n_init(): rnd = np.random.RandomState(0) X = rnd.normal(size=(40, 2)) # two regression tests on bad n_init argument # previous bug: n_init <= 0 threw non-informative TypeError (#3858) assert_raises_regexp(ValueError, "n_init", KMeans(n_init=0).fit, X) assert_raises_regexp(ValueError, "n_init", KMeans(n_init=-1).fit, X) def test_k_means_fortran_aligned_data(): # Check the KMeans will work well, even if X is a fortran-aligned data. X = np.asfortranarray([[0, 0], [0, 1], [0, 1]]) centers = np.array([[0, 0], [0, 1]]) labels = np.array([0, 1, 1]) km = KMeans(n_init=1, init=centers, precompute_distances=False, random_state=42) km.fit(X) assert_array_equal(km.cluster_centers_, centers) assert_array_equal(km.labels_, labels) def test_mb_k_means_plus_plus_init_dense_array(): mb_k_means = MiniBatchKMeans(init="k-means++", n_clusters=n_clusters, random_state=42) mb_k_means.fit(X) _check_fitted_model(mb_k_means) def test_mb_kmeans_verbose(): mb_k_means = MiniBatchKMeans(init="k-means++", n_clusters=n_clusters, random_state=42, verbose=1) old_stdout = sys.stdout sys.stdout = StringIO() try: mb_k_means.fit(X) finally: sys.stdout = old_stdout def test_mb_k_means_plus_plus_init_sparse_matrix(): mb_k_means = MiniBatchKMeans(init="k-means++", n_clusters=n_clusters, random_state=42) mb_k_means.fit(X_csr) _check_fitted_model(mb_k_means) def test_minibatch_init_with_large_k(): mb_k_means = MiniBatchKMeans(init='k-means++', init_size=10, n_clusters=20) # Check that a warning is raised, as the number clusters is larger # than the init_size assert_warns(RuntimeWarning, mb_k_means.fit, X) def test_minibatch_k_means_random_init_dense_array(): # increase n_init to make random init stable enough mb_k_means = MiniBatchKMeans(init="random", n_clusters=n_clusters, random_state=42, n_init=10).fit(X) _check_fitted_model(mb_k_means) def test_minibatch_k_means_random_init_sparse_csr(): # increase n_init to make random init stable enough mb_k_means = MiniBatchKMeans(init="random", n_clusters=n_clusters, random_state=42, n_init=10).fit(X_csr) _check_fitted_model(mb_k_means) def test_minibatch_k_means_perfect_init_dense_array(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42, n_init=1).fit(X) _check_fitted_model(mb_k_means) def test_minibatch_k_means_init_multiple_runs_with_explicit_centers(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42, n_init=10) assert_warns(RuntimeWarning, mb_k_means.fit, X) def test_minibatch_k_means_perfect_init_sparse_csr(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42, n_init=1).fit(X_csr) _check_fitted_model(mb_k_means) def test_minibatch_sensible_reassign_fit(): # check if identical initial clusters are reassigned # also a regression test for when there are more desired reassignments than # samples. zeroed_X, true_labels = make_blobs(n_samples=100, centers=5, cluster_std=1., random_state=42) zeroed_X[::2, :] = 0 mb_k_means = MiniBatchKMeans(n_clusters=20, batch_size=10, random_state=42, init="random") mb_k_means.fit(zeroed_X) # there should not be too many exact zero cluster centers assert_greater(mb_k_means.cluster_centers_.any(axis=1).sum(), 10) # do the same with batch-size > X.shape[0] (regression test) mb_k_means = MiniBatchKMeans(n_clusters=20, batch_size=201, random_state=42, init="random") mb_k_means.fit(zeroed_X) # there should not be too many exact zero cluster centers assert_greater(mb_k_means.cluster_centers_.any(axis=1).sum(), 10) def test_minibatch_sensible_reassign_partial_fit(): zeroed_X, true_labels = make_blobs(n_samples=n_samples, centers=5, cluster_std=1., random_state=42) zeroed_X[::2, :] = 0 mb_k_means = MiniBatchKMeans(n_clusters=20, random_state=42, init="random") for i in range(100): mb_k_means.partial_fit(zeroed_X) # there should not be too many exact zero cluster centers assert_greater(mb_k_means.cluster_centers_.any(axis=1).sum(), 10) def test_minibatch_reassign(): # Give a perfect initialization, but a large reassignment_ratio, # as a result all the centers should be reassigned and the model # should not longer be good for this_X in (X, X_csr): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, random_state=42) mb_k_means.fit(this_X) score_before = mb_k_means.score(this_X) try: old_stdout = sys.stdout sys.stdout = StringIO() # Turn on verbosity to smoke test the display code _mini_batch_step(this_X, (X 2).sum(axis=1), mb_k_means.cluster_centers_, mb_k_means.counts_, np.zeros(X.shape[1], np.double), False, distances=np.zeros(X.shape[0]), random_reassign=True, random_state=42, reassignment_ratio=1, verbose=True) finally: sys.stdout = old_stdout assert_greater(score_before, mb_k_means.score(this_X)) # Give a perfect initialization, with a small reassignment_ratio, # no center should be reassigned for this_X in (X, X_csr): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, init=centers.copy(), random_state=42, n_init=1) mb_k_means.fit(this_X) clusters_before = mb_k_means.cluster_centers_ # Turn on verbosity to smoke test the display code _mini_batch_step(this_X, (X ** 2).sum(axis=1), mb_k_means.cluster_centers_, mb_k_means.counts_, np.zeros(X.shape[1], np.double), False, distances=np.zeros(X.shape[0]), random_reassign=True, random_state=42, reassignment_ratio=1e-15) assert_array_almost_equal(clusters_before, mb_k_means.cluster_centers_) def test_minibatch_with_many_reassignments(): # Test for the case that the number of clusters to reassign is bigger # than the batch_size n_samples = 550 rnd = np.random.RandomState(42) X = rnd.uniform(size=(n_samples, 10)) # Check that the fit works if n_clusters is bigger than the batch_size. # Run the test with 550 clusters and 550 samples, because it turned out # that this values ensure that the number of clusters to reassign # is always bigger than the batch_size n_clusters = 550 MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, init_size=n_samples, random_state=42).fit(X) def test_sparse_mb_k_means_callable_init(): def test_init(X, k, random_state): return centers # Small test to check that giving the wrong number of centers # raises a meaningful error assert_raises(ValueError, MiniBatchKMeans(init=test_init, random_state=42).fit, X_csr) # Now check that the fit actually works mb_k_means = MiniBatchKMeans(n_clusters=3, init=test_init, random_state=42).fit(X_csr) _check_fitted_model(mb_k_means) def test_mini_batch_k_means_random_init_partial_fit(): km = MiniBatchKMeans(n_clusters=n_clusters, init="random", random_state=42) # use the partial_fit API for online learning for X_minibatch in np.array_split(X, 10): km.partial_fit(X_minibatch) # compute the labeling on the complete dataset labels = km.predict(X) assert_equal(v_measure_score(true_labels, labels), 1.0) def test_minibatch_default_init_size(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, batch_size=10, random_state=42, n_init=1).fit(X) assert_equal(mb_k_means.init_size_, 3 * mb_k_means.batch_size) _check_fitted_model(mb_k_means) def test_minibatch_tol(): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=10, random_state=42, tol=.01).fit(X) _check_fitted_model(mb_k_means) def test_minibatch_set_init_size(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, init_size=666, random_state=42, n_init=1).fit(X) assert_equal(mb_k_means.init_size, 666) assert_equal(mb_k_means.init_size_, n_samples) _check_fitted_model(mb_k_means) def test_k_means_invalid_init(): km = KMeans(init="invalid", n_init=1, n_clusters=n_clusters) assert_raises(ValueError, km.fit, X) def test_mini_match_k_means_invalid_init(): km = MiniBatchKMeans(init="invalid", n_init=1, n_clusters=n_clusters) assert_raises(ValueError, km.fit, X) def test_k_means_copyx(): # Check if copy_x=False returns nearly equal X after de-centering. my_X = X.copy() km = KMeans(copy_x=False, n_clusters=n_clusters, random_state=42) km.fit(my_X) _check_fitted_model(km) # check if my_X is centered assert_array_almost_equal(my_X, X) def test_k_means_non_collapsed(): # Check k_means with a bad initialization does not yield a singleton # Starting with bad centers that are quickly ignored should not # result in a repositioning of the centers to the center of mass that # would lead to collapsed centers which in turns make the clustering # dependent of the numerical unstabilities. my_X = np.array([[1.1, 1.1], [0.9, 1.1], [1.1, 0.9], [0.9, 1.1]]) array_init = np.array([[1.0, 1.0], [5.0, 5.0], [-5.0, -5.0]]) km = KMeans(init=array_init, n_clusters=3, random_state=42, n_init=1) km.fit(my_X) # centers must not been collapsed assert_equal(len(np.unique(km.labels_)), 3) centers = km.cluster_centers_ assert_true(np.linalg.norm(centers[0] - centers[1]) >= 0.1) assert_true(np.linalg.norm(centers[0] - centers[2]) >= 0.1) assert_true(np.linalg.norm(centers[1] - centers[2]) >= 0.1) def test_predict(): km = KMeans(n_clusters=n_clusters, random_state=42) km.fit(X) # sanity check: predict centroid labels pred = km.predict(km.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # sanity check: re-predict labeling for training set samples pred = km.predict(X) assert_array_equal(pred, km.labels_) # re-predict labels for training set using fit_predict pred = km.fit_predict(X) assert_array_equal(pred, km.labels_) def test_score(): km1 = KMeans(n_clusters=n_clusters, max_iter=1, random_state=42) s1 = km1.fit(X).score(X) km2 = KMeans(n_clusters=n_clusters, max_iter=10, random_state=42) s2 = km2.fit(X).score(X) assert_greater(s2, s1) def test_predict_minibatch_dense_input(): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, random_state=40).fit(X) # sanity check: predict centroid labels pred = mb_k_means.predict(mb_k_means.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # sanity check: re-predict labeling for training set samples pred = mb_k_means.predict(X) assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_) def test_predict_minibatch_kmeanspp_init_sparse_input(): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, init='k-means++', n_init=10).fit(X_csr) # sanity check: re-predict labeling for training set samples assert_array_equal(mb_k_means.predict(X_csr), mb_k_means.labels_) # sanity check: predict centroid labels pred = mb_k_means.predict(mb_k_means.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # check that models trained on sparse input also works for dense input at # predict time assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_) def test_predict_minibatch_random_init_sparse_input(): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, init='random', n_init=10).fit(X_csr) # sanity check: re-predict labeling for training set samples assert_array_equal(mb_k_means.predict(X_csr), mb_k_means.labels_) # sanity check: predict centroid labels pred = mb_k_means.predict(mb_k_means.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # check that models trained on sparse input also works for dense input at # predict time assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_) def test_input_dtypes(): X_list = [[0, 0], [10, 10], [12, 9], [-1, 1], [2, 0], [8, 10]] X_int = np.array(X_list, dtype=np.int32) X_int_csr = sp.csr_matrix(X_int) init_int = X_int[:2] fitted_models = [ KMeans(n_clusters=2).fit(X_list), KMeans(n_clusters=2).fit(X_int), KMeans(n_clusters=2, init=init_int, n_init=1).fit(X_list), KMeans(n_clusters=2, init=init_int, n_init=1).fit(X_int), # mini batch kmeans is very unstable on such a small dataset hence # we use many inits MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit(X_list), MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit(X_int), MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit(X_int_csr), MiniBatchKMeans(n_clusters=2, batch_size=2, init=init_int, n_init=1).fit(X_list), MiniBatchKMeans(n_clusters=2, batch_size=2, init=init_int, n_init=1).fit(X_int), MiniBatchKMeans(n_clusters=2, batch_size=2, init=init_int, n_init=1).fit(X_int_csr), ] expected_labels = [0, 1, 1, 0, 0, 1] scores = np.array([v_measure_score(expected_labels, km.labels_) for km in fitted_models]) assert_array_equal(scores, np.ones(scores.shape[0])) def test_transform(): km = KMeans(n_clusters=n_clusters) km.fit(X) X_new = km.transform(km.cluster_centers_) for c in range(n_clusters): assert_equal(X_new[c, c], 0) for c2 in range(n_clusters): if c != c2: assert_greater(X_new[c, c2], 0) def test_fit_transform(): X1 = KMeans(n_clusters=3, random_state=51).fit(X).transform(X) X2 = KMeans(n_clusters=3, random_state=51).fit_transform(X) assert_array_equal(X1, X2) def test_n_init(): # Check that increasing the number of init increases the quality n_runs = 5 n_init_range = [1, 5, 10] inertia = np.zeros((len(n_init_range), n_runs)) for i, n_init in enumerate(n_init_range): for j in range(n_runs): km = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, random_state=j).fit(X) inertia[i, j] = km.inertia_ inertia = inertia.mean(axis=1) failure_msg = ("Inertia %r should be decreasing" " when n_init is increasing.") % list(inertia) for i in range(len(n_init_range) - 1): assert_true(inertia[i] >= inertia[i + 1], failure_msg) def test_k_means_function(): # test calling the k_means function directly # catch output old_stdout = sys.stdout sys.stdout = StringIO() try: cluster_centers, labels, inertia = k_means(X, n_clusters=n_clusters, verbose=True) finally: sys.stdout = old_stdout centers = cluster_centers assert_equal(centers.shape, (n_clusters, n_features)) labels = labels assert_equal(np.unique(labels).shape[0], n_clusters) # check that the labels assignment are perfect (up to a permutation) assert_equal(v_measure_score(true_labels, labels), 1.0) assert_greater(inertia, 0.0) # check warning when centers are passed assert_warns(RuntimeWarning, k_means, X, n_clusters=n_clusters, init=centers) # to many clusters desired assert_raises(ValueError, k_means, X, n_clusters=X.shape[0] + 1) def test_x_squared_norms_init_centroids(): """Test that x_squared_norms can be None in _init_centroids""" from sklearn.cluster.k_means_ import _init_centroids X_norms = np.sum(X**2, axis=1) precompute = _init_centroids( X, 3, "k-means++", random_state=0, x_squared_norms=X_norms) assert_array_equal( precompute, _init_centroids(X, 3, "k-means++", random_state=0))	bsd-3-clause
wlamond/scikit-learn	sklearn/metrics/classification.py	4	72788	"""Metrics to assess performance on classification task given class prediction Functions named as ``_score`` return a scalar value to maximize: the higher the better Function named as ``_error`` or ``_loss`` return a scalar value to minimize: the lower the better """ # Authors: Alexandre Gramfort <[email protected]> # Mathieu Blondel <[email protected]> # Olivier Grisel <[email protected]> # Arnaud Joly <[email protected]> # Jochen Wersdorfer <[email protected]> # Lars Buitinck # Joel Nothman <[email protected]> # Noel Dawe <[email protected]> # Jatin Shah <[email protected]> # Saurabh Jha <[email protected]> # Bernardo Stein <[email protected]> # License: BSD 3 clause from __future__ import division import warnings import numpy as np from scipy.sparse import coo_matrix from scipy.sparse import csr_matrix from ..preprocessing import LabelBinarizer, label_binarize from ..preprocessing import LabelEncoder from ..utils import assert_all_finite from ..utils import check_array from ..utils import check_consistent_length from ..utils import column_or_1d from ..utils.multiclass import unique_labels from ..utils.multiclass import type_of_target from ..utils.validation import _num_samples from ..utils.sparsefuncs import count_nonzero from ..utils.fixes import bincount from ..exceptions import UndefinedMetricWarning def _check_targets(y_true, y_pred): """Check that y_true and y_pred belong to the same classification task This converts multiclass or binary types to a common shape, and raises a ValueError for a mix of multilabel and multiclass targets, a mix of multilabel formats, for the presence of continuous-valued or multioutput targets, or for targets of different lengths. Column vectors are squeezed to 1d, while multilabel formats are returned as CSR sparse label indicators. Parameters ---------- y_true : array-like y_pred : array-like Returns ------- type_true : one of {'multilabel-indicator', 'multiclass', 'binary'} The type of the true target data, as output by ``utils.multiclass.type_of_target`` y_true : array or indicator matrix y_pred : array or indicator matrix """ check_consistent_length(y_true, y_pred) type_true = type_of_target(y_true) type_pred = type_of_target(y_pred) y_type = set([type_true, type_pred]) if y_type == set(["binary", "multiclass"]): y_type = set(["multiclass"]) if len(y_type) > 1: raise ValueError("Can't handle mix of {0} and {1}" "".format(type_true, type_pred)) # We can't have more than one value on y_type => The set is no more needed y_type = y_type.pop() # No metrics support "multiclass-multioutput" format if (y_type not in ["binary", "multiclass", "multilabel-indicator"]): raise ValueError("{0} is not supported".format(y_type)) if y_type in ["binary", "multiclass"]: y_true = column_or_1d(y_true) y_pred = column_or_1d(y_pred) if y_type == "binary": unique_values = np.union1d(y_true, y_pred) if len(unique_values) > 2: y_type = "multiclass" if y_type.startswith('multilabel'): y_true = csr_matrix(y_true) y_pred = csr_matrix(y_pred) y_type = 'multilabel-indicator' return y_type, y_true, y_pred def _weighted_sum(sample_score, sample_weight, normalize=False): if normalize: return np.average(sample_score, weights=sample_weight) elif sample_weight is not None: return np.dot(sample_score, sample_weight) else: return sample_score.sum() def accuracy_score(y_true, y_pred, normalize=True, sample_weight=None): """Accuracy classification score. In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must exactly* match the corresponding set of labels in y_true. Read more in the :ref:`User Guide <accuracy_score>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) labels. y_pred : 1d array-like, or label indicator array / sparse matrix Predicted labels, as returned by a classifier. normalize : bool, optional (default=True) If ``False``, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- score : float If ``normalize == True``, return the correctly classified samples (float), else it returns the number of correctly classified samples (int). The best performance is 1 with ``normalize == True`` and the number of samples with ``normalize == False``. See also -------- jaccard_similarity_score, hamming_loss, zero_one_loss Notes ----- In binary and multiclass classification, this function is equal to the ``jaccard_similarity_score`` function. Examples -------- >>> import numpy as np >>> from sklearn.metrics import accuracy_score >>> y_pred = [0, 2, 1, 3] >>> y_true = [0, 1, 2, 3] >>> accuracy_score(y_true, y_pred) 0.5 >>> accuracy_score(y_true, y_pred, normalize=False) 2 In the multilabel case with binary label indicators: >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2))) 0.5 """ # Compute accuracy for each possible representation y_type, y_true, y_pred = _check_targets(y_true, y_pred) if y_type.startswith('multilabel'): differing_labels = count_nonzero(y_true - y_pred, axis=1) score = differing_labels == 0 else: score = y_true == y_pred return _weighted_sum(score, sample_weight, normalize) def confusion_matrix(y_true, y_pred, labels=None, sample_weight=None): """Compute confusion matrix to evaluate the accuracy of a classification By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}` is equal to the number of observations known to be in group :math:`i` but predicted to be in group :math:`j`. Thus in binary classification, the count of true negatives is :math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is :math:`C_{1,1}` and false positives is :math:`C_{0,1}`. Read more in the :ref:`User Guide <confusion_matrix>`. Parameters ---------- y_true : array, shape = [n_samples] Ground truth (correct) target values. y_pred : array, shape = [n_samples] Estimated targets as returned by a classifier. labels : array, shape = [n_classes], optional List of labels to index the matrix. This may be used to reorder or select a subset of labels. If none is given, those that appear at least once in ``y_true`` or ``y_pred`` are used in sorted order. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- C : array, shape = [n_classes, n_classes] Confusion matrix References ---------- .. [1] `Wikipedia entry for the Confusion matrix <https://en.wikipedia.org/wiki/Confusion_matrix>`_ Examples -------- >>> from sklearn.metrics import confusion_matrix >>> y_true = [2, 0, 2, 2, 0, 1] >>> y_pred = [0, 0, 2, 2, 0, 2] >>> confusion_matrix(y_true, y_pred) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]]) >>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"] >>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"] >>> confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"]) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]]) In the binary case, we can extract true positives, etc as follows: >>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel() >>> (tn, fp, fn, tp) (0, 2, 1, 1) """ y_type, y_true, y_pred = _check_targets(y_true, y_pred) if y_type not in ("binary", "multiclass"): raise ValueError("%s is not supported" % y_type) if labels is None: labels = unique_labels(y_true, y_pred) else: labels = np.asarray(labels) if np.all([l not in y_true for l in labels]): raise ValueError("At least one label specified must be in y_true") if sample_weight is None: sample_weight = np.ones(y_true.shape[0], dtype=np.int) else: sample_weight = np.asarray(sample_weight) check_consistent_length(sample_weight, y_true, y_pred) n_labels = labels.size label_to_ind = dict((y, x) for x, y in enumerate(labels)) # convert yt, yp into index y_pred = np.array([label_to_ind.get(x, n_labels + 1) for x in y_pred]) y_true = np.array([label_to_ind.get(x, n_labels + 1) for x in y_true]) # intersect y_pred, y_true with labels, eliminate items not in labels ind = np.logical_and(y_pred < n_labels, y_true < n_labels) y_pred = y_pred[ind] y_true = y_true[ind] # also eliminate weights of eliminated items sample_weight = sample_weight[ind] CM = coo_matrix((sample_weight, (y_true, y_pred)), shape=(n_labels, n_labels) ).toarray() return CM def cohen_kappa_score(y1, y2, labels=None, weights=None, sample_weight=None): """Cohen's kappa: a statistic that measures inter-annotator agreement. This function computes Cohen's kappa [1]_, a score that expresses the level of agreement between two annotators on a classification problem. It is defined as .. math:: \kappa = (p_o - p_e) / (1 - p_e) where :math:`p_o` is the empirical probability of agreement on the label assigned to any sample (the observed agreement ratio), and :math:`p_e` is the expected agreement when both annotators assign labels randomly. :math:`p_e` is estimated using a per-annotator empirical prior over the class labels [2]_. Read more in the :ref:`User Guide <cohen_kappa>`. Parameters ---------- y1 : array, shape = [n_samples] Labels assigned by the first annotator. y2 : array, shape = [n_samples] Labels assigned by the second annotator. The kappa statistic is symmetric, so swapping ``y1`` and ``y2`` doesn't change the value. labels : array, shape = [n_classes], optional List of labels to index the matrix. This may be used to select a subset of labels. If None, all labels that appear at least once in ``y1`` or ``y2`` are used. weights : str, optional List of weighting type to calculate the score. None means no weighted; "linear" means linear weighted; "quadratic" means quadratic weighted. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- kappa : float The kappa statistic, which is a number between -1 and 1. The maximum value means complete agreement; zero or lower means chance agreement. References ---------- .. [1] J. Cohen (1960). "A coefficient of agreement for nominal scales". Educational and Psychological Measurement 20(1):37-46. doi:10.1177/001316446002000104. .. [2] `R. Artstein and M. Poesio (2008). "Inter-coder agreement for computational linguistics". Computational Linguistics 34(4):555-596. <http://www.mitpressjournals.org/doi/abs/10.1162/coli.07-034-R2#.V0J1MJMrIWo>`_ .. [3] `Wikipedia entry for the Cohen's kappa. <https://en.wikipedia.org/wiki/Cohen%27s_kappa>`_ """ confusion = confusion_matrix(y1, y2, labels=labels, sample_weight=sample_weight) n_classes = confusion.shape[0] sum0 = np.sum(confusion, axis=0) sum1 = np.sum(confusion, axis=1) expected = np.outer(sum0, sum1) / np.sum(sum0) if weights is None: w_mat = np.ones([n_classes, n_classes], dtype=np.int) w_mat.flat[:: n_classes + 1] = 0 elif weights == "linear" or weights == "quadratic": w_mat = np.zeros([n_classes, n_classes], dtype=np.int) w_mat += np.arange(n_classes) if weights == "linear": w_mat = np.abs(w_mat - w_mat.T) else: w_mat = (w_mat - w_mat.T) ** 2 else: raise ValueError("Unknown kappa weighting type.") k = np.sum(w_mat * confusion) / np.sum(w_mat * expected) return 1 - k def jaccard_similarity_score(y_true, y_pred, normalize=True, sample_weight=None): """Jaccard similarity coefficient score The Jaccard index [1], or Jaccard similarity coefficient, defined as the size of the intersection divided by the size of the union of two label sets, is used to compare set of predicted labels for a sample to the corresponding set of labels in ``y_true``. Read more in the :ref:`User Guide <jaccard_similarity_score>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) labels. y_pred : 1d array-like, or label indicator array / sparse matrix Predicted labels, as returned by a classifier. normalize : bool, optional (default=True) If ``False``, return the sum of the Jaccard similarity coefficient over the sample set. Otherwise, return the average of Jaccard similarity coefficient. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- score : float If ``normalize == True``, return the average Jaccard similarity coefficient, else it returns the sum of the Jaccard similarity coefficient over the sample set. The best performance is 1 with ``normalize == True`` and the number of samples with ``normalize == False``. See also -------- accuracy_score, hamming_loss, zero_one_loss Notes ----- In binary and multiclass classification, this function is equivalent to the ``accuracy_score``. It differs in the multilabel classification problem. References ---------- .. [1] `Wikipedia entry for the Jaccard index <https://en.wikipedia.org/wiki/Jaccard_index>`_ Examples -------- >>> import numpy as np >>> from sklearn.metrics import jaccard_similarity_score >>> y_pred = [0, 2, 1, 3] >>> y_true = [0, 1, 2, 3] >>> jaccard_similarity_score(y_true, y_pred) 0.5 >>> jaccard_similarity_score(y_true, y_pred, normalize=False) 2 In the multilabel case with binary label indicators: >>> jaccard_similarity_score(np.array([[0, 1], [1, 1]]),\ np.ones((2, 2))) 0.75 """ # Compute accuracy for each possible representation y_type, y_true, y_pred = _check_targets(y_true, y_pred) if y_type.startswith('multilabel'): with np.errstate(divide='ignore', invalid='ignore'): # oddly, we may get an "invalid" rather than a "divide" error here pred_or_true = count_nonzero(y_true + y_pred, axis=1) pred_and_true = count_nonzero(y_true.multiply(y_pred), axis=1) score = pred_and_true / pred_or_true score[pred_or_true == 0.0] = 1.0 else: score = y_true == y_pred return _weighted_sum(score, sample_weight, normalize) def matthews_corrcoef(y_true, y_pred, sample_weight=None): """Compute the Matthews correlation coefficient (MCC) for binary classes The Matthews correlation coefficient is used in machine learning as a measure of the quality of binary (two-class) classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The MCC is in essence a correlation coefficient value between -1 and +1. A coefficient of +1 represents a perfect prediction, 0 an average random prediction and -1 an inverse prediction. The statistic is also known as the phi coefficient. [source: Wikipedia] Only in the binary case does this relate to information about true and false positives and negatives. See references below. Read more in the :ref:`User Guide <matthews_corrcoef>`. Parameters ---------- y_true : array, shape = [n_samples] Ground truth (correct) target values. y_pred : array, shape = [n_samples] Estimated targets as returned by a classifier. sample_weight : array-like of shape = [n_samples], default None Sample weights. Returns ------- mcc : float The Matthews correlation coefficient (+1 represents a perfect prediction, 0 an average random prediction and -1 and inverse prediction). References ---------- .. [1] `Baldi, Brunak, Chauvin, Andersen and Nielsen, (2000). Assessing the accuracy of prediction algorithms for classification: an overview <http://dx.doi.org/10.1093/bioinformatics/16.5.412>`_ .. [2] `Wikipedia entry for the Matthews Correlation Coefficient <https://en.wikipedia.org/wiki/Matthews_correlation_coefficient>`_ Examples -------- >>> from sklearn.metrics import matthews_corrcoef >>> y_true = [+1, +1, +1, -1] >>> y_pred = [+1, -1, +1, +1] >>> matthews_corrcoef(y_true, y_pred) # doctest: +ELLIPSIS -0.33... """ y_type, y_true, y_pred = _check_targets(y_true, y_pred) if y_type != "binary": raise ValueError("%s is not supported" % y_type) lb = LabelEncoder() lb.fit(np.hstack([y_true, y_pred])) y_true = lb.transform(y_true) y_pred = lb.transform(y_pred) mean_yt = np.average(y_true, weights=sample_weight) mean_yp = np.average(y_pred, weights=sample_weight) y_true_u_cent = y_true - mean_yt y_pred_u_cent = y_pred - mean_yp cov_ytyp = np.average(y_true_u_cent * y_pred_u_cent, weights=sample_weight) var_yt = np.average(y_true_u_cent 2, weights=sample_weight) var_yp = np.average(y_pred_u_cent 2, weights=sample_weight) mcc = cov_ytyp / np.sqrt(var_yt * var_yp) if np.isnan(mcc): return 0. else: return mcc def zero_one_loss(y_true, y_pred, normalize=True, sample_weight=None): """Zero-one classification loss. If normalize is ``True``, return the fraction of misclassifications (float), else it returns the number of misclassifications (int). The best performance is 0. Read more in the :ref:`User Guide <zero_one_loss>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) labels. y_pred : 1d array-like, or label indicator array / sparse matrix Predicted labels, as returned by a classifier. normalize : bool, optional (default=True) If ``False``, return the number of misclassifications. Otherwise, return the fraction of misclassifications. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- loss : float or int, If ``normalize == True``, return the fraction of misclassifications (float), else it returns the number of misclassifications (int). Notes ----- In multilabel classification, the zero_one_loss function corresponds to the subset zero-one loss: for each sample, the entire set of labels must be correctly predicted, otherwise the loss for that sample is equal to one. See also -------- accuracy_score, hamming_loss, jaccard_similarity_score Examples -------- >>> from sklearn.metrics import zero_one_loss >>> y_pred = [1, 2, 3, 4] >>> y_true = [2, 2, 3, 4] >>> zero_one_loss(y_true, y_pred) 0.25 >>> zero_one_loss(y_true, y_pred, normalize=False) 1 In the multilabel case with binary label indicators: >>> zero_one_loss(np.array([[0, 1], [1, 1]]), np.ones((2, 2))) 0.5 """ score = accuracy_score(y_true, y_pred, normalize=normalize, sample_weight=sample_weight) if normalize: return 1 - score else: if sample_weight is not None: n_samples = np.sum(sample_weight) else: n_samples = _num_samples(y_true) return n_samples - score def f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None): """Compute the F1 score, also known as balanced F-score or F-measure The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is:: F1 = 2 * (precision * recall) / (precision + recall) In the multi-class and multi-label case, this is the weighted average of the F1 score of each class. Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels : list, optional The set of labels to include when ``average != 'binary'``, and their order if ``average is None``. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in ``y_true`` and ``y_pred`` are used in sorted order. .. versionchanged:: 0.17 parameter labels improved for multiclass problem. pos_label : str or int, 1 by default The class to report if ``average='binary'`` and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting ``labels=[pos_label]`` and ``average != 'binary'`` will report scores for that label only. average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', \ 'weighted'] This parameter is required for multiclass/multilabel targets. If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: ``'binary'``: Only report results for the class specified by ``pos_label``. This is applicable only if targets (``y_{true,pred}``) are binary. ``'micro'``: Calculate metrics globally by counting the total true positives, false negatives and false positives. ``'macro'``: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. ``'weighted'``: Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters 'macro' to account for label imbalance; it can result in an F-score that is not between precision and recall. ``'samples'``: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from :func:`accuracy_score`). sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- f1_score : float or array of float, shape = [n_unique_labels] F1 score of the positive class in binary classification or weighted average of the F1 scores of each class for the multiclass task. References ---------- .. [1] `Wikipedia entry for the F1-score <https://en.wikipedia.org/wiki/F1_score>`_ Examples -------- >>> from sklearn.metrics import f1_score >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> f1_score(y_true, y_pred, average='macro') # doctest: +ELLIPSIS 0.26... >>> f1_score(y_true, y_pred, average='micro') # doctest: +ELLIPSIS 0.33... >>> f1_score(y_true, y_pred, average='weighted') # doctest: +ELLIPSIS 0.26... >>> f1_score(y_true, y_pred, average=None) array([ 0.8, 0. , 0. ]) """ return fbeta_score(y_true, y_pred, 1, labels=labels, pos_label=pos_label, average=average, sample_weight=sample_weight) def fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1, average='binary', sample_weight=None): """Compute the F-beta score The F-beta score is the weighted harmonic mean of precision and recall, reaching its optimal value at 1 and its worst value at 0. The `beta` parameter determines the weight of precision in the combined score. ``beta < 1`` lends more weight to precision, while ``beta > 1`` favors recall (``beta -> 0`` considers only precision, ``beta -> inf`` only recall). Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. beta : float Weight of precision in harmonic mean. labels : list, optional The set of labels to include when ``average != 'binary'``, and their order if ``average is None``. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in ``y_true`` and ``y_pred`` are used in sorted order. .. versionchanged:: 0.17 parameter labels improved for multiclass problem. pos_label : str or int, 1 by default The class to report if ``average='binary'`` and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting ``labels=[pos_label]`` and ``average != 'binary'`` will report scores for that label only. average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', \ 'weighted'] This parameter is required for multiclass/multilabel targets. If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: ``'binary'``: Only report results for the class specified by ``pos_label``. This is applicable only if targets (``y_{true,pred}``) are binary. ``'micro'``: Calculate metrics globally by counting the total true positives, false negatives and false positives. ``'macro'``: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. ``'weighted'``: Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters 'macro' to account for label imbalance; it can result in an F-score that is not between precision and recall. ``'samples'``: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from :func:`accuracy_score`). sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- fbeta_score : float (if average is not None) or array of float, shape =\ [n_unique_labels] F-beta score of the positive class in binary classification or weighted average of the F-beta score of each class for the multiclass task. References ---------- .. [1] R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 327-328. .. [2] `Wikipedia entry for the F1-score <https://en.wikipedia.org/wiki/F1_score>`_ Examples -------- >>> from sklearn.metrics import fbeta_score >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> fbeta_score(y_true, y_pred, average='macro', beta=0.5) ... # doctest: +ELLIPSIS 0.23... >>> fbeta_score(y_true, y_pred, average='micro', beta=0.5) ... # doctest: +ELLIPSIS 0.33... >>> fbeta_score(y_true, y_pred, average='weighted', beta=0.5) ... # doctest: +ELLIPSIS 0.23... >>> fbeta_score(y_true, y_pred, average=None, beta=0.5) ... # doctest: +ELLIPSIS array([ 0.71..., 0. , 0. ]) """ _, _, f, _ = precision_recall_fscore_support(y_true, y_pred, beta=beta, labels=labels, pos_label=pos_label, average=average, warn_for=('f-score',), sample_weight=sample_weight) return f def _prf_divide(numerator, denominator, metric, modifier, average, warn_for): """Performs division and handles divide-by-zero. On zero-division, sets the corresponding result elements to zero and raises a warning. The metric, modifier and average arguments are used only for determining an appropriate warning. """ result = numerator / denominator mask = denominator == 0.0 if not np.any(mask): return result # remove infs result[mask] = 0.0 # build appropriate warning # E.g. "Precision and F-score are ill-defined and being set to 0.0 in # labels with no predicted samples" axis0 = 'sample' axis1 = 'label' if average == 'samples': axis0, axis1 = axis1, axis0 if metric in warn_for and 'f-score' in warn_for: msg_start = '{0} and F-score are'.format(metric.title()) elif metric in warn_for: msg_start = '{0} is'.format(metric.title()) elif 'f-score' in warn_for: msg_start = 'F-score is' else: return result msg = ('{0} ill-defined and being set to 0.0 {{0}} ' 'no {1} {2}s.'.format(msg_start, modifier, axis0)) if len(mask) == 1: msg = msg.format('due to') else: msg = msg.format('in {0}s with'.format(axis1)) warnings.warn(msg, UndefinedMetricWarning, stacklevel=2) return result def precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None, pos_label=1, average=None, warn_for=('precision', 'recall', 'f-score'), sample_weight=None): """Compute precision, recall, F-measure and support for each class The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of true positives and ``fp`` the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of true positives and ``fn`` the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples. The F-beta score can be interpreted as a weighted harmonic mean of the precision and recall, where an F-beta score reaches its best value at 1 and worst score at 0. The F-beta score weights recall more than precision by a factor of ``beta``. ``beta == 1.0`` means recall and precision are equally important. The support is the number of occurrences of each class in ``y_true``. If ``pos_label is None`` and in binary classification, this function returns the average precision, recall and F-measure if ``average`` is one of ``'micro'``, ``'macro'``, ``'weighted'`` or ``'samples'``. Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. beta : float, 1.0 by default The strength of recall versus precision in the F-score. labels : list, optional The set of labels to include when ``average != 'binary'``, and their order if ``average is None``. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in ``y_true`` and ``y_pred`` are used in sorted order. pos_label : str or int, 1 by default The class to report if ``average='binary'`` and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting ``labels=[pos_label]`` and ``average != 'binary'`` will report scores for that label only. average : string, [None (default), 'binary', 'micro', 'macro', 'samples', \ 'weighted'] If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: ``'binary'``: Only report results for the class specified by ``pos_label``. This is applicable only if targets (``y_{true,pred}``) are binary. ``'micro'``: Calculate metrics globally by counting the total true positives, false negatives and false positives. ``'macro'``: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. ``'weighted'``: Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters 'macro' to account for label imbalance; it can result in an F-score that is not between precision and recall. ``'samples'``: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from :func:`accuracy_score`). warn_for : tuple or set, for internal use This determines which warnings will be made in the case that this function is being used to return only one of its metrics. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- precision : float (if average is not None) or array of float, shape =\ [n_unique_labels] recall : float (if average is not None) or array of float, , shape =\ [n_unique_labels] fbeta_score : float (if average is not None) or array of float, shape =\ [n_unique_labels] support : int (if average is not None) or array of int, shape =\ [n_unique_labels] The number of occurrences of each label in ``y_true``. References ---------- .. [1] `Wikipedia entry for the Precision and recall <https://en.wikipedia.org/wiki/Precision_and_recall>`_ .. [2] `Wikipedia entry for the F1-score <https://en.wikipedia.org/wiki/F1_score>`_ .. [3] `Discriminative Methods for Multi-labeled Classification Advances in Knowledge Discovery and Data Mining (2004), pp. 22-30 by Shantanu Godbole, Sunita Sarawagi <http://www.godbole.net/shantanu/pubs/multilabelsvm-pakdd04.pdf>`_ Examples -------- >>> from sklearn.metrics import precision_recall_fscore_support >>> y_true = np.array(['cat', 'dog', 'pig', 'cat', 'dog', 'pig']) >>> y_pred = np.array(['cat', 'pig', 'dog', 'cat', 'cat', 'dog']) >>> precision_recall_fscore_support(y_true, y_pred, average='macro') ... # doctest: +ELLIPSIS (0.22..., 0.33..., 0.26..., None) >>> precision_recall_fscore_support(y_true, y_pred, average='micro') ... # doctest: +ELLIPSIS (0.33..., 0.33..., 0.33..., None) >>> precision_recall_fscore_support(y_true, y_pred, average='weighted') ... # doctest: +ELLIPSIS (0.22..., 0.33..., 0.26..., None) It is possible to compute per-label precisions, recalls, F1-scores and supports instead of averaging: >>> precision_recall_fscore_support(y_true, y_pred, average=None, ... labels=['pig', 'dog', 'cat']) ... # doctest: +ELLIPSIS,+NORMALIZE_WHITESPACE (array([ 0. , 0. , 0.66...]), array([ 0., 0., 1.]), array([ 0. , 0. , 0.8]), array([2, 2, 2])) """ average_options = (None, 'micro', 'macro', 'weighted', 'samples') if average not in average_options and average != 'binary': raise ValueError('average has to be one of ' + str(average_options)) if beta <= 0: raise ValueError("beta should be >0 in the F-beta score") y_type, y_true, y_pred = _check_targets(y_true, y_pred) present_labels = unique_labels(y_true, y_pred) if average == 'binary': if y_type == 'binary': if pos_label not in present_labels: if len(present_labels) < 2: # Only negative labels return (0., 0., 0., 0) else: raise ValueError("pos_label=%r is not a valid label: %r" % (pos_label, present_labels)) labels = [pos_label] else: raise ValueError("Target is %s but average='binary'. Please " "choose another average setting." % y_type) elif pos_label not in (None, 1): warnings.warn("Note that pos_label (set to %r) is ignored when " "average != 'binary' (got %r). You may use " "labels=[pos_label] to specify a single positive class." % (pos_label, average), UserWarning) if labels is None: labels = present_labels n_labels = None else: n_labels = len(labels) labels = np.hstack([labels, np.setdiff1d(present_labels, labels, assume_unique=True)]) # Calculate tp_sum, pred_sum, true_sum ### if y_type.startswith('multilabel'): sum_axis = 1 if average == 'samples' else 0 # All labels are index integers for multilabel. # Select labels: if not np.all(labels == present_labels): if np.max(labels) > np.max(present_labels): raise ValueError('All labels must be in [0, n labels). ' 'Got %d > %d' % (np.max(labels), np.max(present_labels))) if np.min(labels) < 0: raise ValueError('All labels must be in [0, n labels). ' 'Got %d < 0' % np.min(labels)) y_true = y_true[:, labels[:n_labels]] y_pred = y_pred[:, labels[:n_labels]] # calculate weighted counts true_and_pred = y_true.multiply(y_pred) tp_sum = count_nonzero(true_and_pred, axis=sum_axis, sample_weight=sample_weight) pred_sum = count_nonzero(y_pred, axis=sum_axis, sample_weight=sample_weight) true_sum = count_nonzero(y_true, axis=sum_axis, sample_weight=sample_weight) elif average == 'samples': raise ValueError("Sample-based precision, recall, fscore is " "not meaningful outside multilabel " "classification. See the accuracy_score instead.") else: le = LabelEncoder() le.fit(labels) y_true = le.transform(y_true) y_pred = le.transform(y_pred) sorted_labels = le.classes_ # labels are now from 0 to len(labels) - 1 -> use bincount tp = y_true == y_pred tp_bins = y_true[tp] if sample_weight is not None: tp_bins_weights = np.asarray(sample_weight)[tp] else: tp_bins_weights = None if len(tp_bins): tp_sum = bincount(tp_bins, weights=tp_bins_weights, minlength=len(labels)) else: # Pathological case true_sum = pred_sum = tp_sum = np.zeros(len(labels)) if len(y_pred): pred_sum = bincount(y_pred, weights=sample_weight, minlength=len(labels)) if len(y_true): true_sum = bincount(y_true, weights=sample_weight, minlength=len(labels)) # Retain only selected labels indices = np.searchsorted(sorted_labels, labels[:n_labels]) tp_sum = tp_sum[indices] true_sum = true_sum[indices] pred_sum = pred_sum[indices] if average == 'micro': tp_sum = np.array([tp_sum.sum()]) pred_sum = np.array([pred_sum.sum()]) true_sum = np.array([true_sum.sum()]) # Finally, we have all our sufficient statistics. Divide! # beta2 = beta ** 2 with np.errstate(divide='ignore', invalid='ignore'): # Divide, and on zero-division, set scores to 0 and warn: # Oddly, we may get an "invalid" rather than a "divide" error # here. precision = _prf_divide(tp_sum, pred_sum, 'precision', 'predicted', average, warn_for) recall = _prf_divide(tp_sum, true_sum, 'recall', 'true', average, warn_for) # Don't need to warn for F: either P or R warned, or tp == 0 where pos # and true are nonzero, in which case, F is well-defined and zero f_score = ((1 + beta2) * precision * recall / (beta2 * precision + recall)) f_score[tp_sum == 0] = 0.0 # Average the results if average == 'weighted': weights = true_sum if weights.sum() == 0: return 0, 0, 0, None elif average == 'samples': weights = sample_weight else: weights = None if average is not None: assert average != 'binary' or len(precision) == 1 precision = np.average(precision, weights=weights) recall = np.average(recall, weights=weights) f_score = np.average(f_score, weights=weights) true_sum = None # return no support return precision, recall, f_score, true_sum def precision_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None): """Compute the precision The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of true positives and ``fp`` the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The best value is 1 and the worst value is 0. Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels : list, optional The set of labels to include when ``average != 'binary'``, and their order if ``average is None``. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in ``y_true`` and ``y_pred`` are used in sorted order. .. versionchanged:: 0.17 parameter labels improved for multiclass problem. pos_label : str or int, 1 by default The class to report if ``average='binary'`` and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting ``labels=[pos_label]`` and ``average != 'binary'`` will report scores for that label only. average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', \ 'weighted'] This parameter is required for multiclass/multilabel targets. If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: ``'binary'``: Only report results for the class specified by ``pos_label``. This is applicable only if targets (``y_{true,pred}``) are binary. ``'micro'``: Calculate metrics globally by counting the total true positives, false negatives and false positives. ``'macro'``: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. ``'weighted'``: Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters 'macro' to account for label imbalance; it can result in an F-score that is not between precision and recall. ``'samples'``: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from :func:`accuracy_score`). sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- precision : float (if average is not None) or array of float, shape =\ [n_unique_labels] Precision of the positive class in binary classification or weighted average of the precision of each class for the multiclass task. Examples -------- >>> from sklearn.metrics import precision_score >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> precision_score(y_true, y_pred, average='macro') # doctest: +ELLIPSIS 0.22... >>> precision_score(y_true, y_pred, average='micro') # doctest: +ELLIPSIS 0.33... >>> precision_score(y_true, y_pred, average='weighted') ... # doctest: +ELLIPSIS 0.22... >>> precision_score(y_true, y_pred, average=None) # doctest: +ELLIPSIS array([ 0.66..., 0. , 0. ]) """ p, _, _, _ = precision_recall_fscore_support(y_true, y_pred, labels=labels, pos_label=pos_label, average=average, warn_for=('precision',), sample_weight=sample_weight) return p def recall_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None): """Compute the recall The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of true positives and ``fn`` the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples. The best value is 1 and the worst value is 0. Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels : list, optional The set of labels to include when ``average != 'binary'``, and their order if ``average is None``. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in ``y_true`` and ``y_pred`` are used in sorted order. .. versionchanged:: 0.17 parameter labels improved for multiclass problem. pos_label : str or int, 1 by default The class to report if ``average='binary'`` and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting ``labels=[pos_label]`` and ``average != 'binary'`` will report scores for that label only. average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', \ 'weighted'] This parameter is required for multiclass/multilabel targets. If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: ``'binary'``: Only report results for the class specified by ``pos_label``. This is applicable only if targets (``y_{true,pred}``) are binary. ``'micro'``: Calculate metrics globally by counting the total true positives, false negatives and false positives. ``'macro'``: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. ``'weighted'``: Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters 'macro' to account for label imbalance; it can result in an F-score that is not between precision and recall. ``'samples'``: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from :func:`accuracy_score`). sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- recall : float (if average is not None) or array of float, shape =\ [n_unique_labels] Recall of the positive class in binary classification or weighted average of the recall of each class for the multiclass task. Examples -------- >>> from sklearn.metrics import recall_score >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> recall_score(y_true, y_pred, average='macro') # doctest: +ELLIPSIS 0.33... >>> recall_score(y_true, y_pred, average='micro') # doctest: +ELLIPSIS 0.33... >>> recall_score(y_true, y_pred, average='weighted') # doctest: +ELLIPSIS 0.33... >>> recall_score(y_true, y_pred, average=None) array([ 1., 0., 0.]) """ _, r, _, _ = precision_recall_fscore_support(y_true, y_pred, labels=labels, pos_label=pos_label, average=average, warn_for=('recall',), sample_weight=sample_weight) return r def classification_report(y_true, y_pred, labels=None, target_names=None, sample_weight=None, digits=2): """Build a text report showing the main classification metrics Read more in the :ref:`User Guide <classification_report>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels : array, shape = [n_labels] Optional list of label indices to include in the report. target_names : list of strings Optional display names matching the labels (same order). sample_weight : array-like of shape = [n_samples], optional Sample weights. digits : int Number of digits for formatting output floating point values Returns ------- report : string Text summary of the precision, recall, F1 score for each class. The reported averages are a prevalence-weighted macro-average across classes (equivalent to :func:`precision_recall_fscore_support` with ``average='weighted'``). Note that in binary classification, recall of the positive class is also known as "sensitivity"; recall of the negative class is "specificity". Examples -------- >>> from sklearn.metrics import classification_report >>> y_true = [0, 1, 2, 2, 2] >>> y_pred = [0, 0, 2, 2, 1] >>> target_names = ['class 0', 'class 1', 'class 2'] >>> print(classification_report(y_true, y_pred, target_names=target_names)) precision recall f1-score support <BLANKLINE> class 0 0.50 1.00 0.67 1 class 1 0.00 0.00 0.00 1 class 2 1.00 0.67 0.80 3 <BLANKLINE> avg / total 0.70 0.60 0.61 5 <BLANKLINE> """ if labels is None: labels = unique_labels(y_true, y_pred) else: labels = np.asarray(labels) if target_names is not None and len(labels) != len(target_names): warnings.warn( "labels size, {0}, does not match size of target_names, {1}" .format(len(labels), len(target_names)) ) last_line_heading = 'avg / total' if target_names is None: target_names = [u'%s' % l for l in labels] name_width = max(len(cn) for cn in target_names) width = max(name_width, len(last_line_heading), digits) headers = ["precision", "recall", "f1-score", "support"] head_fmt = u'{:>{width}s} ' + u' {:>9}' * len(headers) report = head_fmt.format(u'', headers, width=width) report += u'\n\n' p, r, f1, s = precision_recall_fscore_support(y_true, y_pred, labels=labels, average=None, sample_weight=sample_weight) row_fmt = u'{:>{width}s} ' + u' {:>9.{digits}f}' 3 + u' {:>9}\n' rows = zip(target_names, p, r, f1, s) for row in rows: report += row_fmt.format(row, width=width, digits=digits) report += u'\n' # compute averages report += row_fmt.format(last_line_heading, np.average(p, weights=s), np.average(r, weights=s), np.average(f1, weights=s), np.sum(s), width=width, digits=digits) return report def hamming_loss(y_true, y_pred, labels=None, sample_weight=None, classes=None): """Compute the average Hamming loss. The Hamming loss is the fraction of labels that are incorrectly predicted. Read more in the :ref:`User Guide <hamming_loss>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) labels. y_pred : 1d array-like, or label indicator array / sparse matrix Predicted labels, as returned by a classifier. labels : array, shape = [n_labels], optional (default=None) Integer array of labels. If not provided, labels will be inferred from y_true and y_pred. .. versionadded:: 0.18 sample_weight : array-like of shape = [n_samples], optional Sample weights. .. versionadded:: 0.18 classes : array, shape = [n_labels], optional Integer array of labels. .. deprecated:: 0.18 This parameter has been deprecated in favor of ``labels`` in version 0.18 and will be removed in 0.20. Use ``labels`` instead. Returns ------- loss : float or int, Return the average Hamming loss between element of ``y_true`` and ``y_pred``. See Also -------- accuracy_score, jaccard_similarity_score, zero_one_loss Notes ----- In multiclass classification, the Hamming loss correspond to the Hamming distance between ``y_true`` and ``y_pred`` which is equivalent to the subset ``zero_one_loss`` function. In multilabel classification, the Hamming loss is different from the subset zero-one loss. The zero-one loss considers the entire set of labels for a given sample incorrect if it does entirely match the true set of labels. Hamming loss is more forgiving in that it penalizes the individual labels. The Hamming loss is upperbounded by the subset zero-one loss. When normalized over samples, the Hamming loss is always between 0 and 1. References ---------- .. [1] Grigorios Tsoumakas, Ioannis Katakis. Multi-Label Classification: An Overview. International Journal of Data Warehousing & Mining, 3(3), 1-13, July-September 2007. .. [2] `Wikipedia entry on the Hamming distance <https://en.wikipedia.org/wiki/Hamming_distance>`_ Examples -------- >>> from sklearn.metrics import hamming_loss >>> y_pred = [1, 2, 3, 4] >>> y_true = [2, 2, 3, 4] >>> hamming_loss(y_true, y_pred) 0.25 In the multilabel case with binary label indicators: >>> hamming_loss(np.array([[0, 1], [1, 1]]), np.zeros((2, 2))) 0.75 """ if classes is not None: warnings.warn("'classes' was renamed to 'labels' in version 0.18 and " "will be removed in 0.20.", DeprecationWarning) labels = classes y_type, y_true, y_pred = _check_targets(y_true, y_pred) if labels is None: labels = unique_labels(y_true, y_pred) else: labels = np.asarray(labels) if sample_weight is None: weight_average = 1. else: weight_average = np.mean(sample_weight) if y_type.startswith('multilabel'): n_differences = count_nonzero(y_true - y_pred, sample_weight=sample_weight) return (n_differences / (y_true.shape[0] len(labels) * weight_average)) elif y_type in ["binary", "multiclass"]: return _weighted_sum(y_true != y_pred, sample_weight, normalize=True) else: raise ValueError("{0} is not supported".format(y_type)) def log_loss(y_true, y_pred, eps=1e-15, normalize=True, sample_weight=None, labels=None): """Log loss, aka logistic loss or cross-entropy loss. This is the loss function used in (multinomial) logistic regression and extensions of it such as neural networks, defined as the negative log-likelihood of the true labels given a probabilistic classifier's predictions. The log loss is only defined for two or more labels. For a single sample with true label yt in {0,1} and estimated probability yp that yt = 1, the log loss is -log P(yt\|yp) = -(yt log(yp) + (1 - yt) log(1 - yp)) Read more in the :ref:`User Guide <log_loss>`. Parameters ---------- y_true : array-like or label indicator matrix Ground truth (correct) labels for n_samples samples. y_pred : array-like of float, shape = (n_samples, n_classes) or (n_samples,) Predicted probabilities, as returned by a classifier's predict_proba method. If ``y_pred.shape = (n_samples,)`` the probabilities provided are assumed to be that of the positive class. The labels in ``y_pred`` are assumed to be ordered alphabetically, as done by :class:`preprocessing.LabelBinarizer`. eps : float Log loss is undefined for p=0 or p=1, so probabilities are clipped to max(eps, min(1 - eps, p)). normalize : bool, optional (default=True) If true, return the mean loss per sample. Otherwise, return the sum of the per-sample losses. sample_weight : array-like of shape = [n_samples], optional Sample weights. labels : array-like, optional (default=None) If not provided, labels will be inferred from y_true. If ``labels`` is ``None`` and ``y_pred`` has shape (n_samples,) the labels are assumed to be binary and are inferred from ``y_true``. .. versionadded:: 0.18 Returns ------- loss : float Examples -------- >>> log_loss(["spam", "ham", "ham", "spam"], # doctest: +ELLIPSIS ... [[.1, .9], [.9, .1], [.8, .2], [.35, .65]]) 0.21616... References ---------- C.M. Bishop (2006). Pattern Recognition and Machine Learning. Springer, p. 209. Notes ----- The logarithm used is the natural logarithm (base-e). """ y_pred = check_array(y_pred, ensure_2d=False) check_consistent_length(y_pred, y_true) lb = LabelBinarizer() if labels is not None: lb.fit(labels) else: lb.fit(y_true) if len(lb.classes_) == 1: if labels is None: raise ValueError('y_true contains only one label ({0}). Please ' 'provide the true labels explicitly through the ' 'labels argument.'.format(lb.classes_[0])) else: raise ValueError('The labels array needs to contain at least two ' 'labels for log_loss, ' 'got {0}.'.format(lb.classes_)) transformed_labels = lb.transform(y_true) if transformed_labels.shape[1] == 1: transformed_labels = np.append(1 - transformed_labels, transformed_labels, axis=1) # Clipping y_pred = np.clip(y_pred, eps, 1 - eps) # If y_pred is of single dimension, assume y_true to be binary # and then check. if y_pred.ndim == 1: y_pred = y_pred[:, np.newaxis] if y_pred.shape[1] == 1: y_pred = np.append(1 - y_pred, y_pred, axis=1) # Check if dimensions are consistent. transformed_labels = check_array(transformed_labels) if len(lb.classes_) != y_pred.shape[1]: if labels is None: raise ValueError("y_true and y_pred contain different number of " "classes {0}, {1}. Please provide the true " "labels explicitly through the labels argument. " "Classes found in " "y_true: {2}".format(transformed_labels.shape[1], y_pred.shape[1], lb.classes_)) else: raise ValueError('The number of classes in labels is different ' 'from that in y_pred. Classes found in ' 'labels: {0}'.format(lb.classes_)) # Renormalize y_pred /= y_pred.sum(axis=1)[:, np.newaxis] loss = -(transformed_labels * np.log(y_pred)).sum(axis=1) return _weighted_sum(loss, sample_weight, normalize) def hinge_loss(y_true, pred_decision, labels=None, sample_weight=None): """Average hinge loss (non-regularized) In binary class case, assuming labels in y_true are encoded with +1 and -1, when a prediction mistake is made, ``margin = y_true * pred_decision`` is always negative (since the signs disagree), implying ``1 - margin`` is always greater than 1. The cumulated hinge loss is therefore an upper bound of the number of mistakes made by the classifier. In multiclass case, the function expects that either all the labels are included in y_true or an optional labels argument is provided which contains all the labels. The multilabel margin is calculated according to Crammer-Singer's method. As in the binary case, the cumulated hinge loss is an upper bound of the number of mistakes made by the classifier. Read more in the :ref:`User Guide <hinge_loss>`. Parameters ---------- y_true : array, shape = [n_samples] True target, consisting of integers of two values. The positive label must be greater than the negative label. pred_decision : array, shape = [n_samples] or [n_samples, n_classes] Predicted decisions, as output by decision_function (floats). labels : array, optional, default None Contains all the labels for the problem. Used in multiclass hinge loss. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- loss : float References ---------- .. [1] `Wikipedia entry on the Hinge loss <https://en.wikipedia.org/wiki/Hinge_loss>`_ .. [2] Koby Crammer, Yoram Singer. On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines. Journal of Machine Learning Research 2, (2001), 265-292 .. [3] `L1 AND L2 Regularization for Multiclass Hinge Loss Models by Robert C. Moore, John DeNero. <http://www.ttic.edu/sigml/symposium2011/papers/ Moore+DeNero_Regularization.pdf>`_ Examples -------- >>> from sklearn import svm >>> from sklearn.metrics import hinge_loss >>> X = [[0], [1]] >>> y = [-1, 1] >>> est = svm.LinearSVC(random_state=0) >>> est.fit(X, y) LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True, intercept_scaling=1, loss='squared_hinge', max_iter=1000, multi_class='ovr', penalty='l2', random_state=0, tol=0.0001, verbose=0) >>> pred_decision = est.decision_function([[-2], [3], [0.5]]) >>> pred_decision # doctest: +ELLIPSIS array([-2.18..., 2.36..., 0.09...]) >>> hinge_loss([-1, 1, 1], pred_decision) # doctest: +ELLIPSIS 0.30... In the multiclass case: >>> X = np.array([[0], [1], [2], [3]]) >>> Y = np.array([0, 1, 2, 3]) >>> labels = np.array([0, 1, 2, 3]) >>> est = svm.LinearSVC() >>> est.fit(X, Y) LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True, intercept_scaling=1, loss='squared_hinge', max_iter=1000, multi_class='ovr', penalty='l2', random_state=None, tol=0.0001, verbose=0) >>> pred_decision = est.decision_function([[-1], [2], [3]]) >>> y_true = [0, 2, 3] >>> hinge_loss(y_true, pred_decision, labels) #doctest: +ELLIPSIS 0.56... """ check_consistent_length(y_true, pred_decision, sample_weight) pred_decision = check_array(pred_decision, ensure_2d=False) y_true = column_or_1d(y_true) y_true_unique = np.unique(y_true) if y_true_unique.size > 2: if (labels is None and pred_decision.ndim > 1 and (np.size(y_true_unique) != pred_decision.shape[1])): raise ValueError("Please include all labels in y_true " "or pass labels as third argument") if labels is None: labels = y_true_unique le = LabelEncoder() le.fit(labels) y_true = le.transform(y_true) mask = np.ones_like(pred_decision, dtype=bool) mask[np.arange(y_true.shape[0]), y_true] = False margin = pred_decision[~mask] margin -= np.max(pred_decision[mask].reshape(y_true.shape[0], -1), axis=1) else: # Handles binary class case # this code assumes that positive and negative labels # are encoded as +1 and -1 respectively pred_decision = column_or_1d(pred_decision) pred_decision = np.ravel(pred_decision) lbin = LabelBinarizer(neg_label=-1) y_true = lbin.fit_transform(y_true)[:, 0] try: margin = y_true * pred_decision except TypeError: raise TypeError("pred_decision should be an array of floats.") losses = 1 - margin # The hinge_loss doesn't penalize good enough predictions. losses[losses <= 0] = 0 return np.average(losses, weights=sample_weight) def _check_binary_probabilistic_predictions(y_true, y_prob): """Check that y_true is binary and y_prob contains valid probabilities""" check_consistent_length(y_true, y_prob) labels = np.unique(y_true) if len(labels) > 2: raise ValueError("Only binary classification is supported. " "Provided labels %s." % labels) if y_prob.max() > 1: raise ValueError("y_prob contains values greater than 1.") if y_prob.min() < 0: raise ValueError("y_prob contains values less than 0.") return label_binarize(y_true, labels)[:, 0] def brier_score_loss(y_true, y_prob, sample_weight=None, pos_label=None): """Compute the Brier score. The smaller the Brier score, the better, hence the naming with "loss". Across all items in a set N predictions, the Brier score measures the mean squared difference between (1) the predicted probability assigned to the possible outcomes for item i, and (2) the actual outcome. Therefore, the lower the Brier score is for a set of predictions, the better the predictions are calibrated. Note that the Brier score always takes on a value between zero and one, since this is the largest possible difference between a predicted probability (which must be between zero and one) and the actual outcome (which can take on values of only 0 and 1). The Brier score is appropriate for binary and categorical outcomes that can be structured as true or false, but is inappropriate for ordinal variables which can take on three or more values (this is because the Brier score assumes that all possible outcomes are equivalently "distant" from one another). Which label is considered to be the positive label is controlled via the parameter pos_label, which defaults to 1. Read more in the :ref:`User Guide <calibration>`. Parameters ---------- y_true : array, shape (n_samples,) True targets. y_prob : array, shape (n_samples,) Probabilities of the positive class. sample_weight : array-like of shape = [n_samples], optional Sample weights. pos_label : int or str, default=None Label of the positive class. If None, the maximum label is used as positive class Returns ------- score : float Brier score Examples -------- >>> import numpy as np >>> from sklearn.metrics import brier_score_loss >>> y_true = np.array([0, 1, 1, 0]) >>> y_true_categorical = np.array(["spam", "ham", "ham", "spam"]) >>> y_prob = np.array([0.1, 0.9, 0.8, 0.3]) >>> brier_score_loss(y_true, y_prob) # doctest: +ELLIPSIS 0.037... >>> brier_score_loss(y_true, 1-y_prob, pos_label=0) # doctest: +ELLIPSIS 0.037... >>> brier_score_loss(y_true_categorical, y_prob, \ pos_label="ham") # doctest: +ELLIPSIS 0.037... >>> brier_score_loss(y_true, np.array(y_prob) > 0.5) 0.0 References ---------- .. [1] `Wikipedia entry for the Brier score. <https://en.wikipedia.org/wiki/Brier_score>`_ """ y_true = column_or_1d(y_true) y_prob = column_or_1d(y_prob) assert_all_finite(y_true) assert_all_finite(y_prob) if pos_label is None: pos_label = y_true.max() y_true = np.array(y_true == pos_label, int) y_true = _check_binary_probabilistic_predictions(y_true, y_prob) return np.average((y_true - y_prob) ** 2, weights=sample_weight)	bsd-3-clause
gfyoung/pandas	pandas/tests/arrays/categorical/test_sorting.py	3	5040	import numpy as np import pytest from pandas import Categorical, Index import pandas._testing as tm class TestCategoricalSort: def test_argsort(self): c = Categorical([5, 3, 1, 4, 2], ordered=True) expected = np.array([2, 4, 1, 3, 0]) tm.assert_numpy_array_equal( c.argsort(ascending=True), expected, check_dtype=False ) expected = expected[::-1] tm.assert_numpy_array_equal( c.argsort(ascending=False), expected, check_dtype=False ) def test_numpy_argsort(self): c = Categorical([5, 3, 1, 4, 2], ordered=True) expected = np.array([2, 4, 1, 3, 0]) tm.assert_numpy_array_equal(np.argsort(c), expected, check_dtype=False) tm.assert_numpy_array_equal( np.argsort(c, kind="mergesort"), expected, check_dtype=False ) msg = "the 'axis' parameter is not supported" with pytest.raises(ValueError, match=msg): np.argsort(c, axis=0) msg = "the 'order' parameter is not supported" with pytest.raises(ValueError, match=msg): np.argsort(c, order="C") def test_sort_values(self): # unordered cats are sortable cat = Categorical(["a", "b", "b", "a"], ordered=False) cat.sort_values() cat = Categorical(["a", "c", "b", "d"], ordered=True) # sort_values res = cat.sort_values() exp = np.array(["a", "b", "c", "d"], dtype=object) tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, cat.categories) cat = Categorical( ["a", "c", "b", "d"], categories=["a", "b", "c", "d"], ordered=True ) res = cat.sort_values() exp = np.array(["a", "b", "c", "d"], dtype=object) tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, cat.categories) res = cat.sort_values(ascending=False) exp = np.array(["d", "c", "b", "a"], dtype=object) tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, cat.categories) # sort (inplace order) cat1 = cat.copy() orig_codes = cat1._codes cat1.sort_values(inplace=True) assert cat1._codes is orig_codes exp = np.array(["a", "b", "c", "d"], dtype=object) tm.assert_numpy_array_equal(cat1.__array__(), exp) tm.assert_index_equal(res.categories, cat.categories) # reverse cat = Categorical(["a", "c", "c", "b", "d"], ordered=True) res = cat.sort_values(ascending=False) exp_val = np.array(["d", "c", "c", "b", "a"], dtype=object) exp_categories = Index(["a", "b", "c", "d"]) tm.assert_numpy_array_equal(res.__array__(), exp_val) tm.assert_index_equal(res.categories, exp_categories) def test_sort_values_na_position(self): # see gh-12882 cat = Categorical([5, 2, np.nan, 2, np.nan], ordered=True) exp_categories = Index([2, 5]) exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan]) res = cat.sort_values() # default arguments tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, exp_categories) exp = np.array([np.nan, np.nan, 2.0, 2.0, 5.0]) res = cat.sort_values(ascending=True, na_position="first") tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, exp_categories) exp = np.array([np.nan, np.nan, 5.0, 2.0, 2.0]) res = cat.sort_values(ascending=False, na_position="first") tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, exp_categories) exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan]) res = cat.sort_values(ascending=True, na_position="last") tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, exp_categories) exp = np.array([5.0, 2.0, 2.0, np.nan, np.nan]) res = cat.sort_values(ascending=False, na_position="last") tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, exp_categories) cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) res = cat.sort_values(ascending=False, na_position="last") exp_val = np.array(["d", "c", "b", "a", np.nan], dtype=object) exp_categories = Index(["a", "b", "c", "d"]) tm.assert_numpy_array_equal(res.__array__(), exp_val) tm.assert_index_equal(res.categories, exp_categories) cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) res = cat.sort_values(ascending=False, na_position="first") exp_val = np.array([np.nan, "d", "c", "b", "a"], dtype=object) exp_categories = Index(["a", "b", "c", "d"]) tm.assert_numpy_array_equal(res.__array__(), exp_val) tm.assert_index_equal(res.categories, exp_categories)	bsd-3-clause
printedheart/h2o-3	h2o-py/tests/testdir_algos/gbm/pyunit_bernoulliGBM.py	5	2569	import sys, os sys.path.insert(1, "../../../") import h2o, tests import numpy as np from sklearn import ensemble from sklearn.metrics import roc_auc_score def bernoulliGBM(): #Log.info("Importing prostate.csv data...\n") prostate_train = h2o.import_file(path=h2o.locate("smalldata/logreg/prostate_train.csv")) #Log.info("Converting CAPSULE and RACE columns to factors...\n") prostate_train["CAPSULE"] = prostate_train["CAPSULE"].asfactor() #Log.info("H2O Summary of prostate frame:\n") #prostate.summary() # Import prostate_train.csv as numpy array for scikit comparison trainData = np.loadtxt(h2o.locate("smalldata/logreg/prostate_train.csv"), delimiter=',', skiprows=1) trainDataResponse = trainData[:,0] trainDataFeatures = trainData[:,1:] ntrees = 100 learning_rate = 0.1 depth = 5 min_rows = 10 # Build H2O GBM classification model: #Log.info(paste("H2O GBM with parameters:\ndistribution = 'bernoulli', ntrees = ", ntrees, ", max_depth = 5, # min_rows = 10, learn_rate = 0.1\n", sep = "")) gbm_h2o = h2o.gbm(x=prostate_train[1:], y=prostate_train["CAPSULE"], ntrees=ntrees, learn_rate=learning_rate, max_depth=depth, min_rows=min_rows, distribution="bernoulli") # Build scikit GBM classification model #Log.info("scikit GBM with same parameters\n") gbm_sci = ensemble.GradientBoostingClassifier(learning_rate=learning_rate, n_estimators=ntrees, max_depth=depth, min_samples_leaf=min_rows, max_features=None) gbm_sci.fit(trainDataFeatures,trainDataResponse) #Log.info("Importing prostate_test.csv data...\n") prostate_test = h2o.import_file(path=h2o.locate("smalldata/logreg/prostate_test.csv")) #Log.info("Converting CAPSULE and RACE columns to factors...\n") prostate_test["CAPSULE"] = prostate_test["CAPSULE"].asfactor() # Import prostate_test.csv as numpy array for scikit comparison testData = np.loadtxt(h2o.locate("smalldata/logreg/prostate_test.csv"), delimiter=',', skiprows=1) testDataResponse = testData[:,0] testDataFeatures = testData[:,1:] # Score on the test data and compare results # scikit auc_sci = roc_auc_score(testDataResponse, gbm_sci.predict_proba(testDataFeatures)[:,1]) # h2o gbm_perf = gbm_h2o.model_performance(prostate_test) auc_h2o = gbm_perf.auc() #Log.info(paste("scikit AUC:", auc_sci, "\tH2O AUC:", auc_h2o)) assert auc_h2o >= auc_sci, "h2o (auc) performance degradation, with respect to scikit" if __name__ == "__main__": tests.run_test(sys.argv, bernoulliGBM)	apache-2.0
OSUrobotics/privacy-interfaces	filtering/probability_filters/scripts/test/test_xyz_to_rpy.py	1	2816	#!/usr/bin/env python # TEST PLATFORM for step #3 below. # 3) Convert <x, y, a> to <r, theta, yaw> # 4) Choose four corner poses (each containing one of r_min, r_max, yaw_min, yaw_max) # 5) Project our PolygonStamped onto those four poses # 6) (Run convex hull and then) plot the rectangle (polygon) import rospy import numpy import random from math import sqrt, atan2 from tf.transformations import quaternion_about_axis, quaternion_matrix from matplotlib import pyplot # Premise: object is at (0, 0) and robot has random (x, y, theta) centered at (0, 0, 0) if __name__ == '__main__': # Locate object obj = [0.0, 0.0, 0.0] # Locate robots dev = 5.0 robots_xyw = [] for i in range(1000): robot = [random.gauss(0.0, dev), random.gauss(0.0, dev), random.gauss(0.0, dev)] robots_xyw.append(robot) # Add robots for permuted min & max bounds x = [robot[0] for robot in robots_xyw] y = [robot[1] for robot in robots_xyw] w = [robot[2] for robot in robots_xyw] ranges = [[min(x), max(x)], [min(y), max(y)], [min(w), max(w)]] for x_i in ranges[0]: for y_i in ranges[1]: for w_i in ranges[2]: robots_xyw.append([x_i, y_i, w_i]) # Do conversions robots_rty = [] for robot in robots_xyw: # Calculate radius dx = obj[0] - robot[0] dy = obj[1] - robot[1] r = sqrt(dx2 + dy2) offset = [dx, dy, 0.0] # vector offset = [el / r for el in offset] # normalize # Calculate theta (angle of vector from object to robot) theta = atan2(-1 * offset[1], -1 * offset[0]) # positive is counter-clockwise # Calculate heading vector of robot q_robot = quaternion_about_axis(robot[2], (0, 0, 1)) R_robot = quaternion_matrix(q_robot) heading_robot = numpy.matrix([1, 0, 0, 1]) * numpy.matrix(R_robot).I heading_robot /= heading_robot[0, 3] # ensure homogeneity isn't messing stuff up heading_robot = heading_robot[0, 0:3].tolist()[0] # convert from homogeneous to...not # Calculate camera yaw (angle from gaze vector to object line-of-sight vector) cosine = numpy.dot(heading_robot, offset) cross = numpy.cross(heading_robot, offset) sine = cross[2] yaw = -1 * atan2(sine, cosine) # positive is counter-clockwise robots_rty.append([r, theta, yaw]) robots_xywrty = [xyz + rty for xyz, rty in zip(robots_xyw, robots_rty)] radii = [robot[3] for robot in robots_xywrty] yaws = [robot[5] for robot in robots_xywrty] for robot in robots_xywrty: print robot print min(radii), max(radii) print min(yaws), max(yaws) # RESULT: extremes in XYW do not yield the extremes in RTY	mit
HIPS/pgmult	setup.py	2	1189	from distutils.core import setup import numpy as np from Cython.Build import cythonize setup( name='pgmult', version='0.1', description= "Learning and inference for models with multinomial observations and " "underlying Gaussian correlation structure. Examples include correlated " "topic model, multinomial linear dynamical systems, and multinomial " "Gaussian processes. ", author='Scott W. Linderman and Matthew James Johnson', author_email='[email protected], [email protected]', license="MIT", url='https://github.com/HIPS/pgmult', packages=['pgmult'], install_requires=[ 'Cython >= 0.20.1', 'numpy', 'scipy', 'matplotlib', 'pybasicbayes', 'pypolyagamma', 'gslrandom', 'pylds'], ext_modules=cythonize('pgmult/*/.pyx'), include_dirs=[np.get_include(),], classifiers=[ 'Intended Audience :: Science/Research', 'Programming Language :: Python', 'Programming Language :: C++', ], keywords=[ 'multinomial', 'polya', 'gamma', 'correlated topic model', 'ctm', 'lds', 'linear dynamical system', 'gaussian process', 'gp'], platforms="ALL" )	mit
tkaitchuck/nupic	external/darwin64/lib/python2.6/site-packages/matplotlib/fontconfig_pattern.py	72	6429	""" A module for parsing and generating fontconfig patterns. See the `fontconfig pattern specification <http://www.fontconfig.org/fontconfig-user.html>`_ for more information. """ # Author : Michael Droettboom <[email protected]> # License : matplotlib license (PSF compatible) # This class is defined here because it must be available in: # - The old-style config framework (:file:`rcsetup.py`) # - The traits-based config framework (:file:`mpltraits.py`) # - The font manager (:file:`font_manager.py`) # It probably logically belongs in :file:`font_manager.py`, but # placing it in any of these places would have created cyclical # dependency problems, or an undesired dependency on traits even # when the traits-based config framework is not used. import re from matplotlib.pyparsing import Literal, ZeroOrMore, \ Optional, Regex, StringEnd, ParseException, Suppress family_punc = r'\\\-:,' family_unescape = re.compile(r'\\([%s])' % family_punc).sub family_escape = re.compile(r'([%s])' % family_punc).sub value_punc = r'\\=_:,' value_unescape = re.compile(r'\\([%s])' % value_punc).sub value_escape = re.compile(r'([%s])' % value_punc).sub class FontconfigPatternParser: """A simple pyparsing-based parser for fontconfig-style patterns. See the `fontconfig pattern specification <http://www.fontconfig.org/fontconfig-user.html>`_ for more information. """ _constants = { 'thin' : ('weight', 'light'), 'extralight' : ('weight', 'light'), 'ultralight' : ('weight', 'light'), 'light' : ('weight', 'light'), 'book' : ('weight', 'book'), 'regular' : ('weight', 'regular'), 'normal' : ('weight', 'normal'), 'medium' : ('weight', 'medium'), 'demibold' : ('weight', 'demibold'), 'semibold' : ('weight', 'semibold'), 'bold' : ('weight', 'bold'), 'extrabold' : ('weight', 'extra bold'), 'black' : ('weight', 'black'), 'heavy' : ('weight', 'heavy'), 'roman' : ('slant', 'normal'), 'italic' : ('slant', 'italic'), 'oblique' : ('slant', 'oblique'), 'ultracondensed' : ('width', 'ultra-condensed'), 'extracondensed' : ('width', 'extra-condensed'), 'condensed' : ('width', 'condensed'), 'semicondensed' : ('width', 'semi-condensed'), 'expanded' : ('width', 'expanded'), 'extraexpanded' : ('width', 'extra-expanded'), 'ultraexpanded' : ('width', 'ultra-expanded') } def __init__(self): family = Regex(r'([^%s]\|(\\[%s]))' % (family_punc, family_punc)) \ .setParseAction(self._family) size = Regex(r"([0-9]+\.?[0-9]\|\.[0-9]+)") \ .setParseAction(self._size) name = Regex(r'[a-z]+') \ .setParseAction(self._name) value = Regex(r'([^%s]\|(\\[%s]))' % (value_punc, value_punc)) \ .setParseAction(self._value) families =(family + ZeroOrMore( Literal(',') + family) ).setParseAction(self._families) point_sizes =(size + ZeroOrMore( Literal(',') + size) ).setParseAction(self._point_sizes) property =( (name + Suppress(Literal('=')) + value + ZeroOrMore( Suppress(Literal(',')) + value) ) \| name ).setParseAction(self._property) pattern =(Optional( families) + Optional( Literal('-') + point_sizes) + ZeroOrMore( Literal(':') + property) + StringEnd() ) self._parser = pattern self.ParseException = ParseException def parse(self, pattern): """ Parse the given fontconfig pattern* and return a dictionary of key/value pairs useful for initializing a :class:`font_manager.FontProperties` object. """ props = self._properties = {} try: self._parser.parseString(pattern) except self.ParseException, e: raise ValueError("Could not parse font string: '%s'\n%s" % (pattern, e)) self._properties = None return props def _family(self, s, loc, tokens): return [family_unescape(r'\1', str(tokens[0]))] def _size(self, s, loc, tokens): return [float(tokens[0])] def _name(self, s, loc, tokens): return [str(tokens[0])] def _value(self, s, loc, tokens): return [value_unescape(r'\1', str(tokens[0]))] def _families(self, s, loc, tokens): self._properties['family'] = [str(x) for x in tokens] return [] def _point_sizes(self, s, loc, tokens): self._properties['size'] = [str(x) for x in tokens] return [] def _property(self, s, loc, tokens): if len(tokens) == 1: if tokens[0] in self._constants: key, val = self._constants[tokens[0]] self._properties.setdefault(key, []).append(val) else: key = tokens[0] val = tokens[1:] self._properties.setdefault(key, []).extend(val) return [] parse_fontconfig_pattern = FontconfigPatternParser().parse def generate_fontconfig_pattern(d): """ Given a dictionary of key/value pairs, generates a fontconfig pattern string. """ props = [] families = '' size = '' for key in 'family style variant weight stretch file size'.split(): val = getattr(d, 'get_' + key)() if val is not None and val != []: if type(val) == list: val = [value_escape(r'\\\1', str(x)) for x in val if x is not None] if val != []: val = ','.join(val) props.append(":%s=%s" % (key, val)) return ''.join(props)	gpl-3.0
aewhatley/scikit-learn	sklearn/decomposition/tests/test_incremental_pca.py	297	8265	"""Tests for Incremental PCA.""" import numpy as np from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_raises from sklearn import datasets from sklearn.decomposition import PCA, IncrementalPCA iris = datasets.load_iris() def test_incremental_pca(): # Incremental PCA on dense arrays. X = iris.data batch_size = X.shape[0] // 3 ipca = IncrementalPCA(n_components=2, batch_size=batch_size) pca = PCA(n_components=2) pca.fit_transform(X) X_transformed = ipca.fit_transform(X) np.testing.assert_equal(X_transformed.shape, (X.shape[0], 2)) assert_almost_equal(ipca.explained_variance_ratio_.sum(), pca.explained_variance_ratio_.sum(), 1) for n_components in [1, 2, X.shape[1]]: ipca = IncrementalPCA(n_components, batch_size=batch_size) ipca.fit(X) cov = ipca.get_covariance() precision = ipca.get_precision() assert_array_almost_equal(np.dot(cov, precision), np.eye(X.shape[1])) def test_incremental_pca_check_projection(): # Test that the projection of data is correct. rng = np.random.RandomState(1999) n, p = 100, 3 X = rng.randn(n, p) * .1 X[:10] += np.array([3, 4, 5]) Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5]) # Get the reconstruction of the generated data X # Note that Xt has the same "components" as X, just separated # This is what we want to ensure is recreated correctly Yt = IncrementalPCA(n_components=2).fit(X).transform(Xt) # Normalize Yt /= np.sqrt((Yt ** 2).sum()) # Make sure that the first element of Yt is ~1, this means # the reconstruction worked as expected assert_almost_equal(np.abs(Yt[0][0]), 1., 1) def test_incremental_pca_inverse(): # Test that the projection of data can be inverted. rng = np.random.RandomState(1999) n, p = 50, 3 X = rng.randn(n, p) # spherical data X[:, 1] = .00001 # make middle component relatively small X += [5, 4, 3] # make a large mean # same check that we can find the original data from the transformed # signal (since the data is almost of rank n_components) ipca = IncrementalPCA(n_components=2, batch_size=10).fit(X) Y = ipca.transform(X) Y_inverse = ipca.inverse_transform(Y) assert_almost_equal(X, Y_inverse, decimal=3) def test_incremental_pca_validation(): # Test that n_components is >=1 and <= n_features. X = [[0, 1], [1, 0]] for n_components in [-1, 0, .99, 3]: assert_raises(ValueError, IncrementalPCA(n_components, batch_size=10).fit, X) def test_incremental_pca_set_params(): # Test that components_ sign is stable over batch sizes. rng = np.random.RandomState(1999) n_samples = 100 n_features = 20 X = rng.randn(n_samples, n_features) X2 = rng.randn(n_samples, n_features) X3 = rng.randn(n_samples, n_features) ipca = IncrementalPCA(n_components=20) ipca.fit(X) # Decreasing number of components ipca.set_params(n_components=10) assert_raises(ValueError, ipca.partial_fit, X2) # Increasing number of components ipca.set_params(n_components=15) assert_raises(ValueError, ipca.partial_fit, X3) # Returning to original setting ipca.set_params(n_components=20) ipca.partial_fit(X) def test_incremental_pca_num_features_change(): # Test that changing n_components will raise an error. rng = np.random.RandomState(1999) n_samples = 100 X = rng.randn(n_samples, 20) X2 = rng.randn(n_samples, 50) ipca = IncrementalPCA(n_components=None) ipca.fit(X) assert_raises(ValueError, ipca.partial_fit, X2) def test_incremental_pca_batch_signs(): # Test that components_ sign is stable over batch sizes. rng = np.random.RandomState(1999) n_samples = 100 n_features = 3 X = rng.randn(n_samples, n_features) all_components = [] batch_sizes = np.arange(10, 20) for batch_size in batch_sizes: ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X) all_components.append(ipca.components_) for i, j in zip(all_components[:-1], all_components[1:]): assert_almost_equal(np.sign(i), np.sign(j), decimal=6) def test_incremental_pca_batch_values(): # Test that components_ values are stable over batch sizes. rng = np.random.RandomState(1999) n_samples = 100 n_features = 3 X = rng.randn(n_samples, n_features) all_components = [] batch_sizes = np.arange(20, 40, 3) for batch_size in batch_sizes: ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X) all_components.append(ipca.components_) for i, j in zip(all_components[:-1], all_components[1:]): assert_almost_equal(i, j, decimal=1) def test_incremental_pca_partial_fit(): # Test that fit and partial_fit get equivalent results. rng = np.random.RandomState(1999) n, p = 50, 3 X = rng.randn(n, p) # spherical data X[:, 1] = .00001 # make middle component relatively small X += [5, 4, 3] # make a large mean # same check that we can find the original data from the transformed # signal (since the data is almost of rank n_components) batch_size = 10 ipca = IncrementalPCA(n_components=2, batch_size=batch_size).fit(X) pipca = IncrementalPCA(n_components=2, batch_size=batch_size) # Add one to make sure endpoint is included batch_itr = np.arange(0, n + 1, batch_size) for i, j in zip(batch_itr[:-1], batch_itr[1:]): pipca.partial_fit(X[i:j, :]) assert_almost_equal(ipca.components_, pipca.components_, decimal=3) def test_incremental_pca_against_pca_iris(): # Test that IncrementalPCA and PCA are approximate (to a sign flip). X = iris.data Y_pca = PCA(n_components=2).fit_transform(X) Y_ipca = IncrementalPCA(n_components=2, batch_size=25).fit_transform(X) assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1) def test_incremental_pca_against_pca_random_data(): # Test that IncrementalPCA and PCA are approximate (to a sign flip). rng = np.random.RandomState(1999) n_samples = 100 n_features = 3 X = rng.randn(n_samples, n_features) + 5 * rng.rand(1, n_features) Y_pca = PCA(n_components=3).fit_transform(X) Y_ipca = IncrementalPCA(n_components=3, batch_size=25).fit_transform(X) assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1) def test_explained_variances(): # Test that PCA and IncrementalPCA calculations match X = datasets.make_low_rank_matrix(1000, 100, tail_strength=0., effective_rank=10, random_state=1999) prec = 3 n_samples, n_features = X.shape for nc in [None, 99]: pca = PCA(n_components=nc).fit(X) ipca = IncrementalPCA(n_components=nc, batch_size=100).fit(X) assert_almost_equal(pca.explained_variance_, ipca.explained_variance_, decimal=prec) assert_almost_equal(pca.explained_variance_ratio_, ipca.explained_variance_ratio_, decimal=prec) assert_almost_equal(pca.noise_variance_, ipca.noise_variance_, decimal=prec) def test_whitening(): # Test that PCA and IncrementalPCA transforms match to sign flip. X = datasets.make_low_rank_matrix(1000, 10, tail_strength=0., effective_rank=2, random_state=1999) prec = 3 n_samples, n_features = X.shape for nc in [None, 9]: pca = PCA(whiten=True, n_components=nc).fit(X) ipca = IncrementalPCA(whiten=True, n_components=nc, batch_size=250).fit(X) Xt_pca = pca.transform(X) Xt_ipca = ipca.transform(X) assert_almost_equal(np.abs(Xt_pca), np.abs(Xt_ipca), decimal=prec) Xinv_ipca = ipca.inverse_transform(Xt_ipca) Xinv_pca = pca.inverse_transform(Xt_pca) assert_almost_equal(X, Xinv_ipca, decimal=prec) assert_almost_equal(X, Xinv_pca, decimal=prec) assert_almost_equal(Xinv_pca, Xinv_ipca, decimal=prec)	bsd-3-clause
michaelpacer/networkx	examples/drawing/atlas.py	54	2609	#!/usr/bin/env python """ Atlas of all graphs of 6 nodes or less. """ __author__ = """Aric Hagberg ([email protected])""" # Copyright (C) 2004 by # Aric Hagberg <[email protected]> # Dan Schult <[email protected]> # Pieter Swart <[email protected]> # All rights reserved. # BSD license. import networkx as nx from networkx.generators.atlas import * from networkx.algorithms.isomorphism.isomorph import graph_could_be_isomorphic as isomorphic import random def atlas6(): """ Return the atlas of all connected graphs of 6 nodes or less. Attempt to check for isomorphisms and remove. """ Atlas=graph_atlas_g()[0:208] # 208 # remove isolated nodes, only connected graphs are left U=nx.Graph() # graph for union of all graphs in atlas for G in Atlas: zerodegree=[n for n in G if G.degree(n)==0] for n in zerodegree: G.remove_node(n) U=nx.disjoint_union(U,G) # list of graphs of all connected components C=nx.connected_component_subgraphs(U) UU=nx.Graph() # do quick isomorphic-like check, not a true isomorphism checker nlist=[] # list of nonisomorphic graphs for G in C: # check against all nonisomorphic graphs so far if not iso(G,nlist): nlist.append(G) UU=nx.disjoint_union(UU,G) # union the nonisomorphic graphs return UU def iso(G1, glist): """Quick and dirty nonisomorphism checker used to check isomorphisms.""" for G2 in glist: if isomorphic(G1,G2): return True return False if __name__ == '__main__': import networkx as nx G=atlas6() print("graph has %d nodes with %d edges"\ %(nx.number_of_nodes(G),nx.number_of_edges(G))) print(nx.number_connected_components(G),"connected components") try: from networkx import graphviz_layout except ImportError: raise ImportError("This example needs Graphviz and either PyGraphviz or Pydot") import matplotlib.pyplot as plt plt.figure(1,figsize=(8,8)) # layout graphs with positions using graphviz neato pos=nx.graphviz_layout(G,prog="neato") # color nodes the same in each connected subgraph C=nx.connected_component_subgraphs(G) for g in C: c=[random.random()]*nx.number_of_nodes(g) # random color... nx.draw(g, pos, node_size=40, node_color=c, vmin=0.0, vmax=1.0, with_labels=False ) plt.savefig("atlas.png",dpi=75)	bsd-3-clause
SeonghoBaek/RealtimeCamera	openface/training/plot-loss.py	8	3032	#!/usr/bin/env python3 # # Copyright 2015-2016 Carnegie Mellon University # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import matplotlib as mpl mpl.use('Agg') import matplotlib.pyplot as plt plt.style.use('bmh') import pandas as pd import os import sys scriptDir = os.path.dirname(os.path.realpath(__file__)) plotDir = os.path.join(scriptDir, 'plots') # workDir = os.path.join(scriptDir, 'work') def plot(workDirs): trainDfs = [] testDfs = [] for d in workDirs: trainF = os.path.join(d, 'train.log') testF = os.path.join(d, 'test.log') trainDfs.append(pd.read_csv(trainF, sep='\t')) testDfs.append(pd.read_csv(testF, sep='\t')) if len(trainDfs[-1]) != len(testDfs[-1]): print("Error: Train/test dataframe shapes " "for '{}' don't match: {}, {}".format( d, trainDfs[-1].shape, testDfs[-1].shape)) sys.exit(-1) trainDf = pd.concat(trainDfs, ignore_index=True) testDf = pd.concat(testDfs, ignore_index=True) # print("train, test:") # print("\n".join(["{:0.2e}, {:0.2e}".format(x, y) for (x, y) in # zip(trainDf['avg triplet loss (train set)'].values[-5:], # testDf['avg triplet loss (test set)'].values[-5:])])) fig, ax = plt.subplots(1, 1) trainDf.index += 1 trainDf['avg triplet loss (train set)'].plot(ax=ax) plt.xlabel("Epoch") plt.ylabel("Average Triplet Loss, Training") plt.ylim(ymin=0) # plt.xlim(xmin=1) plt.grid(b=True, which='major', color='k', linestyle='-') plt.grid(b=True, which='minor', color='k', linestyle='-', alpha=0.2) plt.minorticks_on() # ax.set_yscale('log') d = os.path.join(plotDir, "train-loss.pdf") fig.savefig(d) print("Created {}".format(d)) fig, ax = plt.subplots(1, 1) testDf.index += 1 testDf['lfwAcc'].plot(ax=ax) plt.xlabel("Epoch") plt.ylabel("LFW Accuracy") plt.ylim(ymin=0, ymax=1) plt.grid(b=True, which='major', color='k', linestyle='-') plt.grid(b=True, which='minor', color='k', linestyle='-', alpha=0.2) plt.minorticks_on() # plt.xlim(xmin=1) # ax.set_yscale('log') d = os.path.join(plotDir, "lfw-accuracy.pdf") fig.savefig(d) print("Created {}".format(d)) if __name__ == '__main__': os.makedirs(plotDir, exist_ok=True) parser = argparse.ArgumentParser() parser.add_argument('workDirs', type=str, nargs='+') args = parser.parse_args() plot(args.workDirs)	apache-2.0
jereze/scikit-learn	sklearn/datasets/svmlight_format.py	79	15976	"""This module implements a loader and dumper for the svmlight format This format is a text-based format, with one sample per line. It does not store zero valued features hence is suitable for sparse dataset. The first element of each line can be used to store a target variable to predict. This format is used as the default format for both svmlight and the libsvm command line programs. """ # Authors: Mathieu Blondel <[email protected]> # Lars Buitinck <[email protected]> # Olivier Grisel <[email protected]> # License: BSD 3 clause from contextlib import closing import io import os.path import numpy as np import scipy.sparse as sp from ._svmlight_format import _load_svmlight_file from .. import __version__ from ..externals import six from ..externals.six import u, b from ..externals.six.moves import range, zip from ..utils import check_array from ..utils.fixes import frombuffer_empty def load_svmlight_file(f, n_features=None, dtype=np.float64, multilabel=False, zero_based="auto", query_id=False): """Load datasets in the svmlight / libsvm format into sparse CSR matrix This format is a text-based format, with one sample per line. It does not store zero valued features hence is suitable for sparse dataset. The first element of each line can be used to store a target variable to predict. This format is used as the default format for both svmlight and the libsvm command line programs. Parsing a text based source can be expensive. When working on repeatedly on the same dataset, it is recommended to wrap this loader with joblib.Memory.cache to store a memmapped backup of the CSR results of the first call and benefit from the near instantaneous loading of memmapped structures for the subsequent calls. In case the file contains a pairwise preference constraint (known as "qid" in the svmlight format) these are ignored unless the query_id parameter is set to True. These pairwise preference constraints can be used to constraint the combination of samples when using pairwise loss functions (as is the case in some learning to rank problems) so that only pairs with the same query_id value are considered. This implementation is written in Cython and is reasonably fast. However, a faster API-compatible loader is also available at: https://github.com/mblondel/svmlight-loader Parameters ---------- f : {str, file-like, int} (Path to) a file to load. If a path ends in ".gz" or ".bz2", it will be uncompressed on the fly. If an integer is passed, it is assumed to be a file descriptor. A file-like or file descriptor will not be closed by this function. A file-like object must be opened in binary mode. n_features : int or None The number of features to use. If None, it will be inferred. This argument is useful to load several files that are subsets of a bigger sliced dataset: each subset might not have examples of every feature, hence the inferred shape might vary from one slice to another. multilabel : boolean, optional, default False Samples may have several labels each (see http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html) zero_based : boolean or "auto", optional, default "auto" Whether column indices in f are zero-based (True) or one-based (False). If column indices are one-based, they are transformed to zero-based to match Python/NumPy conventions. If set to "auto", a heuristic check is applied to determine this from the file contents. Both kinds of files occur "in the wild", but they are unfortunately not self-identifying. Using "auto" or True should always be safe. query_id : boolean, default False If True, will return the query_id array for each file. dtype : numpy data type, default np.float64 Data type of dataset to be loaded. This will be the data type of the output numpy arrays ``X`` and ``y``. Returns ------- X: scipy.sparse matrix of shape (n_samples, n_features) y: ndarray of shape (n_samples,), or, in the multilabel a list of tuples of length n_samples. query_id: array of shape (n_samples,) query_id for each sample. Only returned when query_id is set to True. See also -------- load_svmlight_files: similar function for loading multiple files in this format, enforcing the same number of features/columns on all of them. Examples -------- To use joblib.Memory to cache the svmlight file:: from sklearn.externals.joblib import Memory from sklearn.datasets import load_svmlight_file mem = Memory("./mycache") @mem.cache def get_data(): data = load_svmlight_file("mysvmlightfile") return data[0], data[1] X, y = get_data() """ return tuple(load_svmlight_files([f], n_features, dtype, multilabel, zero_based, query_id)) def _gen_open(f): if isinstance(f, int): # file descriptor return io.open(f, "rb", closefd=False) elif not isinstance(f, six.string_types): raise TypeError("expected {str, int, file-like}, got %s" % type(f)) _, ext = os.path.splitext(f) if ext == ".gz": import gzip return gzip.open(f, "rb") elif ext == ".bz2": from bz2 import BZ2File return BZ2File(f, "rb") else: return open(f, "rb") def _open_and_load(f, dtype, multilabel, zero_based, query_id): if hasattr(f, "read"): actual_dtype, data, ind, indptr, labels, query = \ _load_svmlight_file(f, dtype, multilabel, zero_based, query_id) # XXX remove closing when Python 2.7+/3.1+ required else: with closing(_gen_open(f)) as f: actual_dtype, data, ind, indptr, labels, query = \ _load_svmlight_file(f, dtype, multilabel, zero_based, query_id) # convert from array.array, give data the right dtype if not multilabel: labels = frombuffer_empty(labels, np.float64) data = frombuffer_empty(data, actual_dtype) indices = frombuffer_empty(ind, np.intc) indptr = np.frombuffer(indptr, dtype=np.intc) # never empty query = frombuffer_empty(query, np.intc) data = np.asarray(data, dtype=dtype) # no-op for float{32,64} return data, indices, indptr, labels, query def load_svmlight_files(files, n_features=None, dtype=np.float64, multilabel=False, zero_based="auto", query_id=False): """Load dataset from multiple files in SVMlight format This function is equivalent to mapping load_svmlight_file over a list of files, except that the results are concatenated into a single, flat list and the samples vectors are constrained to all have the same number of features. In case the file contains a pairwise preference constraint (known as "qid" in the svmlight format) these are ignored unless the query_id parameter is set to True. These pairwise preference constraints can be used to constraint the combination of samples when using pairwise loss functions (as is the case in some learning to rank problems) so that only pairs with the same query_id value are considered. Parameters ---------- files : iterable over {str, file-like, int} (Paths of) files to load. If a path ends in ".gz" or ".bz2", it will be uncompressed on the fly. If an integer is passed, it is assumed to be a file descriptor. File-likes and file descriptors will not be closed by this function. File-like objects must be opened in binary mode. n_features: int or None The number of features to use. If None, it will be inferred from the maximum column index occurring in any of the files. This can be set to a higher value than the actual number of features in any of the input files, but setting it to a lower value will cause an exception to be raised. multilabel: boolean, optional Samples may have several labels each (see http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html) zero_based: boolean or "auto", optional Whether column indices in f are zero-based (True) or one-based (False). If column indices are one-based, they are transformed to zero-based to match Python/NumPy conventions. If set to "auto", a heuristic check is applied to determine this from the file contents. Both kinds of files occur "in the wild", but they are unfortunately not self-identifying. Using "auto" or True should always be safe. query_id: boolean, defaults to False If True, will return the query_id array for each file. dtype : numpy data type, default np.float64 Data type of dataset to be loaded. This will be the data type of the output numpy arrays ``X`` and ``y``. Returns ------- [X1, y1, ..., Xn, yn] where each (Xi, yi) pair is the result from load_svmlight_file(files[i]). If query_id is set to True, this will return instead [X1, y1, q1, ..., Xn, yn, qn] where (Xi, yi, qi) is the result from load_svmlight_file(files[i]) Notes ----- When fitting a model to a matrix X_train and evaluating it against a matrix X_test, it is essential that X_train and X_test have the same number of features (X_train.shape[1] == X_test.shape[1]). This may not be the case if you load the files individually with load_svmlight_file. See also -------- load_svmlight_file """ r = [_open_and_load(f, dtype, multilabel, bool(zero_based), bool(query_id)) for f in files] if (zero_based is False or zero_based == "auto" and all(np.min(tmp[1]) > 0 for tmp in r)): for ind in r: indices = ind[1] indices -= 1 n_f = max(ind[1].max() for ind in r) + 1 if n_features is None: n_features = n_f elif n_features < n_f: raise ValueError("n_features was set to {}," " but input file contains {} features" .format(n_features, n_f)) result = [] for data, indices, indptr, y, query_values in r: shape = (indptr.shape[0] - 1, n_features) X = sp.csr_matrix((data, indices, indptr), shape) X.sort_indices() result += X, y if query_id: result.append(query_values) return result def _dump_svmlight(X, y, f, multilabel, one_based, comment, query_id): is_sp = int(hasattr(X, "tocsr")) if X.dtype.kind == 'i': value_pattern = u("%d:%d") else: value_pattern = u("%d:%.16g") if y.dtype.kind == 'i': label_pattern = u("%d") else: label_pattern = u("%.16g") line_pattern = u("%s") if query_id is not None: line_pattern += u(" qid:%d") line_pattern += u(" %s\n") if comment: f.write(b("# Generated by dump_svmlight_file from scikit-learn %s\n" % __version__)) f.write(b("# Column indices are %s-based\n" % ["zero", "one"][one_based])) f.write(b("#\n")) f.writelines(b("# %s\n" % line) for line in comment.splitlines()) for i in range(X.shape[0]): if is_sp: span = slice(X.indptr[i], X.indptr[i + 1]) row = zip(X.indices[span], X.data[span]) else: nz = X[i] != 0 row = zip(np.where(nz)[0], X[i, nz]) s = " ".join(value_pattern % (j + one_based, x) for j, x in row) if multilabel: nz_labels = np.where(y[i] != 0)[0] labels_str = ",".join(label_pattern % j for j in nz_labels) else: labels_str = label_pattern % y[i] if query_id is not None: feat = (labels_str, query_id[i], s) else: feat = (labels_str, s) f.write((line_pattern % feat).encode('ascii')) def dump_svmlight_file(X, y, f, zero_based=True, comment=None, query_id=None, multilabel=False): """Dump the dataset in svmlight / libsvm file format. This format is a text-based format, with one sample per line. It does not store zero valued features hence is suitable for sparse dataset. The first element of each line can be used to store a target variable to predict. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_labels] Target values. Class labels must be an integer or float, or array-like objects of integer or float for multilabel classifications. f : string or file-like in binary mode If string, specifies the path that will contain the data. If file-like, data will be written to f. f should be opened in binary mode. zero_based : boolean, optional Whether column indices should be written zero-based (True) or one-based (False). comment : string, optional Comment to insert at the top of the file. This should be either a Unicode string, which will be encoded as UTF-8, or an ASCII byte string. If a comment is given, then it will be preceded by one that identifies the file as having been dumped by scikit-learn. Note that not all tools grok comments in SVMlight files. query_id : array-like, shape = [n_samples] Array containing pairwise preference constraints (qid in svmlight format). multilabel: boolean, optional Samples may have several labels each (see http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html) """ if comment is not None: # Convert comment string to list of lines in UTF-8. # If a byte string is passed, then check whether it's ASCII; # if a user wants to get fancy, they'll have to decode themselves. # Avoid mention of str and unicode types for Python 3.x compat. if isinstance(comment, bytes): comment.decode("ascii") # just for the exception else: comment = comment.encode("utf-8") if six.b("\0") in comment: raise ValueError("comment string contains NUL byte") y = np.asarray(y) if y.ndim != 1 and not multilabel: raise ValueError("expected y of shape (n_samples,), got %r" % (y.shape,)) Xval = check_array(X, accept_sparse='csr') if Xval.shape[0] != y.shape[0]: raise ValueError("X.shape[0] and y.shape[0] should be the same, got" " %r and %r instead." % (Xval.shape[0], y.shape[0])) # We had some issues with CSR matrices with unsorted indices (e.g. #1501), # so sort them here, but first make sure we don't modify the user's X. # TODO We can do this cheaper; sorted_indices copies the whole matrix. if Xval is X and hasattr(Xval, "sorted_indices"): X = Xval.sorted_indices() else: X = Xval if hasattr(X, "sort_indices"): X.sort_indices() if query_id is not None: query_id = np.asarray(query_id) if query_id.shape[0] != y.shape[0]: raise ValueError("expected query_id of shape (n_samples,), got %r" % (query_id.shape,)) one_based = not zero_based if hasattr(f, "write"): _dump_svmlight(X, y, f, multilabel, one_based, comment, query_id) else: with open(f, "wb") as f: _dump_svmlight(X, y, f, multilabel, one_based, comment, query_id)	bsd-3-clause
tosolveit/scikit-learn	examples/ensemble/plot_adaboost_regression.py	311	1529	""" ====================================== Decision Tree Regression with AdaBoost ====================================== A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D sinusoidal dataset with a small amount of Gaussian noise. 299 boosts (300 decision trees) is compared with a single decision tree regressor. As the number of boosts is increased the regressor can fit more detail. .. [1] H. Drucker, "Improving Regressors using Boosting Techniques", 1997. """ print(__doc__) # Author: Noel Dawe <[email protected]> # # License: BSD 3 clause # importing necessary libraries import numpy as np import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import AdaBoostRegressor # Create the dataset rng = np.random.RandomState(1) X = np.linspace(0, 6, 100)[:, np.newaxis] y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0]) # Fit regression model regr_1 = DecisionTreeRegressor(max_depth=4) regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4), n_estimators=300, random_state=rng) regr_1.fit(X, y) regr_2.fit(X, y) # Predict y_1 = regr_1.predict(X) y_2 = regr_2.predict(X) # Plot the results plt.figure() plt.scatter(X, y, c="k", label="training samples") plt.plot(X, y_1, c="g", label="n_estimators=1", linewidth=2) plt.plot(X, y_2, c="r", label="n_estimators=300", linewidth=2) plt.xlabel("data") plt.ylabel("target") plt.title("Boosted Decision Tree Regression") plt.legend() plt.show()	bsd-3-clause
vishalpant/Sentiment-analysis-of-streaming-tweets	test.py	1	1528	import load import os import Sentiment import Filter from sklearn.metrics import classification_report from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix def evaluate_model(target_true,target_predicted): print("The confusion matrix is as follows:") print(confusion_matrix(target_true,target_predicted)) print("The classification report is as follows:") print(classification_report(target_true,target_predicted)) print("The accuracy score is {:.2%}".format(accuracy_score(target_true,target_predicted))) if __name__ == "__main__": if not os.path.isfile("sentiments.pickle") or not os.path.isfile("tweets.pickle"): datafile = str(input("Enter path of training data(Should be in .csv):")) # replace 4th parameter with the column number of your tweets # replace 5th parameter with the column number of your sentiments data, target = load.load_data(datafile, ",", '"', 5, 0) load.save_sentiments(target) load.save_tweets(data) else: data = load.load_tweets() target = load.load_sentiments() tf_idf = Filter.filter(data) #test data is taken to be 40% of total data and remaining 60% is training data data_train, data_test, target_train, target_test = Sentiment.data_generate(tf_idf, 0.4, target) classifier = Sentiment.learn(data_train, target_train) prediction = Sentiment.predict(data_test, classifier) evaluate_model(target_test, prediction)	mit
james4424/nest-simulator	topology/pynest/hl_api.py	4	68298	# -- coding: utf-8 -- # # hl_api.py # # This file is part of NEST. # # Copyright (C) 2004 The NEST Initiative # # NEST is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # # NEST is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with NEST. If not, see <http://www.gnu.org/licenses/>. """ High-level API of PyNEST Topology Module This file defines the user-level functions of NEST's Python interface to the Topology module. The basic approach is the same as for the PyNEST interface to NEST: 1. Function names are the same as in SLI. 2. Nodes are identified by their GIDs. 3. GIDs are always given as tuples or lists of integer(s). 4. Commands returning GIDs return them as tuples. 5. Other arguments can be * single items that are applied to all entries in a GID list * a list of the same length as the given list of GID(s) where each item is matched with the pertaining GID. Example :: layers = CreateLayer(({...}, {...}, {...})) creates three layers and returns a tuple of three GIDs. :: ConnectLayers(layers[:2], layers[1:], {...}) connects `layers[0]` to `layers[1]` and `layers[1]` to `layers[2]` \ using the same dictionary to specify both connections. :: ConnectLayers(layers[:2], layers[1:], ({...}, {...})) connects the same layers, but the `layers[0]` to `layers[1]` connection is specified by the first dictionary, the `layers[1]` to `layers[2]` connection by the second. :Authors: Kittel Austvoll, Hans Ekkehard Plesser, Hakon Enger """ import nest def topology_func(slifunc, args): """ Execute SLI function `slifunc` with arguments `args` in Topology namespace. Parameters ---------- slifunc : str SLI namespace expression Other parameters ---------------- args : dict An arbitrary number of arguments Returns ------- out : Values from SLI function `slifunc` See also -------- nest.sli_func """ return nest.sli_func(slifunc, args) class Mask(object): """ Class for spatial masks. Masks are used when creating connections in the Topology module. A mask describes which area of the pool layer shall be searched for nodes to connect for any given node in the driver layer. Masks are created using the ``CreateMask`` command. """ _datum = None # The constructor should not be called by the user def __init__(self, datum): """Masks must be created using the CreateMask command.""" if not isinstance(datum, nest.SLIDatum) or datum.dtype != "masktype": raise TypeError("expected mask Datum") self._datum = datum # Generic binary operation def _binop(self, op, other): if not isinstance(other, Mask): return NotImplemented return Mask(topology_func(op, self._datum, other._datum)) def __or__(self, other): return self._binop("or", other) def __and__(self, other): return self._binop("and", other) def __sub__(self, other): return self._binop("sub", other) def Inside(self, point): """ Test if a point is inside a mask. Parameters ---------- point : tuple/list of float values Coordinate of point Returns ------- out : bool True if the point is inside the mask, False otherwise """ return topology_func("Inside", point, self._datum) def CreateMask(masktype, specs, anchor=None): """ Create a spatial mask for connections. Masks are used when creating connections in the Topology module. A mask describes the area of the pool layer that is searched for nodes to connect for any given node in the driver layer. Several mask types are available. Examples are the grid region, the rectangular, circular or doughnut region. The command ``CreateMask`` creates a Mask object which may be combined with other ``Mask`` objects using Boolean operators. The mask is specified in a dictionary. ``Mask`` objects can be passed to ``ConnectLayers`` in a connection dictionary with the key `'mask'`. Parameters ---------- masktype : str, ['rectangular' \| 'circular' \| 'doughnut'] for 2D masks, \ ['box' \| 'spherical'] for 3D masks, ['grid'] only for grid-based layers in 2D The mask name corresponds to the geometrical shape of the mask. There are different types for 2- and 3-dimensional layers. specs : dict Dictionary specifying the parameters of the provided `masktype`, see Notes. anchor : [tuple/list of floats \| dict with the keys `'column'` and \ `'row'` (for grid masks only)], optional, default: None By providing anchor coordinates, the location of the mask relative to the driver node can be changed. The list of coordinates has a length of 2 or 3 dependent on the number of dimensions. Returns ------- out : ``Mask`` object See also -------- ConnectLayers: Connect two (lists of) layers pairwise according to specified projections. ``Mask`` objects can be passed in a connection dictionary with the key `'mask'`. Notes ----- - Mask types Available mask types (`masktype`) and their corresponding parameter dictionaries: * 2D free and grid-based layers :: 'rectangular' : {'lower_left' : [float, float], 'upper_right': [float, float]} #or 'circular' : {'radius' : float} #or 'doughnut' : {'inner_radius' : float, 'outer_radius' : float} * 3D free and grid-based layers :: 'box' : {'lower_left' : [float, float, float], 'upper_right' : [float, float, float]} #or 'spherical' : {'radius' : float} * 2D grid-based layers only :: 'grid' : {'rows' : float, 'columns' : float} By default the top-left corner of a grid mask, i.e., the grid mask element with grid index [0, 0], is aligned with the driver node. It can be changed by means of the 'anchor' parameter: :: 'anchor' : {'row' : float, 'column' : float} Example :: import nest.topology as tp # create a grid-based layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # create a circular mask m = tp.CreateMask('circular', {'radius': 0.2}) # connectivity specifications conndict = {'connection_type': 'divergent', 'mask' : m} # connect layer l with itself according to the specifications tp.ConnectLayers(l, l, conndict) """ if anchor is None: return Mask(topology_func('CreateMask', {masktype: specs})) else: return Mask( topology_func('CreateMask', {masktype: specs, 'anchor': anchor})) class Parameter(object): """ Class for parameters for distance dependency or randomization. Parameters are spatial functions which are used when creating connections in the Topology module. A parameter may be used as a probability kernel when creating connections or as synaptic parameters (such as weight and delay). Parameters are created using the ``CreateParameter`` command. """ _datum = None # The constructor should not be called by the user def __init__(self, datum): """Parameters must be created using the CreateParameter command.""" if not isinstance(datum, nest.SLIDatum) or datum.dtype != "parametertype": raise TypeError("expected parameter datum") self._datum = datum # Generic binary operation def _binop(self, op, other): if not isinstance(other, Parameter): return NotImplemented return Parameter(topology_func(op, self._datum, other._datum)) def __add__(self, other): return self._binop("add", other) def __sub__(self, other): return self._binop("sub", other) def __mul__(self, other): return self._binop("mul", other) def __div__(self, other): return self._binop("div", other) def __truediv__(self, other): return self._binop("div", other) def GetValue(self, point): """ Compute value of parameter at a point. Parameters ---------- point : tuple/list of float values coordinate of point Returns ------- out : value The value of the parameter at the point See also -------- CreateParameter : create parameter for e.g., distance dependency Notes ----- - Example :: import nest.topology as tp #linear dependent parameter P = tp.CreateParameter('linear', {'a' : 2., 'c' : 0.}) #get out value P.GetValue(point=[3., 4.]) """ return topology_func("GetValue", point, self._datum) def CreateParameter(parametertype, specs): """ Create a parameter for distance dependency or randomization. Parameters are (spatial) functions which are used when creating connections in the Topology module for distance dependency or randomization. This command creates a Parameter object which may be combined with other ``Parameter`` objects using arithmetic operators. The parameter is specified in a dictionary. A parameter may be used as a probability kernel when creating connections or as synaptic parameters (such as weight and delay), i.e., for specifying the parameters `'kernel'`, `'weights'` and `'delays'` in the connection dictionary passed to ``ConnectLayers``. Parameters ---------- parametertype : {'constant', 'linear', 'exponential', 'gaussian', \ 'gaussian2D', 'uniform', 'normal', 'lognormal'} Function types with or without distance dependency specs : dict Dictionary specifying the parameters of the provided `'parametertype'`, see Notes. Returns ------- out : ``Parameter`` object See also -------- ConnectLayers : Connect two (lists of) layers pairwise according to specified projections. Parameters can be used to specify the parameters `'kernel'`, `'weights'` and `'delays'` in the connection dictionary. Parameters : Class for parameters for distance dependency or randomization. Notes ----- - Parameter types Available parameter types (`parametertype` parameter), their function and acceptable keys for their corresponding specification dictionaries * Constant :: 'constant' : {'value' : float} # constant value * With dependence on the distance `d` :: # p(d) = c + a * d 'linear' : {'a' : float, # slope, default: 1.0 'c' : float} # constant offset, default: 0.0 # or # p(d) = c + aexp(-d/tau) 'exponential' : {'a' : float, # coefficient of exponential term, default: 1.0 'c' : float, # constant offset, default: 0.0 'tau' : float} # length scale factor, default: 1.0 # or # p(d) = c + p_centerexp(-(d-mean)^2/(2sigma^2)) 'gaussian' : {'p_center' : float, # value at center, default: 1.0 'mean' : float, # distance to center, default: 0.0 'sigma' : float, # width of Gaussian, default: 1.0 'c' : float} # constant offset, default: 0.0 Bivariate Gaussian parameter: :: # p(x,y) = c + p_center * # exp( -( (x-mean_x)^2/sigma_x^2 + (y-mean_y)^2/sigma_y^2 # + 2rho(x-mean_x)(y-mean_y)/(sigma_xsigma_y) ) / # (2(1-rho^2)) ) 'gaussian2D' : {'p_center' : float, # value at center, default: 1.0 'mean_x' : float, # x-coordinate of center, default: 0.0 'mean_y' : float, # y-coordinate of center, default: 0.0 'sigma_x' : float, # width in x-direction, default: 1.0 'sigma_y' : float, # width in y-direction, default: 1.0 'rho' : float, # correlation of x and y, default: 0.0 'c' : float} # constant offset, default: 0.0 Without distance dependency, for randomization :: # random parameter with uniform distribution in [min,max) 'uniform' : {'min' : float, # minimum value, default: 0.0 'max' : float} # maximum value, default: 1.0 # or # random parameter with normal distribution, optionally truncated # to [min,max) 'normal': {'mean' : float, # mean value, default: 0.0 'sigma': float, # standard deviation, default: 1.0 'min' : float, # minimum value, default: -inf 'max' : float} # maximum value, default: +inf # or # random parameter with lognormal distribution, # optionally truncated to [min,max) 'lognormal' : {'mu' : float, # mean value of logarithm, default: 0.0 'sigma': float, # standard deviation of log, default: 1.0 'min' : float, # minimum value, default: -inf 'max' : float} # maximum value, default: +inf Example :: import nest.topology as tp # create a grid-based layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # parameter for delay with linear distance dependency d = tp.CreateParameter('linear', {'a': 0.2, 'c': 0.2}) # connectivity specifications conndict = {'connection_type': 'divergent', 'delays': d} tp.ConnectLayers(l, l, conndict) """ return Parameter(topology_func('CreateParameter', {parametertype: specs})) def CreateLayer(specs): """ Create one ore more Topology layer(s) according to given specifications. The Topology module organizes neuronal networks in layers. A layer is a special type of subnet which contains information about the spatial position of its nodes (simple or composite elements) in 2 or 3 dimensions. If `specs` is a dictionary, a single layer is created. If it is a list of dictionaries, one layer is created for each dictionary. Topology distinguishes between two classes of layers: * grid-based layers in which each element is placed at a location in a regular grid * free layers in which elements can be placed arbitrarily Obligatory dictionary entries define the class of layer (grid-based layers: 'columns' and 'rows'; free layers: 'positions') and the 'elements'. Parameters ---------- specs : (tuple/list of) dict(s) Dictionary or list of dictionaries with layer specifications, see Notes. Returns ------- out : tuple of int(s) GID(s) of created layer(s) See also -------- ConnectLayers: Connect two (lists of) layers which were created with ``CreateLayer`` pairwise according to specified projections. Other parameters ---------------- Available parameters for the layer-specifying dictionary `specs` center : tuple/list of floats, optional, default: (0.0, 0.0) Layers are centered about the origin by default, but the center coordinates can also be changed. 'center' has length 2 or 3 dependent on the number of dimensions. columns : int, obligatory for grid-based layers Number of columns. Needs `'rows'`; mutually exclusive with `'positions'`. edge_wrap : bool, default: False Periodic boundary conditions. elements : (tuple/list of) str or str followed by int Elements of layers are NEST network nodes such as neuron models or devices. For network elements with several nodes of the same type, the number of nodes to be created must follow the model name. For composite elements, a collection of nodes can be passed as list or tuple. extent : tuple of floats, optional, default in 2D: (1.0, 1.0) Size of the layer. It has length 2 or 3 dependent on the number of dimensions. positions : tuple/list of coordinates (lists/tuples of floats), obligatory for free layers Explicit specification of the positions of all elements. The coordinates have a length 2 or 3 dependent on the number of dimensions. All element positions must be within the layer’s extent. Mutually exclusive with 'rows' and 'columns'. rows : int, obligatory for grid-based layers Number of rows. Needs `'columns'`; mutually exclusive with `'positions'`. Notes ----- - Example :: import nest import nest.topology as tp # grid-based layer gl = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # free layer import numpy as np pos = [[np.random.uniform(-0.5, 0.5), np.random.uniform(-0.5,0.5)] for i in range(50)] fl = tp.CreateLayer({'positions' : pos, 'elements' : 'iaf_neuron'}) # extent, center and edge_wrap el = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'extent' : [2.0, 3.0], 'center' : [1.0, 1.5], 'edge_wrap' : True, 'elements' : 'iaf_neuron'}) # composite layer with several nodes of the same type cl = tp.CreateLayer({'rows' : 1, 'columns' : 2, 'elements' : ['iaf_cond_alpha', 10, 'poisson_generator', 'noise_generator', 2]}) # investigate the status dictionary of a layer nest.GetStatus(gl)[0]['topology'] """ if isinstance(specs, dict): specs = (specs, ) elif not all(isinstance(spec, dict) for spec in specs): raise TypeError("specs must be a dictionary or a list of dictionaries") return topology_func('{ CreateLayer } Map', specs) def ConnectLayers(pre, post, projections): """ Pairwise connect of pre- and postsynaptic (lists of) layers. `pre` and `post` must be a tuple/list of GIDs of equal length. The GIDs must refer to layers created with ``CreateLayers``. Layers in the `pre` and `post` lists are connected pairwise. * If `projections` is a single dictionary, it applies to all pre-post pairs. * If `projections` is a tuple/list of dictionaries, it must have the same length as `pre` and `post` and each dictionary is matched with the proper pre-post pair. A minimal call of ``ConnectLayers`` expects a source layer `pre`, a target layer `post` and a connection dictionary `projections` containing at least the entry `'connection_type'` (either `'convergent'` or `'divergent'`). When connecting two layers, the driver layer is the one in which each node is considered in turn. The pool layer is the one from which nodes are chosen for each node in the driver layer. Parameters ---------- pre : tuple/list of int(s) List of GIDs of presynaptic layers (sources) post : tuple/list of int(s) List of GIDs of postsynaptic layers (targets) projections : (tuple/list of) dict(s) Dictionary or list of dictionaries specifying projection properties Returns ------- out : None ConnectLayers returns `None` See also -------- CreateLayer : Create one or more Topology layer(s). CreateMask : Create a ``Mask`` object. Documentation on available spatial masks. Masks can be used to specify the key `'mask'` of the connection dictionary. CreateParameter : Create a ``Parameter`` object. Documentation on available parameters for distance dependency and randomization. Parameters can be used to specify the parameters `'kernel'`, `'weights'` and `'delays'` of the connection dictionary. nest.GetConnections : Retrieve connections. Other parameters ---------------- Available keys for the layer-specifying dictionary `projections` allow_autapses : bool, optional, default: True An autapse is a synapse (connection) from a node onto itself. It is used together with the `'number_of_connections'` option. allow_multapses : bool, optional, default: True Node A is connected to node B by a multapse if there are synapses (connections) from A to B. It is used together with the `'number_of_connections'` option. connection_type : str The type of connections can be either `'convergent'` or `'divergent'`. In case of convergent connections, the target layer is considered as driver layer and the source layer as pool layer - and vice versa for divergent connections. delays : [float \| dict \| Parameter object], optional, default: 1.0 Delays can be constant, randomized or distance-dependent according to a provided function. Information on available functions can be found in the documentation on the function ``CreateParameter``. kernel : [float \| dict \| Parameter object], optional, default: 1.0 A kernel is a function mapping the distance (or displacement) between a driver and a pool node to a connection probability. The default kernel is 1.0, i.e., connections are created with certainty. Information on available functions can be found in the documentation on the function ``CreateParameter``. mask : [dict \| Mask object], optional The mask defines which pool nodes are considered as potential targets for each driver node. Parameters of the different available masks in 2 and 3 dimensions are also defined in dictionaries. If no mask is specified, all neurons from the pool layer are possible targets for each driver node. Information on available masks can be found in the documentation on the function ``CreateMask``. number_of_connections : int, optional Prescribed number of connections for each driver node. The actual connections being created are picked at random from all the candidate connections. synapse_model : str, optional The default synapse model in NEST is used if not specified otherwise. weights : [float \| dict \| Parameter object], optional, default: 1.0 Weights can be constant, randomized or distance-dependent according to a provided function. Information on available functions can be found in the documentation on the function ``CreateParameter``. Notes ----- * In the case of free probabilistic connections (in contrast to prescribing the number of connections), each possible driver-pool pair is inspected exactly once so that there will be at most one connection between each driver-pool pair. * Periodic boundary conditions are always applied in the pool layer. It is irrelevant whether the driver layer has periodic boundary conditions or not. * By default, Topology does not accept masks that are wider than the pool layer when using periodic boundary conditions. Kernel, weight and delay functions always consider the shortest distance (displacement) between driver and pool node. Example :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 11, 'columns' : 11, 'extent' : [11.0, 11.0], 'elements' : 'iaf_neuron'}) # connectivity specifications with a mask conndict1 = {'connection_type': 'divergent', 'mask': {'rectangular': {'lower_left' : [-2.0, -1.0], 'upper_right' : [2.0, 1.0]}}} # connect layer l with itself according to the given # specifications tp.ConnectLayers(l, l, conndict1) # connection dictionary with distance-dependent kernel # (given as Parameter object) and randomized weights # (given as a dictionary) gauss_kernel = tp.CreateParameter('gaussian', {'p_center' : 1.0, 'sigma' : 1.0}) conndict2 = {'connection_type': 'divergent', 'mask': {'circular': {'radius': 2.0}}, 'kernel': gauss_kernel, 'weights': {'uniform': {'min': 0.2, 'max': 0.8}}} """ if not nest.is_sequence_of_gids(pre): raise TypeError("pre must be a sequence of GIDs") if not nest.is_sequence_of_gids(pre): raise TypeError("post must be a sequence of GIDs") if not len(pre) == len(post): raise nest.NESTError("pre and post must have the same length.") # ensure projections is list of full length projections = nest.broadcast(projections, len(pre), (dict, ), "projections") # Replace python classes with SLI datums def fixdict(d): d = d.copy() for k, v in d.items(): if isinstance(v, dict): d[k] = fixdict(v) elif isinstance(v, Mask) or isinstance(v, Parameter): d[k] = v._datum return d projections = [fixdict(p) for p in projections] topology_func('3 arraystore { ConnectLayers } ScanThread', pre, post, projections) def GetPosition(nodes): """ Return the spatial locations of nodes. Parameters ---------- nodes : tuple/list of int(s) List of GIDs Returns ------- out : tuple of tuple(s) List of positions as 2- or 3-element lists See also -------- Displacement : Get vector of lateral displacement between nodes. Distance : Get lateral distance between nodes. DumpLayerConnections : Write connectivity information to file. DumpLayerNodes : Write layer node positions to file. Notes ----- * The functions ``GetPosition``, ``Displacement`` and ``Distance`` now only works for nodes local to the current MPI process, if used in a MPI-parallel simulation. Example :: import nest import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # retrieve positions of all (local) nodes belonging to the layer gids = nest.GetNodes(l, {'local_only': True})[0] tp.GetPosition(gids) """ if not nest.is_sequence_of_gids(nodes): raise TypeError("nodes must be a sequence of GIDs") return topology_func('{ GetPosition } Map', nodes) def GetLayer(nodes): """ Return the layer to which nodes belong. Parameters ---------- nodes : tuple/list of int(s) List of neuron GIDs Returns ------- out : tuple of int(s) List of layer GIDs See also -------- GetElement : Return the node(s) at the location(s) in the given layer(s). GetPosition : Return the spatial locations of nodes. Notes ----- - Example :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # get layer GID of nodes in layer tp.GetLayer(nest.GetNodes(l)[0]) """ if not nest.is_sequence_of_gids(nodes): raise TypeError("nodes must be a sequence of GIDs") return topology_func('{ GetLayer } Map', nodes) def GetElement(layers, locations): """ Return the node(s) at the location(s) in the given layer(s). This function works for fixed grid layers only. * If layers contains a single GID and locations is a single 2-element array giving a grid location, return a list of GIDs of layer elements at the given location. * If layers is a list with a single GID and locations is a list of coordinates, the function returns a list of lists with GIDs of the nodes at all locations. * If layers is a list of GIDs and locations single 2-element array giving a grid location, the function returns a list of lists with the GIDs of the nodes in all layers at the given location. * If layers and locations are lists, it returns a nested list of GIDs, one list for each layer and each location. Parameters ---------- layers : tuple/list of int(s) List of layer GIDs locations : [tuple/list of floats \| tuple/list of tuples/lists of floats] 2-element list with coordinates of a single grid location, or list of 2-element lists of coordinates for 2-dimensional layers, i.e., on the format [column, row] Returns ------- out : tuple of int(s) List of GIDs See also -------- GetLayer : Return the layer to which nodes belong. FindNearestElement: Return the node(s) closest to the location(s) in the given layer(s). GetPosition : Return the spatial locations of nodes. Notes ----- - Example :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 4, 'elements' : 'iaf_neuron'}) # get GID of element in last row and column tp.GetElement(l, [3, 4]) """ if not nest.is_sequence_of_gids(layers): raise TypeError("layers must be a sequence of GIDs") if not len(layers) > 0: raise nest.NESTError("layers cannot be empty") if not (nest.is_iterable(locations) and len(locations) > 0): raise nest.NESTError( "locations must be coordinate array or list of coordinate arrays") # ensure that all layers are grid-based, otherwise one ends up with an # incomprehensible error message try: topology_func('{ [ /topology [ /rows /columns ] ] get ; } forall', layers) except: raise nest.NESTError( "layers must contain only grid-based topology layers") # SLI GetElement returns either single GID or list def make_tuple(x): if not nest.is_iterable(x): return (x, ) else: return x if nest.is_iterable(locations[0]): # layers and locations are now lists nodes = topology_func( '/locs Set { /lyr Set locs { lyr exch GetElement } Map } Map', layers, locations) node_list = tuple( tuple(make_tuple(nodes_at_loc) for nodes_at_loc in nodes_in_lyr) for nodes_in_lyr in nodes) else: # layers is list, locations is a single location nodes = topology_func('/loc Set { loc GetElement } Map', layers, locations) node_list = tuple(make_tuple(nodes_in_lyr) for nodes_in_lyr in nodes) # If only a single layer is given, un-nest list if len(layers) == 1: node_list = node_list[0] return node_list def FindNearestElement(layers, locations, find_all=False): """ Return the node(s) closest to the location(s) in the given layer(s). This function works for fixed grid layers only. * If layers contains a single GID and locations is a single 2-element array giving a grid location, return a list of GIDs of layer elements at the given location. * If layers is a list with a single GID and locations is a list of coordinates, the function returns a list of lists with GIDs of the nodes at all locations. * If layers is a list of GIDs and locations single 2-element array giving a grid location, the function returns a list of lists with the GIDs of the nodes in all layers at the given location. * If layers and locations are lists, it returns a nested list of GIDs, one list for each layer and each location. Parameters ---------- layers : tuple/list of int(s) List of layer GIDs locations : tuple(s)/list(s) of tuple(s)/list(s) 2-element list with coordinates of a single position, or list of 2-element list of positions find_all : bool, default: False If there are several nodes with same minimal distance, return only the first found, if `False`. If `True`, instead of returning a single GID, return a list of GIDs containing all nodes with minimal distance. Returns ------- out : tuple of int(s) List of node GIDs See also -------- FindCenterElement : Return GID(s) of node closest to center of layers. GetElement : Return the node(s) at the location(s) in the given layer(s). GetPosition : Return the spatial locations of nodes. Notes ----- - Example :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # get GID of element closest to some location tp.FindNearestElement(l, [3.0, 4.0], True) """ import numpy if not nest.is_sequence_of_gids(layers): raise TypeError("layers must be a sequence of GIDs") if not len(layers) > 0: raise nest.NESTError("layers cannot be empty") if not nest.is_iterable(locations): raise TypeError( "locations must be coordinate array or list of coordinate arrays") # ensure locations is sequence, keeps code below simpler if not nest.is_iterable(locations[0]): locations = (locations, ) result = [] # collect one list per layer # loop over layers for lyr in layers: els = nest.GetChildren((lyr, ))[0] lyr_result = [] # loop over locations for loc in locations: d = Distance(numpy.array(loc), els) if not find_all: dx = numpy.argmin(d) # finds location of one minimum lyr_result.append(els[dx]) else: mingids = list(els[:1]) minval = d[0] for idx in range(1, len(els)): if d[idx] < minval: mingids = [els[idx]] minval = d[idx] elif numpy.abs(d[idx] - minval) <= 1e-14 * minval: mingids.append(els[idx]) lyr_result.append(tuple(mingids)) result.append(tuple(lyr_result)) # If both layers and locations are multi-element lists, result shall remain # a nested list. Otherwise, either the top or the second level is a single # element list and we flatten. assert (len(result) > 0) if len(result) == 1: assert (len(layers) == 1) return result[0] elif len(result[0]) == 1: assert (len(locations) == 1) return tuple(el[0] for el in result) else: return tuple(result) def _check_displacement_args(from_arg, to_arg, caller): """ Internal helper function to check arguments to Displacement and Distance and make them lists of equal length. """ import numpy if isinstance(from_arg, numpy.ndarray): from_arg = (from_arg, ) elif not (nest.is_iterable(from_arg) and len(from_arg) > 0): raise nest.NESTError( "%s: from_arg must be lists of GIDs or positions" % caller) # invariant: from_arg is list if not nest.is_sequence_of_gids(to_arg): raise nest.NESTError("%s: to_arg must be lists of GIDs" % caller) # invariant: from_arg and to_arg are sequences if len(from_arg) > 1 and len(to_arg) > 1 and not len(from_arg) == len( to_arg): raise nest.NESTError( "%s: If to_arg and from_arg are lists, they must have same length." % caller) # invariant: from_arg and to_arg have equal length, # or (at least) one has length 1 if len(from_arg) == 1: from_arg = from_arg * len(to_arg) # this is a no-op if len(to_arg)==1 if len(to_arg) == 1: to_arg = to_arg * len(from_arg) # this is a no-op if len(from_arg)==1 # invariant: from_arg and to_arg have equal length return from_arg, to_arg def Displacement(from_arg, to_arg): """ Get vector of lateral displacement from node(s) `from_arg` to node(s) `to_arg`. Displacement is always measured in the layer to which the `to_arg` node belongs. If a node in the `from_arg` list belongs to a different layer, its location is projected into the `to_arg` layer. If explicit positions are given in the `from_arg` list, they are interpreted in the `to_arg` layer. Displacement is the shortest displacement, taking into account periodic boundary conditions where applicable. * If one of `from_arg` or `to_arg` has length 1, and the other is longer, the displacement from/to the single item to all other items is given. * If `from_arg` and `to_arg` both have more than two elements, they have to be lists of the same length and the displacement for each pair is returned. Parameters ---------- from_arg : [tuple/list of int(s) \| tuple/list of tuples/lists of floats] List of GIDs or position(s) to_arg : tuple/list of int(s) List of GIDs Returns ------- out : tuple Displacement vectors between pairs of nodes in `from_arg` and `to_arg` See also -------- Distance : Get lateral distances between nodes. DumpLayerConnections : Write connectivity information to file. GetPosition : Return the spatial locations of nodes. Notes ----- * The functions ``GetPosition``, ``Displacement`` and ``Distance`` now only works for nodes local to the current MPI process, if used in a MPI-parallel simulation. Example :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # displacement between node 2 and 3 print tp.Displacement([2], [3]) # displacment between the position (0.0., 0.0) and node 2 print tp.Displacement([(0.0, 0.0)], [2]) """ from_arg, to_arg = _check_displacement_args(from_arg, to_arg, 'Displacement') return topology_func('{ Displacement } MapThread', [from_arg, to_arg]) def Distance(from_arg, to_arg): """ Get lateral distances from node(s) from_arg to node(s) to_arg. The distance between two nodes is the length of its displacement. Distance is always measured in the layer to which the `to_arg` node belongs. If a node in the `from_arg` list belongs to a different layer, its location is projected into the `to_arg` layer. If explicit positions are given in the `from_arg` list, they are interpreted in the `to_arg` layer. Distance is the shortest distance, taking into account periodic boundary conditions where applicable. * If one of `from_arg` or `to_arg` has length 1, and the other is longer, the displacement from/to the single item to all other items is given. * If `from_arg` and `to_arg` both have more than two elements, they have to be lists of the same length and the distance for each pair is returned. Parameters ---------- from_arg : [tuple/list of ints \| tuple/list with tuples/lists of floats] List of GIDs or position(s) to_arg : tuple/list of ints List of GIDs Returns ------- out : tuple Distances between from and to See also -------- Displacement : Get vector of lateral displacements between nodes. DumpLayerConnections : Write connectivity information to file. GetPosition : Return the spatial locations of nodes. Notes ----- * The functions ``GetPosition``, ``Displacement`` and ``Distance`` now only works for nodes local to the current MPI process, if used in a MPI-parallel simulation. Example :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # distance between node 2 and 3 print tp.Distance([2], [3]) # distance between the position (0.0., 0.0) and node 2 print tp.Distance([(0.0, 0.0)], [2]) """ from_arg, to_arg = _check_displacement_args(from_arg, to_arg, 'Distance') return topology_func('{ Distance } MapThread', [from_arg, to_arg]) def _rank_specific_filename(basename): """Returns file name decorated with rank.""" if nest.NumProcesses() == 1: return basename else: np = nest.NumProcesses() np_digs = len(str(np - 1)) # for pretty formatting rk = nest.Rank() dot = basename.find('.') if dot < 0: return '%s-%0d' % (basename, np_digs, rk) else: return '%s-%0d%s' % (basename[:dot], np_digs, rk, basename[dot:]) def DumpLayerNodes(layers, outname): """ Write GID and position data of layer(s) to file. Write GID and position data to layer(s) file. For each node in a layer, a line with the following information is written: :: GID x-position y-position [z-position] If `layers` contains several GIDs, data for all layers will be written to a single file. Parameters ---------- layers : tuple/list of int(s) List of GIDs of a Topology layer outname : str Name of file to write to (existing files are overwritten) Returns ------- out : None See also -------- DumpLayerConnections : Write connectivity information to file. GetPosition : Return the spatial locations of nodes. Notes ----- * If calling this function from a distributed simulation, this function will write to one file per MPI rank. * File names are formed by adding the MPI Rank into the file name before the file name suffix. * Each file stores data for nodes local to that file. Example :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # write layer node positions to file tp.DumpLayerNodes(l, 'positions.txt') """ topology_func(""" (w) file exch { DumpLayerNodes } forall close """, layers, _rank_specific_filename(outname)) def DumpLayerConnections(layers, synapse_model, outname): """ Write connectivity information to file. This function writes connection information to file for all outgoing connections from the given layers with the given synapse model. Data for all layers in the list is combined. For each connection, one line is stored, in the following format: :: source_gid target_gid weight delay dx dy [dz] where (dx, dy [, dz]) is the displacement from source to target node. If targets do not have positions (eg spike detectors outside any layer), NaN is written for each displacement coordinate. Parameters ---------- layers : tuple/list of int(s) List of GIDs of a Topology layer synapse_model : str NEST synapse model outname : str Name of file to write to (will be overwritten if it exists) Returns ------- out : None See also -------- DumpLayerNodes : Write layer node positions to file. GetPosition : Return the spatial locations of nodes. nest.GetConnections : Return connection identifiers between sources and targets Notes ----- * If calling this function from a distributed simulation, this function will write to one file per MPI rank. * File names are formed by inserting the MPI Rank into the file name before the file name suffix. * Each file stores data for local nodes. Example :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) tp.ConnectLayers(l,l, {'connection_type': 'divergent', 'synapse_model': 'static_synapse'}) # write connectivity information to file tp.DumpLayerConnections(l, 'static_synapse', 'connections.txt') """ topology_func(""" /oname Set cvlit /synmod Set /lyrs Set oname (w) file lyrs { synmod DumpLayerConnections } forall close """, layers, synapse_model, _rank_specific_filename(outname)) def FindCenterElement(layers): """ Return GID(s) of node closest to center of layers. Parameters ---------- layers : tuple/list of int(s) List of layer GIDs Returns ------- out : tuple of int(s) A list containing for each layer the GID of the node closest to the center of the layer, as specified in the layer parameters. If several nodes are equally close to the center, an arbitrary one of them is returned. See also -------- FindNearestElement : Return the node(s) closest to the location(s) in the given layer(s). GetElement : Return the node(s) at the location(s) in the given layer(s). GetPosition : Return the spatial locations of nodes. Notes ----- - Example :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # get GID of the element closest to the center of the layer tp.FindCenterElement(l) """ if not nest.is_sequence_of_gids(layers): raise TypeError("layers must be a sequence of GIDs") # Do each layer on its own since FindNearestElement does not thread return tuple(FindNearestElement((lyr, ), nest.GetStatus((lyr, ), 'topology')[0][ 'center'])[0] for lyr in layers) def GetTargetNodes(sources, tgt_layer, tgt_model=None, syn_model=None): """ Obtain targets of a list of sources in given target layer. Parameters ---------- sources : tuple/list of int(s) List of GID(s) of source neurons tgt_layer : tuple/list of int(s) Single-element list with GID of tgt_layer tgt_model : [None \| str], optional, default: None Return only target positions for a given neuron model. syn_model : [None \| str], optional, default: None Return only target positions for a given synapse model. Returns ------- out : tuple of list(s) of int(s) List of GIDs of target neurons fulfilling the given criteria. It is a list of lists, one list per source. For each neuron in `sources`, this function finds all target elements in `tgt_layer`. If `tgt_model` is not given (default), all targets are returned, otherwise only targets of specific type, and similarly for syn_model. See also -------- GetTargetPositions : Obtain positions of targets of a list of sources in a given target layer. nest.GetConnections : Return connection identifiers between sources and targets Notes ----- * For distributed simulations, this function only returns targets on the local MPI process. Example :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 11, 'columns' : 11, 'extent' : [11.0, 11.0], 'elements' : 'iaf_neuron'}) # connectivity specifications with a mask conndict = {'connection_type': 'divergent', 'mask': {'rectangular': {'lower_left' : [-2.0, -1.0], 'upper_right': [2.0, 1.0]}}} # connect layer l with itself according to the given # specifications tp.ConnectLayers(l, l, conndict) # get the GIDs of the targets of the source neuron with GID 5 tp.GetTargetNodes([5], l) """ if not nest.is_sequence_of_gids(sources): raise TypeError("sources must be a sequence of GIDs") if not nest.is_sequence_of_gids(tgt_layer): raise TypeError("tgt_layer must be a sequence of GIDs") if len(tgt_layer) != 1: raise nest.NESTError("tgt_layer must be a one-element list") # obtain local nodes in target layer, to pass to GetConnections tgt_nodes = nest.GetLeaves(tgt_layer, properties={ 'model': tgt_model} if tgt_model else None, local_only=True)[0] conns = nest.GetConnections(sources, tgt_nodes, synapse_model=syn_model) # conns is a flat list of connections. # Re-organize into one list per source, containing only target GIDs. src_tgt_map = dict((sgid, []) for sgid in sources) for conn in conns: src_tgt_map[conn[0]].append(conn[1]) # convert dict to nested list in same order as sources return tuple(src_tgt_map[sgid] for sgid in sources) def GetTargetPositions(sources, tgt_layer, tgt_model=None, syn_model=None): """ Obtain positions of targets of a list of sources in a given target layer. Parameters ---------- sources : tuple/list of int(s) List of GID(s) of source neurons tgt_layer : tuple/list of int(s) Single-element list with GID of tgt_layer tgt_model : [None \| str], optional, default: None Return only target positions for a given neuron model. syn_type : [None \| str], optional, default: None Return only target positions for a given synapse model. Returns ------- out : tuple of tuple(s) of tuple(s) of floats Positions of target neurons fulfilling the given criteria as a nested list, containing one list of positions per node in sources. For each neuron in `sources`, this function finds all target elements in `tgt_layer`. If `tgt_model` is not given (default), all targets are returned, otherwise only targets of specific type, and similarly for syn_model. See also -------- GetTargetNodes : Obtain targets of a list of sources in a given target layer. Notes ----- * For distributed simulations, this function only returns targets on the local MPI process. Example :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 11, 'columns' : 11, 'extent' : [11.0, 11.0], 'elements' : 'iaf_neuron'}) # connectivity specifications with a mask conndict1 = {'connection_type': 'divergent', 'mask': {'rectangular': {'lower_left' : [-2.0, -1.0], 'upper_right' : [2.0, 1.0]}}} # connect layer l with itself according to the given # specifications tp.ConnectLayers(l, l, conndict1) # get the positions of the targets of the source neuron with GID 5 tp.GetTargetPositions([5], l) """ return tuple(GetPosition(nodes) for nodes in GetTargetNodes(sources, tgt_layer, tgt_model, syn_model)) def _draw_extent(ax, xctr, yctr, xext, yext): """Draw extent and set aspect ration, limits""" import matplotlib.pyplot as plt # thin gray line indicating extent llx, lly = xctr - xext / 2.0, yctr - yext / 2.0 urx, ury = llx + xext, lly + yext ax.add_patch( plt.Rectangle((llx, lly), xext, yext, fc='none', ec='0.5', lw=1, zorder=1)) # set limits slightly outside extent ax.set(aspect='equal', xlim=(llx - 0.05 * xext, urx + 0.05 * xext), ylim=(lly - 0.05 * yext, ury + 0.05 * yext), xticks=tuple(), yticks=tuple()) def PlotLayer(layer, fig=None, nodecolor='b', nodesize=20): """ Plot all nodes in a layer. This function plots only top-level nodes, not the content of composite nodes. Parameters ---------- layer : tuple/list of int(s) GID of layer to plot, must be tuple/list of length 1 fig : [None \| matplotlib.figure.Figure object], optional, default: None Matplotlib figure to plot to. If not given, a new figure is created. nodecolor : [None \| any matplotlib color], optional, default: 'b' Color for nodes nodesize : float, optional, default: 20 Marker size for nodes Returns ------- out : `matplotlib.figure.Figure` object See also -------- PlotKernel : Add indication of mask and kernel to axes. PlotTargets : Plot all targets of a given source. matplotlib.figure.Figure : matplotlib Figure class Notes ----- * Do not use this function in distributed simulations. Example :: import nest.topology as tp import matplotlib.pyplot as plt # create a layer l = tp.CreateLayer({'rows' : 11, 'columns' : 11, 'extent' : [11.0, 11.0], 'elements' : 'iaf_neuron'}) # plot layer with all its nodes tp.PlotLayer(l) plt.show() """ import matplotlib.pyplot as plt if len(layer) != 1: raise ValueError("layer must contain exactly one GID.") # get layer extent ext = nest.GetStatus(layer, 'topology')[0]['extent'] if len(ext) == 2: # 2D layer # get layer extent and center, x and y xext, yext = ext xctr, yctr = nest.GetStatus(layer, 'topology')[0]['center'] # extract position information, transpose to list of x and y positions xpos, ypos = zip(GetPosition(nest.GetChildren(layer)[0])) if fig is None: fig = plt.figure() ax = fig.add_subplot(111) else: ax = fig.gca() ax.scatter(xpos, ypos, s=nodesize, facecolor=nodecolor, edgecolor='none') _draw_extent(ax, xctr, yctr, xext, yext) elif len(ext) == 3: # 3D layer from mpl_toolkits.mplot3d import Axes3D # extract position information, transpose to list of x,y,z positions pos = zip(GetPosition(nest.GetChildren(layer)[0])) if fig is None: fig = plt.figure() ax = fig.add_subplot(111, projection='3d') else: ax = fig.gca() ax.scatter3D(pos, s=nodesize, facecolor=nodecolor, edgecolor='none') plt.draw_if_interactive() else: raise nest.NESTError("unexpected dimension of layer") return fig def PlotTargets(src_nrn, tgt_layer, tgt_model=None, syn_type=None, fig=None, mask=None, kernel=None, src_color='red', src_size=50, tgt_color='blue', tgt_size=20, mask_color='red', kernel_color='red'): """ Plot all targets of source neuron `src_nrn` in a target layer `tgt_layer`. Parameters ---------- src_nrn : int GID of source neuron (as single-element list) tgt_layer : tuple/list of int(s) GID of tgt_layer (as single-element list) tgt_model : [None \| str], optional, default: None Show only targets of a given model. syn_type : [None \| str], optional, default: None Show only targets connected to with a given synapse type fig : [None \| matplotlib.figure.Figure object], optional, default: None Matplotlib figure to plot to. If not given, a new figure is created. mask : [None \| dict], optional, default: None Draw topology mask with targets; see ``PlotKernel`` for details. kernel : [None \| dict], optional, default: None Draw topology kernel with targets; see ``PlotKernel`` for details. src_color : [None \| any matplotlib color], optional, default: 'red' Color used to mark source node position src_size : float, optional, default: 50 Size of source marker (see scatter for details) tgt_color : [None \| any matplotlib color], optional, default: 'blue' Color used to mark target node positions tgt_size : float, optional, default: 20 Size of target markers (see scatter for details) mask_color : [None \| any matplotlib color], optional, default: 'red' Color used for line marking mask kernel_color : [None \| any matplotlib color], optional, default: 'red' Color used for lines marking kernel Returns ------- out : matplotlib.figure.Figure object See also -------- GetTargetNodes : Obtain targets of a list of sources in a given target layer. GetTargetPositions : Obtain positions of targets of a list of sources in a given target layer. PlotKernel : Add indication of mask and kernel to axes. PlotLayer : Plot all nodes in a layer. matplotlib.pyplot.scatter : matplotlib scatter plot. Notes ----- Do not use this function in distributed simulations. Example :: import nest.topology as tp import matplotlib.pyplot as plt # create a layer l = tp.CreateLayer({'rows' : 11, 'columns' : 11, 'extent' : [11.0, 11.0], 'elements' : 'iaf_neuron'}) # connectivity specifications with a mask conndict = {'connection_type': 'divergent', 'mask': {'rectangular': {'lower_left' : [-2.0, -1.0], 'upper_right' : [2.0, 1.0]}}} # connect layer l with itself according to the given # specifications tp.ConnectLayers(l, l, conndict) # plot the targets of the source neuron with GID 5 tp.PlotTargets([5], l) plt.show() """ import matplotlib.pyplot as plt # get position of source srcpos = GetPosition(src_nrn)[0] # get layer extent and center, x and y ext = nest.GetStatus(tgt_layer, 'topology')[0]['extent'] if len(ext) == 2: # 2D layer # get layer extent and center, x and y xext, yext = ext xctr, yctr = nest.GetStatus(tgt_layer, 'topology')[0]['center'] if fig is None: fig = plt.figure() ax = fig.add_subplot(111) else: ax = fig.gca() # get positions, reorganize to x and y vectors tgtpos = GetTargetPositions(src_nrn, tgt_layer, tgt_model, syn_type) if tgtpos: xpos, ypos = zip(tgtpos[0]) ax.scatter(xpos, ypos, s=tgt_size, facecolor=tgt_color, edgecolor='none') ax.scatter(srcpos[:1], srcpos[1:], s=src_size, facecolor=src_color, edgecolor='none', alpha=0.4, zorder=-10) _draw_extent(ax, xctr, yctr, xext, yext) if mask is not None or kernel is not None: PlotKernel(ax, src_nrn, mask, kernel, mask_color, kernel_color) else: # 3D layer from mpl_toolkits.mplot3d import Axes3D if fig is None: fig = plt.figure() ax = fig.add_subplot(111, projection='3d') else: ax = fig.gca() # get positions, reorganize to x,y,z vectors tgtpos = GetTargetPositions(src_nrn, tgt_layer, tgt_model, syn_type) if tgtpos: xpos, ypos, zpos = zip(tgtpos[0]) ax.scatter3D(xpos, ypos, zpos, s=tgt_size, facecolor=tgt_color, edgecolor='none') ax.scatter3D(srcpos[:1], srcpos[1:2], srcpos[2:], s=src_size, facecolor=src_color, edgecolor='none', alpha=0.4, zorder=-10) plt.draw_if_interactive() return fig def PlotKernel(ax, src_nrn, mask, kern=None, mask_color='red', kernel_color='red'): """ Add indication of mask and kernel to axes. Adds solid red line for mask. For doughnut mask show inner and outer line. If kern is Gaussian, add blue dashed lines marking 1, 2, 3 sigma. This function ignores periodic boundary conditions. Usually, this function is invoked by ``PlotTargets``. Parameters ---------- ax : matplotlib.axes.AxesSubplot, subplot reference returned by PlotTargets src_nrn : int GID of source neuron (as single element list), mask and kernel plotted relative to it mask : dict Mask used in creating connections. kern : [None \| dict], optional, default: None Kernel used in creating connections mask_color : [None \| any matplotlib color], optional, default: 'red' Color used for line marking mask kernel_color : [None \| any matplotlib color], optional, default: 'red' Color used for lines marking kernel Returns ------- out : None See also -------- CreateMask : Create a ``Mask`` object. Documentation on available spatial masks. CreateParameter : Create a ``Parameter`` object. Documentation on available parameters for distance dependency and randomization. PlotLayer : Plot all nodes in a layer. Notes ----- * Do not use this function in distributed simulations. Example :: import nest.topology as tp import matplotlib.pyplot as plt # create a layer l = tp.CreateLayer({'rows' : 11, 'columns' : 11, 'extent' : [11.0, 11.0], 'elements' : 'iaf_neuron'}) # connectivity specifications mask_dict = {'rectangular': {'lower_left' : [-2.0, -1.0], 'upper_right' : [2.0, 1.0]}} kernel_dict = {'gaussian': {'p_center' : 1.0, 'sigma' : 1.0}} conndict = {'connection_type': 'divergent', 'mask' : mask_dict, 'kernel' : kernel_dict} # connect layer l with itself according to the given # specifications tp.ConnectLayers(l, l, conndict) # set up figure fig, ax = plt.subplots() # plot layer nodes tp.PlotLayer(l, fig) # choose center element of the layer as source node ctr_elem = tp.FindCenterElement(l) # plot mask and kernel of the center element tp.PlotKernel(ax, ctr_elem, mask=mask_dict, kern=kernel_dict) """ import matplotlib import matplotlib.pyplot as plt import numpy as np # minimal checks for ax having been created by PlotKernel if ax and not isinstance(ax, matplotlib.axes.Axes): raise ValueError('ax must be matplotlib.axes.Axes instance.') srcpos = np.array(GetPosition(src_nrn)[0]) if 'anchor' in mask: offs = np.array(mask['anchor']) else: offs = np.array([0., 0.]) if 'circular' in mask: r = mask['circular']['radius'] ax.add_patch(plt.Circle(srcpos + offs, radius=r, zorder=-1000, fc='none', ec=mask_color, lw=3)) elif 'doughnut' in mask: r_in = mask['doughnut']['inner_radius'] r_out = mask['doughnut']['outer_radius'] ax.add_patch(plt.Circle(srcpos + offs, radius=r_in, zorder=-1000, fc='none', ec=mask_color, lw=3)) ax.add_patch(plt.Circle(srcpos + offs, radius=r_out, zorder=-1000, fc='none', ec=mask_color, lw=3)) elif 'rectangular' in mask: ll = mask['rectangular']['lower_left'] ur = mask['rectangular']['upper_right'] ax.add_patch( plt.Rectangle(srcpos + ll + offs, ur[0] - ll[0], ur[1] - ll[1], zorder=-1000, fc='none', ec=mask_color, lw=3)) else: raise ValueError( 'Mask type cannot be plotted with this version of PyTopology.') if kern is not None and isinstance(kern, dict): if 'gaussian' in kern: sigma = kern['gaussian']['sigma'] for r in range(3): ax.add_patch(plt.Circle(srcpos + offs, radius=(r + 1) * sigma, zorder=-1000, fc='none', ec=kernel_color, lw=3, ls='dashed')) else: raise ValueError('Kernel type cannot be plotted with this ' + 'version of PyTopology') plt.draw()	gpl-2.0
jkbradley/spark	python/pyspark/sql/tests/test_dataframe.py	2	37690	# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import os import pydoc import time import unittest from pyspark.sql import SparkSession, Row from pyspark.sql.types import * from pyspark.sql.utils import AnalysisException, IllegalArgumentException from pyspark.testing.sqlutils import ReusedSQLTestCase, SQLTestUtils, have_pyarrow, have_pandas, \ pandas_requirement_message, pyarrow_requirement_message from pyspark.testing.utils import QuietTest class DataFrameTests(ReusedSQLTestCase): def test_range(self): self.assertEqual(self.spark.range(1, 1).count(), 0) self.assertEqual(self.spark.range(1, 0, -1).count(), 1) self.assertEqual(self.spark.range(0, 1 << 40, 1 << 39).count(), 2) self.assertEqual(self.spark.range(-2).count(), 0) self.assertEqual(self.spark.range(3).count(), 3) def test_duplicated_column_names(self): df = self.spark.createDataFrame([(1, 2)], ["c", "c"]) row = df.select('').first() self.assertEqual(1, row[0]) self.assertEqual(2, row[1]) self.assertEqual("Row(c=1, c=2)", str(row)) # Cannot access columns self.assertRaises(AnalysisException, lambda: df.select(df[0]).first()) self.assertRaises(AnalysisException, lambda: df.select(df.c).first()) self.assertRaises(AnalysisException, lambda: df.select(df["c"]).first()) def test_freqItems(self): vals = [Row(a=1, b=-2.0) if i % 2 == 0 else Row(a=i, b=i 1.0) for i in range(100)] df = self.sc.parallelize(vals).toDF() items = df.stat.freqItems(("a", "b"), 0.4).collect()[0] self.assertTrue(1 in items[0]) self.assertTrue(-2.0 in items[1]) def test_help_command(self): # Regression test for SPARK-5464 rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}']) df = self.spark.read.json(rdd) # render_doc() reproduces the help() exception without printing output pydoc.render_doc(df) pydoc.render_doc(df.foo) pydoc.render_doc(df.take(1)) def test_dropna(self): schema = StructType([ StructField("name", StringType(), True), StructField("age", IntegerType(), True), StructField("height", DoubleType(), True)]) # shouldn't drop a non-null row self.assertEqual(self.spark.createDataFrame( [(u'Alice', 50, 80.1)], schema).dropna().count(), 1) # dropping rows with a single null value self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, 80.1)], schema).dropna().count(), 0) self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, 80.1)], schema).dropna(how='any').count(), 0) # if how = 'all', only drop rows if all values are null self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, 80.1)], schema).dropna(how='all').count(), 1) self.assertEqual(self.spark.createDataFrame( [(None, None, None)], schema).dropna(how='all').count(), 0) # how and subset self.assertEqual(self.spark.createDataFrame( [(u'Alice', 50, None)], schema).dropna(how='any', subset=['name', 'age']).count(), 1) self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, None)], schema).dropna(how='any', subset=['name', 'age']).count(), 0) # threshold self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, 80.1)], schema).dropna(thresh=2).count(), 1) self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, None)], schema).dropna(thresh=2).count(), 0) # threshold and subset self.assertEqual(self.spark.createDataFrame( [(u'Alice', 50, None)], schema).dropna(thresh=2, subset=['name', 'age']).count(), 1) self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, 180.9)], schema).dropna(thresh=2, subset=['name', 'age']).count(), 0) # thresh should take precedence over how self.assertEqual(self.spark.createDataFrame( [(u'Alice', 50, None)], schema).dropna( how='any', thresh=2, subset=['name', 'age']).count(), 1) def test_fillna(self): schema = StructType([ StructField("name", StringType(), True), StructField("age", IntegerType(), True), StructField("height", DoubleType(), True), StructField("spy", BooleanType(), True)]) # fillna shouldn't change non-null values row = self.spark.createDataFrame([(u'Alice', 10, 80.1, True)], schema).fillna(50).first() self.assertEqual(row.age, 10) # fillna with int row = self.spark.createDataFrame([(u'Alice', None, None, None)], schema).fillna(50).first() self.assertEqual(row.age, 50) self.assertEqual(row.height, 50.0) # fillna with double row = self.spark.createDataFrame( [(u'Alice', None, None, None)], schema).fillna(50.1).first() self.assertEqual(row.age, 50) self.assertEqual(row.height, 50.1) # fillna with bool row = self.spark.createDataFrame( [(u'Alice', None, None, None)], schema).fillna(True).first() self.assertEqual(row.age, None) self.assertEqual(row.spy, True) # fillna with string row = self.spark.createDataFrame([(None, None, None, None)], schema).fillna("hello").first() self.assertEqual(row.name, u"hello") self.assertEqual(row.age, None) # fillna with subset specified for numeric cols row = self.spark.createDataFrame( [(None, None, None, None)], schema).fillna(50, subset=['name', 'age']).first() self.assertEqual(row.name, None) self.assertEqual(row.age, 50) self.assertEqual(row.height, None) self.assertEqual(row.spy, None) # fillna with subset specified for string cols row = self.spark.createDataFrame( [(None, None, None, None)], schema).fillna("haha", subset=['name', 'age']).first() self.assertEqual(row.name, "haha") self.assertEqual(row.age, None) self.assertEqual(row.height, None) self.assertEqual(row.spy, None) # fillna with subset specified for bool cols row = self.spark.createDataFrame( [(None, None, None, None)], schema).fillna(True, subset=['name', 'spy']).first() self.assertEqual(row.name, None) self.assertEqual(row.age, None) self.assertEqual(row.height, None) self.assertEqual(row.spy, True) # fillna with dictionary for boolean types row = self.spark.createDataFrame([Row(a=None), Row(a=True)]).fillna({"a": True}).first() self.assertEqual(row.a, True) def test_repartitionByRange_dataframe(self): schema = StructType([ StructField("name", StringType(), True), StructField("age", IntegerType(), True), StructField("height", DoubleType(), True)]) df1 = self.spark.createDataFrame( [(u'Bob', 27, 66.0), (u'Alice', 10, 10.0), (u'Bob', 10, 66.0)], schema) df2 = self.spark.createDataFrame( [(u'Alice', 10, 10.0), (u'Bob', 10, 66.0), (u'Bob', 27, 66.0)], schema) # test repartitionByRange(numPartitions, cols) df3 = df1.repartitionByRange(2, "name", "age") self.assertEqual(df3.rdd.getNumPartitions(), 2) self.assertEqual(df3.rdd.first(), df2.rdd.first()) self.assertEqual(df3.rdd.take(3), df2.rdd.take(3)) # test repartitionByRange(numPartitions, cols) df4 = df1.repartitionByRange(3, "name", "age") self.assertEqual(df4.rdd.getNumPartitions(), 3) self.assertEqual(df4.rdd.first(), df2.rdd.first()) self.assertEqual(df4.rdd.take(3), df2.rdd.take(3)) # test repartitionByRange(cols) df5 = df1.repartitionByRange("name", "age") self.assertEqual(df5.rdd.first(), df2.rdd.first()) self.assertEqual(df5.rdd.take(3), df2.rdd.take(3)) def test_replace(self): schema = StructType([ StructField("name", StringType(), True), StructField("age", IntegerType(), True), StructField("height", DoubleType(), True)]) # replace with int row = self.spark.createDataFrame([(u'Alice', 10, 10.0)], schema).replace(10, 20).first() self.assertEqual(row.age, 20) self.assertEqual(row.height, 20.0) # replace with double row = self.spark.createDataFrame( [(u'Alice', 80, 80.0)], schema).replace(80.0, 82.1).first() self.assertEqual(row.age, 82) self.assertEqual(row.height, 82.1) # replace with string row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace(u'Alice', u'Ann').first() self.assertEqual(row.name, u"Ann") self.assertEqual(row.age, 10) # replace with subset specified by a string of a column name w/ actual change row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace(10, 20, subset='age').first() self.assertEqual(row.age, 20) # replace with subset specified by a string of a column name w/o actual change row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace(10, 20, subset='height').first() self.assertEqual(row.age, 10) # replace with subset specified with one column replaced, another column not in subset # stays unchanged. row = self.spark.createDataFrame( [(u'Alice', 10, 10.0)], schema).replace(10, 20, subset=['name', 'age']).first() self.assertEqual(row.name, u'Alice') self.assertEqual(row.age, 20) self.assertEqual(row.height, 10.0) # replace with subset specified but no column will be replaced row = self.spark.createDataFrame( [(u'Alice', 10, None)], schema).replace(10, 20, subset=['name', 'height']).first() self.assertEqual(row.name, u'Alice') self.assertEqual(row.age, 10) self.assertEqual(row.height, None) # replace with lists row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace([u'Alice'], [u'Ann']).first() self.assertTupleEqual(row, (u'Ann', 10, 80.1)) # replace with dict row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace({10: 11}).first() self.assertTupleEqual(row, (u'Alice', 11, 80.1)) # test backward compatibility with dummy value dummy_value = 1 row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace({'Alice': 'Bob'}, dummy_value).first() self.assertTupleEqual(row, (u'Bob', 10, 80.1)) # test dict with mixed numerics row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace({10: -10, 80.1: 90.5}).first() self.assertTupleEqual(row, (u'Alice', -10, 90.5)) # replace with tuples row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace((u'Alice', ), (u'Bob', )).first() self.assertTupleEqual(row, (u'Bob', 10, 80.1)) # replace multiple columns row = self.spark.createDataFrame( [(u'Alice', 10, 80.0)], schema).replace((10, 80.0), (20, 90)).first() self.assertTupleEqual(row, (u'Alice', 20, 90.0)) # test for mixed numerics row = self.spark.createDataFrame( [(u'Alice', 10, 80.0)], schema).replace((10, 80), (20, 90.5)).first() self.assertTupleEqual(row, (u'Alice', 20, 90.5)) row = self.spark.createDataFrame( [(u'Alice', 10, 80.0)], schema).replace({10: 20, 80: 90.5}).first() self.assertTupleEqual(row, (u'Alice', 20, 90.5)) # replace with boolean row = (self .spark.createDataFrame([(u'Alice', 10, 80.0)], schema) .selectExpr("name = 'Bob'", 'age <= 15') .replace(False, True).first()) self.assertTupleEqual(row, (True, True)) # replace string with None and then drop None rows row = self.spark.createDataFrame( [(u'Alice', 10, 80.0)], schema).replace(u'Alice', None).dropna() self.assertEqual(row.count(), 0) # replace with number and None row = self.spark.createDataFrame( [(u'Alice', 10, 80.0)], schema).replace([10, 80], [20, None]).first() self.assertTupleEqual(row, (u'Alice', 20, None)) # should fail if subset is not list, tuple or None with self.assertRaises(ValueError): self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace({10: 11}, subset=1).first() # should fail if to_replace and value have different length with self.assertRaises(ValueError): self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace(["Alice", "Bob"], ["Eve"]).first() # should fail if when received unexpected type with self.assertRaises(ValueError): from datetime import datetime self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace(datetime.now(), datetime.now()).first() # should fail if provided mixed type replacements with self.assertRaises(ValueError): self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace(["Alice", 10], ["Eve", 20]).first() with self.assertRaises(ValueError): self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace({u"Alice": u"Bob", 10: 20}).first() with self.assertRaisesRegexp( TypeError, 'value argument is required when to_replace is not a dictionary.'): self.spark.createDataFrame( [(u'Alice', 10, 80.0)], schema).replace(["Alice", "Bob"]).first() def test_with_column_with_existing_name(self): keys = self.df.withColumn("key", self.df.key).select("key").collect() self.assertEqual([r.key for r in keys], list(range(100))) # regression test for SPARK-10417 def test_column_iterator(self): def foo(): for x in self.df.key: break self.assertRaises(TypeError, foo) def test_generic_hints(self): from pyspark.sql import DataFrame df1 = self.spark.range(10e10).toDF("id") df2 = self.spark.range(10e10).toDF("id") self.assertIsInstance(df1.hint("broadcast"), DataFrame) self.assertIsInstance(df1.hint("broadcast", []), DataFrame) # Dummy rules self.assertIsInstance(df1.hint("broadcast", "foo", "bar"), DataFrame) self.assertIsInstance(df1.hint("broadcast", ["foo", "bar"]), DataFrame) plan = df1.join(df2.hint("broadcast"), "id")._jdf.queryExecution().executedPlan() self.assertEqual(1, plan.toString().count("BroadcastHashJoin")) # add tests for SPARK-23647 (test more types for hint) def test_extended_hint_types(self): from pyspark.sql import DataFrame df = self.spark.range(10e10).toDF("id") such_a_nice_list = ["itworks1", "itworks2", "itworks3"] hinted_df = df.hint("my awesome hint", 1.2345, "what", such_a_nice_list) logical_plan = hinted_df._jdf.queryExecution().logical() self.assertEqual(1, logical_plan.toString().count("1.2345")) self.assertEqual(1, logical_plan.toString().count("what")) self.assertEqual(3, logical_plan.toString().count("itworks")) def test_sample(self): self.assertRaisesRegexp( TypeError, "should be a bool, float and number", lambda: self.spark.range(1).sample()) self.assertRaises( TypeError, lambda: self.spark.range(1).sample("a")) self.assertRaises( TypeError, lambda: self.spark.range(1).sample(seed="abc")) self.assertRaises( IllegalArgumentException, lambda: self.spark.range(1).sample(-1.0)) def test_toDF_with_schema_string(self): data = [Row(key=i, value=str(i)) for i in range(100)] rdd = self.sc.parallelize(data, 5) df = rdd.toDF("key: int, value: string") self.assertEqual(df.schema.simpleString(), "struct<key:int,value:string>") self.assertEqual(df.collect(), data) # different but compatible field types can be used. df = rdd.toDF("key: string, value: string") self.assertEqual(df.schema.simpleString(), "struct<key:string,value:string>") self.assertEqual(df.collect(), [Row(key=str(i), value=str(i)) for i in range(100)]) # field names can differ. df = rdd.toDF(" a: int, b: string ") self.assertEqual(df.schema.simpleString(), "struct<a:int,b:string>") self.assertEqual(df.collect(), data) # number of fields must match. self.assertRaisesRegexp(Exception, "Length of object", lambda: rdd.toDF("key: int").collect()) # field types mismatch will cause exception at runtime. self.assertRaisesRegexp(Exception, "FloatType can not accept", lambda: rdd.toDF("key: float, value: string").collect()) # flat schema values will be wrapped into row. df = rdd.map(lambda row: row.key).toDF("int") self.assertEqual(df.schema.simpleString(), "struct<value:int>") self.assertEqual(df.collect(), [Row(key=i) for i in range(100)]) # users can use DataType directly instead of data type string. df = rdd.map(lambda row: row.key).toDF(IntegerType()) self.assertEqual(df.schema.simpleString(), "struct<value:int>") self.assertEqual(df.collect(), [Row(key=i) for i in range(100)]) def test_join_without_on(self): df1 = self.spark.range(1).toDF("a") df2 = self.spark.range(1).toDF("b") with self.sql_conf({"spark.sql.crossJoin.enabled": False}): self.assertRaises(AnalysisException, lambda: df1.join(df2, how="inner").collect()) with self.sql_conf({"spark.sql.crossJoin.enabled": True}): actual = df1.join(df2, how="inner").collect() expected = [Row(a=0, b=0)] self.assertEqual(actual, expected) # Regression test for invalid join methods when on is None, Spark-14761 def test_invalid_join_method(self): df1 = self.spark.createDataFrame([("Alice", 5), ("Bob", 8)], ["name", "age"]) df2 = self.spark.createDataFrame([("Alice", 80), ("Bob", 90)], ["name", "height"]) self.assertRaises(IllegalArgumentException, lambda: df1.join(df2, how="invalid-join-type")) # Cartesian products require cross join syntax def test_require_cross(self): df1 = self.spark.createDataFrame([(1, "1")], ("key", "value")) df2 = self.spark.createDataFrame([(1, "1")], ("key", "value")) with self.sql_conf({"spark.sql.crossJoin.enabled": False}): # joins without conditions require cross join syntax self.assertRaises(AnalysisException, lambda: df1.join(df2).collect()) # works with crossJoin self.assertEqual(1, df1.crossJoin(df2).count()) def test_cache(self): spark = self.spark with self.tempView("tab1", "tab2"): spark.createDataFrame([(2, 2), (3, 3)]).createOrReplaceTempView("tab1") spark.createDataFrame([(2, 2), (3, 3)]).createOrReplaceTempView("tab2") self.assertFalse(spark.catalog.isCached("tab1")) self.assertFalse(spark.catalog.isCached("tab2")) spark.catalog.cacheTable("tab1") self.assertTrue(spark.catalog.isCached("tab1")) self.assertFalse(spark.catalog.isCached("tab2")) spark.catalog.cacheTable("tab2") spark.catalog.uncacheTable("tab1") self.assertFalse(spark.catalog.isCached("tab1")) self.assertTrue(spark.catalog.isCached("tab2")) spark.catalog.clearCache() self.assertFalse(spark.catalog.isCached("tab1")) self.assertFalse(spark.catalog.isCached("tab2")) self.assertRaisesRegexp( AnalysisException, "does_not_exist", lambda: spark.catalog.isCached("does_not_exist")) self.assertRaisesRegexp( AnalysisException, "does_not_exist", lambda: spark.catalog.cacheTable("does_not_exist")) self.assertRaisesRegexp( AnalysisException, "does_not_exist", lambda: spark.catalog.uncacheTable("does_not_exist")) def _to_pandas(self): from datetime import datetime, date schema = StructType().add("a", IntegerType()).add("b", StringType())\ .add("c", BooleanType()).add("d", FloatType())\ .add("dt", DateType()).add("ts", TimestampType()) data = [ (1, "foo", True, 3.0, date(1969, 1, 1), datetime(1969, 1, 1, 1, 1, 1)), (2, "foo", True, 5.0, None, None), (3, "bar", False, -1.0, date(2012, 3, 3), datetime(2012, 3, 3, 3, 3, 3)), (4, "bar", False, 6.0, date(2100, 4, 4), datetime(2100, 4, 4, 4, 4, 4)), ] df = self.spark.createDataFrame(data, schema) return df.toPandas() @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_to_pandas(self): import numpy as np pdf = self._to_pandas() types = pdf.dtypes self.assertEquals(types[0], np.int32) self.assertEquals(types[1], np.object) self.assertEquals(types[2], np.bool) self.assertEquals(types[3], np.float32) self.assertEquals(types[4], np.object) # datetime.date self.assertEquals(types[5], 'datetime64[ns]') @unittest.skipIf(have_pandas, "Required Pandas was found.") def test_to_pandas_required_pandas_not_found(self): with QuietTest(self.sc): with self.assertRaisesRegexp(ImportError, 'Pandas >= . must be installed'): self._to_pandas() @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_to_pandas_avoid_astype(self): import numpy as np schema = StructType().add("a", IntegerType()).add("b", StringType())\ .add("c", IntegerType()) data = [(1, "foo", 16777220), (None, "bar", None)] df = self.spark.createDataFrame(data, schema) types = df.toPandas().dtypes self.assertEquals(types[0], np.float64) # doesn't convert to np.int32 due to NaN value. self.assertEquals(types[1], np.object) self.assertEquals(types[2], np.float64) @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_to_pandas_from_empty_dataframe(self): # SPARK-29188 test that toPandas() on an empty dataframe has the correct dtypes import numpy as np sql = """ SELECT CAST(1 AS TINYINT) AS tinyint, CAST(1 AS SMALLINT) AS smallint, CAST(1 AS INT) AS int, CAST(1 AS BIGINT) AS bigint, CAST(0 AS FLOAT) AS float, CAST(0 AS DOUBLE) AS double, CAST(1 AS BOOLEAN) AS boolean, CAST('foo' AS STRING) AS string, CAST('2019-01-01' AS TIMESTAMP) AS timestamp """ dtypes_when_nonempty_df = self.spark.sql(sql).toPandas().dtypes dtypes_when_empty_df = self.spark.sql(sql).filter("False").toPandas().dtypes self.assertTrue(np.all(dtypes_when_empty_df == dtypes_when_nonempty_df)) @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_to_pandas_from_null_dataframe(self): # SPARK-29188 test that toPandas() on a dataframe with only nulls has correct dtypes import numpy as np sql = """ SELECT CAST(NULL AS TINYINT) AS tinyint, CAST(NULL AS SMALLINT) AS smallint, CAST(NULL AS INT) AS int, CAST(NULL AS BIGINT) AS bigint, CAST(NULL AS FLOAT) AS float, CAST(NULL AS DOUBLE) AS double, CAST(NULL AS BOOLEAN) AS boolean, CAST(NULL AS STRING) AS string, CAST(NULL AS TIMESTAMP) AS timestamp """ pdf = self.spark.sql(sql).toPandas() types = pdf.dtypes self.assertEqual(types[0], np.float64) self.assertEqual(types[1], np.float64) self.assertEqual(types[2], np.float64) self.assertEqual(types[3], np.float64) self.assertEqual(types[4], np.float32) self.assertEqual(types[5], np.float64) self.assertEqual(types[6], np.object) self.assertEqual(types[7], np.object) self.assertTrue(np.can_cast(np.datetime64, types[8])) @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_to_pandas_from_mixed_dataframe(self): # SPARK-29188 test that toPandas() on a dataframe with some nulls has correct dtypes import numpy as np sql = """ SELECT CAST(col1 AS TINYINT) AS tinyint, CAST(col2 AS SMALLINT) AS smallint, CAST(col3 AS INT) AS int, CAST(col4 AS BIGINT) AS bigint, CAST(col5 AS FLOAT) AS float, CAST(col6 AS DOUBLE) AS double, CAST(col7 AS BOOLEAN) AS boolean, CAST(col8 AS STRING) AS string, CAST(col9 AS TIMESTAMP) AS timestamp FROM VALUES (1, 1, 1, 1, 1, 1, 1, 1, 1), (NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL) """ pdf_with_some_nulls = self.spark.sql(sql).toPandas() pdf_with_only_nulls = self.spark.sql(sql).filter('tinyint is null').toPandas() self.assertTrue(np.all(pdf_with_only_nulls.dtypes == pdf_with_some_nulls.dtypes)) def test_create_dataframe_from_array_of_long(self): import array data = [Row(longarray=array.array('l', [-9223372036854775808, 0, 9223372036854775807]))] df = self.spark.createDataFrame(data) self.assertEqual(df.first(), Row(longarray=[-9223372036854775808, 0, 9223372036854775807])) @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_create_dataframe_from_pandas_with_timestamp(self): import pandas as pd from datetime import datetime pdf = pd.DataFrame({"ts": [datetime(2017, 10, 31, 1, 1, 1)], "d": [pd.Timestamp.now().date()]}, columns=["d", "ts"]) # test types are inferred correctly without specifying schema df = self.spark.createDataFrame(pdf) self.assertTrue(isinstance(df.schema['ts'].dataType, TimestampType)) self.assertTrue(isinstance(df.schema['d'].dataType, DateType)) # test with schema will accept pdf as input df = self.spark.createDataFrame(pdf, schema="d date, ts timestamp") self.assertTrue(isinstance(df.schema['ts'].dataType, TimestampType)) self.assertTrue(isinstance(df.schema['d'].dataType, DateType)) @unittest.skipIf(have_pandas, "Required Pandas was found.") def test_create_dataframe_required_pandas_not_found(self): with QuietTest(self.sc): with self.assertRaisesRegexp( ImportError, "(Pandas >= .* must be installed\|No module named '?pandas'?)"): import pandas as pd from datetime import datetime pdf = pd.DataFrame({"ts": [datetime(2017, 10, 31, 1, 1, 1)], "d": [pd.Timestamp.now().date()]}) self.spark.createDataFrame(pdf) # Regression test for SPARK-23360 @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_create_dataframe_from_pandas_with_dst(self): import pandas as pd from pandas.util.testing import assert_frame_equal from datetime import datetime pdf = pd.DataFrame({'time': [datetime(2015, 10, 31, 22, 30)]}) df = self.spark.createDataFrame(pdf) assert_frame_equal(pdf, df.toPandas()) orig_env_tz = os.environ.get('TZ', None) try: tz = 'America/Los_Angeles' os.environ['TZ'] = tz time.tzset() with self.sql_conf({'spark.sql.session.timeZone': tz}): df = self.spark.createDataFrame(pdf) assert_frame_equal(pdf, df.toPandas()) finally: del os.environ['TZ'] if orig_env_tz is not None: os.environ['TZ'] = orig_env_tz time.tzset() def test_repr_behaviors(self): import re pattern = re.compile(r'^ \\|', re.MULTILINE) df = self.spark.createDataFrame([(1, "1"), (22222, "22222")], ("key", "value")) # test when eager evaluation is enabled and _repr_html_ will not be called with self.sql_conf({"spark.sql.repl.eagerEval.enabled": True}): expected1 = """+-----+-----+ \|\| key\|value\| \|+-----+-----+ \|\| 1\| 1\| \|\|22222\|22222\| \|+-----+-----+ \|""" self.assertEquals(re.sub(pattern, '', expected1), df.__repr__()) with self.sql_conf({"spark.sql.repl.eagerEval.truncate": 3}): expected2 = """+---+-----+ \|\|key\|value\| \|+---+-----+ \|\| 1\| 1\| \|\|222\| 222\| \|+---+-----+ \|""" self.assertEquals(re.sub(pattern, '', expected2), df.__repr__()) with self.sql_conf({"spark.sql.repl.eagerEval.maxNumRows": 1}): expected3 = """+---+-----+ \|\|key\|value\| \|+---+-----+ \|\| 1\| 1\| \|+---+-----+ \|only showing top 1 row \|""" self.assertEquals(re.sub(pattern, '', expected3), df.__repr__()) # test when eager evaluation is enabled and _repr_html_ will be called with self.sql_conf({"spark.sql.repl.eagerEval.enabled": True}): expected1 = """<table border='1'> \|<tr><th>key</th><th>value</th></tr> \|<tr><td>1</td><td>1</td></tr> \|<tr><td>22222</td><td>22222</td></tr> \|</table> \|""" self.assertEquals(re.sub(pattern, '', expected1), df._repr_html_()) with self.sql_conf({"spark.sql.repl.eagerEval.truncate": 3}): expected2 = """<table border='1'> \|<tr><th>key</th><th>value</th></tr> \|<tr><td>1</td><td>1</td></tr> \|<tr><td>222</td><td>222</td></tr> \|</table> \|""" self.assertEquals(re.sub(pattern, '', expected2), df._repr_html_()) with self.sql_conf({"spark.sql.repl.eagerEval.maxNumRows": 1}): expected3 = """<table border='1'> \|<tr><th>key</th><th>value</th></tr> \|<tr><td>1</td><td>1</td></tr> \|</table> \|only showing top 1 row \|""" self.assertEquals(re.sub(pattern, '', expected3), df._repr_html_()) # test when eager evaluation is disabled and _repr_html_ will be called with self.sql_conf({"spark.sql.repl.eagerEval.enabled": False}): expected = "DataFrame[key: bigint, value: string]" self.assertEquals(None, df._repr_html_()) self.assertEquals(expected, df.__repr__()) with self.sql_conf({"spark.sql.repl.eagerEval.truncate": 3}): self.assertEquals(None, df._repr_html_()) self.assertEquals(expected, df.__repr__()) with self.sql_conf({"spark.sql.repl.eagerEval.maxNumRows": 1}): self.assertEquals(None, df._repr_html_()) self.assertEquals(expected, df.__repr__()) def test_to_local_iterator(self): df = self.spark.range(8, numPartitions=4) expected = df.collect() it = df.toLocalIterator() self.assertEqual(expected, list(it)) # Test DataFrame with empty partition df = self.spark.range(3, numPartitions=4) it = df.toLocalIterator() expected = df.collect() self.assertEqual(expected, list(it)) def test_to_local_iterator_prefetch(self): df = self.spark.range(8, numPartitions=4) expected = df.collect() it = df.toLocalIterator(prefetchPartitions=True) self.assertEqual(expected, list(it)) def test_to_local_iterator_not_fully_consumed(self): # SPARK-23961: toLocalIterator throws exception when not fully consumed # Create a DataFrame large enough so that write to socket will eventually block df = self.spark.range(1 << 20, numPartitions=2) it = df.toLocalIterator() self.assertEqual(df.take(1)[0], next(it)) with QuietTest(self.sc): it = None # remove iterator from scope, socket is closed when cleaned up # Make sure normal df operations still work result = [] for i, row in enumerate(df.toLocalIterator()): result.append(row) if i == 7: break self.assertEqual(df.take(8), result) class QueryExecutionListenerTests(unittest.TestCase, SQLTestUtils): # These tests are separate because it uses 'spark.sql.queryExecutionListeners' which is # static and immutable. This can't be set or unset, for example, via `spark.conf`. @classmethod def setUpClass(cls): import glob from pyspark.find_spark_home import _find_spark_home SPARK_HOME = _find_spark_home() filename_pattern = ( "sql/core/target/scala-/test-classes/org/apache/spark/sql/" "TestQueryExecutionListener.class") cls.has_listener = bool(glob.glob(os.path.join(SPARK_HOME, filename_pattern))) if cls.has_listener: # Note that 'spark.sql.queryExecutionListeners' is a static immutable configuration. cls.spark = SparkSession.builder \ .master("local[4]") \ .appName(cls.__name__) \ .config( "spark.sql.queryExecutionListeners", "org.apache.spark.sql.TestQueryExecutionListener") \ .getOrCreate() def setUp(self): if not self.has_listener: raise self.skipTest( "'org.apache.spark.sql.TestQueryExecutionListener' is not " "available. Will skip the related tests.") @classmethod def tearDownClass(cls): if hasattr(cls, "spark"): cls.spark.stop() def tearDown(self): self.spark._jvm.OnSuccessCall.clear() def test_query_execution_listener_on_collect(self): self.assertFalse( self.spark._jvm.OnSuccessCall.isCalled(), "The callback from the query execution listener should not be called before 'collect'") self.spark.sql("SELECT * FROM range(1)").collect() self.spark.sparkContext._jsc.sc().listenerBus().waitUntilEmpty(10000) self.assertTrue( self.spark._jvm.OnSuccessCall.isCalled(), "The callback from the query execution listener should be called after 'collect'") @unittest.skipIf( not have_pandas or not have_pyarrow, pandas_requirement_message or pyarrow_requirement_message) def test_query_execution_listener_on_collect_with_arrow(self): with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": True}): self.assertFalse( self.spark._jvm.OnSuccessCall.isCalled(), "The callback from the query execution listener should not be " "called before 'toPandas'") self.spark.sql("SELECT * FROM range(1)").toPandas() self.spark.sparkContext._jsc.sc().listenerBus().waitUntilEmpty(10000) self.assertTrue( self.spark._jvm.OnSuccessCall.isCalled(), "The callback from the query execution listener should be called after 'toPandas'") if __name__ == "__main__": from pyspark.sql.tests.test_dataframe import * try: import xmlrunner testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2) except ImportError: testRunner = None unittest.main(testRunner=testRunner, verbosity=2)	apache-2.0
iamshang1/Projects	Advanced_ML/Text_Classification/tf_han.py	1	14525	''' hierarchical attention network for document classification https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf ''' import os os.environ["CUDA_VISIBLE_DEVICES"]="0" import numpy as np import tensorflow as tf from tensorflow.contrib.rnn import LSTMCell, GRUCell import sys import time class hierarchical_attention_network(object): ''' hierarchical attention network for document classification https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf parameters: - embedding_matrix: numpy array numpy array of word embeddings each row should represent a word embedding NOTE: the word index 0 is dropped, so the first row is ignored - num classes: int number of output classes - max_sents: int maximum number of sentences per document - max_words: int maximum number of words per sentence - rnn_type: string (default: "gru") rnn cells to use, can be "gru" or "lstm" - rnn_units: int (default: 100) number of rnn units to use for embedding layers - attention_context: int (default: 50) number of dimensions to use for attention context layer - dropout_keep: float (default: 0.5) dropout keep rate for final softmax layer methods: - train(data,labels,epochs=30,savebest=False,filepath=None) train network on given data - predict(data) return the one-hot-encoded predicted labels for given data - score(data,labels,bootstrap=False,bs_samples=100) return the accuracy of predicted labels on given data - save(filepath) save the model weights to a file - load(filepath) load model weights from a file ''' def __init__(self,embedding_matrix,num_classes,max_sents,max_words,rnn_type="gru", rnn_units=200,attention_context=300,dropout_keep=1.0): self.rnn_units = rnn_units if rnn_type == "gru": self.rnn_cell = GRUCell elif rnn_type == "lstm": self.rnn_cell = LSTMCell else: raise Exception("rnn_type parameter must be set to gru or lstm") self.dropout_keep = dropout_keep self.vocab = embedding_matrix self.embedding_size = embedding_matrix.shape[1] self.ms = max_sents self.mw = max_words #shared variables with tf.variable_scope('words'): self.word_atten_W = tf.Variable(self._ortho_weight(2rnn_units,attention_context),name='word_atten_W') self.word_atten_b = tf.Variable(np.asarray(np.zeros(attention_context),dtype=np.float32),name='word_atten_b') self.word_softmax = tf.Variable(self._ortho_weight(attention_context,1),name='word_softmax') with tf.variable_scope('sentence'): self.sent_atten_W = tf.Variable(self._ortho_weight(2rnn_units,attention_context),name='sent_atten_W') self.sent_atten_b = tf.Variable(np.asarray(np.zeros(attention_context),dtype=np.float32),name='sent_atten_b') self.sent_softmax = tf.Variable(self._ortho_weight(attention_context,1),name='sent_softmax') with tf.variable_scope('classify'): self.W_softmax = tf.Variable(self._ortho_weight(rnn_units2,num_classes),name='W_softmax') self.b_softmax = tf.Variable(np.asarray(np.zeros(num_classes),dtype=np.float32),name='b_softmax') self.embeddings = tf.constant(self.vocab,tf.float32) self.dropout = tf.placeholder(tf.float32) #doc input and mask self.doc_input = tf.placeholder(tf.int32, shape=[max_sents,max_words]) self.words_per_line = tf.reduce_sum(tf.sign(self.doc_input),1) self.max_lines = tf.reduce_sum(tf.sign(self.words_per_line)) self.max_words = tf.reduce_max(self.words_per_line) self.doc_input_reduced = self.doc_input[:self.max_lines,:self.max_words] self.num_words = self.words_per_line[:self.max_lines] #word rnn layer self.word_embeds = tf.gather(tf.get_variable('embeddings',initializer=self.embeddings,dtype=tf.float32),self.doc_input_reduced) with tf.variable_scope('words'): [word_outputs_fw,word_outputs_bw],_ = \ tf.nn.bidirectional_dynamic_rnn( tf.contrib.rnn.DropoutWrapper(self.rnn_cell(self.rnn_units),state_keep_prob=self.dropout), tf.contrib.rnn.DropoutWrapper(self.rnn_cell(self.rnn_units),state_keep_prob=self.dropout), self.word_embeds,sequence_length=self.num_words,dtype=tf.float32) word_outputs = tf.concat((word_outputs_fw, word_outputs_bw),2) #word attention seq_mask = tf.reshape(tf.sequence_mask(self.num_words,self.max_words),[-1]) u = tf.nn.tanh(tf.matmul(tf.reshape(word_outputs,[-1,self.rnn_units2]),self.word_atten_W)+self.word_atten_b) exps = tf.exp(tf.matmul(u,self.word_softmax)) exps = tf.where(seq_mask,exps,tf.ones_like(exps)0.000000001) alpha = tf.reshape(exps,[-1,self.max_words,1]) alpha /= tf.reshape(tf.reduce_sum(alpha,1),[-1,1,1]) self.sent_embeds = tf.reduce_sum(word_outputsalpha,1) self.sent_embeds = tf.expand_dims(self.sent_embeds,0) #sentence rnn layer with tf.variable_scope('sentence'): [self.sent_outputs_fw,self.sent_outputs_bw],_ = \ tf.nn.bidirectional_dynamic_rnn( tf.contrib.rnn.DropoutWrapper(self.rnn_cell(self.rnn_units),state_keep_prob=self.dropout), tf.contrib.rnn.DropoutWrapper(self.rnn_cell(self.rnn_units),state_keep_prob=self.dropout), self.sent_embeds,sequence_length=tf.expand_dims(self.max_lines,0),dtype=tf.float32) self.sent_outputs = tf.concat((tf.squeeze(self.sent_outputs_fw,[0]),tf.squeeze(self.sent_outputs_bw,[0])),1) #sentence attention self.sent_u = tf.nn.tanh(tf.matmul(self.sent_outputs,self.sent_atten_W) + self.sent_atten_b) self.sent_exp = tf.exp(tf.matmul(self.sent_u,self.sent_softmax)) self.sent_atten = self.sent_exp/tf.reduce_sum(self.sent_exp) self.doc_embed = tf.transpose(tf.matmul(tf.transpose(self.sent_outputs),self.sent_atten)) #classification functions self.output = tf.matmul(self.doc_embed,self.W_softmax)+self.b_softmax self.prediction = tf.nn.softmax(self.output) #loss, accuracy, and training functions self.labels = tf.placeholder(tf.float32, shape=[num_classes]) self.labels_rs = tf.expand_dims(self.labels,0) self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.output,labels=self.labels_rs)) self.optimizer = tf.train.AdamOptimizer(0.00001,0.9,0.99).minimize(self.loss) #init op self.init_op = tf.global_variables_initializer() self.saver = tf.train.Saver() self.sess = tf.Session() self.sess.run(self.init_op) def _ortho_weight(self,fan_in,fan_out): ''' generate orthogonal weight matrix ''' bound = np.sqrt(2./(fan_in+fan_out)) W = np.random.randn(fan_in,fan_out)bound u, s, v = np.linalg.svd(W,full_matrices=False) if u.shape[0] != u.shape[1]: W = u else: W = v return W.astype(np.float32) def _list_to_numpy(self,inputval): ''' convert variable length lists of input values to zero padded numpy array ''' if type(inputval) == list: retval = np.zeros((self.ms,self.mw)) for i,line in enumerate(inputval): for j, word in enumerate(line): retval[i,j] = word return retval elif type(inputval) == np.ndarray: return inputval else: raise Exception("invalid input type") def train(self,data,labels,epochs=30,validation_data=None,savebest=False,filepath=None): ''' train network on given data parameters: - data: numpy array 3d numpy array (doc x sentence x word ids) of input data - labels: numpy array 2d numpy array of one-hot-encoded labels - epochs: int (default: 30) number of epochs to train for - validation_data: tuple (optional) tuple of numpy arrays (X,y) representing validation data - savebest: boolean (default: False) set to True to save the best model based on validation score per epoch - filepath: string (optional) path to save model if savebest is set to True outputs: None ''' if savebest==True and filepath==None: raise Exception("Please enter a path to save the network") if validation_data: validation_size = len(validation_data[0]) else: validation_size = len(data) print('training network on %i documents, validating on %i documents' \ % (len(data), validation_size)) #track best model for saving prevbest = 0 for i in range(epochs): correct = 0. start = time.time() #train for doc in range(len(data)): inputval = self._list_to_numpy(data[doc]) feed_dict = {self.doc_input:inputval,self.labels:labels[doc],self.dropout:self.dropout_keep} pred,cost,_ = self.sess.run([self.prediction,self.loss,self.optimizer],feed_dict=feed_dict) if np.argmax(pred) == np.argmax(labels[doc]): correct += 1 sys.stdout.write("epoch %i, sample %i of %i, loss: %f \r"\ % (i+1,doc+1,len(data),cost)) sys.stdout.flush() if (doc+1) % 50000 == 0: score = self.score(validation_data[0],validation_data[1]) print("iteration %i validation accuracy: %.4f%%" % (doc+1, score100)) print() #print("training time: %.2f" % (time.time()-start)) trainscore = correct/len(data) print("epoch %i training accuracy: %.4f%%" % (i+1, trainscore100)) #validate if validation_data: score = self.score(validation_data[0],validation_data[1]) print("epoch %i validation accuracy: %.4f%%" % (i+1, score100)) #save if performance better than previous best if savebest and score >= prevbest: prevbest = score self.save(filepath) def predict(self,data): ''' return the one-hot-encoded predicted labels for given data parameters: - data: numpy array 3d numpy array (doc x sentence x word ids) of input data outputs: numpy array of one-hot-encoded predicted labels for input data ''' labels = [] for doc in range(len(data)): inputval = self._list_to_numpy(data[doc]) feed_dict = {self.doc_input:inputval,self.dropout:1.0} prob = self.sess.run(self.prediction,feed_dict=feed_dict) prob = np.squeeze(prob,0) one_hot = np.zeros_like(prob) one_hot[np.argmax(prob)] = 1 labels.append(one_hot) labels = np.array(labels) return labels def score(self,data,labels): ''' return the accuracy of predicted labels on given data parameters: - data: numpy array 3d numpy array (doc x sentence x word ids) of input data - labels: numpy array 2d numpy array of one-hot-encoded labels outputs: float representing accuracy of predicted labels on given data ''' correct = 0. for doc in range(len(data)): inputval = self._list_to_numpy(data[doc]) feed_dict = {self.doc_input:inputval,self.dropout:1.0} prob = self.sess.run(self.prediction,feed_dict=feed_dict) if np.argmax(prob) == np.argmax(labels[doc]): correct +=1 accuracy = correct/len(labels) return accuracy def save(self,filename): ''' save the model weights to a file parameters: - filepath: string path to save model weights outputs: None ''' self.saver.save(self.sess,filename) def load(self,filename): ''' load model weights from a file parameters: - filepath: string path from which to load model weights outputs: None ''' self.saver.restore(self.sess,filename) if __name__ == "__main__": from sklearn.preprocessing import LabelEncoder, LabelBinarizer from sklearn.model_selection import train_test_split import pickle import os #load saved files print "loading data" vocab = np.load('embeddings.npy') with open('data.pkl', 'rb') as f: data = pickle.load(f) num_docs = len(data) #convert data to numpy arrays print "converting data to arrays" max_sents = 0 max_words = 0 docs = [] labels = [] for i in range(num_docs): sys.stdout.write("processing record %i of %i \r" % (i+1,num_docs)) sys.stdout.flush() doc = data[i]['idx'] docs.append(doc) labels.append(data[i]['label']) if len(doc) > max_sents: max_sents = len(doc) if len(max(doc,key=len)) > max_words: max_words = len(max(doc,key=len)) del data print #label encoder le = LabelEncoder() y = le.fit_transform(labels) classes = len(le.classes_) lb = LabelBinarizer() y_bin = lb.fit_transform(y) del labels #test train split X_train,X_test,y_train,y_test = train_test_split(docs,y_bin,test_size=0.1, random_state=1234,stratify=y) #train nn print "building hierarchical attention network" nn = hierarchical_attention_network(vocab,classes,max_sents,max_words) nn.train(X_train,y_train,epochs=5,validation_data=(X_test,y_test))	mit
thorwhalen/ut	ml/text/topic_analysis.py	1	6849	__author__ = 'thor' from wordcloud import WordCloud import colorsys import seaborn as sns from numpy import sqrt, linspace, ceil, where, arange, array, any, floor, ceil, ndarray from pandas import Series import matplotlib.pyplot as plt class TopicExplorer(object): def __init__(self, url_vectorizer, topic_model, topic_weight_normalization=None, word_preprocessor=None, wordcloud_params={'ranks_only': True, 'width': 300, 'height': 300, 'margin': 1, 'background_color': "black"}, replace_empty_feature_with='EMPTY', word_art_params={}): self.url_vectorizer = url_vectorizer self.feature_names = self.url_vectorizer.get_feature_names() if word_preprocessor is None: self.word_preprocessor = lambda x: x else: self.word_preprocessor = word_preprocessor # some features might have empty names: Replace them with replace_empty_feature_with if replace_empty_feature_with is not None: lidx = array(self.feature_names) == '' if any(lidx): self.feature_names[lidx] = replace_empty_feature_with self.topic_model = topic_model self.wordcloud_params = wordcloud_params self.word_art_params = word_art_params self.n_topics = len(self.topic_model.components_) topic_components = self.topic_model.components_ if topic_weight_normalization is not None: if isinstance(topic_weight_normalization, str): if topic_weight_normalization == 'tf_normal': def topic_weight_normalization(topic_components): topic_components /= topic_components.sum(axis=1)[:, None] topic_components = 1 / sqrt((topic_components * 2).sum(axis=0)) return topic_components else: ValueError("Unknown topic_weight_normalization name") if callable(topic_weight_normalization): topic_components = topic_weight_normalization(topic_components) self.topic_word_weights = list() for topic_idx, topic in enumerate(topic_components): topic_ww = dict() for i in topic.argsort(): topic_ww[self.feature_names[i]] = topic_components[topic_idx, i] self.topic_word_weights.append(Series(topic_ww).sort_values(ascending=False, inplace=False)) self.topic_color = ["hsl(0, 100%, 100%)"] h_list = list(map(int, linspace(0, 360, len(self.topic_model.components_))))[:-1] for h in h_list: self.topic_color.append("hsl({}, 100%, 50%)".format(h)) def topic_weights(self, text_collection): if isinstance(text_collection, str): urls = [text_collection] return self.topic_model.transform(self.url_vectorizer.transform(text_collection)) def topic_word_art(self, topic_idx=None, n_words=20, save_file=None, color_func=None, random_state=1, fig_row_size=16, *kwargs): if topic_idx is None: ncols = int(floor(sqrt(self.n_topics))) nrows = int(ceil(self.n_topics / float(ncols))) ncols_to_nrows_ratio = ncols / nrows plt.figure(figsize=(fig_row_size, ncols_to_nrows_ratio fig_row_size)) for i in range(self.n_topics): plt.subplot(nrows, ncols, i + 1) self.topic_word_art(topic_idx=i, n_words=n_words, save_file=save_file, color_func=color_func, random_state=random_state, *kwargs) plt.gcf().subplots_adjust(wspace=.1, hspace=.1) # elif isinstance(topic_idx, (list, tuple, ndarray)) and len(topic_idx) == self.n_topics: # ncols = int(floor(sqrt(self.n_topics))) # nrows = int(ceil(self.n_topics / float(ncols))) # ncols_to_nrows_ratio = ncols / nrows # plt.figure(figsize=(fig_row_size, ncols_to_nrows_ratio fig_row_size)) # for i in range(self.n_topics): # plt.subplot(nrows, ncols, i + 1) # self.topic_word_art(topic_idx=i, n_words=n_words, save_file=save_file, # color_func=color_func, random_state=random_state, # width=int(self.wordcloud_params['width'] * topic_idx[i]), # height=int(self.wordcloud_params['height'] * topic_idx[i])) # plt.gcf().subplots_adjust(wspace=.1, hspace=.1) else: kwargs = dict(self.wordcloud_params, *kwargs) if color_func is None: color_func = self.word_art_params.get('color_func', self.topic_color[topic_idx]) if isinstance(color_func, tuple): color_func = "rgb({}, {}, {})".format(list(map(int, color_func))) if isinstance(color_func, str): color = color_func def color_func(word, font_size, position, orientation, random_state=None, kwargs): return color elif not callable(color_func): TypeError("Unrecognized hsl_color type ()".format(type(color_func))) # kwargs = dict(self.word_art_params, kwargs) wc = WordCloud(random_state=random_state, **kwargs) wc.fit_words([(self.word_preprocessor(k), v) for k, v in self.topic_word_weights[topic_idx].iloc[:n_words].to_dict().items()]) # wc.recolor(color_func=kwargs['color_func'], random_state=random_state) plt.imshow(wc.recolor(color_func=color_func, random_state=random_state)) plt.grid(False) plt.xticks([]) plt.yticks([]) def plot_topic_trajectory(self, urls): _topic_weights = self.topic_weights(urls) _topic_weights = (_topic_weights.T / _topic_weights.max(axis=1)) sns.heatmap(_topic_weights, cbar=False, linewidths=1) plt.ylabel('Topic') plt.xlabel('Page view') ax = plt.gca() start, end = ax.get_xlim() if _topic_weights.shape[1] > 20: ax.xaxis.set_ticks(arange(start, end, 10)) ax.xaxis.set_ticklabels(arange(start, end, 10).astype(int)) return ax def plot_topic_trajectory_of_tcid(self, tcid, data): d = data[data.tc_id == tcid].sort_values(by='timestamp', ascending=True) urls = d.data_url_test ax = self.plot_topic_trajectory(urls) conversion_idx = where(array(d.data_env_template == 'funnel_confirmation'))[0] if len(conversion_idx): min_y, max_y = plt.ylim() for idx in conversion_idx: plt.plot((idx + 0.5, idx + 0.5), (min_y, max_y), 'b-')	mit
kkk669/mxnet	docs/mxdoc.py	13	12762	# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """A sphnix-doc plugin to build mxnet docs""" import subprocess import re import os import json import sys from recommonmark import transform import pypandoc # import StringIO from io for python3 compatibility from io import StringIO import contextlib # white list to evaluate the code block output, such as ['tutorials/gluon'] _EVAL_WHILTELIST = [] # start or end of a code block _CODE_MARK = re.compile('^([ ])```([\w])') # language names and the according file extensions and comment symbol _LANGS = {'python' : ('py', '#'), 'r' : ('R','#'), 'scala' : ('scala', '#'), 'julia' : ('jl', '#'), 'perl' : ('pl', '#'), 'cpp' : ('cc', '//'), 'bash' : ('sh', '#')} _LANG_SELECTION_MARK = 'INSERT SELECTION BUTTONS' _SRC_DOWNLOAD_MARK = 'INSERT SOURCE DOWNLOAD BUTTONS' def _run_cmd(cmds): """Run commands, raise exception if failed""" if not isinstance(cmds, str): cmds = "".join(cmds) print("Execute \"%s\"" % cmds) try: subprocess.check_call(cmds, shell=True) except subprocess.CalledProcessError as err: print(err) raise err def generate_doxygen(app): """Run the doxygen make commands""" _run_cmd("cd %s/.. && make doxygen" % app.builder.srcdir) _run_cmd("cp -rf doxygen/html %s/doxygen" % app.builder.outdir) def build_mxnet(app): """Build mxnet .so lib""" _run_cmd("cd %s/.. && cp make/config.mk config.mk && make -j$(nproc) DEBUG=1" % app.builder.srcdir) def build_r_docs(app): """build r pdf""" r_root = app.builder.srcdir + '/../R-package' pdf_path = root_path + '/docs/api/r/mxnet-r-reference-manual.pdf' _run_cmd('cd ' + r_root + '; R -e "roxygen2::roxygenize()"; R CMD Rd2pdf . --no-preview -o ' + pdf_path) dest_path = app.builder.outdir + '/api/r/' _run_cmd('mkdir -p ' + dest_path + '; mv ' + pdf_path + ' ' + dest_path) def build_scala_docs(app): """build scala doc and then move the outdir""" scala_path = app.builder.srcdir + '/../scala-package/core/src/main/scala/ml/dmlc/mxnet' # scaldoc fails on some apis, so exit 0 to pass the check _run_cmd('cd ' + scala_path + '; scaladoc `find . \| grep .scala`; exit 0') dest_path = app.builder.outdir + '/api/scala/docs' _run_cmd('rm -rf ' + dest_path) _run_cmd('mkdir -p ' + dest_path) scaladocs = ['index', 'index.html', 'ml', 'lib', 'index.js', 'package.html'] for doc_file in scaladocs: _run_cmd('cd ' + scala_path + ' && mv -f ' + doc_file + ' ' + dest_path) def _convert_md_table_to_rst(table): """Convert a markdown table to rst format""" if len(table) < 3: return '' out = '```eval_rst\n.. list-table::\n :header-rows: 1\n\n' for i,l in enumerate(table): cols = l.split('\|')[1:-1] if i == 0: ncol = len(cols) else: if len(cols) != ncol: return '' if i == 1: for c in cols: if len(c) is not 0 and '---' not in c: return '' else: for j,c in enumerate(cols): out += ' - ' if j == 0 else ' - ' out += pypandoc.convert_text( c, 'rst', format='md').replace('\n', ' ').replace('\r', '') + '\n' out += '```\n' return out def convert_table(app, docname, source): """Find tables in a markdown and then convert them into the rst format""" num_tables = 0 for i,j in enumerate(source): table = [] output = '' in_table = False for l in j.split('\n'): r = l.strip() if r.startswith('\|'): table.append(r) in_table = True else: if in_table is True: converted = _convert_md_table_to_rst(table) if converted is '': print("Failed to convert the markdown table") print(table) else: num_tables += 1 output += converted in_table = False table = [] output += l + '\n' source[i] = output if num_tables > 0: print('Converted %d tables in %s' % (num_tables, docname)) def _parse_code_lines(lines): """A iterator that returns if a line is within a code block Returns ------- iterator of (str, bool, str, int) - line: the line - in_code: if this line is in a code block - lang: the code block langunage - indent: the code indent """ in_code = False lang = None indent = None for l in lines: m = _CODE_MARK.match(l) if m is not None: if not in_code: if m.groups()[1].lower() in _LANGS: lang = m.groups()[1].lower() indent = len(m.groups()[0]) in_code = True yield (l, in_code, lang, indent) else: yield (l, in_code, lang, indent) lang = None indent = None in_code = False else: yield (l, in_code, lang, indent) def _get_lang_selection_btn(langs): active = True btngroup = '<div class="text-center">\n<div class="btn-group opt-group" role="group">' for l in langs: btngroup += '<button type="button" class="btn btn-default opt %s">%s</button>\n' % ( 'active' if active else '', l[0].upper()+l[1:].lower()) active = False btngroup += '</div>\n</div> <script type="text/javascript" src="../../_static/js/options.js"></script>' return btngroup def _get_blocks(lines): """split lines into code and non-code blocks Returns ------- iterator of (bool, str, list of str) - if it is a code block - source language - lines of source """ cur_block = [] pre_lang = None pre_in_code = None for (l, in_code, cur_lang, _) in _parse_code_lines(lines): if in_code != pre_in_code: if pre_in_code and len(cur_block) >= 2: cur_block = cur_block[1:-1] # remove ``` # remove empty lines at head while len(cur_block) > 0: if len(cur_block[0]) == 0: cur_block.pop(0) else: break # remove empty lines at tail while len(cur_block) > 0: if len(cur_block[-1]) == 0: cur_block.pop() else: break if len(cur_block): yield (pre_in_code, pre_lang, cur_block) cur_block = [] cur_block.append(l) pre_lang = cur_lang pre_in_code = in_code if len(cur_block): yield (pre_in_code, pre_lang, cur_block) def _get_mk_code_block(src, lang): """Return a markdown code block E.g. ```python import mxnet ```` """ if lang is None: lang = '' return '```'+lang+'\n'+src.rstrip()+'\n'+'```\n' @contextlib.contextmanager def _string_io(): oldout = sys.stdout olderr = sys.stderr strio = StringIO.StringIO() sys.stdout = strio sys.stderr = strio yield strio sys.stdout = oldout sys.stderr = olderr def _get_python_block_output(src, global_dict, local_dict): """Evaluate python source codes Returns (bool, str): - True if success - output """ src = '\n'.join([l for l in src.split('\n') if not l.startswith('%') and not 'plt.show()' in l]) ret_status = True err = '' with _string_io() as s: try: exec(src, global_dict, global_dict) except Exception as e: err = str(e) ret_status = False return (ret_status, s.getvalue()+err) def _get_jupyter_notebook(lang, lines): cells = [] for in_code, blk_lang, lines in _get_blocks(lines): if blk_lang != lang: in_code = False src = '\n'.join(lines) cell = { "cell_type": "code" if in_code else "markdown", "metadata": {}, "source": src } if in_code: cell.update({ "outputs": [], "execution_count": None, }) cells.append(cell) ipynb = {"nbformat" : 4, "nbformat_minor" : 2, "metadata" : {"language":lang, "display_name":'', "name":''}, "cells" : cells} return ipynb def _get_source(lang, lines): cmt = _LANGS[lang][1] + ' ' out = [] for in_code, lines in _get_blocks(lang, lines): if in_code: out.append('') for l in lines: if in_code: if '%matplotlib' not in l: out.append(l) else: if ('<div>' in l or '</div>' in l or '<script>' in l or '</script>' in l or '<!--' in l or '-->' in l or '%matplotlib' in l ): continue out.append(cmt+l) if in_code: out.append('') return out def _get_src_download_btn(out_prefix, langs, lines): btn = '<div class="btn-group" role="group">\n' for lang in langs: ipynb = out_prefix if lang == 'python': ipynb += '.ipynb' else: ipynb += '_' + lang + '.ipynb' with open(ipynb, 'w') as f: json.dump(_get_jupyter_notebook(lang, lines), f) f = ipynb.split('/')[-1] btn += '<div class="download-btn"><a href="%s" download="%s">' \ '<span class="glyphicon glyphicon-download-alt"></span> %s</a></div>' % (f, f, f) btn += '</div>\n' return btn def add_buttons(app, docname, source): out_prefix = app.builder.outdir + '/' + docname dirname = os.path.dirname(out_prefix) if not os.path.exists(dirname): os.makedirs(dirname) for i,j in enumerate(source): local_dict = {} global_dict = {} lines = j.split('\n') langs = set([l for (_, _, l, _) in _parse_code_lines(lines) if l is not None and l in _LANGS]) # first convert for k,l in enumerate(lines): if _SRC_DOWNLOAD_MARK in l: lines[k] = _get_src_download_btn( out_prefix, langs, lines) # # then add lang buttons # for k,l in enumerate(lines): # if _LANG_SELECTION_MARK in l: # lines[k] = _get_lang_selection_btn(langs) output = '' for in_code, lang, lines in _get_blocks(lines): src = '\n'.join(lines)+'\n' if in_code: output += _get_mk_code_block(src, lang) if lang == 'python' and any([w in docname for w in _EVAL_WHILTELIST]): status, blk_out = _get_python_block_output(src, global_dict, local_dict) if len(blk_out): output += '<div class=\"cell-results-header\">Output:</div>\n\n' output += _get_mk_code_block(blk_out, 'results') else: output += src source[i] = output # source[i] = '\n'.join(lines) def setup(app): app.connect("builder-inited", build_mxnet) app.connect("builder-inited", generate_doxygen) app.connect("builder-inited", build_scala_docs) # skipped to build r, it requires to install latex, which is kinds of too heavy # app.connect("builder-inited", build_r_docs) app.connect('source-read', convert_table) app.connect('source-read', add_buttons) app.add_config_value('recommonmark_config', { 'url_resolver': lambda url: 'http://mxnet.io/' + url, 'enable_eval_rst': True, }, True) app.add_transform(transform.AutoStructify)	apache-2.0
Clyde-fare/scikit-learn	examples/decomposition/plot_image_denoising.py	181	5819	""" ========================================= Image denoising using dictionary learning ========================================= An example comparing the effect of reconstructing noisy fragments of the Lena image using firstly online :ref:`DictionaryLearning` and various transform methods. The dictionary is fitted on the distorted left half of the image, and subsequently used to reconstruct the right half. Note that even better performance could be achieved by fitting to an undistorted (i.e. noiseless) image, but here we start from the assumption that it is not available. A common practice for evaluating the results of image denoising is by looking at the difference between the reconstruction and the original image. If the reconstruction is perfect this will look like Gaussian noise. It can be seen from the plots that the results of :ref:`omp` with two non-zero coefficients is a bit less biased than when keeping only one (the edges look less prominent). It is in addition closer from the ground truth in Frobenius norm. The result of :ref:`least_angle_regression` is much more strongly biased: the difference is reminiscent of the local intensity value of the original image. Thresholding is clearly not useful for denoising, but it is here to show that it can produce a suggestive output with very high speed, and thus be useful for other tasks such as object classification, where performance is not necessarily related to visualisation. """ print(__doc__) from time import time import matplotlib.pyplot as plt import numpy as np from scipy.misc import lena from sklearn.decomposition import MiniBatchDictionaryLearning from sklearn.feature_extraction.image import extract_patches_2d from sklearn.feature_extraction.image import reconstruct_from_patches_2d ############################################################################### # Load Lena image and extract patches lena = lena() / 256.0 # downsample for higher speed lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2] lena /= 4.0 height, width = lena.shape # Distort the right half of the image print('Distorting image...') distorted = lena.copy() distorted[:, height // 2:] += 0.075 * np.random.randn(width, height // 2) # Extract all reference patches from the left half of the image print('Extracting reference patches...') t0 = time() patch_size = (7, 7) data = extract_patches_2d(distorted[:, :height // 2], patch_size) data = data.reshape(data.shape[0], -1) data -= np.mean(data, axis=0) data /= np.std(data, axis=0) print('done in %.2fs.' % (time() - t0)) ############################################################################### # Learn the dictionary from reference patches print('Learning the dictionary...') t0 = time() dico = MiniBatchDictionaryLearning(n_components=100, alpha=1, n_iter=500) V = dico.fit(data).components_ dt = time() - t0 print('done in %.2fs.' % dt) plt.figure(figsize=(4.2, 4)) for i, comp in enumerate(V[:100]): plt.subplot(10, 10, i + 1) plt.imshow(comp.reshape(patch_size), cmap=plt.cm.gray_r, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.suptitle('Dictionary learned from Lena patches\n' + 'Train time %.1fs on %d patches' % (dt, len(data)), fontsize=16) plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23) ############################################################################### # Display the distorted image def show_with_diff(image, reference, title): """Helper function to display denoising""" plt.figure(figsize=(5, 3.3)) plt.subplot(1, 2, 1) plt.title('Image') plt.imshow(image, vmin=0, vmax=1, cmap=plt.cm.gray, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.subplot(1, 2, 2) difference = image - reference plt.title('Difference (norm: %.2f)' % np.sqrt(np.sum(difference 2))) plt.imshow(difference, vmin=-0.5, vmax=0.5, cmap=plt.cm.PuOr, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.suptitle(title, size=16) plt.subplots_adjust(0.02, 0.02, 0.98, 0.79, 0.02, 0.2) show_with_diff(distorted, lena, 'Distorted image') ############################################################################### # Extract noisy patches and reconstruct them using the dictionary print('Extracting noisy patches... ') t0 = time() data = extract_patches_2d(distorted[:, height // 2:], patch_size) data = data.reshape(data.shape[0], -1) intercept = np.mean(data, axis=0) data -= intercept print('done in %.2fs.' % (time() - t0)) transform_algorithms = [ ('Orthogonal Matching Pursuit\n1 atom', 'omp', {'transform_n_nonzero_coefs': 1}), ('Orthogonal Matching Pursuit\n2 atoms', 'omp', {'transform_n_nonzero_coefs': 2}), ('Least-angle regression\n5 atoms', 'lars', {'transform_n_nonzero_coefs': 5}), ('Thresholding\n alpha=0.1', 'threshold', {'transform_alpha': .1})] reconstructions = {} for title, transform_algorithm, kwargs in transform_algorithms: print(title + '...') reconstructions[title] = lena.copy() t0 = time() dico.set_params(transform_algorithm=transform_algorithm, kwargs) code = dico.transform(data) patches = np.dot(code, V) if transform_algorithm == 'threshold': patches -= patches.min() patches /= patches.max() patches += intercept patches = patches.reshape(len(data), *patch_size) if transform_algorithm == 'threshold': patches -= patches.min() patches /= patches.max() reconstructions[title][:, height // 2:] = reconstruct_from_patches_2d( patches, (width, height // 2)) dt = time() - t0 print('done in %.2fs.' % dt) show_with_diff(reconstructions[title], lena, title + ' (time: %.1fs)' % dt) plt.show()	bsd-3-clause
gfyoung/pandas	pandas/tests/extension/test_sparse.py	1	15154	""" This file contains a minimal set of tests for compliance with the extension array interface test suite, and should contain no other tests. The test suite for the full functionality of the array is located in `pandas/tests/arrays/`. The tests in this file are inherited from the BaseExtensionTests, and only minimal tweaks should be applied to get the tests passing (by overwriting a parent method). Additional tests should either be added to one of the BaseExtensionTests classes (if they are relevant for the extension interface for all dtypes), or be added to the array-specific tests in `pandas/tests/arrays/`. """ import numpy as np import pytest from pandas.errors import PerformanceWarning from pandas.core.dtypes.common import is_object_dtype import pandas as pd from pandas import SparseDtype import pandas._testing as tm from pandas.arrays import SparseArray from pandas.tests.extension import base def make_data(fill_value): if np.isnan(fill_value): data = np.random.uniform(size=100) else: data = np.random.randint(1, 100, size=100) if data[0] == data[1]: data[0] += 1 data[2::3] = fill_value return data @pytest.fixture def dtype(): return SparseDtype() @pytest.fixture(params=[0, np.nan]) def data(request): """Length-100 PeriodArray for semantics test.""" res = SparseArray(make_data(request.param), fill_value=request.param) return res @pytest.fixture def data_for_twos(request): return SparseArray(np.ones(100) * 2) @pytest.fixture(params=[0, np.nan]) def data_missing(request): """Length 2 array with [NA, Valid]""" return SparseArray([np.nan, 1], fill_value=request.param) @pytest.fixture(params=[0, np.nan]) def data_repeated(request): """Return different versions of data for count times""" def gen(count): for _ in range(count): yield SparseArray(make_data(request.param), fill_value=request.param) yield gen @pytest.fixture(params=[0, np.nan]) def data_for_sorting(request): return SparseArray([2, 3, 1], fill_value=request.param) @pytest.fixture(params=[0, np.nan]) def data_missing_for_sorting(request): return SparseArray([2, np.nan, 1], fill_value=request.param) @pytest.fixture def na_value(): return np.nan @pytest.fixture def na_cmp(): return lambda left, right: pd.isna(left) and pd.isna(right) @pytest.fixture(params=[0, np.nan]) def data_for_grouping(request): return SparseArray([1, 1, np.nan, np.nan, 2, 2, 1, 3], fill_value=request.param) class BaseSparseTests: def _check_unsupported(self, data): if data.dtype == SparseDtype(int, 0): pytest.skip("Can't store nan in int array.") @pytest.mark.xfail(reason="SparseArray does not support setitem") def test_ravel(self, data): super().test_ravel(data) class TestDtype(BaseSparseTests, base.BaseDtypeTests): def test_array_type_with_arg(self, data, dtype): assert dtype.construct_array_type() is SparseArray class TestInterface(BaseSparseTests, base.BaseInterfaceTests): def test_no_values_attribute(self, data): pytest.skip("We have values") def test_copy(self, data): # __setitem__ does not work, so we only have a smoke-test data.copy() def test_view(self, data): # __setitem__ does not work, so we only have a smoke-test data.view() class TestConstructors(BaseSparseTests, base.BaseConstructorsTests): pass class TestReshaping(BaseSparseTests, base.BaseReshapingTests): def test_concat_mixed_dtypes(self, data): # https://github.com/pandas-dev/pandas/issues/20762 # This should be the same, aside from concat([sparse, float]) df1 = pd.DataFrame({"A": data[:3]}) df2 = pd.DataFrame({"A": [1, 2, 3]}) df3 = pd.DataFrame({"A": ["a", "b", "c"]}).astype("category") dfs = [df1, df2, df3] # dataframes result = pd.concat(dfs) expected = pd.concat( [x.apply(lambda s: np.asarray(s).astype(object)) for x in dfs] ) self.assert_frame_equal(result, expected) def test_concat_columns(self, data, na_value): self._check_unsupported(data) super().test_concat_columns(data, na_value) def test_concat_extension_arrays_copy_false(self, data, na_value): self._check_unsupported(data) super().test_concat_extension_arrays_copy_false(data, na_value) def test_align(self, data, na_value): self._check_unsupported(data) super().test_align(data, na_value) def test_align_frame(self, data, na_value): self._check_unsupported(data) super().test_align_frame(data, na_value) def test_align_series_frame(self, data, na_value): self._check_unsupported(data) super().test_align_series_frame(data, na_value) def test_merge(self, data, na_value): self._check_unsupported(data) super().test_merge(data, na_value) @pytest.mark.xfail(reason="SparseArray does not support setitem") def test_transpose(self, data): super().test_transpose(data) class TestGetitem(BaseSparseTests, base.BaseGetitemTests): def test_get(self, data): s = pd.Series(data, index=[2 * i for i in range(len(data))]) if np.isnan(s.values.fill_value): assert np.isnan(s.get(4)) and np.isnan(s.iloc[2]) else: assert s.get(4) == s.iloc[2] assert s.get(2) == s.iloc[1] def test_reindex(self, data, na_value): self._check_unsupported(data) super().test_reindex(data, na_value) # Skipping TestSetitem, since we don't implement it. class TestMissing(BaseSparseTests, base.BaseMissingTests): def test_isna(self, data_missing): expected_dtype = SparseDtype(bool, pd.isna(data_missing.dtype.fill_value)) expected = SparseArray([True, False], dtype=expected_dtype) result = pd.isna(data_missing) self.assert_equal(result, expected) result = pd.Series(data_missing).isna() expected = pd.Series(expected) self.assert_series_equal(result, expected) # GH 21189 result = pd.Series(data_missing).drop([0, 1]).isna() expected = pd.Series([], dtype=expected_dtype) self.assert_series_equal(result, expected) def test_fillna_limit_pad(self, data_missing): with tm.assert_produces_warning(PerformanceWarning): super().test_fillna_limit_pad(data_missing) def test_fillna_limit_backfill(self, data_missing): with tm.assert_produces_warning(PerformanceWarning): super().test_fillna_limit_backfill(data_missing) def test_fillna_series_method(self, data_missing): with tm.assert_produces_warning(PerformanceWarning): super().test_fillna_limit_backfill(data_missing) @pytest.mark.skip(reason="Unsupported") def test_fillna_series(self): # this one looks doable. pass def test_fillna_frame(self, data_missing): # Have to override to specify that fill_value will change. fill_value = data_missing[1] result = pd.DataFrame({"A": data_missing, "B": [1, 2]}).fillna(fill_value) if pd.isna(data_missing.fill_value): dtype = SparseDtype(data_missing.dtype, fill_value) else: dtype = data_missing.dtype expected = pd.DataFrame( { "A": data_missing._from_sequence([fill_value, fill_value], dtype=dtype), "B": [1, 2], } ) self.assert_frame_equal(result, expected) class TestMethods(BaseSparseTests, base.BaseMethodsTests): def test_combine_le(self, data_repeated): # We return a Series[SparseArray].__le__ returns a # Series[Sparse[bool]] # rather than Series[bool] orig_data1, orig_data2 = data_repeated(2) s1 = pd.Series(orig_data1) s2 = pd.Series(orig_data2) result = s1.combine(s2, lambda x1, x2: x1 <= x2) expected = pd.Series( SparseArray( [a <= b for (a, b) in zip(list(orig_data1), list(orig_data2))], fill_value=False, ) ) self.assert_series_equal(result, expected) val = s1.iloc[0] result = s1.combine(val, lambda x1, x2: x1 <= x2) expected = pd.Series( SparseArray([a <= val for a in list(orig_data1)], fill_value=False) ) self.assert_series_equal(result, expected) def test_fillna_copy_frame(self, data_missing): arr = data_missing.take([1, 1]) df = pd.DataFrame({"A": arr}) filled_val = df.iloc[0, 0] result = df.fillna(filled_val) assert df.values.base is not result.values.base assert df.A._values.to_dense() is arr.to_dense() def test_fillna_copy_series(self, data_missing): arr = data_missing.take([1, 1]) ser = pd.Series(arr) filled_val = ser[0] result = ser.fillna(filled_val) assert ser._values is not result._values assert ser._values.to_dense() is arr.to_dense() @pytest.mark.skip(reason="Not Applicable") def test_fillna_length_mismatch(self, data_missing): pass def test_where_series(self, data, na_value): assert data[0] != data[1] cls = type(data) a, b = data[:2] ser = pd.Series(cls._from_sequence([a, a, b, b], dtype=data.dtype)) cond = np.array([True, True, False, False]) result = ser.where(cond) new_dtype = SparseDtype("float", 0.0) expected = pd.Series( cls._from_sequence([a, a, na_value, na_value], dtype=new_dtype) ) self.assert_series_equal(result, expected) other = cls._from_sequence([a, b, a, b], dtype=data.dtype) cond = np.array([True, False, True, True]) result = ser.where(cond, other) expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype)) self.assert_series_equal(result, expected) def test_combine_first(self, data): if data.dtype.subtype == "int": # Right now this is upcasted to float, just like combine_first # for Series[int] pytest.skip("TODO(SparseArray.__setitem__ will preserve dtype.") super().test_combine_first(data) def test_searchsorted(self, data_for_sorting, as_series): with tm.assert_produces_warning(PerformanceWarning): super().test_searchsorted(data_for_sorting, as_series) def test_shift_0_periods(self, data): # GH#33856 shifting with periods=0 should return a copy, not same obj result = data.shift(0) data._sparse_values[0] = data._sparse_values[1] assert result._sparse_values[0] != result._sparse_values[1] @pytest.mark.parametrize("method", ["argmax", "argmin"]) def test_argmin_argmax_all_na(self, method, data, na_value): # overriding because Sparse[int64, 0] cannot handle na_value self._check_unsupported(data) super().test_argmin_argmax_all_na(method, data, na_value) @pytest.mark.parametrize("box", [pd.array, pd.Series, pd.DataFrame]) def test_equals(self, data, na_value, as_series, box): self._check_unsupported(data) super().test_equals(data, na_value, as_series, box) class TestCasting(BaseSparseTests, base.BaseCastingTests): def test_astype_object_series(self, all_data): # Unlike the base class, we do not expect the resulting Block # to be ObjectBlock ser = pd.Series(all_data, name="A") result = ser.astype(object) assert is_object_dtype(result._data.blocks[0].dtype) def test_astype_object_frame(self, all_data): # Unlike the base class, we do not expect the resulting Block # to be ObjectBlock df = pd.DataFrame({"A": all_data}) result = df.astype(object) assert is_object_dtype(result._data.blocks[0].dtype) # FIXME: these currently fail; dont leave commented-out # check that we can compare the dtypes # comp = result.dtypes.equals(df.dtypes) # assert not comp.any() def test_astype_str(self, data): result = pd.Series(data[:5]).astype(str) expected_dtype = SparseDtype(str, str(data.fill_value)) expected = pd.Series([str(x) for x in data[:5]], dtype=expected_dtype) self.assert_series_equal(result, expected) @pytest.mark.xfail(raises=TypeError, reason="no sparse StringDtype") def test_astype_string(self, data): super().test_astype_string(data) class TestArithmeticOps(BaseSparseTests, base.BaseArithmeticOpsTests): series_scalar_exc = None frame_scalar_exc = None divmod_exc = None series_array_exc = None def _skip_if_different_combine(self, data): if data.fill_value == 0: # arith ops call on dtype.fill_value so that the sparsity # is maintained. Combine can't be called on a dtype in # general, so we can't make the expected. This is tested elsewhere raise pytest.skip("Incorrected expected from Series.combine") def test_error(self, data, all_arithmetic_operators): pass def test_arith_series_with_scalar(self, data, all_arithmetic_operators): self._skip_if_different_combine(data) super().test_arith_series_with_scalar(data, all_arithmetic_operators) def test_arith_series_with_array(self, data, all_arithmetic_operators): self._skip_if_different_combine(data) super().test_arith_series_with_array(data, all_arithmetic_operators) class TestComparisonOps(BaseSparseTests, base.BaseComparisonOpsTests): def _compare_other(self, s, data, op_name, other): op = self.get_op_from_name(op_name) # array result = pd.Series(op(data, other)) # hard to test the fill value, since we don't know what expected # is in general. # Rely on tests in `tests/sparse` to validate that. assert isinstance(result.dtype, SparseDtype) assert result.dtype.subtype == np.dtype("bool") with np.errstate(all="ignore"): expected = pd.Series( SparseArray( op(np.asarray(data), np.asarray(other)), fill_value=result.values.fill_value, ) ) tm.assert_series_equal(result, expected) # series s = pd.Series(data) result = op(s, other) tm.assert_series_equal(result, expected) class TestPrinting(BaseSparseTests, base.BasePrintingTests): @pytest.mark.xfail(reason="Different repr", strict=True) def test_array_repr(self, data, size): super().test_array_repr(data, size) class TestParsing(BaseSparseTests, base.BaseParsingTests): @pytest.mark.parametrize("engine", ["c", "python"]) def test_EA_types(self, engine, data): expected_msg = r".must implement _from_sequence_of_strings." with pytest.raises(NotImplementedError, match=expected_msg): super().test_EA_types(engine, data)	bsd-3-clause
grahesh/Stock-Market-Event-Analysis	qstkutil/tsutil.py	1	29904	''' (c) 2011, 2012 Georgia Tech Research Corporation This source code is released under the New BSD license. Please see http://wiki.quantsoftware.org/index.php?title=QSTK_License for license details. Created on Jan 1, 2011 @author:Drew Bratcher @contact: [email protected] @summary: Contains tutorial for backtester and report. ''' import math import datetime as dt import numpy as np from qstkutil import qsdateutil from math import sqrt from copy import deepcopy import random as rand from qstkutil import DataAccess as da from qstkutil import qsdateutil as du import numpy as np def daily(lfFunds): """ @summary Computes daily returns centered around 0 @param funds: A time series containing daily fund values @return an array of daily returns """ nds = np.asarray(deepcopy(lfFunds)) s= np.shape(nds) if len(s)==1: nds=np.expand_dims(nds,1) returnize0(nds) return(nds) def daily1(lfFunds): """ @summary Computes daily returns centered around 1 @param funds: A time series containing daily fund values @return an array of daily returns """ nds = np.asarray(deepcopy(lfFunds)) s= np.shape(nds) if len(s)==1: nds=np.expand_dims(nds,1) returnize1(nds) return(nds) def monthly(funds): """ @summary Computes monthly returns centered around 0 @param funds: A time series containing daily fund values @return an array of monthly returns """ funds2 = [] last_last_month = -1 years = qsdateutil.getYears(funds) for year in years: months = qsdateutil.getMonths(funds, year) for month in months: last_this_month = qsdateutil.getLastDay(funds, year, month) if last_last_month == -1 : last_last_month=qsdateutil.getFirstDay(funds, year, month) if type(funds).__name__=='TimeSeries': funds2.append(funds[last_this_month]/funds[last_last_month]-1) else: funds2.append(funds.xs(last_this_month)/funds.xs(last_last_month)-1) last_last_month = last_this_month return(funds2) def average_monthly(funds): """ @summary Computes average monthly returns centered around 0 @param funds: A time series containing daily fund values @return an array of average monthly returns """ rets = daily(funds) ret_i = 0 years = qsdateutil.getYears(funds) averages = [] for year in years: months = qsdateutil.getMonths(funds, year) for month in months: avg = 0 count = 0 days = qsdateutil.getDays(funds, year, month) for day in days: avg += rets[ret_i] ret_i += 1 count += 1 averages.append(float(avg) / count) return(averages) def fillforward(nds): """ @summary Removes NaNs from a 2D array by scanning forward in the 1st dimension. If a cell is NaN, the value above it is carried forward. @param nds: the array to fill forward @return the array is revised in place """ for col in range(nds.shape[1]): for row in range(1, nds.shape[0]): if math.isnan(nds[row, col]): nds[row, col] = nds[row-1, col] def fillbackward(nds): """ @summary Removes NaNs from a 2D array by scanning backward in the 1st dimension. If a cell is NaN, the value above it is carried backward. @param nds: the array to fill backward @return the array is revised in place """ for col in range(nds.shape[1]): for row in range(nds.shape[0] - 2, -1, -1): if math.isnan(nds[row, col]): nds[row, col] = nds[row+1, col] def returnize0(nds): """ @summary Computes stepwise (usually daily) returns relative to 0, where 0 implies no change in value. @return the array is revised in place """ s= np.shape(nds) if len(s)==1: nds=np.expand_dims(nds,1) nds[1:, :] = (nds[1:, :] / nds[0:-1]) - 1 nds[0, :] = np.zeros(nds.shape[1]) def returnize1(nds): """ @summary Computes stepwise (usually daily) returns relative to 1, where 1 implies no change in value. @param nds: the array to fill backward @return the array is revised in place """ s= np.shape(nds) if len(s)==1: nds=np.expand_dims(nds,1) nds[1:, :] = (nds[1:, :]/nds[0:-1]) nds[0, :] = np.ones(nds.shape[1]) def priceize1(nds): """ @summary Computes stepwise (usually daily) returns relative to 1, where 1 implies no change in value. @param nds: the array to fill backward @return the array is revised in place """ nds[0, :] = 100 for i in range(1, nds.shape[0]): nds[i, :] = nds[i-1, :] * nds[i, :] def logreturnize(nds): """ @summary Computes stepwise (usually daily) logarithmic returns. @param nds: the array to fill backward @return the array is revised in place """ returnize1(nds) nds = np.log(nds) return nds def get_winning_days( rets): """ @summary Returns the percentage of winning days of the returns. @param rets: 1d numpy array or fund list of daily returns (centered on 0) @return Percentage of winning days """ negative_rets = [] for i in rets: if(i<0): negative_rets.append(i) return 100 * (1 - float(len(negative_rets)) / float(len(rets))) def get_max_draw_down(ts_vals): """ @summary Returns the max draw down of the returns. @param ts_vals: 1d numpy array or fund list @return Max draw down """ MDD = 0 DD = 0 peak = -99999 for value in ts_vals: if (value > peak): peak = value else: DD = (peak - value) / peak if (DD > MDD): MDD = DD return -1MDD def get_sortino_ratio( rets, risk_free=0.00 ): """ @summary Returns the daily Sortino ratio of the returns. @param rets: 1d numpy array or fund list of daily returns (centered on 0) @param risk_free: risk free return, default is 0% @return Sortino Ratio, computed off daily returns """ rets = np.asarray(rets) f_mean = np.mean( rets, axis=0 ) negative_rets = rets[rets < 0] f_dev = np.std( negative_rets, axis=0 ) f_sortino = (f_mean252 - risk_free) / (f_dev * np.sqrt(252)) return f_sortino def get_sharpe_ratio( rets, risk_free=0.00 ): """ @summary Returns the daily Sharpe ratio of the returns. @param rets: 1d numpy array or fund list of daily returns (centered on 0) @param risk_free: risk free returns, default is 0% @return Annualized rate of return, not converted to percent """ f_dev = np.std( rets, axis=0 ) f_mean = np.mean( rets, axis=0 ) f_sharpe = (f_mean 252 - risk_free) / ( f_dev np.sqrt(252) ) return f_sharpe def get_ror_annual( rets ): """ @summary Returns the rate of return annualized. Assumes len(rets) is number of days. @param rets: 1d numpy array or list of daily returns @return Annualized rate of return, not converted to percent """ f_inv = 1.0 for f_ret in rets: f_inv = f_inv * f_ret f_ror_ytd = f_inv - 1.0 #print ' RorYTD =', f_inv, 'Over days:', len(rets) return ( (1.0 + f_ror_ytd)*( 1.0/(len(rets)/252.0) ) ) - 1.0 def getPeriodicRets( dmPrice, sOffset ): """ @summary Reindexes a DataMatrix price array and returns the new periodic returns. @param dmPrice: DataMatrix of stock prices @param sOffset: Offset string to use, choose from _offsetMap in pandas/core/datetools.py e.g. 'EOM', 'WEEKDAY', 'W@FRI', 'A@JAN'. Or use a pandas DateOffset. """ # Could possibly use DataMatrix.asfreq here """ # Use pandas DateRange to create the dates we want, use 4:00 """ drNewRange = DateRange(dmPrice.index[0], dmPrice.index[-1], timeRule=sOffset) drNewRange += DateOffset(hours=16) dmPrice = dmPrice.reindex( drNewRange, method='ffill' ) returnize1( dmPrice.values ) # Do not leave return of 1.0 for first time period: not accurate """ return dmPrice[1:] def getReindexedRets( rets, l_period ): """ @summary Reindexes returns using the cumulative product. E.g. if returns are 1.5 and 1.5, a period of 2 will produce a 2-day return of 2.25. Note, these must be returns centered around 1. @param rets: Daily returns of the various stocks (using returnize1) @param l_period: New target period. @note: Note that this function does not track actual weeks or months, it only approximates with trading days. You can use 5 for week, or 21 for month, etc. """ naCumData = np.cumprod(rets, axis=0) lNewRows =(rets.shape[0]-1) / (l_period) # We compress data into height / l_period + 1 new rows """ for i in range( lNewRows ): lCurInd = -1 - il_period # Just hold new data in same array""" # new return is cumprod on day x / cumprod on day x-l_period """ start=naCumData[lCurInd - l_period, :] naCumData[-1 - i, :] = naCumData[lCurInd, :] / start # Select new returns from end of cumulative array """ return naCumData[-lNewRows:, ] def getOptPort(rets, f_target, l_period=1, naLower=None, naUpper=None, lNagDebug=0): """ @summary Returns the Markowitz optimum portfolio for a specific return. @param rets: Daily returns of the various stocks (using returnize1) @param f_target: Target return, i.e. 0.04 = 4% per period @param l_period: Period to compress the returns to, e.g. 7 = weekly @param naLower: List of floats which corresponds to lower portfolio% for each stock @param naUpper: List of floats which corresponds to upper portfolio% for each stock @return tuple: (weights of portfolio, min possible return, max possible return) """ # Attempt to import library """ try: pass import nagint as nag except ImportError: print 'Could not import NAG library' print 'make sure nagint.so is in your python path' return ([], 0, 0) # Get number of stocks """ lStocks = rets.shape[1] # If period != 1 we need to restructure the data """ if( l_period != 1 ): rets = getReindexedRets( rets, l_period) # Calculate means and covariance """ naAvgRets = np.average( rets, axis=0 ) naCov = np.cov( rets, rowvar=False ) # Special case for None == f_target""" # simply return average returns and cov """ if( f_target is None ): return naAvgRets, np.std(rets, axis=0) # Calculate upper and lower limits of variables as well as constraints """ if( naUpper is None ): naUpper = np.ones( lStocks ) # max portfolio % is 1 if( naLower is None ): naLower = np.zeros( lStocks ) # min is 0, set negative for shorting # Two extra constraints for linear conditions""" # result = desired return, and sum of weights = 1 """ naUpper = np.append( naUpper, [f_target, 1.0] ) naLower = np.append( naLower, [f_target, 1.0] ) # Initial estimate of portfolio """ naInitial = np.array([1.0/lStocks]lStocks) # Set up constraints matrix""" # composed of expected returns in row one, unity row in row two """ naConstraints = np.vstack( (naAvgRets, np.ones(lStocks)) ) # Get portfolio weights, last entry in array is actually variance """ try: naReturn = nag.optPort( naConstraints, naLower, naUpper, \ naCov, naInitial, lNagDebug ) except RuntimeError: print 'NAG Runtime error with target: %.02lf'%(f_target) return ( naInitial, sqrt( naCov[0][0] ) ) #return semi-junk to not mess up the rest of the plot # Calculate stdev of entire portfolio to return""" # what NAG returns is slightly different """ fPortDev = np.std( np.dot(rets, naReturn[0,0:-1]) ) # Show difference between above stdev and sqrt NAG covariance""" # possibly not taking correlation into account """ #print fPortDev / sqrt(naReturn[0, -1]) # Return weights and stdDev of portfolio.""" # note again the last value of naReturn is NAG's reported variance """ return (naReturn[0, 0:-1], fPortDev) def OptPort( naData, fTarget, naLower=None, naUpper=None, naExpected=None, s_type = "long"): """ @summary Returns the Markowitz optimum portfolio for a specific return. @param naData: Daily returns of the various stocks (using returnize1) @param fTarget: Target return, i.e. 0.04 = 4% per period @param lPeriod: Period to compress the returns to, e.g. 7 = weekly @param naLower: List of floats which corresponds to lower portfolio% for each stock @param naUpper: List of floats which corresponds to upper portfolio% for each stock @return tuple: (weights of portfolio, min possible return, max possible return) """ ''' Attempt to import library ''' try: pass from cvxopt import matrix from cvxopt.blas import dot from cvxopt.solvers import qp, options except ImportError: print 'Could not import CVX library' return ([],0, True) ''' Get number of stocks ''' length = naData.shape[1] b_error = False naLower = deepcopy(naLower) naUpper = deepcopy(naUpper) naExpected = deepcopy(naExpected) # Assuming AvgReturns as the expected returns if parameter is not specified if (naExpected==None): naExpected = np.average( naData, axis=0 ) na_signs = np.sign(naExpected) indices, = np.where(na_signs == 0) na_signs[indices] = 1 if s_type == "long": na_signs = np.ones(len(na_signs)) elif s_type == "short": na_signs = np.ones(len(na_signs))(-1) naData = na_signsnaData naExpected = na_signsnaExpected # Covariance matrix of the Data Set naCov=np.cov(naData, rowvar=False) # If length is one, just return 100% single symbol if length == 1: return (list(na_signs), np.std(naData, axis=0)[0], False) if length == 0: return ([], [0], False) # If we have 0/1 "free" equity we can't optimize # We just use limits since we are stuck with 0 degrees of freedom ''' Special case for None == fTarget, simply return average returns and cov ''' if( fTarget is None ): return (naExpected, np.std(naData, axis=0), b_error) # Upper bound of the Weights of a equity, If not specified, assumed to be 1. if(naUpper is None): naUpper= np.ones(length) # Lower bound of the Weights of a equity, If not specified assumed to be 0 (No shorting case) if(naLower is None): naLower= np.zeros(length) if sum(naLower) == 1: fPortDev = np.std(np.dot(naData, naLower)) return (naLower, fPortDev, False) if sum(naUpper) == 1: fPortDev = np.std(np.dot(naData, naUpper)) return (naUpper, fPortDev, False) naFree = naUpper != naLower if naFree.sum() <= 1: lnaPortfolios = naUpper.copy() # If there is 1 free we need to modify it to make the total # Add up to 1 if naFree.sum() == 1: f_rest = naUpper[~naFree].sum() lnaPortfolios[naFree] = 1.0 - f_rest lnaPortfolios = na_signs * lnaPortfolios fPortDev = np.std(np.dot(naData, lnaPortfolios)) return (lnaPortfolios, fPortDev, False) # Double the covariance of the diagonal elements for calculating risk. for i in range(length): naCov[i][i]=2naCov[i][i] # Note, returns are modified to all be long from here on out (fMin, fMax) = getRetRange(False, naLower, naUpper, naExpected, "long") #print (fTarget, fMin, fMax) if fTarget<fMin or fTarget>fMax: print "Target not possible", fTarget, fMin, fMax b_error = True naLower = naLower(-1) # Setting up the parameters for the CVXOPT Library, it takes inputs in Matrix format. ''' The Risk minimization problem is a standard Quadratic Programming problem according to the Markowitz Theory. ''' S=matrix(naCov) #pbar=matrix(naExpected) naLower.shape=(length,1) naUpper.shape=(length,1) naExpected.shape = (1,length) zeo=matrix(0.0,(length,1)) I = np.eye(length) minusI=-1I G=matrix(np.vstack((I, minusI))) h=matrix(np.vstack((naUpper, naLower))) ones=matrix(1.0,(1,length)) A=matrix(np.vstack((naExpected, ones))) b=matrix([float(fTarget),1.0]) # Optional Settings for CVXOPT options['show_progress'] = False options['abstol']=1e-25 options['reltol']=1e-24 options['feastol']=1e-25 # Optimization Calls # Optimal Portfolio try: lnaPortfolios = qp(S, -zeo, G, h, A, b)['x'] except: b_error = True if b_error == True: print "Optimization not Possible" na_port = naLower-1 if sum(na_port) < 1: if sum(naUpper) == 1: na_port = naUpper else: i=0 while(sum(na_port)<1 and i<25): naOrder = naUpper - na_port i = i+1 indices = np.where(naOrder > 0) na_port[indices]= na_port[indices] + (1-sum(na_port))/len(indices[0]) naOrder = naUpper - na_port indices = np.where(naOrder < 0) na_port[indices]= naUpper[indices] lnaPortfolios = matrix(na_port) lnaPortfolios = (na_signs.reshape(-1,1) * lnaPortfolios).reshape(-1) # Expected Return of the Portfolio # lfReturn = dot(pbar, lnaPortfolios) # Risk of the portfolio fPortDev = np.std(np.dot(naData, lnaPortfolios)) return (lnaPortfolios, fPortDev, b_error) def getRetRange( rets, naLower, naUpper, naExpected = "False", s_type = "long"): """ @summary Returns the range of possible returns with upper and lower bounds on the portfolio participation @param rets: Expected returns @param naLower: List of lower percentages by stock @param naUpper: List of upper percentages by stock @return tuple containing (fMin, fMax) """ # Calculate theoretical minimum and maximum theoretical returns """ fMin = 0 fMax = 0 rets = deepcopy(rets) if naExpected == "False": naExpected = np.average( rets, axis=0 ) na_signs = np.sign(naExpected) indices, = np.where(na_signs == 0) na_signs[indices] = 1 if s_type == "long": na_signs = np.ones(len(na_signs)) elif s_type == "short": na_signs = np.ones(len(na_signs))(-1) rets = na_signsrets naExpected = na_signsnaExpected naSortInd = naExpected.argsort() # First add the lower bounds on portfolio participation """ for i, fRet in enumerate(naExpected): fMin = fMin + fRetnaLower[i] fMax = fMax + fRetnaLower[i] # Now calculate minimum returns""" # allocate the max possible in worst performing equities """ # Subtract min since we have already counted it """ naUpperAdd = naUpper - naLower fTotalPercent = np.sum(naLower[:]) for i, lInd in enumerate(naSortInd): fRetAdd = naUpperAdd[lInd] naExpected[lInd] fTotalPercent = fTotalPercent + naUpperAdd[lInd] fMin = fMin + fRetAdd # Check if this additional percent puts us over the limit """ if fTotalPercent > 1.0: fMin = fMin - naExpected[lInd] * (fTotalPercent - 1.0) break # Repeat for max, just reverse the sort, i.e. high to low """ naUpperAdd = naUpper - naLower fTotalPercent = np.sum(naLower[:]) for i, lInd in enumerate(naSortInd[::-1]): fRetAdd = naUpperAdd[lInd] * naExpected[lInd] fTotalPercent = fTotalPercent + naUpperAdd[lInd] fMax = fMax + fRetAdd # Check if this additional percent puts us over the limit """ if fTotalPercent > 1.0: fMax = fMax - naExpected[lInd] * (fTotalPercent - 1.0) break return (fMin, fMax) def _create_dict(df_rets, lnaPortfolios): allocations = {} for i, sym in enumerate(df_rets.columns): allocations[sym] = lnaPortfolios[i] return allocations def optimizePortfolio(df_rets, list_min, list_max, list_price_target, target_risk, direction="long"): naLower = np.array(list_min) naUpper = np.array(list_max) naExpected = np.array(list_price_target) b_same_flag = np.all( naExpected == naExpected[0]) if b_same_flag and (naExpected[0] == 0): naExpected = naExpected + 0.1 if b_same_flag: na_randomness = np.ones(naExpected.shape) target_risk = 0 for i in range(len(na_randomness)): if i%2 ==0: na_randomness[i] = -1 naExpected = naExpected + naExpected0.0000001na_randomness (fMin, fMax) = getRetRange( df_rets.values, naLower, naUpper, naExpected, direction) # Try to avoid intractible endpoints due to rounding errors """ fMin += abs(fMin) * 0.00000000001 fMax -= abs(fMax) * 0.00000000001 if target_risk == 1: (naPortWeights, fPortDev, b_error) = OptPort( df_rets.values, fMax, naLower, naUpper, naExpected, direction) allocations = _create_dict(df_rets, naPortWeights) return {'allocations': allocations, 'std_dev': fPortDev, 'expected_return': fMax, 'error': b_error} fStep = (fMax - fMin) / 50.0 lfReturn = [fMin + x * fStep for x in range(51)] lfStd = [] lnaPortfolios = [] for fTarget in lfReturn: (naWeights, fStd, b_error) = OptPort( df_rets.values, fTarget, naLower, naUpper, naExpected, direction) if b_error == False: lfStd.append(fStd) lnaPortfolios.append( naWeights ) else: # Return error on ANY failed optimization allocations = _create_dict(df_rets, np.zeros(df_rets.shape[1])) return {'allocations': allocations, 'std_dev': 0.0, 'expected_return': fMax, 'error': True} if len(lfStd) == 0: (naPortWeights, fPortDev, b_error) = OptPort( df_rets.values, fMax, naLower, naUpper, naExpected, direction) allocations = _create_dict(df_rets, naPortWeights) return {'allocations': allocations, 'std_dev': fPortDev, 'expected_return': fMax, 'error': True} f_return = lfReturn[lfStd.index(min(lfStd))] if target_risk == 0: naPortWeights=lnaPortfolios[lfStd.index(min(lfStd))] allocations = _create_dict(df_rets, naPortWeights) return {'allocations': allocations, 'std_dev': min(lfStd), 'expected_return': f_return, 'error': False} # Otherwise try to hit custom target between 0-1 min-max risk fTarget = f_return + ((fMax - f_return) * target_risk) (naPortWeights, fPortDev, b_error) = OptPort( df_rets.values, fTarget, naLower, naUpper, naExpected, direction) allocations = _create_dict(df_rets, naPortWeights) return {'allocations': allocations, 'std_dev': fPortDev, 'expected_return': fTarget, 'error': b_error} def getFrontier( rets, lRes=100, fUpper=0.2, fLower=0.00): """ @summary Generates an efficient frontier based on average returns. @param rets: Array of returns to use @param lRes: Resolution of the curve, default=100 @param fUpper: Upper bound on portfolio percentage @param fLower: Lower bound on portfolio percentage @return tuple containing (lf_ret, lfStd, lnaPortfolios) lf_ret: List of returns provided by each point lfStd: list of standard deviations provided by each point lnaPortfolios: list of numpy arrays containing weights for each portfolio """ # Limit/enforce percent participation """ naUpper = np.ones(rets.shape[1]) * fUpper naLower = np.ones(rets.shape[1]) * fLower (fMin, fMax) = getRetRange( rets, naLower, naUpper ) # Try to avoid intractible endpoints due to rounding errors """ fMin = 1.0000001 fMax = 0.9999999 # Calculate target returns from min and max """ lf_ret = [] for i in range(lRes): lf_ret.append( (fMax - fMin) * i / (lRes - 1) + fMin ) lfStd = [] lnaPortfolios = [] # Call the function lRes times for the given range, use 1 for period """ for f_target in lf_ret: (naWeights, fStd) = getOptPort( rets, f_target, 1, \ naUpper=naUpper, naLower=naLower ) lfStd.append(fStd) lnaPortfolios.append( naWeights ) # plot frontier """ #plt.plot( lfStd, lf_ret ) plt.plot( np.std( rets, axis=0 ), np.average( rets, axis=0 ), \ 'g+', markersize=10 ) #plt.show()""" return (lf_ret, lfStd, lnaPortfolios) def stockFilter( dmPrice, dmVolume, fNonNan=0.95, fPriceVolume=1001000 ): """ @summary Returns the list of stocks filtered based on various criteria. @param dmPrice: DataMatrix of stock prices @param dmVolume: DataMatrix of stock volumes @param fNonNan: Optional non-nan percent, default is .95 @param fPriceVolume: Optional pricevolume, default is 100,000 @return list of stocks which meet the criteria """ lsRetStocks = list( dmPrice.columns ) for sStock in dmPrice.columns: fValid = 0.0 print sStock # loop through all dates """ for dtDate in dmPrice.index: # Count null (nan/inf/etc) values """ fPrice = dmPrice[sStock][dtDate] if( not isnull(fPrice) ): fValid = fValid + 1 # else test price volume """ fVol = dmVolume[sStock][dtDate] if( not isnull(fVol) and fVol * fPrice < fPriceVolume ): lsRetStocks.remove( sStock ) break # Remove if too many nan values """ if( fValid / len(dmPrice.index) < fNonNan and sStock in lsRetStocks ): lsRetStocks.remove( sStock ) return lsRetStocks def getRandPort( lNum, dtStart=None, dtEnd=None, lsStocks=None,\ dmPrice=None, dmVolume=None, bFilter=True, fNonNan=0.95,\ fPriceVolume=1001000, lSeed=None ): """ @summary Returns a random portfolio based on certain criteria. @param lNum: Number of stocks to be included @param dtStart: Start date for portfolio @param dtEnd: End date for portfolio @param lsStocks: Optional list of ticker symbols, if not provided all symbols will be used @param bFilter: If False, stocks are not filtered by price or volume data, simply return random Portfolio. @param dmPrice: Optional price data, if not provided, data access will be queried @param dmVolume: Optional volume data, if not provided, data access will be queried @param fNonNan: Optional non-nan percent for filter, default is .95 @param fPriceVolume: Optional pricevolume for filter, default is 100,000 @warning: Does not work for all sets of optional inputs, e.g. if you don't include dtStart, dtEnd, you need to include dmPrice/dmVolume @return list of stocks which meet the criteria """ if( lsStocks is None ): if( dmPrice is None and dmVolume is None ): norObj = da.DataAccess('Norgate') lsStocks = norObj.get_all_symbols() elif( not dmPrice is None ): lsStocks = list(dmPrice.columns) else: lsStocks = list(dmVolume.columns) if( dmPrice is None and dmVolume is None and bFilter == True ): norObj = da.DataAccess('Norgate') ldtTimestamps = du.getNYSEdays( dtStart, dtEnd, dt.timedelta(hours=16) ) # if dmPrice and dmVol are provided then we don't query it every time """ bPullPrice = False bPullVol = False if( dmPrice is None ): bPullPrice = True if( dmVolume is None ): bPullVol = True # Default seed (none) uses system clock """ rand.seed(lSeed) lsRetStocks = [] # Loop until we have enough randomly selected stocks """ llRemainingIndexes = range(0,len(lsStocks)) lsValid = None while( len(lsRetStocks) != lNum ): lsCheckStocks = [] for i in range( lNum - len(lsRetStocks) ): lRemaining = len(llRemainingIndexes) if( lRemaining == 0 ): print 'Error in getRandPort: ran out of stocks' return lsRetStocks # Pick a stock and remove it from the list of remaining stocks """ lPicked = rand.randint(0, lRemaining-1) lsCheckStocks.append( lsStocks[ llRemainingIndexes.pop(lPicked) ] ) # If bFilter is false""" # simply return our first list of stocks, don't check prive/vol """ if( not bFilter ): return sorted(lsCheckStocks) # Get data if needed """ if( bPullPrice ): dmPrice = norObj.get_data( ldtTimestamps, lsCheckStocks, 'close' ) # Get data if needed """ if( bPullVol ): dmVolume = norObj.get_data(ldtTimestamps, lsCheckStocks, 'volume' ) # Only query this once if data is provided""" # else query every time with new data """ if( lsValid is None or bPullVol or bPullPrice ): lsValid = stockFilter(dmPrice, dmVolume, fNonNan, fPriceVolume) for sAdd in lsValid: if sAdd in lsCheckStocks: lsRetStocks.append( sAdd ) return sorted(lsRetStocks)	bsd-3-clause
RPGOne/Skynet	scikit-learn-0.18.1/sklearn/utils/tests/test_testing.py	13	7883	import warnings import unittest import sys from sklearn.utils.testing import ( assert_raises, _assert_less, _assert_greater, assert_less_equal, assert_greater_equal, assert_warns, assert_no_warnings, assert_equal, set_random_state, assert_raise_message, ignore_warnings) from sklearn.tree import DecisionTreeClassifier from sklearn.discriminant_analysis import LinearDiscriminantAnalysis try: from nose.tools import assert_less def test_assert_less(): # Check that the nose implementation of assert_less gives the # same thing as the scikit's assert_less(0, 1) _assert_less(0, 1) assert_raises(AssertionError, assert_less, 1, 0) assert_raises(AssertionError, _assert_less, 1, 0) except ImportError: pass try: from nose.tools import assert_greater def test_assert_greater(): # Check that the nose implementation of assert_less gives the # same thing as the scikit's assert_greater(1, 0) _assert_greater(1, 0) assert_raises(AssertionError, assert_greater, 0, 1) assert_raises(AssertionError, _assert_greater, 0, 1) except ImportError: pass def test_assert_less_equal(): assert_less_equal(0, 1) assert_less_equal(1, 1) assert_raises(AssertionError, assert_less_equal, 1, 0) def test_assert_greater_equal(): assert_greater_equal(1, 0) assert_greater_equal(1, 1) assert_raises(AssertionError, assert_greater_equal, 0, 1) def test_set_random_state(): lda = LinearDiscriminantAnalysis() tree = DecisionTreeClassifier() # Linear Discriminant Analysis doesn't have random state: smoke test set_random_state(lda, 3) set_random_state(tree, 3) assert_equal(tree.random_state, 3) def test_assert_raise_message(): def _raise_ValueError(message): raise ValueError(message) def _no_raise(): pass assert_raise_message(ValueError, "test", _raise_ValueError, "test") assert_raises(AssertionError, assert_raise_message, ValueError, "something else", _raise_ValueError, "test") assert_raises(ValueError, assert_raise_message, TypeError, "something else", _raise_ValueError, "test") assert_raises(AssertionError, assert_raise_message, ValueError, "test", _no_raise) # multiple exceptions in a tuple assert_raises(AssertionError, assert_raise_message, (ValueError, AttributeError), "test", _no_raise) def test_ignore_warning(): # This check that ignore_warning decorateur and context manager are working # as expected def _warning_function(): warnings.warn("deprecation warning", DeprecationWarning) def _multiple_warning_function(): warnings.warn("deprecation warning", DeprecationWarning) warnings.warn("deprecation warning") # Check the function directly assert_no_warnings(ignore_warnings(_warning_function)) assert_no_warnings(ignore_warnings(_warning_function, category=DeprecationWarning)) assert_warns(DeprecationWarning, ignore_warnings(_warning_function, category=UserWarning)) assert_warns(UserWarning, ignore_warnings(_multiple_warning_function, category=DeprecationWarning)) assert_warns(DeprecationWarning, ignore_warnings(_multiple_warning_function, category=UserWarning)) assert_no_warnings(ignore_warnings(_warning_function, category=(DeprecationWarning, UserWarning))) # Check the decorator @ignore_warnings def decorator_no_warning(): _warning_function() _multiple_warning_function() @ignore_warnings(category=(DeprecationWarning, UserWarning)) def decorator_no_warning_multiple(): _multiple_warning_function() @ignore_warnings(category=DeprecationWarning) def decorator_no_deprecation_warning(): _warning_function() @ignore_warnings(category=UserWarning) def decorator_no_user_warning(): _warning_function() @ignore_warnings(category=DeprecationWarning) def decorator_no_deprecation_multiple_warning(): _multiple_warning_function() @ignore_warnings(category=UserWarning) def decorator_no_user_multiple_warning(): _multiple_warning_function() assert_no_warnings(decorator_no_warning) assert_no_warnings(decorator_no_warning_multiple) assert_no_warnings(decorator_no_deprecation_warning) assert_warns(DeprecationWarning, decorator_no_user_warning) assert_warns(UserWarning, decorator_no_deprecation_multiple_warning) assert_warns(DeprecationWarning, decorator_no_user_multiple_warning) # Check the context manager def context_manager_no_warning(): with ignore_warnings(): _warning_function() def context_manager_no_warning_multiple(): with ignore_warnings(category=(DeprecationWarning, UserWarning)): _multiple_warning_function() def context_manager_no_deprecation_warning(): with ignore_warnings(category=DeprecationWarning): _warning_function() def context_manager_no_user_warning(): with ignore_warnings(category=UserWarning): _warning_function() def context_manager_no_deprecation_multiple_warning(): with ignore_warnings(category=DeprecationWarning): _multiple_warning_function() def context_manager_no_user_multiple_warning(): with ignore_warnings(category=UserWarning): _multiple_warning_function() assert_no_warnings(context_manager_no_warning) assert_no_warnings(context_manager_no_warning_multiple) assert_no_warnings(context_manager_no_deprecation_warning) assert_warns(DeprecationWarning, context_manager_no_user_warning) assert_warns(UserWarning, context_manager_no_deprecation_multiple_warning) assert_warns(DeprecationWarning, context_manager_no_user_multiple_warning) # This class is inspired from numpy 1.7 with an alteration to check # the reset warning filters after calls to assert_warns. # This assert_warns behavior is specific to scikit-learn because #`clean_warning_registry()` is called internally by assert_warns # and clears all previous filters. class TestWarns(unittest.TestCase): def test_warn(self): def f(): warnings.warn("yo") return 3 # Test that assert_warns is not impacted by externally set # filters and is reset internally. # This is because `clean_warning_registry()` is called internally by # assert_warns and clears all previous filters. warnings.simplefilter("ignore", UserWarning) assert_equal(assert_warns(UserWarning, f), 3) # Test that the warning registry is empty after assert_warns assert_equal(sys.modules['warnings'].filters, []) assert_raises(AssertionError, assert_no_warnings, f) assert_equal(assert_no_warnings(lambda x: x, 1), 1) def test_warn_wrong_warning(self): def f(): warnings.warn("yo", DeprecationWarning) failed = False filters = sys.modules['warnings'].filters[:] try: try: # Should raise an AssertionError assert_warns(UserWarning, f) failed = True except AssertionError: pass finally: sys.modules['warnings'].filters = filters if failed: raise AssertionError("wrong warning caught by assert_warn")	bsd-3-clause
nesterione/scikit-learn	sklearn/decomposition/tests/test_truncated_svd.py	240	6055	"""Test truncated SVD transformer.""" import numpy as np import scipy.sparse as sp from sklearn.decomposition import TruncatedSVD from sklearn.utils import check_random_state from sklearn.utils.testing import (assert_array_almost_equal, assert_equal, assert_raises, assert_greater, assert_array_less) # Make an X that looks somewhat like a small tf-idf matrix. # XXX newer versions of SciPy have scipy.sparse.rand for this. shape = 60, 55 n_samples, n_features = shape rng = check_random_state(42) X = rng.randint(-100, 20, np.product(shape)).reshape(shape) X = sp.csr_matrix(np.maximum(X, 0), dtype=np.float64) X.data[:] = 1 + np.log(X.data) Xdense = X.A def test_algorithms(): svd_a = TruncatedSVD(30, algorithm="arpack") svd_r = TruncatedSVD(30, algorithm="randomized", random_state=42) Xa = svd_a.fit_transform(X)[:, :6] Xr = svd_r.fit_transform(X)[:, :6] assert_array_almost_equal(Xa, Xr) comp_a = np.abs(svd_a.components_) comp_r = np.abs(svd_r.components_) # All elements are equal, but some elements are more equal than others. assert_array_almost_equal(comp_a[:9], comp_r[:9]) assert_array_almost_equal(comp_a[9:], comp_r[9:], decimal=3) def test_attributes(): for n_components in (10, 25, 41): tsvd = TruncatedSVD(n_components).fit(X) assert_equal(tsvd.n_components, n_components) assert_equal(tsvd.components_.shape, (n_components, n_features)) def test_too_many_components(): for algorithm in ["arpack", "randomized"]: for n_components in (n_features, n_features+1): tsvd = TruncatedSVD(n_components=n_components, algorithm=algorithm) assert_raises(ValueError, tsvd.fit, X) def test_sparse_formats(): for fmt in ("array", "csr", "csc", "coo", "lil"): Xfmt = Xdense if fmt == "dense" else getattr(X, "to" + fmt)() tsvd = TruncatedSVD(n_components=11) Xtrans = tsvd.fit_transform(Xfmt) assert_equal(Xtrans.shape, (n_samples, 11)) Xtrans = tsvd.transform(Xfmt) assert_equal(Xtrans.shape, (n_samples, 11)) def test_inverse_transform(): for algo in ("arpack", "randomized"): # We need a lot of components for the reconstruction to be "almost # equal" in all positions. XXX Test means or sums instead? tsvd = TruncatedSVD(n_components=52, random_state=42) Xt = tsvd.fit_transform(X) Xinv = tsvd.inverse_transform(Xt) assert_array_almost_equal(Xinv, Xdense, decimal=1) def test_integers(): Xint = X.astype(np.int64) tsvd = TruncatedSVD(n_components=6) Xtrans = tsvd.fit_transform(Xint) assert_equal(Xtrans.shape, (n_samples, tsvd.n_components)) def test_explained_variance(): # Test sparse data svd_a_10_sp = TruncatedSVD(10, algorithm="arpack") svd_r_10_sp = TruncatedSVD(10, algorithm="randomized", random_state=42) svd_a_20_sp = TruncatedSVD(20, algorithm="arpack") svd_r_20_sp = TruncatedSVD(20, algorithm="randomized", random_state=42) X_trans_a_10_sp = svd_a_10_sp.fit_transform(X) X_trans_r_10_sp = svd_r_10_sp.fit_transform(X) X_trans_a_20_sp = svd_a_20_sp.fit_transform(X) X_trans_r_20_sp = svd_r_20_sp.fit_transform(X) # Test dense data svd_a_10_de = TruncatedSVD(10, algorithm="arpack") svd_r_10_de = TruncatedSVD(10, algorithm="randomized", random_state=42) svd_a_20_de = TruncatedSVD(20, algorithm="arpack") svd_r_20_de = TruncatedSVD(20, algorithm="randomized", random_state=42) X_trans_a_10_de = svd_a_10_de.fit_transform(X.toarray()) X_trans_r_10_de = svd_r_10_de.fit_transform(X.toarray()) X_trans_a_20_de = svd_a_20_de.fit_transform(X.toarray()) X_trans_r_20_de = svd_r_20_de.fit_transform(X.toarray()) # helper arrays for tests below svds = (svd_a_10_sp, svd_r_10_sp, svd_a_20_sp, svd_r_20_sp, svd_a_10_de, svd_r_10_de, svd_a_20_de, svd_r_20_de) svds_trans = ( (svd_a_10_sp, X_trans_a_10_sp), (svd_r_10_sp, X_trans_r_10_sp), (svd_a_20_sp, X_trans_a_20_sp), (svd_r_20_sp, X_trans_r_20_sp), (svd_a_10_de, X_trans_a_10_de), (svd_r_10_de, X_trans_r_10_de), (svd_a_20_de, X_trans_a_20_de), (svd_r_20_de, X_trans_r_20_de), ) svds_10_v_20 = ( (svd_a_10_sp, svd_a_20_sp), (svd_r_10_sp, svd_r_20_sp), (svd_a_10_de, svd_a_20_de), (svd_r_10_de, svd_r_20_de), ) svds_sparse_v_dense = ( (svd_a_10_sp, svd_a_10_de), (svd_a_20_sp, svd_a_20_de), (svd_r_10_sp, svd_r_10_de), (svd_r_20_sp, svd_r_20_de), ) # Assert the 1st component is equal for svd_10, svd_20 in svds_10_v_20: assert_array_almost_equal( svd_10.explained_variance_ratio_, svd_20.explained_variance_ratio_[:10], decimal=5, ) # Assert that 20 components has higher explained variance than 10 for svd_10, svd_20 in svds_10_v_20: assert_greater( svd_20.explained_variance_ratio_.sum(), svd_10.explained_variance_ratio_.sum(), ) # Assert that all the values are greater than 0 for svd in svds: assert_array_less(0.0, svd.explained_variance_ratio_) # Assert that total explained variance is less than 1 for svd in svds: assert_array_less(svd.explained_variance_ratio_.sum(), 1.0) # Compare sparse vs. dense for svd_sparse, svd_dense in svds_sparse_v_dense: assert_array_almost_equal(svd_sparse.explained_variance_ratio_, svd_dense.explained_variance_ratio_) # Test that explained_variance is correct for svd, transformed in svds_trans: total_variance = np.var(X.toarray(), axis=0).sum() variances = np.var(transformed, axis=0) true_explained_variance_ratio = variances / total_variance assert_array_almost_equal( svd.explained_variance_ratio_, true_explained_variance_ratio, )	bsd-3-clause
FluidityStokes/fluidity	examples/tides_in_the_Mediterranean_Sea/Med-tides-probe.py	2	6073	#!/usr/bin/env python3 import vtktools import math from numpy import array u=vtktools.vtu("tidesmedsea-flat.vtu") g = open("Med-GEBCO-5m-gauges-fes2004-O1-102", "w") pts=vtktools.arr([ #[-5.3500, 36.1333, -2.00], #[-4.4500, 36.7000, 0.00], #[-3.9167, 35.2500, 0.00], #[-2.4500, 36.8333, 0.00], #[-0.5833, 38.3333, 0.00], [2.6333, 39.5833, 0.00], #[3.1000, 42.4833, 0.00], #[5.3500, 43.3000, 0.00], #[6.9167, 36.8833, 0.00], [8.0167, 43.8667, 0.00], [8.3000, 39.1500, 0.00], [8.9000, 44.4000, 0.00], [9.1667, 39.2000, 0.00], #[9.8500, 44.0667, 0.00], #[10.1167, 33.8833, 0.00], [10.3000, 43.5333, 0.00], #[10.7667, 34.7333, 0.00], #[11.1167, 33.5000, 0.00], #[11.7833, 42.1000, 0.00], [12.0000, 36.7833, 0.00], #[12.3333, 45.4167, 0.00], [12.5000, 35.5000, 0.00], [12.5833, 37.6333, 0.00], [12.8167, 36.1667, 0.00], [13.2000, 32.9000, 0.00], #[13.3333, 38.1333, 0.00], #[13.5000, 43.6167, 0.00], [13.5000, 37.2500, 0.00], [13.7500, 45.6500, 0.00], [13.9333, 40.7333, 0.00], #[14.2667, 40.8667, 0.00], [14.4000, 42.5167, 0.00], [14.5167, 35.9000, 0.00], #[14.5333, 45.3000, 0.00], [14.9667, 38.4833, 0.00], #[15.1000, 38.5000, 0.00], [15.1500, 36.6833, 0.00], #[15.2500, 38.2167, 0.00], #[15.6500, 38.1167, 0.00], [15.7667, 43.0333, 0.00], [16.1833, 41.8833, 0.00], #[16.4333, 43.5000, 0.00], #[17.2167, 40.4667, 0.00], #[17.9333, 40.6333, 0.00], #[18.5000, 40.1500, 0.00], [19.1000, 42.0667, 0.00], #[20.7000, 38.8333, 0.00], [21.3167, 37.6333, 0.00], #[22.1333, 37.0167, 0.00], #[23.0333, 40.6167, 0.00], [23.8000, 32.1833, 0.00], #[24.0500, 35.5000, 0.00], [24.9167, 37.4333, 0.00], #[25.1333, 35.3333, 0.00], [25.3833, 40.8500, 0.00], [25.7000, 31.7667, 0.00], [26.1500, 38.3833, 0.00], [26.8833, 37.0833, 0.00], [28.2333, 36.4333, 0.00], [29.8667, 31.2000, 0.00], [32.3167, 31.2667, 0.00], #[33.3167, 35.3333, 0.00], #[33.9500, 35.1167, 0.00] ]) M2amp = u.ProbeData(pts, "M2amp") (ilen, jlen) = M2amp.shape S2amp = u.ProbeData(pts, "S2amp") K1amp = u.ProbeData(pts, "K1amp") O1amp = u.ProbeData(pts, "O1amp") for i in range(ilen): g.write("%f\n" % M2amp[i][0]) ampcm=M2amp100 M2_tideGauge_amp = array([ [29.8], [18.0], [18.0], [9.0], [2.0], [3.0], [4.6], [7.0], [5.6], [8.3], [6.5], [8.6], [7.6], [9.4], [51.1], [8.5], [41.6], [21.9], [10.9], [1.6], [23.4], [6.6], [4.3], [4.8], [11.1], [10.6], [6.6], [4.5], [26.3], [12.0], [11.1], [6.4], [6.0], [10.6], [12.0], #[6.4], [6.7], [12.0], [6.2], [6.8], [7.9], [8.0], [6.5], [8.7], [7.0], [9.2], [4.0], [3.3], [2.2], [9.0], [1.4], [1.0], [2.0], [1.5], [7.1], [2.9], [4.4], [2.1], [4.4], [7.2], [11.2], [10.1], [11.0] ]) from math import sqrt ampdiff=ampcm-M2_tideGauge_amp ampdiff2=ampdiff2 a = sum(sum(ampdiff2))/62 RMS=sqrt(a) print("RMS difference of M2 Amp (cm):",RMS) S2_tide_guage_data_amp = array([ [10.7], [7], [7], [4], [1], [1], [1.8], [2], [2.2], [3.4], [2.6], [3.2], [2.8], [3.4], [36.4], [3.4], [26.7], [15.3], [4.1], [1.9], [14.1], [4.2], [1.8], [3.1], [5.4], [4.1], [3.6], [3.2], [15.2], [5], [4.4], [4.5], [4], [5.5], [4.5], #[3.4], [3.5], [4.7], [3.1], [4.4], [5.1], [5.6], [3.7], [5.2], [4], [5.6], [2.2], [1.6], [1.1], [6.1], [1.3], [0.8], [1], [1.1], [5], [2.9], [2.9], [1.3], [2.7], [4.1], [6.9], [6.4], [7.3] ]) S2ampcm=S2amp100 S2ampdiff=S2ampcm-S2_tide_guage_data_amp S2ampdiff2=S2ampdiff*2 S2a = sum(sum(S2ampdiff2))/62 S2RMS=sqrt(S2a) print("RMS difference of S2 Amp (cm):",S2RMS) K1_tide_guage_data_amp= array([ [2], [3], [4], [3], [4], [4], [3.2], [3], [2.3], [3.6], [3.2], [3.3], [3.2], [3.7], [2.5], [4], [1.8], [2], [2.7], [2], [17.9], [0.9], [3.5], [0.5], [2], [3.2], [13], [1.8], [19.7], [3], [2.8], [9.7], [1], [13.8], [3.1], #[1.5], [1.9], [3.3], [1.3], [6.8], [4.2], [8.8], [1.8], [4.6], [2.3], [4.8], [1.4], [1.3], [1.2], [2.6], [0.6], [1.4], [1.9], [1.8], [0.3], [1.2], [2.3], [2], [1.8], [1.7], [2.1], [2.4], [2.1] ]) K1ampcm=K1amp100 K1ampdiff=K1ampcm-K1_tide_guage_data_amp K1ampdiff2=K1ampdiff*2 K1a = sum(sum(K1ampdiff2))/62 K1RMS=sqrt(K1a) print("RMS difference of K1 Amp (cm):",K1RMS) O1_tide_guage_data_amp= array([ [0.9], [2], [1], [2], [2], [2], [1.9], [2], [2], [1.6], [1.9], [1.4], [1.8], [1.4], [0.5], [1.8], [0.8], [0.9], [1.2], [1.4], [5.6], [0.7], [1.6], [0.9], [0.6], [1.2], [4.2], [1.4], [6.1], [1], [1], [3.4], [1], [4.1], [1.1], #[1.1], [0.9], [1.1], [0.9], [2.5], [1.5], [2.7], [0.8], [1.5], [1], [1.4], [0.6], [0.5], [0.5], [1.3], [0.5], [0.6], [1], [0.9], [1.3], [0.8], [1.3], [1.1], [1.1], [1.3], [1.7], [1.8], [1.8] ]) O1ampcm=O1amp100 O1ampdiff=O1ampcm-O1_tide_guage_data_amp O1ampdiff2=O1ampdiff**2 O1a = sum(sum(O1ampdiff2))/62 O1RMS=sqrt(O1a) print("RMS difference of O1 Amp (cm):",O1RMS) import fluidity_tools from matplotlib import pylab pylab.plot(ampcm,M2_tideGauge_amp) pylab.xlabel("Fluidity") pylab.ylabel("Tide Gauge") pylab.show() import matplotlib matplotlib.pyplot.scatter(M2_tideGauge_amp,ampcm,s=20, c='b', marker='o') #pylab.xlabel("Tide Gauge M2 Amplitude (cm)") #pylab.ylabel("Fluidity M2 Amplitude (cm)") #x=([0,70]) #y=([0,70]) #matplotlib.pyplot.plot(y,x, label="y=x") #pylab.ylim(ymax=70.0,ymin=0.0) #pylab.xlim(xmax=70.0,xmin=0.0) #pylab.show() #matplotlib.pyplot.scatter(S2_tide_guage_data_amp,S2ampcm,s=20, c='b', marker='o') #pylab.xlabel("Tide Gauge S2 Amplitude (cm)") #pylab.ylabel("Fluidity S2 Amplitude (cm)") #x=([0,70]) #y=([0,70]) #matplotlib.pyplot.plot(y,x, label="y=x") #pylab.ylim(ymax=70.0,ymin=0.0) #pylab.xlim(xmax=70.0,xmin=0.0) #pylab.show() #matplotlib.pyplot.scatter(K1_tide_guage_data_amp,K1ampcm,s=20, c='b', marker='o') #pylab.xlabel("Tide Gauge K1 Amplitude (cm)") #pylab.ylabel("Fluidity K1 Amplitude (cm)") #x=([0,20]) #y=([0,20]) #matplotlib.pyplot.plot(y,x, label="y=x") #pylab.ylim(ymax=20.0,ymin=0.0) #pylab.xlim(xmax=20.0,xmin=0.0) #pylab.show() #matplotlib.pyplot.scatter(O1_tide_guage_data_amp,O1ampcm,s=20, c='b', marker='o') #pylab.xlabel("Tide Gauge O1 Amplitude (cm)") #pylab.ylabel("Fluidity O1 Amplitude (cm)") #x=([0,20]) #y=([0,20]) #matplotlib.pyplot.plot(y,x, label="y=x") #pylab.ylim(ymax=20.0,ymin=0.0) #pylab.xlim(xmax=20.0,xmin=0.0) #pylab.show()	lgpl-2.1
kirangonella/BuildingMachineLearningSystemsWithPython	ch08/chapter.py	21	6372	import numpy as np # NOT IN BOOK from matplotlib import pyplot as plt # NOT IN BOOK def load(): import numpy as np from scipy import sparse data = np.loadtxt('data/ml-100k/u.data') ij = data[:, :2] ij -= 1 # original data is in 1-based system values = data[:, 2] reviews = sparse.csc_matrix((values, ij.T)).astype(float) return reviews.toarray() reviews = load() U,M = np.where(reviews) import random test_idxs = np.array(random.sample(range(len(U)), len(U)//10)) train = reviews.copy() train[U[test_idxs], M[test_idxs]] = 0 test = np.zeros_like(reviews) test[U[test_idxs], M[test_idxs]] = reviews[U[test_idxs], M[test_idxs]] class NormalizePositive(object): def __init__(self, axis=0): self.axis = axis def fit(self, features, y=None): if self.axis == 1: features = features.T # count features that are greater than zero in axis 0: binary = (features > 0) count0 = binary.sum(axis=0) # to avoid division by zero, set zero counts to one: count0[count0 == 0] = 1. # computing the mean is easy: self.mean = features.sum(axis=0)/count0 # only consider differences where binary is True: diff = (features - self.mean) * binary diff *= 2 # regularize the estimate of std by adding 0.1 self.std = np.sqrt(0.1 + diff.sum(axis=0)/count0) return self def transform(self, features): if self.axis == 1: features = features.T binary = (features > 0) features = features - self.mean features /= self.std features = binary if self.axis == 1: features = features.T return features def inverse_transform(self, features, copy=True): if copy: features = features.copy() if self.axis == 1: features = features.T features = self.std features += self.mean if self.axis == 1: features = features.T return features def fit_transform(self, features): return self.fit(features).transform(features) norm = NormalizePositive(axis=1) binary = (train > 0) train = norm.fit_transform(train) # plot just 200x200 area for space reasons plt.imshow(binary[:200, :200], interpolation='nearest') from scipy.spatial import distance # compute all pair-wise distances: dists = distance.pdist(binary, 'correlation') # Convert to square form, so that dists[i,j] # is distance between binary[i] and binary[j]: dists = distance.squareform(dists) neighbors = dists.argsort(axis=1) # We are going to fill this matrix with results filled = train.copy() for u in range(filled.shape[0]): # n_u is neighbors of user n_u = neighbors[u, 1:] for m in range(filled.shape[1]): # get relevant reviews in order! revs = [train[neigh, m] for neigh in n_u if binary [neigh, m]] if len(revs): # n is the number of reviews for this movie n = len(revs) # take half of the reviews plus one into consideration: n //= 2 n += 1 revs = revs[:n] filled[u,m] = np.mean(revs) predicted = norm.inverse_transform(filled) from sklearn import metrics r2 = metrics.r2_score(test[test > 0], predicted[test > 0]) print('R2 score (binary neighbors): {:.1%}'.format(r2)) reviews = reviews.T # use same code as before r2 = metrics.r2_score(test[test > 0], predicted[test > 0]) print('R2 score (binary movie neighbors): {:.1%}'.format(r2)) from sklearn.linear_model import ElasticNetCV # NOT IN BOOK reg = ElasticNetCV(alphas=[ 0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.]) filled = train.copy() # iterate over all users: for u in range(train.shape[0]): curtrain = np.delete(train, u, axis=0) bu = binary[u] reg.fit(curtrain[:,bu].T, train[u, bu]) filled[u, ~bu] = reg.predict(curtrain[:,~bu].T) predicted = norm.inverse_transform(filled) r2 = metrics.r2_score(test[test > 0], predicted[test > 0]) print('R2 score (user regression): {:.1%}'.format(r2)) # SHOPPING BASKET ANALYSIS # This is the slow version of the code, which will take a long time to # complete. from collections import defaultdict from itertools import chain # File is downloaded as a compressed file import gzip # file format is a line per transaction # of the form '12 34 342 5...' dataset = [[int(tok) for tok in line.strip().split()] for line in gzip.open('data/retail.dat.gz')] dataset = [set(d) for d in dataset] # count how often each product was purchased: counts = defaultdict(int) for elem in chain(dataset): counts[elem] += 1 minsupport = 80 valid = set(k for k,v in counts.items() if (v >= minsupport)) itemsets = [frozenset([v]) for v in valid] freqsets = [] for i in range(16): nextsets = [] tested = set() for it in itemsets: for v in valid: if v not in it: # Create a new candidate set by adding v to it c = (it \| frozenset([v])) # check If we have tested it already if c in tested: continue tested.add(c) # Count support by looping over dataset # This step is slow. # Check `apriori.py` for a better implementation. support_c = sum(1 for d in dataset if d.issuperset(c)) if support_c > minsupport: nextsets.append(c) freqsets.extend(nextsets) itemsets = nextsets if not len(itemsets): break print("Finished!") minlift = 5.0 nr_transactions = float(len(dataset)) for itemset in freqsets: for item in itemset: consequent = frozenset([item]) antecedent = itemset-consequent base = 0.0 # acount: antecedent count acount = 0.0 # ccount : consequent count ccount = 0.0 for d in dataset: if item in d: base += 1 if d.issuperset(itemset): ccount += 1 if d.issuperset(antecedent): acount += 1 base /= nr_transactions p_y_given_x = ccount/acount lift = p_y_given_x / base if lift > minlift: print('Rule {0} -> {1} has lift {2}' .format(antecedent, consequent,lift))	mit
ShawnMurd/MetPy	src/metpy/plots/skewt.py	1	36666	# Copyright (c) 2014,2015,2016,2017,2019 MetPy Developers. # Distributed under the terms of the BSD 3-Clause License. # SPDX-License-Identifier: BSD-3-Clause """Make Skew-T Log-P based plots. Contain tools for making Skew-T Log-P plots, including the base plotting class, `SkewT`, as well as a class for making a `Hodograph`. """ from contextlib import ExitStack import warnings import matplotlib from matplotlib.axes import Axes import matplotlib.axis as maxis from matplotlib.collections import LineCollection import matplotlib.colors as mcolors from matplotlib.patches import Circle from matplotlib.projections import register_projection import matplotlib.spines as mspines from matplotlib.ticker import MultipleLocator, NullFormatter, ScalarFormatter import matplotlib.transforms as transforms import numpy as np from ._util import colored_line from ..calc import dewpoint, dry_lapse, el, lcl, moist_lapse, vapor_pressure from ..calc.tools import _delete_masked_points from ..interpolate import interpolate_1d from ..package_tools import Exporter from ..units import concatenate, units exporter = Exporter(globals()) class SkewTTransform(transforms.Affine2D): """Perform Skew transform for Skew-T plotting. This works in pixel space, so is designed to be applied after the normal plotting transformations. """ def __init__(self, bbox, rot): """Initialize skew transform. This needs a reference to the parent bounding box to do the appropriate math and to register it as a child so that the transform is invalidated and regenerated if the bounding box changes. """ super().__init__() self._bbox = bbox self.set_children(bbox) self.invalidate() # We're not trying to support changing the rotation, so go ahead and convert to # the right factor for skewing here and just save that. self._rot_factor = np.tan(np.deg2rad(rot)) def get_matrix(self): """Return transformation matrix.""" if self._invalid: # The following matrix is equivalent to the following: # x0, y0 = self._bbox.xmin, self._bbox.ymin # self.translate(-x0, -y0).skew_deg(self._rot, 0).translate(x0, y0) # Setting it this way is just more efficient. self._mtx = np.array([[1.0, self._rot_factor, -self._rot_factor * self._bbox.ymin], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) # Need to clear both the invalid flag and reset the inverse, which is cached # by the parent class. self._invalid = 0 self._inverted = None return self._mtx class SkewXTick(maxis.XTick): r"""Make x-axis ticks for Skew-T plots. This class adds to the standard :class:`matplotlib.axis.XTick` dynamic checking for whether a top or bottom tick is actually within the data limits at that part and draw as appropriate. It also performs similar checking for gridlines. """ # Taken from matplotlib's SkewT example to update for matplotlib 3.1's changes to # state management for ticks. See matplotlib/matplotlib#10088 def draw(self, renderer): """Draw the tick.""" # When adding the callbacks with `stack.callback`, we fetch the current # visibility state of the artist with `get_visible`; the ExitStack will # restore these states (`set_visible`) at the end of the block (after # the draw). with ExitStack() as stack: for artist in [self.gridline, self.tick1line, self.tick2line, self.label1, self.label2]: stack.callback(artist.set_visible, artist.get_visible()) self.tick1line.set_visible(self.tick1line.get_visible() and self.lower_in_bounds) self.label1.set_visible(self.label1.get_visible() and self.lower_in_bounds) self.tick2line.set_visible(self.tick2line.get_visible() and self.upper_in_bounds) self.label2.set_visible(self.label2.get_visible() and self.upper_in_bounds) self.gridline.set_visible(self.gridline.get_visible() and self.grid_in_bounds) super().draw(renderer) @property def lower_in_bounds(self): """Whether the lower part of the tick is in bounds.""" return transforms.interval_contains(self.axes.lower_xlim, self.get_loc()) @property def upper_in_bounds(self): """Whether the upper part of the tick is in bounds.""" return transforms.interval_contains(self.axes.upper_xlim, self.get_loc()) @property def grid_in_bounds(self): """Whether any of the tick grid line is in bounds.""" return transforms.interval_contains(self.axes.xaxis.get_view_interval(), self.get_loc()) class SkewXAxis(maxis.XAxis): r"""Make an x-axis that works properly for Skew-T plots. This class exists to force the use of our custom :class:`SkewXTick` as well as provide a custom value for interval that combines the extents of the upper and lower x-limits from the axes. """ def _get_tick(self, major): # Warning stuff can go away when we only support Matplotlib >=3.3 with warnings.catch_warnings(): warnings.simplefilter('ignore', getattr( matplotlib, 'MatplotlibDeprecationWarning', DeprecationWarning)) return SkewXTick(self.axes, None, label=None, major=major) # Needed to properly handle tight bbox def _get_tick_bboxes(self, ticks, renderer): """Return lists of bboxes for ticks' label1's and label2's.""" return ([tick.label1.get_window_extent(renderer) for tick in ticks if tick.label1.get_visible() and tick.lower_in_bounds], [tick.label2.get_window_extent(renderer) for tick in ticks if tick.label2.get_visible() and tick.upper_in_bounds]) def get_view_interval(self): """Get the view interval.""" return self.axes.upper_xlim[0], self.axes.lower_xlim[1] class SkewSpine(mspines.Spine): r"""Make an x-axis spine that works properly for Skew-T plots. This class exists to use the separate x-limits from the axes to properly locate the spine. """ def _adjust_location(self): pts = self._path.vertices if self.spine_type == 'top': pts[:, 0] = self.axes.upper_xlim else: pts[:, 0] = self.axes.lower_xlim class SkewXAxes(Axes): r"""Make a set of axes for Skew-T plots. This class handles registration of the skew-xaxes as a projection as well as setting up the appropriate transformations. It also makes sure we use our instances for spines and x-axis: :class:`SkewSpine` and :class:`SkewXAxis`. It provides properties to facilitate finding the x-limits for the bottom and top of the plot as well. """ # The projection must specify a name. This will be used be the # user to select the projection, i.e. ``subplot(111, # projection='skewx')``. name = 'skewx' def __init__(self, args, kwargs): r"""Initialize `SkewXAxes`. Parameters ---------- args : Arbitrary positional arguments Passed to :class:`matplotlib.axes.Axes` position: int, optional The rotation of the x-axis against the y-axis, in degrees. kwargs : Arbitrary keyword arguments Passed to :class:`matplotlib.axes.Axes` """ # This needs to be popped and set before moving on self.rot = kwargs.pop('rotation', 30) super().__init__(args, *kwargs) def _init_axis(self): # Taken from Axes and modified to use our modified X-axis self.xaxis = SkewXAxis(self) self.spines['top'].register_axis(self.xaxis) self.spines['bottom'].register_axis(self.xaxis) self.yaxis = maxis.YAxis(self) self.spines['left'].register_axis(self.yaxis) self.spines['right'].register_axis(self.yaxis) def _gen_axes_spines(self, locations=None, offset=0.0, units='inches'): # pylint: disable=unused-argument spines = {'top': SkewSpine.linear_spine(self, 'top'), 'bottom': mspines.Spine.linear_spine(self, 'bottom'), 'left': mspines.Spine.linear_spine(self, 'left'), 'right': mspines.Spine.linear_spine(self, 'right')} return spines def _set_lim_and_transforms(self): """Set limits and transforms. This is called once when the plot is created to set up all the transforms for the data, text and grids. """ # Get the standard transform setup from the Axes base class super()._set_lim_and_transforms() # This transformation handles the skewing skew_trans = SkewTTransform(self.bbox, self.rot) # Create the full transform from Data to Pixels self.transData += skew_trans # Blended transforms like this need to have the skewing applied using # both axes, in axes coords like before. self._xaxis_transform += skew_trans @property def lower_xlim(self): """Get the data limits for the x-axis along the bottom of the axes.""" return self.axes.viewLim.intervalx @property def upper_xlim(self): """Get the data limits for the x-axis along the top of the axes.""" return self.transData.inverted().transform([[self.bbox.xmin, self.bbox.ymax], self.bbox.max])[:, 0] # Now register the projection with matplotlib so the user can select it. register_projection(SkewXAxes) @exporter.export class SkewT(object): r"""Make Skew-T log-P plots of data. This class simplifies the process of creating Skew-T log-P plots in using matplotlib. It handles requesting the appropriate skewed projection, and provides simplified wrappers to make it easy to plot data, add wind barbs, and add other lines to the plots (e.g. dry adiabats) Attributes ---------- ax : `matplotlib.axes.Axes` The underlying Axes instance, which can be used for calling additional plot functions (e.g. `axvline`) """ def __init__(self, fig=None, rotation=30, subplot=None, rect=None, aspect=80.5): r"""Create SkewT - logP plots. Parameters ---------- fig : matplotlib.figure.Figure, optional Source figure to use for plotting. If none is given, a new :class:`matplotlib.figure.Figure` instance will be created. rotation : float or int, optional Controls the rotation of temperature relative to horizontal. Given in degrees counterclockwise from x-axis. Defaults to 30 degrees. subplot : tuple[int, int, int] or `matplotlib.gridspec.SubplotSpec` instance, optional Controls the size/position of the created subplot. This allows creating the skewT as part of a collection of subplots. If subplot is a tuple, it should conform to the specification used for :meth:`matplotlib.figure.Figure.add_subplot`. The :class:`matplotlib.gridspec.SubplotSpec` can be created by using :class:`matplotlib.gridspec.GridSpec`. rect : tuple[float, float, float, float], optional Rectangle (left, bottom, width, height) in which to place the axes. This allows the user to place the axes at an arbitrary point on the figure. aspect : float, int, or 'auto', optional Aspect ratio (i.e. ratio of y-scale to x-scale) to maintain in the plot. Defaults to 80.5. Passing the string ``'auto'`` tells matplotlib to handle the aspect ratio automatically (this is not recommended for SkewT). """ if fig is None: import matplotlib.pyplot as plt figsize = plt.rcParams.get('figure.figsize', (7, 7)) fig = plt.figure(figsize=figsize) self._fig = fig if rect and subplot: raise ValueError("Specify only one of `rect' and `subplot', but not both") elif rect: self.ax = fig.add_axes(rect, projection='skewx', rotation=rotation) else: if subplot is not None: # Handle being passed a tuple for the subplot, or a GridSpec instance try: len(subplot) except TypeError: subplot = (subplot,) else: subplot = (1, 1, 1) self.ax = fig.add_subplot(subplot, projection='skewx', rotation=rotation) # Set the yaxis as inverted with log scaling self.ax.set_yscale('log') # Override default ticking for log scaling self.ax.yaxis.set_major_formatter(ScalarFormatter()) self.ax.yaxis.set_major_locator(MultipleLocator(100)) self.ax.yaxis.set_minor_formatter(NullFormatter()) # Needed to make sure matplotlib doesn't freak out and create a bunch of ticks # Also takes care of inverting the y-axis self.ax.set_ylim(1050, 100) self.ax.yaxis.set_units(units.hPa) # Try to make sane default temperature plotting ticks self.ax.xaxis.set_major_locator(MultipleLocator(10)) self.ax.xaxis.set_units(units.degC) self.ax.set_xlim(-40, 50) self.ax.grid(True) self.mixing_lines = None self.dry_adiabats = None self.moist_adiabats = None # Maintain a reasonable ratio of data limits. Only works on Matplotlib >= 3.2 if matplotlib.__version__[:3] > '3.1': self.ax.set_aspect(aspect, adjustable='box') def plot(self, pressure, t, args, kwargs): r"""Plot data. Simple wrapper around plot so that pressure is the first (independent) input. This is essentially a wrapper around `plot`. Parameters ---------- pressure : array_like pressure values t : array_like temperature values, can also be used for things like dew point args Other positional arguments to pass to :func:`~matplotlib.pyplot.plot` kwargs Other keyword arguments to pass to :func:`~matplotlib.pyplot.plot` Returns ------- list[matplotlib.lines.Line2D] lines plotted See Also -------- :func:`matplotlib.pyplot.plot` """ # Skew-T logP plotting t, pressure = _delete_masked_points(t, pressure) return self.ax.plot(t, pressure, args, kwargs) def plot_barbs(self, pressure, u, v, c=None, xloc=1.0, x_clip_radius=0.1, y_clip_radius=0.08, kwargs): r"""Plot wind barbs. Adds wind barbs to the skew-T plot. This is a wrapper around the `barbs` command that adds to appropriate transform to place the barbs in a vertical line, located as a function of pressure. Parameters ---------- pressure : array_like pressure values u : array_like U (East-West) component of wind v : array_like V (North-South) component of wind c: An optional array used to map colors to the barbs xloc : float, optional Position for the barbs, in normalized axes coordinates, where 0.0 denotes far left and 1.0 denotes far right. Defaults to far right. x_clip_radius : float, optional Space, in normalized axes coordinates, to leave before clipping wind barbs in the x-direction. Defaults to 0.1. y_clip_radius : float, optional Space, in normalized axes coordinates, to leave above/below plot before clipping wind barbs in the y-direction. Defaults to 0.08. plot_units: `pint.unit` Units to plot in (performing conversion if necessary). Defaults to given units. kwargs Other keyword arguments to pass to :func:`~matplotlib.pyplot.barbs` Returns ------- matplotlib.quiver.Barbs instance created See Also -------- :func:`matplotlib.pyplot.barbs` """ # If plot_units specified, convert the data to those units plotting_units = kwargs.pop('plot_units', None) if plotting_units: if hasattr(u, 'units') and hasattr(v, 'units'): u = u.to(plotting_units) v = v.to(plotting_units) else: raise ValueError('To convert to plotting units, units must be attached to ' 'u and v wind components.') # Assemble array of x-locations in axes space x = np.empty_like(pressure) x.fill(xloc) # Do barbs plot at this location if c is not None: b = self.ax.barbs(x, pressure, u, v, c, transform=self.ax.get_yaxis_transform(which='tick2'), clip_on=True, zorder=2, kwargs) else: b = self.ax.barbs(x, pressure, u, v, transform=self.ax.get_yaxis_transform(which='tick2'), clip_on=True, zorder=2, kwargs) # Override the default clip box, which is the axes rectangle, so we can have # barbs that extend outside. ax_bbox = transforms.Bbox([[xloc - x_clip_radius, -y_clip_radius], [xloc + x_clip_radius, 1.0 + y_clip_radius]]) b.set_clip_box(transforms.TransformedBbox(ax_bbox, self.ax.transAxes)) return b def plot_dry_adiabats(self, t0=None, pressure=None, *kwargs): r"""Plot dry adiabats. Adds dry adiabats (lines of constant potential temperature) to the plot. The default style of these lines is dashed red lines with an alpha value of 0.5. These can be overridden using keyword arguments. Parameters ---------- t0 : array_like, optional Starting temperature values in Kelvin. If none are given, they will be generated using the current temperature range at the bottom of the plot. pressure : array_like, optional Pressure values to be included in the dry adiabats. If not specified, they will be linearly distributed across the current plotted pressure range. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.LineCollection` Returns ------- matplotlib.collections.LineCollection instance created See Also -------- :func:`~metpy.calc.thermo.dry_lapse` :meth:`plot_moist_adiabats` :class:`matplotlib.collections.LineCollection` """ # Remove old lines if self.dry_adiabats: self.dry_adiabats.remove() # Determine set of starting temps if necessary if t0 is None: xmin, xmax = self.ax.get_xlim() t0 = np.arange(xmin, xmax + 1, 10) units.degC # Get pressure levels based on ylims if necessary if pressure is None: pressure = np.linspace(self.ax.get_ylim()) units.mbar # Assemble into data for plotting t = dry_lapse(pressure, t0[:, np.newaxis], 1000. * units.mbar).to(units.degC) linedata = [np.vstack((ti.m, pressure.m)).T for ti in t] # Add to plot kwargs.setdefault('colors', 'r') kwargs.setdefault('linestyles', 'dashed') kwargs.setdefault('alpha', 0.5) self.dry_adiabats = self.ax.add_collection(LineCollection(linedata, kwargs)) return self.dry_adiabats def plot_moist_adiabats(self, t0=None, pressure=None, kwargs): r"""Plot moist adiabats. Adds saturated pseudo-adiabats (lines of constant equivalent potential temperature) to the plot. The default style of these lines is dashed blue lines with an alpha value of 0.5. These can be overridden using keyword arguments. Parameters ---------- t0 : array_like, optional Starting temperature values in Kelvin. If none are given, they will be generated using the current temperature range at the bottom of the plot. pressure : array_like, optional Pressure values to be included in the moist adiabats. If not specified, they will be linearly distributed across the current plotted pressure range. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.LineCollection` Returns ------- matplotlib.collections.LineCollection instance created See Also -------- :func:`~metpy.calc.thermo.moist_lapse` :meth:`plot_dry_adiabats` :class:`matplotlib.collections.LineCollection` """ # Remove old lines if self.moist_adiabats: self.moist_adiabats.remove() # Determine set of starting temps if necessary if t0 is None: xmin, xmax = self.ax.get_xlim() t0 = np.concatenate((np.arange(xmin, 0, 10), np.arange(0, xmax + 1, 5))) * units.degC # Get pressure levels based on ylims if necessary if pressure is None: pressure = np.linspace(self.ax.get_ylim()) units.mbar # Assemble into data for plotting t = moist_lapse(pressure, t0[:, np.newaxis], 1000. * units.mbar).to(units.degC) linedata = [np.vstack((ti.m, pressure.m)).T for ti in t] # Add to plot kwargs.setdefault('colors', 'b') kwargs.setdefault('linestyles', 'dashed') kwargs.setdefault('alpha', 0.5) self.moist_adiabats = self.ax.add_collection(LineCollection(linedata, kwargs)) return self.moist_adiabats def plot_mixing_lines(self, mixing_ratio=None, pressure=None, kwargs): r"""Plot lines of constant mixing ratio. Adds lines of constant mixing ratio (isohumes) to the plot. The default style of these lines is dashed green lines with an alpha value of 0.8. These can be overridden using keyword arguments. Parameters ---------- mixing_ratio : array_like, optional Unitless mixing ratio values to plot. If none are given, default values are used. pressure : array_like, optional Pressure values to be included in the isohumes. If not specified, they will be linearly distributed across the current plotted pressure range up to 600 mb. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.LineCollection` Returns ------- matplotlib.collections.LineCollection instance created See Also -------- :class:`matplotlib.collections.LineCollection` """ # Remove old lines if self.mixing_lines: self.mixing_lines.remove() # Default mixing level values if necessary if mixing_ratio is None: mixing_ratio = np.array([0.0004, 0.001, 0.002, 0.004, 0.007, 0.01, 0.016, 0.024, 0.032]).reshape(-1, 1) # Set pressure range if necessary if pressure is None: pressure = np.linspace(600, max(self.ax.get_ylim())) * units.mbar # Assemble data for plotting td = dewpoint(vapor_pressure(pressure, mixing_ratio)) linedata = [np.vstack((t.m, pressure.m)).T for t in td] # Add to plot kwargs.setdefault('colors', 'g') kwargs.setdefault('linestyles', 'dashed') kwargs.setdefault('alpha', 0.8) self.mixing_lines = self.ax.add_collection(LineCollection(linedata, kwargs)) return self.mixing_lines def shade_area(self, y, x1, x2=0, which='both', kwargs): r"""Shade area between two curves. Shades areas between curves. Area can be where one is greater or less than the other or all areas shaded. Parameters ---------- y : array_like 1-dimensional array of numeric y-values x1 : array_like 1-dimensional array of numeric x-values x2 : array_like 1-dimensional array of numeric x-values which : string Specifies if `positive`, `negative`, or `both` areas are being shaded. Will be overridden by where. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.PolyCollection` Returns ------- :class:`matplotlib.collections.PolyCollection` See Also -------- :class:`matplotlib.collections.PolyCollection` :func:`matplotlib.axes.Axes.fill_betweenx` """ fill_properties = {'positive': {'facecolor': 'tab:red', 'alpha': 0.4, 'where': x1 > x2}, 'negative': {'facecolor': 'tab:blue', 'alpha': 0.4, 'where': x1 < x2}, 'both': {'facecolor': 'tab:green', 'alpha': 0.4, 'where': None}} try: fill_args = fill_properties[which] fill_args.update(kwargs) except KeyError: raise ValueError('Unknown option for which: {0}'.format(str(which))) arrs = y, x1, x2 if fill_args['where'] is not None: arrs = arrs + (fill_args['where'],) fill_args.pop('where', None) fill_args['interpolate'] = True arrs = _delete_masked_points(arrs) return self.ax.fill_betweenx(arrs, fill_args) def shade_cape(self, pressure, t, t_parcel, kwargs): r"""Shade areas of Convective Available Potential Energy (CAPE). Shades areas where the parcel is warmer than the environment (areas of positive buoyancy. Parameters ---------- pressure : array_like Pressure values t : array_like Temperature values dewpoint : array_like Dewpoint values t_parcel : array_like Parcel path temperature values limit_shading : bool Eliminate shading below the LCL or above the EL, default is True kwargs Other keyword arguments to pass to :class:`matplotlib.collections.PolyCollection` Returns ------- :class:`matplotlib.collections.PolyCollection` See Also -------- :class:`matplotlib.collections.PolyCollection` :func:`matplotlib.axes.Axes.fill_betweenx` """ return self.shade_area(pressure, t_parcel, t, which='positive', kwargs) def shade_cin(self, pressure, t, t_parcel, dewpoint=None, kwargs): r"""Shade areas of Convective INhibition (CIN). Shades areas where the parcel is cooler than the environment (areas of negative buoyancy. If `dewpoint` is passed in, negative area below the lifting condensation level or above the equilibrium level is not shaded. Parameters ---------- pressure : array_like Pressure values t : array_like Temperature values t_parcel : array_like Parcel path temperature values dewpoint : array_like Dew point values, optional kwargs Other keyword arguments to pass to :class:`matplotlib.collections.PolyCollection` Returns ------- :class:`matplotlib.collections.PolyCollection` See Also -------- :class:`matplotlib.collections.PolyCollection` :func:`matplotlib.axes.Axes.fill_betweenx` """ if dewpoint is not None: lcl_p, _ = lcl(pressure[0], t[0], dewpoint[0]) el_p, _ = el(pressure, t, dewpoint, t_parcel) idx = np.logical_and(pressure > el_p, pressure < lcl_p) else: idx = np.arange(0, len(pressure)) return self.shade_area(pressure[idx], t_parcel[idx], t[idx], which='negative', *kwargs) @exporter.export class Hodograph(object): r"""Make a hodograph of wind data. Plots the u and v components of the wind along the x and y axes, respectively. This class simplifies the process of creating a hodograph using matplotlib. It provides helpers for creating a circular grid and for plotting the wind as a line colored by another value (such as wind speed). Attributes ---------- ax : `matplotlib.axes.Axes` The underlying Axes instance used for all plotting """ def __init__(self, ax=None, component_range=80): r"""Create a Hodograph instance. Parameters ---------- ax : `matplotlib.axes.Axes`, optional The `Axes` instance used for plotting component_range : value The maximum range of the plot. Used to set plot bounds and control the maximum number of grid rings needed. """ if ax is None: import matplotlib.pyplot as plt self.ax = plt.figure().add_subplot(1, 1, 1) else: self.ax = ax self.ax.set_aspect('equal', 'box') self.ax.set_xlim(-component_range, component_range) self.ax.set_ylim(-component_range, component_range) # == sqrt(2) max_range, which is the distance at the corner self.max_range = 1.4142135 * component_range def add_grid(self, increment=10., kwargs): r"""Add grid lines to hodograph. Creates lines for the x- and y-axes, as well as circles denoting wind speed values. Parameters ---------- increment : value, optional The value increment between rings kwargs Other kwargs to control appearance of lines See Also -------- :class:`matplotlib.patches.Circle` :meth:`matplotlib.axes.Axes.axhline` :meth:`matplotlib.axes.Axes.axvline` """ # Some default arguments. Take those, and update with any # arguments passed in grid_args = {'color': 'grey', 'linestyle': 'dashed'} if kwargs: grid_args.update(kwargs) # Take those args and make appropriate for a Circle circle_args = grid_args.copy() color = circle_args.pop('color', None) circle_args['edgecolor'] = color circle_args['fill'] = False self.rings = [] for r in np.arange(increment, self.max_range, increment): c = Circle((0, 0), radius=r, circle_args) self.ax.add_patch(c) self.rings.append(c) # Add lines for x=0 and y=0 self.yaxis = self.ax.axvline(0, grid_args) self.xaxis = self.ax.axhline(0, grid_args) @staticmethod def _form_line_args(kwargs): """Simplify taking the default line style and extending with kwargs.""" def_args = {'linewidth': 3} def_args.update(kwargs) return def_args def plot(self, u, v, kwargs): r"""Plot u, v data. Plots the wind data on the hodograph. Parameters ---------- u : array_like u-component of wind v : array_like v-component of wind kwargs Other keyword arguments to pass to :meth:`matplotlib.axes.Axes.plot` Returns ------- list[matplotlib.lines.Line2D] lines plotted See Also -------- :meth:`Hodograph.plot_colormapped` """ line_args = self._form_line_args(kwargs) u, v = _delete_masked_points(u, v) return self.ax.plot(u, v, line_args) def wind_vectors(self, u, v, kwargs): r"""Plot u, v data as wind vectors. Plot the wind data as vectors for each level, beginning at the origin. Parameters ---------- u : array_like u-component of wind v : array_like v-component of wind kwargs Other keyword arguments to pass to :meth:`matplotlib.axes.Axes.quiver` Returns ------- matplotlib.quiver.Quiver arrows plotted """ quiver_args = {'units': 'xy', 'scale': 1} quiver_args.update(kwargs) center_position = np.zeros_like(u) return self.ax.quiver(center_position, center_position, u, v, quiver_args) def plot_colormapped(self, u, v, c, intervals=None, colors=None, kwargs): r"""Plot u, v data, with line colored based on a third set of data. Plots the wind data on the hodograph, but with a colormapped line. Takes a third variable besides the winds (e.g. heights or pressure levels) and either a colormap to color it with or a series of contour intervals and colors to create a colormap and norm to control colormapping. The intervals must always be in increasing order. For using custom contour intervals with height data, the function will automatically interpolate to the contour intervals from the height and wind data, as well as convert the input contour intervals from height AGL to MSL to work with the provided heights. Parameters ---------- u : array_like u-component of wind v : array_like v-component of wind c : array_like data to use for colormapping (e.g. heights, pressure, wind speed) intervals: array-like, optional Array of intervals for c to use in coloring the hodograph. colors: list, optional Array of strings representing colors for the hodograph segments. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.LineCollection` Returns ------- matplotlib.collections.LineCollection instance created See Also -------- :meth:`Hodograph.plot` """ u, v, c = _delete_masked_points(u, v, c) # Plotting a color segmented hodograph if colors: cmap = mcolors.ListedColormap(colors) # If we are segmenting by height (a length), interpolate the contour intervals if intervals.dimensionality == {'[length]': 1.0}: # Find any intervals not in the data and interpolate them interpolation_heights = [bound.m for bound in intervals if bound not in c] interpolation_heights = np.array(interpolation_heights) * intervals.units interpolation_heights = (np.sort(interpolation_heights.magnitude) * interpolation_heights.units) (interpolated_heights, interpolated_u, interpolated_v) = interpolate_1d(interpolation_heights, c, c, u, v) # Combine the interpolated data with the actual data c = concatenate([c, interpolated_heights]) u = concatenate([u, interpolated_u]) v = concatenate([v, interpolated_v]) sort_inds = np.argsort(c) c = c[sort_inds] u = u[sort_inds] v = v[sort_inds] # Unit conversion required for coloring of bounds/data in dissimilar units # to work properly. c = c.to_base_units() # TODO: This shouldn't be required! intervals = intervals.to_base_units() # If segmenting by anything else, do not interpolate, just use the data else: intervals = np.asarray(intervals) * intervals.units norm = mcolors.BoundaryNorm(intervals.magnitude, cmap.N) cmap.set_over('none') cmap.set_under('none') kwargs['cmap'] = cmap kwargs['norm'] = norm line_args = self._form_line_args(kwargs) # Plotting a continuously colored line else: line_args = self._form_line_args(kwargs) # Do the plotting lc = colored_line(u, v, c, **line_args) self.ax.add_collection(lc) return lc	bsd-3-clause
coolsgupta/machine_learning_nanodegree	Supervised_Learning/naive bayes/creating_the_GNB_clf.py	1	1349	#doc: http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html from sklearn.naive_bayes import GaussianNB def classify(features_train, labels_train): ### import the sklearn module for GaussianNB ### create classifier ### fit the classifier on the training features and labels ### return the fit classifier ### your code goes here! clf = GaussianNB() clf.fit(features_train, labels_train) return clf def NBAccuracy(features_train, labels_train, features_test, labels_test): """ compute the accuracy of your Naive Bayes classifier """ ### import the sklearn module for GaussianNB from sklearn.naive_bayes import GaussianNB ### create la clf = GaussianNB() ### fit the classifier on the training features and labels #TODO clf.fit(features_train,labels_train) ### use the trained classifier to predict labels for the test features pred = clf.predict(features_test) ### calculate and return the accuracy on the test data ### this is slightly different than the example, ### where we just print the accuracy ### you might need to import an sklearn module accuracy = clf.score(features_test,labels_test) #or from sklearn.metrics import accuracy_score accuracy = accuracy_score(pred,labels_test) return accuracy	mit
dariomangoni/chrono	src/demos/python/chrono-tensorflow/PPO/value_function.py	4	7169	""" State-Value Function """ import tensorflow as tf import numpy as np from sklearn.utils import shuffle import os.path class NNValueFunction(object): """ NN-based state-value function """ def __init__(self, obs_dim, env_name, MultiGPU = False): """ Args: obs_dim: number of dimensions in observation vector (int) """ self.env_name = env_name self.multiGPU = MultiGPU self.replay_buffer_x = None self.replay_buffer_y = None self.obs_dim = obs_dim self.epochs = 10 self.lr = None # learning rate set in _build_graph() self.savedmodel = os.path.isfile("./savedmodel/"+self.env_name+"/VF/checkpoint") directory = "./savedmodel/"+self.env_name+"/VF/" if self.savedmodel : self._restore() else: if not os.path.exists(directory): os.makedirs(directory) self._build_graph() #self.sess = tf.Session(graph=self.g) #self.sess.run(self.init) def _build_graph(self): """ Construct TensorFlow graph, including loss function, init op and train op """ self.g = tf.Graph() with self.g.as_default(): self.obs_ph = tf.placeholder(tf.float32, (None, self.obs_dim), 'obs_valfunc') self.val_ph = tf.placeholder(tf.float32, (None,), 'val_valfunc') # hid1 layer size is 10x obs_dim, hid3 size is 10, and hid2 is geometric mean hid1_size = self.obs_dim * 10 # 10 chosen empirically on 'Hopper-v1' hid3_size = 5 # 5 chosen empirically on 'Hopper-v1' hid2_size = int(np.sqrt(hid1_size * hid3_size)) # heuristic to set learning rate based on NN size (tuned on 'Hopper-v1') self.lr = 1e-2 / np.sqrt(hid2_size) # 1e-3 empirically determined print('Value Params -- h1: {}, h2: {}, h3: {}, lr: {:.3g}' .format(hid1_size, hid2_size, hid3_size, self.lr)) # 3 hidden layers with tanh activations out = tf.layers.dense(self.obs_ph, hid1_size, tf.tanh, kernel_initializer=tf.random_normal_initializer( stddev=np.sqrt(1 / self.obs_dim)), name="h1VF") out = tf.layers.dense(out, hid2_size, tf.tanh, kernel_initializer=tf.random_normal_initializer( stddev=np.sqrt(1 / hid1_size)), name="h2VF") out = tf.layers.dense(out, hid3_size, tf.tanh, kernel_initializer=tf.random_normal_initializer( stddev=np.sqrt(1 / hid2_size)), name="h3VF") out = tf.layers.dense(out, 1, kernel_initializer=tf.random_normal_initializer( stddev=np.sqrt(1 / hid3_size)), name='output') self.out = tf.squeeze(out) self.loss = tf.reduce_mean(tf.square(self.out - self.val_ph), name='lossVF') # squared loss optimizer = tf.train.AdamOptimizer(self.lr) self.train_op = optimizer.minimize(self.loss, name='train_opVF') self.init = tf.global_variables_initializer() self.saverVF = tf.train.Saver() if self.multiGPU : config = tf.ConfigProto() config.gpu_options.allow_growth = True #config.gpu_options.per_process_gpu_memory_fraction = 0.1 self.sess = tf.Session(graph=self.g, config=config) else: self.sess = tf.Session(graph=self.g) self.sess.run(self.init) def _restore(self): if self.multiGPU : config = tf.ConfigProto() config.gpu_options.allow_growth = True #config.gpu_options.per_process_gpu_memory_fraction = 0.1 self.sess = tf.Session(config=config) else: self.sess = tf.Session() loader = tf.train.import_meta_graph("./savedmodel/"+self.env_name+"/VF/trained_VF.ckpt.meta") self.sess.run(tf.global_variables_initializer()) self.g = tf.get_default_graph() self.obs_ph = self.g.get_tensor_by_name('obs_valfunc:0') self.val_ph = self.g.get_tensor_by_name('val_valfunc:0') out = self.g.get_tensor_by_name('output/BiasAdd:0') self.out = tf.squeeze(out) self.loss = self.g.get_tensor_by_name('lossVF:0') self.train_op = self.g.get_operation_by_name('train_opVF') self.lr = 1e-2 / np.sqrt(int(np.sqrt(self.obs_dim * 10 * 5))) self.saverVF = tf.train.Saver() loader.restore(self.sess, tf.train.latest_checkpoint("./savedmodel/"+self.env_name+"/VF")) def fit(self, x, y, logger): """ Fit model to current data batch + previous data batch Args: x: features y: target logger: logger to save training loss and % explained variance """ # minibatches of 256 tuples. Trining set is pretty big when the episode is long (steps_perepisodeepisode_per batch) num_batches = max(x.shape[0] // 256, 1) batch_size = x.shape[0] // num_batches y_hat = self.predict(x) # check explained variance prior to update old_exp_var = 1 - np.var(y - y_hat)/np.var(y) #alla prima iterazione buffer coincide con ultimo dato, a quelle seguenti lo incollo al buffer if self.replay_buffer_x is None: x_train, y_train = x, y else: x_train = np.concatenate([x, self.replay_buffer_x]) y_train = np.concatenate([y, self.replay_buffer_y]) # flush buffar. the last one wil be appended to the next self.replay_buffer_x = x self.replay_buffer_y = y for e in range(self.epochs): x_train, y_train = shuffle(x_train, y_train) for j in range(num_batches): start = j batch_size end = (j + 1) * batch_size feed_dict = {self.obs_ph: x_train[start:end, :], self.val_ph: y_train[start:end]} _, l = self.sess.run([self.train_op, self.loss], feed_dict=feed_dict) y_hat = self.predict(x) loss = np.mean(np.square(y_hat - y)) # explained variance after update exp_var = 1 - np.var(y - y_hat) / np.var(y) # diagnose over-fitting of val func self.saverVF.save(self.sess, "./savedmodel/"+self.env_name+"/VF/trained_VF.ckpt") logger.log({'ValFuncLoss': loss, 'ExplainedVarNew': exp_var, 'ExplainedVarOld': old_exp_var}) def predict(self, x): """ Predict method """ feed_dict = {self.obs_ph: x} y_hat = self.sess.run(self.out, feed_dict=feed_dict) return np.squeeze(y_hat) def close_sess(self): """ Close TensorFlow session """ self.saverVF.save(self.sess, "./savedmodel/"+self.env_name+"/VF/trained_VF.ckpt") self.sess.close()	bsd-3-clause
kose-y/pylearn2	pylearn2/scripts/plot_monitor.py	37	10204	#!/usr/bin/env python """ usage: plot_monitor.py model_1.pkl model_2.pkl ... model_n.pkl Loads any number of .pkl files produced by train.py. Extracts all of their monitoring channels and prompts the user to select a subset of them to be plotted. """ from __future__ import print_function __authors__ = "Ian Goodfellow, Harm Aarts" __copyright__ = "Copyright 2010-2012, Universite de Montreal" __credits__ = ["Ian Goodfellow"] __license__ = "3-clause BSD" __maintainer__ = "LISA Lab" __email__ = "pylearn-dev@googlegroups" import gc import numpy as np import sys from theano.compat.six.moves import input, xrange from pylearn2.utils import serial from theano.printing import _TagGenerator from pylearn2.utils.string_utils import number_aware_alphabetical_key from pylearn2.utils import contains_nan, contains_inf import argparse channels = {} def unique_substring(s, other, min_size=1): """ .. todo:: WRITEME """ size = min(len(s), min_size) while size <= len(s): for pos in xrange(0,len(s)-size+1): rval = s[pos:pos+size] fail = False for o in other: if o.find(rval) != -1: fail = True break if not fail: return rval size += 1 # no unique substring return s def unique_substrings(l, min_size=1): """ .. todo:: WRITEME """ return [unique_substring(s, [x for x in l if x is not s], min_size) for s in l] def main(): """ .. todo:: WRITEME """ parser = argparse.ArgumentParser() parser.add_argument("--out") parser.add_argument("model_paths", nargs='+') parser.add_argument("--yrange", help='The y-range to be used for plotting, e.g. 0:1') options = parser.parse_args() model_paths = options.model_paths if options.out is not None: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt print('generating names...') model_names = [model_path.replace('.pkl', '!') for model_path in model_paths] model_names = unique_substrings(model_names, min_size=10) model_names = [model_name.replace('!','') for model_name in model_names] print('...done') for i, arg in enumerate(model_paths): try: model = serial.load(arg) except Exception: if arg.endswith('.yaml'): print(sys.stderr, arg + " is a yaml config file," + "you need to load a trained model.", file=sys.stderr) quit(-1) raise this_model_channels = model.monitor.channels if len(sys.argv) > 2: postfix = ":" + model_names[i] else: postfix = "" for channel in this_model_channels: channels[channel+postfix] = this_model_channels[channel] del model gc.collect() while True: # Make a list of short codes for each channel so user can specify them # easily tag_generator = _TagGenerator() codebook = {} sorted_codes = [] for channel_name in sorted(channels, key = number_aware_alphabetical_key): code = tag_generator.get_tag() codebook[code] = channel_name codebook['<'+channel_name+'>'] = channel_name sorted_codes.append(code) x_axis = 'example' print('set x_axis to example') if len(channels.values()) == 0: print("there are no channels to plot") break # If there is more than one channel in the monitor ask which ones to # plot prompt = len(channels.values()) > 1 if prompt: # Display the codebook for code in sorted_codes: print(code + '. ' + codebook[code]) print() print("Put e, b, s or h in the list somewhere to plot " + "epochs, batches, seconds, or hours, respectively.") response = input('Enter a list of channels to plot ' + \ '(example: A, C,F-G, h, <test_err>) or q to quit' + \ ' or o for options: ') if response == 'o': print('1: smooth all channels') print('any other response: do nothing, go back to plotting') response = input('Enter your choice: ') if response == '1': for channel in channels.values(): k = 5 new_val_record = [] for i in xrange(len(channel.val_record)): new_val = 0. count = 0. for j in xrange(max(0, i-k), i+1): new_val += channel.val_record[j] count += 1. new_val_record.append(new_val / count) channel.val_record = new_val_record continue if response == 'q': break #Remove spaces response = response.replace(' ','') #Split into list codes = response.split(',') final_codes = set([]) for code in codes: if code == 'e': x_axis = 'epoch' continue elif code == 'b': x_axis = 'batche' elif code == 's': x_axis = 'second' elif code == 'h': x_axis = 'hour' elif code.startswith('<'): assert code.endswith('>') final_codes.add(code) elif code.find('-') != -1: #The current list element is a range of codes rng = code.split('-') if len(rng) != 2: print("Input not understood: "+code) quit(-1) found = False for i in xrange(len(sorted_codes)): if sorted_codes[i] == rng[0]: found = True break if not found: print("Invalid code: "+rng[0]) quit(-1) found = False for j in xrange(i,len(sorted_codes)): if sorted_codes[j] == rng[1]: found = True break if not found: print("Invalid code: "+rng[1]) quit(-1) final_codes = final_codes.union(set(sorted_codes[i:j+1])) else: #The current list element is just a single code final_codes = final_codes.union(set([code])) # end for code in codes else: final_codes ,= set(codebook.keys()) colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k'] styles = list(colors) styles += [color+'--' for color in colors] styles += [color+':' for color in colors] fig = plt.figure() ax = plt.subplot(1,1,1) # plot the requested channels for idx, code in enumerate(sorted(final_codes)): channel_name= codebook[code] channel = channels[channel_name] y = np.asarray(channel.val_record) if contains_nan(y): print(channel_name + ' contains NaNs') if contains_inf(y): print(channel_name + 'contains infinite values') if x_axis == 'example': x = np.asarray(channel.example_record) elif x_axis == 'batche': x = np.asarray(channel.batch_record) elif x_axis == 'epoch': try: x = np.asarray(channel.epoch_record) except AttributeError: # older saved monitors won't have epoch_record x = np.arange(len(channel.batch_record)) elif x_axis == 'second': x = np.asarray(channel.time_record) elif x_axis == 'hour': x = np.asarray(channel.time_record) / 3600. else: assert False ax.plot( x, y, styles[idx % len(styles)], marker = '.', # add point margers to lines label = channel_name) plt.xlabel('# '+x_axis+'s') ax.ticklabel_format( scilimits = (-3,3), axis = 'both') handles, labels = ax.get_legend_handles_labels() lgd = ax.legend(handles, labels, loc = 'upper left', bbox_to_anchor = (1.05, 1.02)) # Get the axis positions and the height and width of the legend plt.draw() ax_pos = ax.get_position() pad_width = ax_pos.x0 * fig.get_size_inches()[0] pad_height = ax_pos.y0 * fig.get_size_inches()[1] dpi = fig.get_dpi() lgd_width = ax.get_legend().get_frame().get_width() / dpi lgd_height = ax.get_legend().get_frame().get_height() / dpi # Adjust the bounding box to encompass both legend and axis. Axis should be 3x3 inches. # I had trouble getting everything to align vertically. ax_width = 3 ax_height = 3 total_width = 2pad_width + ax_width + lgd_width total_height = 2pad_height + np.maximum(ax_height, lgd_height) fig.set_size_inches(total_width, total_height) ax.set_position([pad_width/total_width, 1-6*pad_height/total_height, ax_width/total_width, ax_height/total_height]) if(options.yrange is not None): ymin, ymax = map(float, options.yrange.split(':')) plt.ylim(ymin, ymax) if options.out is None: plt.show() else: plt.savefig(options.out) if not prompt: break if __name__ == "__main__": main()	bsd-3-clause
prheenan/GeneralUtil	python/Plot/Inset.py	1	1864	# force floating point division. Can still use integer with // from __future__ import division # other good compatibility recquirements for python3 from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals # This file is used for importing the common utilities classes. import numpy as np import matplotlib.pyplot as plt import sys from GeneralUtil.python import PlotUtilities from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes,mark_inset def slice_by_x(x,y,xlim): """ slices x and y by xlim: Args: <x/y>: arrays of the same shape: xlim: the limits for x-limits Returns: tuple of (sliced x, sliced y, y limits) """ x = np.array(x) y = np.array(y) # plot the data red where we will zoom in where_region = np.where( (x >= min(xlim)) & (x <= max(xlim))) assert x.shape == y.shape , "Arrays should be the same shape " zoom_x = x[where_region] zoom_y = y[where_region] ylim = [min(zoom_y),max(zoom_y)] return zoom_x,zoom_y,ylim def zoomed_axis(ax=plt.gca(),xlim=[None,None],ylim=[None,None], remove_ticks=True,zoom=1,borderpad=1,loc=4,*kw): """ Creates a (pretty) zoomed axis Args: ax: which axis to zoom on <x/y>_lim: the axes limits remove_ticks: if true, removes the x and y ticks, to reduce clutter remaining args: passed to zoomed_inset_axes Returns: the inset axis """ axins = zoomed_inset_axes(ax, zoom=zoom, loc=loc,borderpad=borderpad) axins.set_xlim(xlim) # apply the x-limits axins.set_ylim(*ylim) # apply the y-limits if (remove_ticks): PlotUtilities.no_x_anything(axins) PlotUtilities.no_y_anything(axins) return axins	gpl-2.0
th0mmeke/toyworld	evaluators/plot_molecular_diversity.py	1	1177	""" Created on 6/05/2013 @author: thom """ from plot import Plot from evaluator import Evaluator from molecular_population import MolecularPopulation import matplotlib.colors as colors class PlotMolecularDiversity(Plot): def draw_figure(self, f1, results_filename, *kwargs): results = Evaluator.load_results(results_filename) population = MolecularPopulation(population=results['initial_population'], reactions=results['reactions']) # , size=500) diversity = [] for t in population.get_times(): slice = population.get_slice_by_time([t]) quantities = [slice.get_quantity(item) for item in slice.get_items() if slice.get_quantity(item) > 0] iteration_diversity = (1.0 len(quantities)) / (1.0 * sum(quantities)) diversity.append(iteration_diversity) ax = f1.add_subplot(1, 1, 1) # one row, one column, first plot ax.set_title('Diversity of Molecular Types') ax.set_xlabel('Time') ax.set_ylabel('Diversity') ax.set_xlim(left=0, right=population.get_times()[-1]) ax.plot(diversity, color=colors.cnames['slategray']) ax.grid()	gpl-3.0
kezilu/pextant	pextant/analysis/loadWaypoints.py	2	5515	import json import pandas as pd from copy import deepcopy from pextant.lib.geoshapely import * import numpy as np import re import os pd.options.display.max_rows = 5 from pathlib2 import Path def loadPointsOld(filename): parsed_json = json.loads(jsonInput) waypoints = [] for element in parsed_json: # identify all of the waypoints if element["type"] == "Station": lon, lat = element["geometry"]["coordinates"] time_cost = element["userDuration"] waypoints.append(GeoPolygon(LAT_LONG, lon, lat)) return waypoints, parsed_json def get_gps_data(filename): """ Gets GPS time series gathered from a traversal :param filename: <String> csv file from GPS team in format \|date\|time\|name\|latitude\|longitude\|heading :return: <pandas DataFrame> time_stamp\|latitude\|longitude """ delimiter = r"\s+" # some of the columns are separated by a space, others by tabs, use regex to include both header_row = 0 # the first row has all the header names df = pd.read_csv(filename, sep=delimiter, header=header_row) df['date_time'] = pd.to_datetime(df['epoch timestamp'], unit='s') time_lat_long = df[['date_time', 'latitude', 'longitude']] gp = GeoPolygon(LAT_LONG, df[['latitude', 'longitude']].as_matrix().transpose()) return gp #TODO: Need to move this over to test file #filename = '../../data/ev_tracks/20161104A_EV1.csv' #time_lat_long = get_gps_data(filename) def sextant_loader(filepath): with open(filepath) as data_file: jsondata = json.load(data_file) latlongInter = np.array(jsondata['geometry']['coordinates']).transpose() return GeoPolygon(LONG_LAT, latlongInter) #this really is a xpjson loader class JSONloader: def __init__(self, sequence, raw, filename=None): self.extension = '_plan.json' if isinstance(filename, Path): filename = str(filename.absolute()) self.filename = filename self.raw = raw self.sequence = sequence @classmethod def from_string(cls, str): return cls(json.loads(str)) @classmethod def from_file(cls, filepath): if isinstance(filepath, Path): filepath = str(filepath.absolute()) stem = os.path.basename(filepath).split('.')[0] parent = os.path.dirname(filepath) fullfilename = os.path.join(parent, stem) with open(filepath) as data_file: jsondata = json.load(data_file) return cls(jsondata['sequence'], jsondata, fullfilename) def get_waypoints(self): #print('HI') #print(self.sequence) #print('Hi again') ways_and_segments = self.sequence s = pd.DataFrame(ways_and_segments) waypoints = s[s['type'] == 'Station']['geometry'] w = waypoints.values.tolist() latlongFull = pd.DataFrame(w) latlongInter = np.array(latlongFull['coordinates'].values.tolist()).transpose() return GeoPolygon(LONG_LAT, latlongInter) def get_segments(self): ways_and_segments = self.sequence s = pd.DataFrame(ways_and_segments) waypoints = s[s['type'] == 'Segment']['geometry'] w = waypoints.values.tolist() latlongFull = pd.DataFrame(w) latlongInter = latlongFull['coordinates'].values.tolist() waypointslatlong = [] for elt in latlongInter: waypointslatlong.extend(elt) return GeoPolygon(LONG_LAT, np.array(waypointslatlong).transpose()) def add_search_sol(self, segments, write_to_file=False): ways_and_segments = deepcopy(self.sequence) segment_iter = iter(segments) for i, element in enumerate(ways_and_segments): if element["type"] == "Segment": segment = segment_iter.next().tojson() ways_and_segments[i]["derivedInfo"].update(segment["derivedInfo"]) #merges our new info ways_and_segments[i]["geometry"] = segment["geometry"] raw_json = json.dumps(ways_and_segments) formatted_json = json.dumps(ways_and_segments, indent=4, sort_keys=True) if write_to_file and self.filename: rawfile = self.raw rawfile["sequence"] = ways_and_segments new_filename = self.filename + self.extension with open(new_filename, 'w') as outfile: json.dump(rawfile, outfile, indent=4, sort_keys=True) return raw_json if __name__ == '__main__': from pextant.settings import * md = JSONloader.from_file(MD_HI[6]) sextantsol = md.get_segments() test =json.dumps([{u'commands': [], u'uuid': u'ccf34b91-86f4-47ee-b03d-3dbbba6ba167', u'geometry': {u'type': u'Point', u'coordinates': [-155.20191861222781, 19.366498026755977]}, u'tolerance': 0.6, u'userDuration': 0, u'boundary': 0.6, u'type': u'Station', u'id': u'HIL11_A_WAY0'}, { u'derivedInfo': {u'durationSeconds': 28, u'straightLineDurationSeconds': 28, u'distanceMeters': 25.15366493675656}, u'commands': [], u'type': u'Segment', u'id': u'HIL11_A_SEG1', u'uuid': u'69aa6e5f-6a10-4568-bfea-5bfbc8417ba7'}, {u'commands': [], u'uuid': u'1a159ed9-77ee-4f79-9163-e3685a01a00c', u'geometry': {u'type': u'Point', u'coordinates': [-155.2016858384008, 19.36644374514718]}, u'tolerance': 0.6, u'userDuration': 0, u'boundary': 0.6, u'type': u'Station', u'id': u'HIL11_A_WAY1'}]) jloader = JSONloader(test) jloader.get_waypoints()	mit
astropy/astropy	astropy/nddata/utils.py	2	31965	# Licensed under a 3-clause BSD style license - see LICENSE.rst """ This module includes helper functions for array operations. """ from copy import deepcopy import sys import types import warnings import numpy as np from astropy import units as u from astropy.coordinates import SkyCoord from astropy.utils import lazyproperty from astropy.utils.decorators import AstropyDeprecationWarning from astropy.wcs.utils import skycoord_to_pixel, proj_plane_pixel_scales from astropy.wcs import Sip from .blocks import block_reduce as _block_reduce from .blocks import block_replicate as _block_replicate __all__ = ['extract_array', 'add_array', 'subpixel_indices', 'overlap_slices', 'NoOverlapError', 'PartialOverlapError', 'Cutout2D'] # this can be replaced with PEP562 when the minimum required Python # version is 3.7 class _ModuleWithDeprecation(types.ModuleType): def __getattribute__(self, name): deprecated = ('block_reduce', 'block_replicate') if name in deprecated: warnings.warn(f'{name} was moved to the astropy.nddata.blocks ' 'module. Please update your import statement.', AstropyDeprecationWarning) return object.__getattribute__(self, f'_{name}') return object.__getattribute__(self, name) sys.modules[__name__].__class__ = _ModuleWithDeprecation class NoOverlapError(ValueError): '''Raised when determining the overlap of non-overlapping arrays.''' pass class PartialOverlapError(ValueError): '''Raised when arrays only partially overlap.''' pass def overlap_slices(large_array_shape, small_array_shape, position, mode='partial'): """ Get slices for the overlapping part of a small and a large array. Given a certain position of the center of the small array, with respect to the large array, tuples of slices are returned which can be used to extract, add or subtract the small array at the given position. This function takes care of the correct behavior at the boundaries, where the small array is cut of appropriately. Integer positions are at the pixel centers. Parameters ---------- large_array_shape : tuple of int or int The shape of the large array (for 1D arrays, this can be an `int`). small_array_shape : int or tuple thereof The shape of the small array (for 1D arrays, this can be an `int`). See the ``mode`` keyword for additional details. position : number or tuple thereof The position of the small array's center with respect to the large array. The pixel coordinates should be in the same order as the array shape. Integer positions are at the pixel centers. For any axis where ``small_array_shape`` is even, the position is rounded up, e.g. extracting two elements with a center of ``1`` will define the extracted region as ``[0, 1]``. mode : {'partial', 'trim', 'strict'}, optional In ``'partial'`` mode, a partial overlap of the small and the large array is sufficient. The ``'trim'`` mode is similar to the ``'partial'`` mode, but ``slices_small`` will be adjusted to return only the overlapping elements. In the ``'strict'`` mode, the small array has to be fully contained in the large array, otherwise an `~astropy.nddata.utils.PartialOverlapError` is raised. In all modes, non-overlapping arrays will raise a `~astropy.nddata.utils.NoOverlapError`. Returns ------- slices_large : tuple of slice A tuple of slice objects for each axis of the large array, such that ``large_array[slices_large]`` extracts the region of the large array that overlaps with the small array. slices_small : tuple of slice A tuple of slice objects for each axis of the small array, such that ``small_array[slices_small]`` extracts the region that is inside the large array. """ if mode not in ['partial', 'trim', 'strict']: raise ValueError('Mode can be only "partial", "trim", or "strict".') if np.isscalar(small_array_shape): small_array_shape = (small_array_shape, ) if np.isscalar(large_array_shape): large_array_shape = (large_array_shape, ) if np.isscalar(position): position = (position, ) if any(~np.isfinite(position)): raise ValueError('Input position contains invalid values (NaNs or ' 'infs).') if len(small_array_shape) != len(large_array_shape): raise ValueError('"large_array_shape" and "small_array_shape" must ' 'have the same number of dimensions.') if len(small_array_shape) != len(position): raise ValueError('"position" must have the same number of dimensions ' 'as "small_array_shape".') # define the min/max pixel indices indices_min = [int(np.ceil(pos - (small_shape / 2.))) for (pos, small_shape) in zip(position, small_array_shape)] indices_max = [int(np.ceil(pos + (small_shape / 2.))) for (pos, small_shape) in zip(position, small_array_shape)] for e_max in indices_max: if e_max < 0: raise NoOverlapError('Arrays do not overlap.') for e_min, large_shape in zip(indices_min, large_array_shape): if e_min >= large_shape: raise NoOverlapError('Arrays do not overlap.') if mode == 'strict': for e_min in indices_min: if e_min < 0: raise PartialOverlapError('Arrays overlap only partially.') for e_max, large_shape in zip(indices_max, large_array_shape): if e_max > large_shape: raise PartialOverlapError('Arrays overlap only partially.') # Set up slices slices_large = tuple(slice(max(0, indices_min), min(large_shape, indices_max)) for (indices_min, indices_max, large_shape) in zip(indices_min, indices_max, large_array_shape)) if mode == 'trim': slices_small = tuple(slice(0, slc.stop - slc.start) for slc in slices_large) else: slices_small = tuple(slice(max(0, -indices_min), min(large_shape - indices_min, indices_max - indices_min)) for (indices_min, indices_max, large_shape) in zip(indices_min, indices_max, large_array_shape)) return slices_large, slices_small def extract_array(array_large, shape, position, mode='partial', fill_value=np.nan, return_position=False): """ Extract a smaller array of the given shape and position from a larger array. Parameters ---------- array_large : ndarray The array from which to extract the small array. shape : int or tuple thereof The shape of the extracted array (for 1D arrays, this can be an `int`). See the ``mode`` keyword for additional details. position : number or tuple thereof The position of the small array's center with respect to the large array. The pixel coordinates should be in the same order as the array shape. Integer positions are at the pixel centers (for 1D arrays, this can be a number). mode : {'partial', 'trim', 'strict'}, optional The mode used for extracting the small array. For the ``'partial'`` and ``'trim'`` modes, a partial overlap of the small array and the large array is sufficient. For the ``'strict'`` mode, the small array has to be fully contained within the large array, otherwise an `~astropy.nddata.utils.PartialOverlapError` is raised. In all modes, non-overlapping arrays will raise a `~astropy.nddata.utils.NoOverlapError`. In ``'partial'`` mode, positions in the small array that do not overlap with the large array will be filled with ``fill_value``. In ``'trim'`` mode only the overlapping elements are returned, thus the resulting small array may be smaller than the requested ``shape``. fill_value : number, optional If ``mode='partial'``, the value to fill pixels in the extracted small array that do not overlap with the input ``array_large``. ``fill_value`` will be changed to have the same ``dtype`` as the ``array_large`` array, with one exception. If ``array_large`` has integer type and ``fill_value`` is ``np.nan``, then a `ValueError` will be raised. return_position : bool, optional If `True`, return the coordinates of ``position`` in the coordinate system of the returned array. Returns ------- array_small : ndarray The extracted array. new_position : tuple If ``return_position`` is true, this tuple will contain the coordinates of the input ``position`` in the coordinate system of ``array_small``. Note that for partially overlapping arrays, ``new_position`` might actually be outside of the ``array_small``; ``array_small[new_position]`` might give wrong results if any element in ``new_position`` is negative. Examples -------- We consider a large array with the shape 11x10, from which we extract a small array of shape 3x5: >>> import numpy as np >>> from astropy.nddata.utils import extract_array >>> large_array = np.arange(110).reshape((11, 10)) >>> extract_array(large_array, (3, 5), (7, 7)) array([[65, 66, 67, 68, 69], [75, 76, 77, 78, 79], [85, 86, 87, 88, 89]]) """ if np.isscalar(shape): shape = (shape, ) if np.isscalar(position): position = (position, ) if mode not in ['partial', 'trim', 'strict']: raise ValueError("Valid modes are 'partial', 'trim', and 'strict'.") large_slices, small_slices = overlap_slices(array_large.shape, shape, position, mode=mode) extracted_array = array_large[large_slices] if return_position: new_position = [i - s.start for i, s in zip(position, large_slices)] # Extracting on the edges is presumably a rare case, so treat special here if (extracted_array.shape != shape) and (mode == 'partial'): extracted_array = np.zeros(shape, dtype=array_large.dtype) try: extracted_array[:] = fill_value except ValueError as exc: exc.args += ('fill_value is inconsistent with the data type of ' 'the input array (e.g., fill_value cannot be set to ' 'np.nan if the input array has integer type). Please ' 'change either the input array dtype or the ' 'fill_value.',) raise exc extracted_array[small_slices] = array_large[large_slices] if return_position: new_position = [i + s.start for i, s in zip(new_position, small_slices)] if return_position: return extracted_array, tuple(new_position) else: return extracted_array def add_array(array_large, array_small, position): """ Add a smaller array at a given position in a larger array. Parameters ---------- array_large : ndarray Large array. array_small : ndarray Small array to add. Can be equal to ``array_large`` in size in a given dimension, but not larger. position : tuple Position of the small array's center, with respect to the large array. Coordinates should be in the same order as the array shape. Returns ------- new_array : ndarray The new array formed from the sum of ``array_large`` and ``array_small``. Notes ----- The addition is done in-place. Examples -------- We consider a large array of zeros with the shape 5x5 and a small array of ones with a shape of 3x3: >>> import numpy as np >>> from astropy.nddata.utils import add_array >>> large_array = np.zeros((5, 5)) >>> small_array = np.ones((3, 3)) >>> add_array(large_array, small_array, (1, 2)) # doctest: +FLOAT_CMP array([[0., 1., 1., 1., 0.], [0., 1., 1., 1., 0.], [0., 1., 1., 1., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]]) """ # Check if large array is not smaller if all(large_shape >= small_shape for (large_shape, small_shape) in zip(array_large.shape, array_small.shape)): large_slices, small_slices = overlap_slices(array_large.shape, array_small.shape, position) array_large[large_slices] += array_small[small_slices] return array_large else: raise ValueError("Can't add array. Small array too large.") def subpixel_indices(position, subsampling): """ Convert decimal points to indices, given a subsampling factor. This discards the integer part of the position and uses only the decimal place, and converts this to a subpixel position depending on the subsampling specified. The center of a pixel corresponds to an integer position. Parameters ---------- position : ndarray or array-like Positions in pixels. subsampling : int Subsampling factor per pixel. Returns ------- indices : ndarray The integer subpixel indices corresponding to the input positions. Examples -------- If no subsampling is used, then the subpixel indices returned are always 0: >>> from astropy.nddata.utils import subpixel_indices >>> subpixel_indices([1.2, 3.4, 5.6], 1) # doctest: +FLOAT_CMP array([0., 0., 0.]) If instead we use a subsampling of 2, we see that for the two first values (1.1 and 3.4) the subpixel position is 1, while for 5.6 it is 0. This is because the values of 1, 3, and 6 lie in the center of pixels, and 1.1 and 3.4 lie in the left part of the pixels and 5.6 lies in the right part. >>> subpixel_indices([1.2, 3.4, 5.5], 2) # doctest: +FLOAT_CMP array([1., 1., 0.]) """ # Get decimal points fractions = np.modf(np.asanyarray(position) + 0.5)[0] return np.floor(fractions * subsampling) class Cutout2D: """ Create a cutout object from a 2D array. The returned object will contain a 2D cutout array. If ``copy=False`` (default), the cutout array is a view into the original ``data`` array, otherwise the cutout array will contain a copy of the original data. If a `~astropy.wcs.WCS` object is input, then the returned object will also contain a copy of the original WCS, but updated for the cutout array. For example usage, see :ref:`cutout_images`. .. warning:: The cutout WCS object does not currently handle cases where the input WCS object contains distortion lookup tables described in the `FITS WCS distortion paper <https://www.atnf.csiro.au/people/mcalabre/WCS/dcs_20040422.pdf>`__. Parameters ---------- data : ndarray The 2D data array from which to extract the cutout array. position : tuple or `~astropy.coordinates.SkyCoord` The position of the cutout array's center with respect to the ``data`` array. The position can be specified either as a ``(x, y)`` tuple of pixel coordinates or a `~astropy.coordinates.SkyCoord`, in which case ``wcs`` is a required input. size : int, array-like, or `~astropy.units.Quantity` The size of the cutout array along each axis. If ``size`` is a scalar number or a scalar `~astropy.units.Quantity`, then a square cutout of ``size`` will be created. If ``size`` has two elements, they should be in ``(ny, nx)`` order. Scalar numbers in ``size`` are assumed to be in units of pixels. ``size`` can also be a `~astropy.units.Quantity` object or contain `~astropy.units.Quantity` objects. Such `~astropy.units.Quantity` objects must be in pixel or angular units. For all cases, ``size`` will be converted to an integer number of pixels, rounding the the nearest integer. See the ``mode`` keyword for additional details on the final cutout size. .. note:: If ``size`` is in angular units, the cutout size is converted to pixels using the pixel scales along each axis of the image at the ``CRPIX`` location. Projection and other non-linear distortions are not taken into account. wcs : `~astropy.wcs.WCS`, optional A WCS object associated with the input ``data`` array. If ``wcs`` is not `None`, then the returned cutout object will contain a copy of the updated WCS for the cutout data array. mode : {'trim', 'partial', 'strict'}, optional The mode used for creating the cutout data array. For the ``'partial'`` and ``'trim'`` modes, a partial overlap of the cutout array and the input ``data`` array is sufficient. For the ``'strict'`` mode, the cutout array has to be fully contained within the ``data`` array, otherwise an `~astropy.nddata.utils.PartialOverlapError` is raised. In all modes, non-overlapping arrays will raise a `~astropy.nddata.utils.NoOverlapError`. In ``'partial'`` mode, positions in the cutout array that do not overlap with the ``data`` array will be filled with ``fill_value``. In ``'trim'`` mode only the overlapping elements are returned, thus the resulting cutout array may be smaller than the requested ``shape``. fill_value : float or int, optional If ``mode='partial'``, the value to fill pixels in the cutout array that do not overlap with the input ``data``. ``fill_value`` must have the same ``dtype`` as the input ``data`` array. copy : bool, optional If `False` (default), then the cutout data will be a view into the original ``data`` array. If `True`, then the cutout data will hold a copy of the original ``data`` array. Attributes ---------- data : 2D `~numpy.ndarray` The 2D cutout array. shape : (2,) tuple The ``(ny, nx)`` shape of the cutout array. shape_input : (2,) tuple The ``(ny, nx)`` shape of the input (original) array. input_position_cutout : (2,) tuple The (unrounded) ``(x, y)`` position with respect to the cutout array. input_position_original : (2,) tuple The original (unrounded) ``(x, y)`` input position (with respect to the original array). slices_original : (2,) tuple of slice object A tuple of slice objects for the minimal bounding box of the cutout with respect to the original array. For ``mode='partial'``, the slices are for the valid (non-filled) cutout values. slices_cutout : (2,) tuple of slice object A tuple of slice objects for the minimal bounding box of the cutout with respect to the cutout array. For ``mode='partial'``, the slices are for the valid (non-filled) cutout values. xmin_original, ymin_original, xmax_original, ymax_original : float The minimum and maximum ``x`` and ``y`` indices of the minimal rectangular region of the cutout array with respect to the original array. For ``mode='partial'``, the bounding box indices are for the valid (non-filled) cutout values. These values are the same as those in `bbox_original`. xmin_cutout, ymin_cutout, xmax_cutout, ymax_cutout : float The minimum and maximum ``x`` and ``y`` indices of the minimal rectangular region of the cutout array with respect to the cutout array. For ``mode='partial'``, the bounding box indices are for the valid (non-filled) cutout values. These values are the same as those in `bbox_cutout`. wcs : `~astropy.wcs.WCS` or None A WCS object associated with the cutout array if a ``wcs`` was input. Examples -------- >>> import numpy as np >>> from astropy.nddata.utils import Cutout2D >>> from astropy import units as u >>> data = np.arange(20.).reshape(5, 4) >>> cutout1 = Cutout2D(data, (2, 2), (3, 3)) >>> print(cutout1.data) # doctest: +FLOAT_CMP [[ 5. 6. 7.] [ 9. 10. 11.] [13. 14. 15.]] >>> print(cutout1.center_original) (2.0, 2.0) >>> print(cutout1.center_cutout) (1.0, 1.0) >>> print(cutout1.origin_original) (1, 1) >>> cutout2 = Cutout2D(data, (2, 2), 3) >>> print(cutout2.data) # doctest: +FLOAT_CMP [[ 5. 6. 7.] [ 9. 10. 11.] [13. 14. 15.]] >>> size = u.Quantity([3, 3], u.pixel) >>> cutout3 = Cutout2D(data, (0, 0), size) >>> print(cutout3.data) # doctest: +FLOAT_CMP [[0. 1.] [4. 5.]] >>> cutout4 = Cutout2D(data, (0, 0), (3 * u.pixel, 3)) >>> print(cutout4.data) # doctest: +FLOAT_CMP [[0. 1.] [4. 5.]] >>> cutout5 = Cutout2D(data, (0, 0), (3, 3), mode='partial') >>> print(cutout5.data) # doctest: +FLOAT_CMP [[nan nan nan] [nan 0. 1.] [nan 4. 5.]] """ def __init__(self, data, position, size, wcs=None, mode='trim', fill_value=np.nan, copy=False): if wcs is None: wcs = getattr(data, 'wcs', None) if isinstance(position, SkyCoord): if wcs is None: raise ValueError('wcs must be input if position is a ' 'SkyCoord') position = skycoord_to_pixel(position, wcs, mode='all') # (x, y) if np.isscalar(size): size = np.repeat(size, 2) # special handling for a scalar Quantity if isinstance(size, u.Quantity): size = np.atleast_1d(size) if len(size) == 1: size = np.repeat(size, 2) if len(size) > 2: raise ValueError('size must have at most two elements') shape = np.zeros(2).astype(int) pixel_scales = None # ``size`` can have a mixture of int and Quantity (and even units), # so evaluate each axis separately for axis, side in enumerate(size): if not isinstance(side, u.Quantity): shape[axis] = int(np.round(size[axis])) # pixels else: if side.unit == u.pixel: shape[axis] = int(np.round(side.value)) elif side.unit.physical_type == 'angle': if wcs is None: raise ValueError('wcs must be input if any element ' 'of size has angular units') if pixel_scales is None: pixel_scales = u.Quantity( proj_plane_pixel_scales(wcs), wcs.wcs.cunit[axis]) shape[axis] = int(np.round( (side / pixel_scales[axis]).decompose())) else: raise ValueError('shape can contain Quantities with only ' 'pixel or angular units') data = np.asanyarray(data) # reverse position because extract_array and overlap_slices # use (y, x), but keep the input position pos_yx = position[::-1] cutout_data, input_position_cutout = extract_array( data, tuple(shape), pos_yx, mode=mode, fill_value=fill_value, return_position=True) if copy: cutout_data = np.copy(cutout_data) self.data = cutout_data self.input_position_cutout = input_position_cutout[::-1] # (x, y) slices_original, slices_cutout = overlap_slices( data.shape, shape, pos_yx, mode=mode) self.slices_original = slices_original self.slices_cutout = slices_cutout self.shape = self.data.shape self.input_position_original = position self.shape_input = shape ((self.ymin_original, self.ymax_original), (self.xmin_original, self.xmax_original)) = self.bbox_original ((self.ymin_cutout, self.ymax_cutout), (self.xmin_cutout, self.xmax_cutout)) = self.bbox_cutout # the true origin pixel of the cutout array, including any # filled cutout values self._origin_original_true = ( self.origin_original[0] - self.slices_cutout[1].start, self.origin_original[1] - self.slices_cutout[0].start) if wcs is not None: self.wcs = deepcopy(wcs) self.wcs.wcs.crpix -= self._origin_original_true self.wcs.array_shape = self.data.shape if wcs.sip is not None: self.wcs.sip = Sip(wcs.sip.a, wcs.sip.b, wcs.sip.ap, wcs.sip.bp, wcs.sip.crpix - self._origin_original_true) else: self.wcs = None def to_original_position(self, cutout_position): """ Convert an ``(x, y)`` position in the cutout array to the original ``(x, y)`` position in the original large array. Parameters ---------- cutout_position : tuple The ``(x, y)`` pixel position in the cutout array. Returns ------- original_position : tuple The corresponding ``(x, y)`` pixel position in the original large array. """ return tuple(cutout_position[i] + self.origin_original[i] for i in [0, 1]) def to_cutout_position(self, original_position): """ Convert an ``(x, y)`` position in the original large array to the ``(x, y)`` position in the cutout array. Parameters ---------- original_position : tuple The ``(x, y)`` pixel position in the original large array. Returns ------- cutout_position : tuple The corresponding ``(x, y)`` pixel position in the cutout array. """ return tuple(original_position[i] - self.origin_original[i] for i in [0, 1]) def plot_on_original(self, ax=None, fill=False, kwargs): """ Plot the cutout region on a matplotlib Axes instance. Parameters ---------- ax : `matplotlib.axes.Axes` instance, optional If `None`, then the current `matplotlib.axes.Axes` instance is used. fill : bool, optional Set whether to fill the cutout patch. The default is `False`. kwargs : optional Any keyword arguments accepted by `matplotlib.patches.Patch`. Returns ------- ax : `matplotlib.axes.Axes` instance The matplotlib Axes instance constructed in the method if ``ax=None``. Otherwise the output ``ax`` is the same as the input ``ax``. """ import matplotlib.pyplot as plt import matplotlib.patches as mpatches kwargs['fill'] = fill if ax is None: ax = plt.gca() height, width = self.shape hw, hh = width / 2., height / 2. pos_xy = self.position_original - np.array([hw, hh]) patch = mpatches.Rectangle(pos_xy, width, height, 0., kwargs) ax.add_patch(patch) return ax @staticmethod def _calc_center(slices): """ Calculate the center position. The center position will be fractional for even-sized arrays. For ``mode='partial'``, the central position is calculated for the valid (non-filled) cutout values. """ return tuple(0.5 * (slices[i].start + slices[i].stop - 1) for i in [1, 0]) @staticmethod def _calc_bbox(slices): """ Calculate a minimal bounding box in the form ``((ymin, ymax), (xmin, xmax))``. Note these are pixel locations, not slice indices. For ``mode='partial'``, the bounding box indices are for the valid (non-filled) cutout values. """ # (stop - 1) to return the max pixel location, not the slice index return ((slices[0].start, slices[0].stop - 1), (slices[1].start, slices[1].stop - 1)) @lazyproperty def origin_original(self): """ The ``(x, y)`` index of the origin pixel of the cutout with respect to the original array. For ``mode='partial'``, the origin pixel is calculated for the valid (non-filled) cutout values. """ return (self.slices_original[1].start, self.slices_original[0].start) @lazyproperty def origin_cutout(self): """ The ``(x, y)`` index of the origin pixel of the cutout with respect to the cutout array. For ``mode='partial'``, the origin pixel is calculated for the valid (non-filled) cutout values. """ return (self.slices_cutout[1].start, self.slices_cutout[0].start) @staticmethod def _round(a): """ Round the input to the nearest integer. If two integers are equally close, the value is rounded up. Note that this is different from `np.round`, which rounds to the nearest even number. """ return int(np.floor(a + 0.5)) @lazyproperty def position_original(self): """ The ``(x, y)`` position index (rounded to the nearest pixel) in the original array. """ return (self._round(self.input_position_original[0]), self._round(self.input_position_original[1])) @lazyproperty def position_cutout(self): """ The ``(x, y)`` position index (rounded to the nearest pixel) in the cutout array. """ return (self._round(self.input_position_cutout[0]), self._round(self.input_position_cutout[1])) @lazyproperty def center_original(self): """ The central ``(x, y)`` position of the cutout array with respect to the original array. For ``mode='partial'``, the central position is calculated for the valid (non-filled) cutout values. """ return self._calc_center(self.slices_original) @lazyproperty def center_cutout(self): """ The central ``(x, y)`` position of the cutout array with respect to the cutout array. For ``mode='partial'``, the central position is calculated for the valid (non-filled) cutout values. """ return self._calc_center(self.slices_cutout) @lazyproperty def bbox_original(self): """ The bounding box ``((ymin, ymax), (xmin, xmax))`` of the minimal rectangular region of the cutout array with respect to the original array. For ``mode='partial'``, the bounding box indices are for the valid (non-filled) cutout values. """ return self._calc_bbox(self.slices_original) @lazyproperty def bbox_cutout(self): """ The bounding box ``((ymin, ymax), (xmin, xmax))`` of the minimal rectangular region of the cutout array with respect to the cutout array. For ``mode='partial'``, the bounding box indices are for the valid (non-filled) cutout values. """ return self._calc_bbox(self.slices_cutout)	bsd-3-clause
f3r/scikit-learn	sklearn/metrics/cluster/__init__.py	312	1322	""" The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for cluster analysis results. There are two forms of evaluation: - supervised, which uses a ground truth class values for each sample. - unsupervised, which does not and measures the 'quality' of the model itself. """ from .supervised import adjusted_mutual_info_score from .supervised import normalized_mutual_info_score from .supervised import adjusted_rand_score from .supervised import completeness_score from .supervised import contingency_matrix from .supervised import expected_mutual_information from .supervised import homogeneity_completeness_v_measure from .supervised import homogeneity_score from .supervised import mutual_info_score from .supervised import v_measure_score from .supervised import entropy from .unsupervised import silhouette_samples from .unsupervised import silhouette_score from .bicluster import consensus_score __all__ = ["adjusted_mutual_info_score", "normalized_mutual_info_score", "adjusted_rand_score", "completeness_score", "contingency_matrix", "expected_mutual_information", "homogeneity_completeness_v_measure", "homogeneity_score", "mutual_info_score", "v_measure_score", "entropy", "silhouette_samples", "silhouette_score", "consensus_score"]	bsd-3-clause
op7ic/LeakGenerator	leakme.py	1	351132	# Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. import random import string import itertools import re import sys import hashlib import binascii import optparse # Resources # https://raw.githubusercontent.com/neo/discourse_heroku/master/lib/common_passwords/10k-common-passwords.txt # https://raw.githubusercontent.com/dominictarr/random-name/master/names.txt # http://code.activestate.com/recipes/65215-e-mail-address-validation/ emailregex = "^[a-zA-Z0-9._%-]+@[a-zA-Z0-9._%-]+.[a-zA-Z]{2,6}$" common_passwords = 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inic","Dominica","Dominick","Dominik","Dominique","Dominus","Dominy","Domonic","Domph","Don","Dona","Donadee","Donaghue","Donahoe","Donahue","Donal","Donald","Donaldson","Donall","Donalt","Donata","Donatelli","Donaugh","Donavon","Donegan","Donela","Donell","Donella","Donelle","Donelson","Donelu","Doner","Donetta","Dong","Donia","Donica","Donielle","Donn","Donna","Donnamarie","Donnell","Donnelly","Donnenfeld","Donni","Donnie","Donny","Donoghue","Donoho","Donohue","Donough","Donovan","Doolittle","Doone","Dopp","Dora","Doralia","Doralin","Doralyn","Doralynn","Doralynne","Doran","Dorca","Dorcas","Dorcea","Dorcia","Dorcus","Dorcy","Dore","Doreen","Dorelia","Dorella","Dorelle","Dorena","Dorene","Doretta","Dorette","Dorey","Dorfman","Dori","Doria","Dorian","Dorice","Dorie","Dorin","Dorina","Dorinda","Dorine","Dorion","Doris","Dorisa","Dorise","Dorison","Dorita","Dorkas","Dorkus","Dorlisa","Dorman","Dorn","Doro","Dorolice","Dorolisa","Dorotea","Doroteya","Dorothea","Dorothee","Dorothi","Dorothy","Dorr","Dorran","Dorree","Dorren","Dorri","Dorrie","Dorris","Dorry","Dorsey","Dorsman","Dorsy","Dorthea","Dorthy","Dorweiler","Dorwin","Dory","Doscher","Dosh","Dosi","Dosia","Doss","Dot","Doti","Dotson","Dott","Dotti","Dottie","Dotty","Doty","Doubler","Doug","Dougal","Dougald","Dougall","Dougherty","Doughman","Doughty","Dougie","Douglas","Douglass","Dougy","Douty","Douville","Dov","Dove","Dovev","Dow","Dowd","Dowdell","Dowell","Dowlen","Dowling","Down","Downall","Downe","Downes","Downey","Downing","Downs","Dowski","Dowzall","Doxia","Doy","Doykos","Doyle","Drabeck","Dragelin","Dragon","Dragone","Dragoon","Drain","Drais","Drake","Drandell","Drape","Draper","Dray","Dre","Dream","Dreda","Dreddy","Dredi","Dreeda","Dreher","Dremann","Drescher","Dressel","Dressler","Drew","Drewett","Drews","Drexler","Dreyer","Dric","Drice","Drida","Dripps","Driscoll","Driskill","Drisko","Drislane","Drobman","Drogin","Drolet","Drona","Dronski","Drooff","Dru","Druce","Druci","Drucie","Drucill","Drucilla","Drucy","Drud","Drue","Drugge","Drugi","Drummond","Drus","Drusi","Drusie","Drusilla","Drusus","Drusy","Dry","Dryden","Drye","Dryfoos","DuBois","Duane","Duarte","Duax","Dubenko","Dublin","Ducan","Duck","Dud","Dudden","Dudley","Duer","Duester","Duff","Duffie","Duffy","Dugaid","Dugald","Dugan","Dugas","Duggan","Duhl","Duke","Dukey","Dukie","Duky","Dulce","Dulcea","Dulci","Dulcia","Dulciana","Dulcie","Dulcine","Dulcinea","Dulcle","Dulcy","Duleba","Dulla","Dulsea","Duma","Dumah","Dumanian","Dumas","Dumm","Dumond","Dun","Dunaville","Dunc","Duncan","Dunham","Dunkin","Dunlavy","Dunn","Dunning","Dunseath","Dunson","Dunstan","Dunston","Dunton","Duntson","Duong","Dupaix","Dupin","Dupre","Dupuis","Dupuy","Duquette","Dur","Durand","Durant","Durante","Durarte","Durer","Durgy","Durham","Durkee","Durkin","Durman","Durnan","Durning","Durno","Durr","Durrace","Durrell","Durrett","Durst","Durstin","Durston","Durtschi","Durward","Durware","Durwin","Durwood","Durwyn","Dusa","Dusen","Dust","Dustan","Duster","Dustie","Dustin","Dustman","Duston","Dusty","Dusza","Dutch","Dutchman","Duthie","Duval","Duvall","Duwalt","Duwe","Duyne","Dwain","Dwaine","Dwan","Dwane","Dwayne","Dweck","Dwight","Dwinnell","Dworman","Dwyer","Dyal","Dyan","Dyana","Dyane","Dyann","Dyanna","Dyanne","Dyche","Dyer","Dygal","Dygall","Dygert","Dyke","Dyl","Dylan","Dylana","Dylane","Dymoke","Dympha","Dymphia","Dyna","Dynah","Dysart","Dyson","Dyun","Dzoba","Eachelle","Eachern","Eada","Eade","Eadie","Eadith","Eadmund","Eads","Eadwina","Eadwine","Eagle","Eal","Ealasaid","Eamon","Eanore","Earl","Earla","Earle","Earleen","Earlene","Earley","Earlie","Early","Eartha","Earvin","East","Easter","Eastlake","Eastman","Easton","Eaton","Eatton","Eaves","Eb","Eba","Ebarta","Ebba","Ebbarta","Ebberta","Ebbie","Ebby","Eben","Ebeneser","Ebenezer","Eberhard","Eberhart","Eberle","Eberly","Ebert","Eberta","Eberto","Ebner","Ebneter","Eboh","Ebonee","Ebony","Ebsen","Echikson","Echo","Eckardt","Eckart","Eckblad","Eckel","Eckhardt","Eckmann","Econah","Ed","Eda","Edan","Edana","Edbert","Edd","Edda","Eddana","Eddi","Eddie","Eddina","Eddra","Eddy","Ede","Edea","Edee","Edeline","Edelman","Edelson","Edelstein","Edelsten","Eden","Edette","Edgar","Edgard","Edgardo","Edge","Edgell","Edgerton","Edholm","Edi","Edie","Edik","Edin","Edina","Edison","Edita","Edith","Editha","Edithe","Ediva","Edla","Edlin","Edlun","Edlyn","Edmanda","Edme","Edmea","Edmead","Edmee","Edmon","Edmond","Edmonda","Edmondo","Edmonds","Edmund","Edmunda","Edna","Edny","Edora","Edouard","Edra","Edrea","Edrei","Edric","Edrick","Edris","Edrock","Edroi","Edsel","Edson","Eduard","Eduardo","Eduino","Edva","Edvard","Edveh","Edward","Edwards","Edwin","Edwina","Edwine","Edwyna","Edy","Edyth","Edythe","Effie","Effy","Efram","Efrem","Efren","Efron","Efthim","Egan","Egarton","Egbert","Egerton","Eggett","Eggleston","Egide","Egidio","Egidius","Egin","Eglanteen","Eglantine","Egon","Egor","Egwan","Egwin","Ehling","Ehlke","Ehman","Ehr","Ehrenberg","Ehrlich","Ehrman","Ehrsam","Ehud","Ehudd",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aryn","Taryne","Tasha","Tasia","Tasiana","Tat","Tate","Tati","Tatia","Tatiana","Tatianas","Tatiania","Tatianna","Tatman","Tattan","Tatum","Taub","Tav","Taveda","Tavey","Tavi","Tavia","Tavie","Tavis","Tavish","Tavy","Tawney","Tawnya","Tawsha","Tay","Tayib","Tayler","Taylor","Tayyebeb","Tchao","Teador","Teagan","Teage","Teague","Teahan","Teak","Tearle","Tecla","Tecu","Ted","Tedd","Tedda","Tedder","Teddi","Teddie","Teddman","Teddy","Tedi","Tedie","Tedman","Tedmann","Tedmund","Tedra","Tedric","Teece","Teena","Teerell","Teeter","Teevens","Teferi","Tega","Tegan","Teillo","Teilo","Tekla","Telfer","Telford","Telfore","Tella","Tellford","Tem","Tema","Temp","Tempa","Tempest","Templa","Templas","Temple","Templer","Templeton","Templia","Ten","Tena","Tench","Tenenbaum","Tengdin","Tengler","Tenn","Tenner","Tennes","Tenney","Tennies","Teodoor","Teodor","Teodora","Teodorico","Teodoro","Teplica","Teplitz","Tepper","Tera","Terbecki","Terchie","Terena","Terence","Terencio","Teresa","Terese","Teresina","Teresita","Teressa","Terhune","Teri","Teria","Teriann","Terina","Terle","Ternan","Terpstra","Terr","Terra","Terrance","Terrel","Terrell","Terrena","Terrence","Terrene","Terri","Terrie","Terrijo","Terrill","Terrilyn","Terris","Terriss","Territus","Terry","Terrye","Terryl","Terryn","Tersina","Terti","Tertia","Tertias","Tertius","Teryl","Teryn","Terza","Terzas","Tesler","Tess","Tessa","Tessi","Tessie","Tessler","Tessy","Teteak","Teufert","Teuton","Tevis","Tewell","Tewfik","Tews","Thacher","Thacker","Thackeray","Thad","Thaddaus","Thaddeus","Thaddus","Thadeus","Thagard","Thain","Thaine","Thais","Thalassa","Thalia","Tham","Thamora","Thamos","Thanasi","Thane","Thanh","Thanos","Thant","Thapa","Thar","Tharp","Thatch","Thatcher","Thaxter","Thay","Thayer","Thayne","The","Thea","Theadora","Theall","Thebault","Thecla","Theda","Thedric","Thedrick","Theis","Thekla","Thelma","Thema","Themis","Thenna","Theo","Theobald","Theodor","Theodora","Theodore","Theodoric","Theodosia","Theola","Theona","Theone","Thera","Theran","Theresa","Therese","Theresina","Theresita","Theressa","Therine","Theron","Therron","Thesda","Thessa","Theta","Thetes","Thetis","Thetisa","Thetos","Theurer","Theurich","Thevenot","Thia","Thibaud","Thibault","Thibaut","Thielen","Thier","Thierry","Thilda","Thilde","Thill","Thin","Thinia","Thirion","Thirza","Thirzi","Thirzia","Thisbe","Thisbee","Thissa","Thistle","Thoer","Thom","Thoma","Thomajan","Thomas","Thomasa","Thomasin","Thomasina","Thomasine","Thomey","Thompson","Thomsen","Thomson","Thor","Thora","Thorbert","Thordia","Thordis","Thorfinn","Thorin","Thorlay","Thorley","Thorlie","Thorma","Thorman","Thormora","Thorn","Thornburg","Thorncombe","Thorndike","Thorne","Thorner","Thornie","Thornton","Thorny","Thorpe","Thorr","Thorrlow","Thorstein","Thorsten","Thorvald","Thorwald","Thrasher","Three","Threlkeld","Thrift","Thun","Thunell","Thurber","Thurlough","Thurlow","Thurman","Thurmann","Thurmond","Thurnau","Thursby","Thurstan","Thurston","Thury","Thynne","Tia","Tiana","Tibbetts","Tibbitts","Tibbs","Tibold","Tica","Tice","Tichon","Tichonn","Ticknor","Ticon","Tidwell","Tiebold","Tiebout","Tiedeman","Tiemroth","Tien","Tiena","Tierell","Tiernan","Tierney","Tiersten","Tiertza","Tierza","Tifanie","Tiff","Tiffa","Tiffani","Tiffanie","Tiffanle","Tiffany","Tiffi","Tiffie","Tiffy","Tiga","Tigges","Tila","Tilda","Tilden","Tildi","Tildie","Tildy","Tiler","Tilford","Till","Tilla","Tillford","Tillfourd","Tillie","Tillinger","Tillio","Tillion","Tillman","Tillo","Tilly","Tilney","Tiloine","Tim","Tima","Timi","Timmi","Timmie","Timmons","Timms","Timmy","Timofei","Timon","Timoteo","Timothea","Timothee","Timotheus","Timothy","Tina","Tinaret","Tindall","Tine","Tingey","Tingley","Tini","Tiny","Tinya","Tiossem","Tiphane","Tiphani","Tiphanie","Tiphany","Tippets","Tips","Tipton","Tirrell","Tirza","Tirzah","Tisbe","Tisbee","Tisdale","Tish","Tisha","Tisman","Tita","Titania","Tito","Titos","Titus","Tizes","Tjaden","Tjader","Tjon","Tletski","Toback","Tobe","Tobey","Tobi","Tobiah","Tobias","Tobie","Tobin","Tobit","Toby","Tobye","Tocci","Tod","Todd","Toddie","Toddy","Todhunter","Toffey","Toffic","Toft","Toh","Toiboid","Toinette","Tol","Toland","Tolkan","Toll","Tolland","Tolley","Tolliver","Tollman","Tollmann","Tolmach","Tolman","Tolmann","Tom","Toma","Tomas","Tomasina","Tomasine","Tomaso","Tomasz","Tombaugh","Tomchay","Tome","Tomi","Tomkiel","Tomkin","Tomkins","Tomlin","Tomlinson","Tommi","Tommie","Tommy","Tompkins","Toms","Toney","Tongue","Toni","Tonia","Tonie","Tonina","Tonjes","Tonkin","Tonl","Tonneson","Tonnie","Tonry","Tony","Tonya","Tonye","Toogood","Toole","Tooley","Toolis","Toomay","Toombs","Toomin","Toor","Tootsie","Topliffe","Topper","Topping","Tor","Torbart","Torbert","Tore","Torey","Torhert","Tori","Torie","Torin","Tormoria","Torosian","Torp","Torr","Torrance","Torras","Torray","Torre","Torrell","Torrence","Torres","Torrey","Torrie","Torrin","Torrlow","Torruella","Torry","Torto","Tortosa","Tory","Toscano","Tosch","Toshiko","Toth","Touber","Toulon","Tound"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domains = ["aol.com", "att.net", "comcast.net", "facebook.com", "gmail.com", "gmx.com", "googlemail.com","google.com", "hotmail.com", "hotmail.co.uk", "mac.com", "me.com", "mail.com", "msn.com","live.com", "sbcglobal.net", "verizon.net", "yahoo.com", "yahoo.co.uk","email.com", "games.com", "gmx.net", "hush.com", "hushmail.com", "icloud.com", "inbox.com","lavabit.com", "love.com" , "outlook.com", "pobox.com", "rocketmail.com","safe-mail.net", "wow.com", "ygm.com", "ymail.com", "zoho.com", "fastmail.fm","yandex.com","bellsouth.net", "charter.net", "comcast.net", "cox.net", "earthlink.net", "juno.com","btinternet.com", "virginmedia.com", "blueyonder.co.uk", "freeserve.co.uk", "live.co.uk","ntlworld.com", "o2.co.uk", "orange.net", "sky.com", "talktalk.co.uk", "tiscali.co.uk","virgin.net", "wanadoo.co.uk", "bt.com","sina.com", "qq.com", "naver.com", "hanmail.net", "daum.net", "nate.com", "yahoo.co.jp", "yahoo.co.kr", "yahoo.co.id", "yahoo.co.in", "yahoo.com.sg", "yahoo.com.ph","hotmail.fr", "live.fr", "laposte.net", "yahoo.fr", "wanadoo.fr", "orange.fr", "gmx.fr", "sfr.fr", "neuf.fr", "free.fr","gmx.de", "hotmail.de", "live.de", "online.de", "t-online.de", "web.de", "yahoo.de","mail.ru", "rambler.ru", "yandex.ru", "ya.ru", "list.ru","hotmail.be", "live.be", "skynet.be", "voo.be", "tvcablenet.be", "telenet.be","hotmail.com.ar", "live.com.ar", "yahoo.com.ar", "fibertel.com.ar", "speedy.com.ar", "arnet.com.ar","hotmail.com", "gmail.com", "yahoo.com.mx", "live.com.mx", "yahoo.com", "hotmail.es", "live.com", "hotmail.com.mx", "prodigy.net.mx", "msn.com"] def generate(max_num,gen_type): if max_num == 0: sys.exit(1) for x in itertools.count(start=0,step=1): password = '' if random.randrange(6,26) > 12: password = random.choice(common_passwords) else: password = ''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(2,8))) if(x==max_num): break sys.exit(1) else: name = '' if random.randrange(1,10) == 3: name = str(random.choice(english_first_names))+"."+str(random.choice(surnames))+"@"+str(random.choice(domains)) elif random.randrange(1,10) == 1: name = str.lower(random.choice(english_first_names))+"_"+''.join(random.choice(string.digits) for _ in range(random.randrange(1,5)))+"@"+random.choice(domains) elif random.randrange(1,10) == 2: name = str.lower(random.choice(surnames))+"-"+''.join(random.choice(string.digits) for _ in range(random.randrange(1,5)))+"@"+random.choice(domains) elif random.randrange(1,10) == 8: name = ''.join(random.choice(string.digits) for _ in range(random.randrange(1,5)))+str.upper(random.choice(surnames))+"@"+random.choice(domains) elif random.randrange(1,10) == 9: name = ''.join(random.choice(string.digits) for _ in range(random.randrange(1,2)))+"-"+str.upper(random.choice(surnames))+"@"+random.choice(domains) elif random.randrange(1,10) == 5: name = str.lower(random.choice(english_first_names))+"-"+''.join(random.choice(string.digits) for _ in range(random.randrange(1,5)))+"@"+random.choice(domains) elif random.randrange(1,10) == 4: name = str.upper(random.choice(english_first_names))+""+str.upper(random.choice(surnames))+"@"+random.choice(domains) elif random.randrange(1,10) == 6: name= ''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,25)))+"@"+random.choice(domains) elif random.randrange(1,10) == 7: name= ''.join(random.choice(string.ascii_letters) for _ in range(random.randrange(1,5)))+"."+str(random.choice(surnames))+"@"+random.choice(domains) elif random.randrange(1,10) == 10: name= ''.join(random.choice(string.ascii_letters + string.digits + ['.','+','-','_']) for _ in range(random.randrange(6,15)))+"."+str(random.choice(surnames))+"@"+random.choice(domains) else: name = str(random.choice(english_first_names))+"_"+str(random.choice(surnames))+"@"+str(random.choice(domains)) if (gen_type == "clear"): if (re.match(emailregex, name)): print name,":",password elif (gen_type == "sha256"): if (re.match(emailregex, name)): hash_object = hashlib.sha256(password).hexdigest() print name,":",password,":",hash_object elif (gen_type == "sha512"): if (re.match(emailregex, name)): hash_object = hashlib.sha512(password).hexdigest() print name,":",password,":",hash_object elif (gen_type == "md5"): if (re.match(emailregex, name)): hash_object = hashlib.md5(password).hexdigest() print name,":",password,":",hash_object elif (gen_type == "sha1"): if (re.match(emailregex, name)): hash_object = hashlib.sha1(password).hexdigest() print name,":",password,":",hash_object elif (gen_type == "ntlm"): if (re.match(emailregex, name)): hash_object = binascii.hexlify(hashlib.new('md4', password.encode('utf-16le')).digest()) print name,":",password,":",hash_object elif (gen_type == "sha1_r_salt"): if (re.match(emailregex, name)): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.sha1(pass_obj).hexdigest() print name,":",pass_obj,":",hash_object elif (gen_type == "sha256_r_salt"): if (re.match(emailregex, name)): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.sha256(pass_obj).hexdigest() print name,":",pass_obj,":",hash_object elif (gen_type == "sha512_r_salt"): if (re.match(emailregex, name)): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.sha512(pass_obj).hexdigest() print name,":",pass_obj,":",hash_object elif (gen_type == "md5_r_salt"): if (re.match(emailregex, name)): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.md5(pass_obj).hexdigest() print name,":",pass_obj,":",hash_object elif (gen_type == "ntlm_r_salt"): if (re.match(emailregex, name)): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = binascii.hexlify(hashlib.new('md4', pass_obj.encode('utf-16le')).digest()) print name,":",pass_obj,":",hash_object elif(gen_type == "md5_hashonly"): hash_object = hashlib.md5(password).hexdigest() print hash_object elif(gen_type == "sha1_hashonly"): hash_object = hashlib.sha1(password).hexdigest() print hash_object elif(gen_type == "sha256_hashonly"): hash_object = hashlib.sha256(password).hexdigest() print hash_object elif(gen_type == "sha512_hashonly"): hash_object = hashlib.sha512(password).hexdigest() print hash_object elif(gen_type == "ntlm_hashonly"): hash_object = binascii.hexlify(hashlib.new('md4', password.encode('utf-16le')).digest()) print hash_object elif(gen_type == "md5_r_salt_hashonly"): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.md5(pass_obj).hexdigest() print hash_object elif(gen_type == "sha1_r_salt_hashonly"): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.sha1(pass_obj).hexdigest() print hash_object elif(gen_type == "sha256_r_salt_hashonly"): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.sha256(pass_obj).hexdigest() print hash_object elif(gen_type == "sha512_r_salt_hashonly"): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.sha512(pass_obj).hexdigest() print hash_object elif(gen_type == "ntlm_r_salt_hashonly"): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits)) hash_object = binascii.hexlify(hashlib.new('md4', pass_obj.encode('utf-16le')).digest()) print hash_object elif(gen_type == "ad_compromise"): #https://technet.microsoft.com/en-us/library/active-directory-maximum-limits-scalability(v=ws.10).aspx # based on above making 6m limit on dump if (max_num is 6000000): break sys.exit(1) else: hash_object = "%s:aad3b435b51404eeaad3b435b51404ee:%s:::" % (str(x),binascii.hexlify(hashlib.new('md4', password.encode('utf-16le')).digest())) print hash_object else: print "[!] Unknown hash type, exit" sys.exit(1) def help(): types=""" -= LeakGenerator v1.1A by op7ic =- Discover your own leak with spoofed emails, random passwords and equally random hashes Supported hash types for '-t' argument: [+] Main hash alghoritms. Will print "email : password : hash" combo. md5 sha1 sha256 sha512 ntlm [+] Hash alghoritms with random salt added. Will print "email : password : hash" combo. md5_r_salt sha1_r_salt sha256_r_salt sha512_r_salt ntlm_r_salt [+] Other type. Will print "email or username : password" combo. clear ad_compromise [+] Hash only types that print only 'hash' values md5_hashonly sha1_hashonly sha256_hashonly sha512_hashonly ntlm_only md5_r_salt_hashonly sha1_r_salt_hashonly sha256_r_salt_hashonly sha512_r_salt_hashonly ntlm_r_salt_hashonly You also need to specify max passwords you want to generate in hex format e.g. 0x00FFFFFF The final command would look like this: python leakme.py -t md5_r_salt -m 0x00FFFFFF """ return types parser = optparse.OptionParser(usage=help()) parser.add_option('-m', '--max', help = "Number of hashes to generate in hex format e.g. --max=0x00FFFFFF",action="store", dest="max_dump") parser.add_option('-t', '--type', help="Hash type to print, use -h or --help to see all applicable hash types",action="store", dest="hash_type") (opts, args) = parser.parse_args() generate(opts.max_dump,opts.hash_type)	mit
jm-begon/scikit-learn	sklearn/utils/fixes.py	133	12882	"""Compatibility fixes for older version of python, numpy and scipy If you add content to this file, please give the version of the package at which the fixe is no longer needed. """ # Authors: Emmanuelle Gouillart <[email protected]> # Gael Varoquaux <[email protected]> # Fabian Pedregosa <[email protected]> # Lars Buitinck # # License: BSD 3 clause import inspect import warnings import sys import functools import os import errno import numpy as np import scipy.sparse as sp import scipy def _parse_version(version_string): version = [] for x in version_string.split('.'): try: version.append(int(x)) except ValueError: # x may be of the form dev-1ea1592 version.append(x) return tuple(version) np_version = _parse_version(np.__version__) sp_version = _parse_version(scipy.__version__) try: from scipy.special import expit # SciPy >= 0.10 with np.errstate(invalid='ignore', over='ignore'): if np.isnan(expit(1000)): # SciPy < 0.14 raise ImportError("no stable expit in scipy.special") except ImportError: def expit(x, out=None): """Logistic sigmoid function, ``1 / (1 + exp(-x))``. See sklearn.utils.extmath.log_logistic for the log of this function. """ if out is None: out = np.empty(np.atleast_1d(x).shape, dtype=np.float64) out[:] = x # 1 / (1 + exp(-x)) = (1 + tanh(x / 2)) / 2 # This way of computing the logistic is both fast and stable. out = .5 np.tanh(out, out) out += 1 out = .5 return out.reshape(np.shape(x)) # little danse to see if np.copy has an 'order' keyword argument if 'order' in inspect.getargspec(np.copy)[0]: def safe_copy(X): # Copy, but keep the order return np.copy(X, order='K') else: # Before an 'order' argument was introduced, numpy wouldn't muck with # the ordering safe_copy = np.copy try: if (not np.allclose(np.divide(.4, 1, casting="unsafe"), np.divide(.4, 1, casting="unsafe", dtype=np.float)) or not np.allclose(np.divide(.4, 1), .4)): raise TypeError('Divide not working with dtype: ' 'https://github.com/numpy/numpy/issues/3484') divide = np.divide except TypeError: # Compat for old versions of np.divide that do not provide support for # the dtype args def divide(x1, x2, out=None, dtype=None): out_orig = out if out is None: out = np.asarray(x1, dtype=dtype) if out is x1: out = x1.copy() else: if out is not x1: out[:] = x1 if dtype is not None and out.dtype != dtype: out = out.astype(dtype) out /= x2 if out_orig is None and np.isscalar(x1): out = np.asscalar(out) return out try: np.array(5).astype(float, copy=False) except TypeError: # Compat where astype accepted no copy argument def astype(array, dtype, copy=True): if not copy and array.dtype == dtype: return array return array.astype(dtype) else: astype = np.ndarray.astype try: with warnings.catch_warnings(record=True): # Don't raise the numpy deprecation warnings that appear in # 1.9, but avoid Python bug due to simplefilter('ignore') warnings.simplefilter('always') sp.csr_matrix([1.0, 2.0, 3.0]).max(axis=0) except (TypeError, AttributeError): # in scipy < 14.0, sparse matrix min/max doesn't accept an `axis` argument # the following code is taken from the scipy 0.14 codebase def _minor_reduce(X, ufunc): major_index = np.flatnonzero(np.diff(X.indptr)) if X.data.size == 0 and major_index.size == 0: # Numpy < 1.8.0 don't handle empty arrays in reduceat value = np.zeros_like(X.data) else: value = ufunc.reduceat(X.data, X.indptr[major_index]) return major_index, value def _min_or_max_axis(X, axis, min_or_max): N = X.shape[axis] if N == 0: raise ValueError("zero-size array to reduction operation") M = X.shape[1 - axis] mat = X.tocsc() if axis == 0 else X.tocsr() mat.sum_duplicates() major_index, value = _minor_reduce(mat, min_or_max) not_full = np.diff(mat.indptr)[major_index] < N value[not_full] = min_or_max(value[not_full], 0) mask = value != 0 major_index = np.compress(mask, major_index) value = np.compress(mask, value) from scipy.sparse import coo_matrix if axis == 0: res = coo_matrix((value, (np.zeros(len(value)), major_index)), dtype=X.dtype, shape=(1, M)) else: res = coo_matrix((value, (major_index, np.zeros(len(value)))), dtype=X.dtype, shape=(M, 1)) return res.A.ravel() def _sparse_min_or_max(X, axis, min_or_max): if axis is None: if 0 in X.shape: raise ValueError("zero-size array to reduction operation") zero = X.dtype.type(0) if X.nnz == 0: return zero m = min_or_max.reduce(X.data.ravel()) if X.nnz != np.product(X.shape): m = min_or_max(zero, m) return m if axis < 0: axis += 2 if (axis == 0) or (axis == 1): return _min_or_max_axis(X, axis, min_or_max) else: raise ValueError("invalid axis, use 0 for rows, or 1 for columns") def sparse_min_max(X, axis): return (_sparse_min_or_max(X, axis, np.minimum), _sparse_min_or_max(X, axis, np.maximum)) else: def sparse_min_max(X, axis): return (X.min(axis=axis).toarray().ravel(), X.max(axis=axis).toarray().ravel()) try: from numpy import argpartition except ImportError: # numpy.argpartition was introduced in v 1.8.0 def argpartition(a, kth, axis=-1, kind='introselect', order=None): return np.argsort(a, axis=axis, order=order) try: from itertools import combinations_with_replacement except ImportError: # Backport of itertools.combinations_with_replacement for Python 2.6, # from Python 3.4 documentation (http://tinyurl.com/comb-w-r), copyright # Python Software Foundation (https://docs.python.org/3/license.html) def combinations_with_replacement(iterable, r): # combinations_with_replacement('ABC', 2) --> AA AB AC BB BC CC pool = tuple(iterable) n = len(pool) if not n and r: return indices = [0] * r yield tuple(pool[i] for i in indices) while True: for i in reversed(range(r)): if indices[i] != n - 1: break else: return indices[i:] = [indices[i] + 1] * (r - i) yield tuple(pool[i] for i in indices) try: from numpy import isclose except ImportError: def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False): """ Returns a boolean array where two arrays are element-wise equal within a tolerance. This function was added to numpy v1.7.0, and the version you are running has been backported from numpy v1.8.1. See its documentation for more details. """ def within_tol(x, y, atol, rtol): with np.errstate(invalid='ignore'): result = np.less_equal(abs(x - y), atol + rtol * abs(y)) if np.isscalar(a) and np.isscalar(b): result = bool(result) return result x = np.array(a, copy=False, subok=True, ndmin=1) y = np.array(b, copy=False, subok=True, ndmin=1) xfin = np.isfinite(x) yfin = np.isfinite(y) if all(xfin) and all(yfin): return within_tol(x, y, atol, rtol) else: finite = xfin & yfin cond = np.zeros_like(finite, subok=True) # Since we're using boolean indexing, x & y must be the same shape. # Ideally, we'd just do x, y = broadcast_arrays(x, y). It's in # lib.stride_tricks, though, so we can't import it here. x = x * np.ones_like(cond) y = y * np.ones_like(cond) # Avoid subtraction with infinite/nan values... cond[finite] = within_tol(x[finite], y[finite], atol, rtol) # Check for equality of infinite values... cond[~finite] = (x[~finite] == y[~finite]) if equal_nan: # Make NaN == NaN cond[np.isnan(x) & np.isnan(y)] = True return cond if np_version < (1, 7): # Prior to 1.7.0, np.frombuffer wouldn't work for empty first arg. def frombuffer_empty(buf, dtype): if len(buf) == 0: return np.empty(0, dtype=dtype) else: return np.frombuffer(buf, dtype=dtype) else: frombuffer_empty = np.frombuffer if np_version < (1, 8): def in1d(ar1, ar2, assume_unique=False, invert=False): # Backport of numpy function in1d 1.8.1 to support numpy 1.6.2 # Ravel both arrays, behavior for the first array could be different ar1 = np.asarray(ar1).ravel() ar2 = np.asarray(ar2).ravel() # This code is significantly faster when the condition is satisfied. if len(ar2) < 10 * len(ar1) ** 0.145: if invert: mask = np.ones(len(ar1), dtype=np.bool) for a in ar2: mask &= (ar1 != a) else: mask = np.zeros(len(ar1), dtype=np.bool) for a in ar2: mask \|= (ar1 == a) return mask # Otherwise use sorting if not assume_unique: ar1, rev_idx = np.unique(ar1, return_inverse=True) ar2 = np.unique(ar2) ar = np.concatenate((ar1, ar2)) # We need this to be a stable sort, so always use 'mergesort' # here. The values from the first array should always come before # the values from the second array. order = ar.argsort(kind='mergesort') sar = ar[order] if invert: bool_ar = (sar[1:] != sar[:-1]) else: bool_ar = (sar[1:] == sar[:-1]) flag = np.concatenate((bool_ar, [invert])) indx = order.argsort(kind='mergesort')[:len(ar1)] if assume_unique: return flag[indx] else: return flag[indx][rev_idx] else: from numpy import in1d if sp_version < (0, 15): # Backport fix for scikit-learn/scikit-learn#2986 / scipy/scipy#4142 from ._scipy_sparse_lsqr_backport import lsqr as sparse_lsqr else: from scipy.sparse.linalg import lsqr as sparse_lsqr if sys.version_info < (2, 7, 0): # partial cannot be pickled in Python 2.6 # http://bugs.python.org/issue1398 class partial(object): def __init__(self, func, args, keywords): functools.update_wrapper(self, func) self.func = func self.args = args self.keywords = keywords def __call__(self, args, *keywords): args = self.args + args kwargs = self.keywords.copy() kwargs.update(keywords) return self.func(args, **kwargs) else: from functools import partial if np_version < (1, 6, 2): # Allow bincount to accept empty arrays # https://github.com/numpy/numpy/commit/40f0844846a9d7665616b142407a3d74cb65a040 def bincount(x, weights=None, minlength=None): if len(x) > 0: return np.bincount(x, weights, minlength) else: if minlength is None: minlength = 0 minlength = np.asscalar(np.asarray(minlength, dtype=np.intp)) return np.zeros(minlength, dtype=np.intp) else: from numpy import bincount if 'exist_ok' in inspect.getargspec(os.makedirs).args: makedirs = os.makedirs else: def makedirs(name, mode=0o777, exist_ok=False): """makedirs(name [, mode=0o777][, exist_ok=False]) Super-mkdir; create a leaf directory and all intermediate ones. Works like mkdir, except that any intermediate path segment (not just the rightmost) will be created if it does not exist. If the target directory already exists, raise an OSError if exist_ok is False. Otherwise no exception is raised. This is recursive. """ try: os.makedirs(name, mode=mode) except OSError as e: if (not exist_ok or e.errno != errno.EEXIST or not os.path.isdir(name)): raise	bsd-3-clause
adamgreenhall/scikit-learn	sklearn/utils/multiclass.py	83	12343	# Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi # # License: BSD 3 clause """ Multi-class / multi-label utility function ========================================== """ from __future__ import division from collections import Sequence from itertools import chain from scipy.sparse import issparse from scipy.sparse.base import spmatrix from scipy.sparse import dok_matrix from scipy.sparse import lil_matrix import numpy as np from ..externals.six import string_types from .validation import check_array from ..utils.fixes import bincount def _unique_multiclass(y): if hasattr(y, '__array__'): return np.unique(np.asarray(y)) else: return set(y) def _unique_indicator(y): return np.arange(check_array(y, ['csr', 'csc', 'coo']).shape[1]) _FN_UNIQUE_LABELS = { 'binary': _unique_multiclass, 'multiclass': _unique_multiclass, 'multilabel-indicator': _unique_indicator, } def unique_labels(ys): """Extract an ordered array of unique labels We don't allow: - mix of multilabel and multiclass (single label) targets - mix of label indicator matrix and anything else, because there are no explicit labels) - mix of label indicator matrices of different sizes - mix of string and integer labels At the moment, we also don't allow "multiclass-multioutput" input type. Parameters ---------- ys : array-likes, Returns ------- out : numpy array of shape [n_unique_labels] An ordered array of unique labels. Examples -------- >>> from sklearn.utils.multiclass import unique_labels >>> unique_labels([3, 5, 5, 5, 7, 7]) array([3, 5, 7]) >>> unique_labels([1, 2, 3, 4], [2, 2, 3, 4]) array([1, 2, 3, 4]) >>> unique_labels([1, 2, 10], [5, 11]) array([ 1, 2, 5, 10, 11]) """ if not ys: raise ValueError('No argument has been passed.') # Check that we don't mix label format ys_types = set(type_of_target(x) for x in ys) if ys_types == set(["binary", "multiclass"]): ys_types = set(["multiclass"]) if len(ys_types) > 1: raise ValueError("Mix type of y not allowed, got types %s" % ys_types) label_type = ys_types.pop() # Check consistency for the indicator format if (label_type == "multilabel-indicator" and len(set(check_array(y, ['csr', 'csc', 'coo']).shape[1] for y in ys)) > 1): raise ValueError("Multi-label binary indicator input with " "different numbers of labels") # Get the unique set of labels _unique_labels = _FN_UNIQUE_LABELS.get(label_type, None) if not _unique_labels: raise ValueError("Unknown label type: %s" % repr(ys)) ys_labels = set(chain.from_iterable(_unique_labels(y) for y in ys)) # Check that we don't mix string type with number type if (len(set(isinstance(label, string_types) for label in ys_labels)) > 1): raise ValueError("Mix of label input types (string and number)") return np.array(sorted(ys_labels)) def _is_integral_float(y): return y.dtype.kind == 'f' and np.all(y.astype(int) == y) def is_multilabel(y): """ Check if ``y`` is in a multilabel format. Parameters ---------- y : numpy array of shape [n_samples] Target values. Returns ------- out : bool, Return ``True``, if ``y`` is in a multilabel format, else ```False``. Examples -------- >>> import numpy as np >>> from sklearn.utils.multiclass import is_multilabel >>> is_multilabel([0, 1, 0, 1]) False >>> is_multilabel([[1], [0, 2], []]) False >>> is_multilabel(np.array([[1, 0], [0, 0]])) True >>> is_multilabel(np.array([[1], [0], [0]])) False >>> is_multilabel(np.array([[1, 0, 0]])) True """ if hasattr(y, '__array__'): y = np.asarray(y) if not (hasattr(y, "shape") and y.ndim == 2 and y.shape[1] > 1): return False if issparse(y): if isinstance(y, (dok_matrix, lil_matrix)): y = y.tocsr() return (len(y.data) == 0 or np.ptp(y.data) == 0 and (y.dtype.kind in 'biu' or # bool, int, uint _is_integral_float(np.unique(y.data)))) else: labels = np.unique(y) return len(labels) < 3 and (y.dtype.kind in 'biu' or # bool, int, uint _is_integral_float(labels)) def type_of_target(y): """Determine the type of data indicated by target `y` Parameters ---------- y : array-like Returns ------- target_type : string One of: * 'continuous': `y` is an array-like of floats that are not all integers, and is 1d or a column vector. * 'continuous-multioutput': `y` is a 2d array of floats that are not all integers, and both dimensions are of size > 1. * 'binary': `y` contains <= 2 discrete values and is 1d or a column vector. * 'multiclass': `y` contains more than two discrete values, is not a sequence of sequences, and is 1d or a column vector. * 'multiclass-multioutput': `y` is a 2d array that contains more than two discrete values, is not a sequence of sequences, and both dimensions are of size > 1. * 'multilabel-indicator': `y` is a label indicator matrix, an array of two dimensions with at least two columns, and at most 2 unique values. * 'unknown': `y` is array-like but none of the above, such as a 3d array, sequence of sequences, or an array of non-sequence objects. Examples -------- >>> import numpy as np >>> type_of_target([0.1, 0.6]) 'continuous' >>> type_of_target([1, -1, -1, 1]) 'binary' >>> type_of_target(['a', 'b', 'a']) 'binary' >>> type_of_target([1.0, 2.0]) 'binary' >>> type_of_target([1, 0, 2]) 'multiclass' >>> type_of_target([1.0, 0.0, 3.0]) 'multiclass' >>> type_of_target(['a', 'b', 'c']) 'multiclass' >>> type_of_target(np.array([[1, 2], [3, 1]])) 'multiclass-multioutput' >>> type_of_target([[1, 2]]) 'multiclass-multioutput' >>> type_of_target(np.array([[1.5, 2.0], [3.0, 1.6]])) 'continuous-multioutput' >>> type_of_target(np.array([[0, 1], [1, 1]])) 'multilabel-indicator' """ valid = ((isinstance(y, (Sequence, spmatrix)) or hasattr(y, '__array__')) and not isinstance(y, string_types)) if not valid: raise ValueError('Expected array-like (array or non-string sequence), ' 'got %r' % y) if is_multilabel(y): return 'multilabel-indicator' try: y = np.asarray(y) except ValueError: # Known to fail in numpy 1.3 for array of arrays return 'unknown' # The old sequence of sequences format try: if (not hasattr(y[0], '__array__') and isinstance(y[0], Sequence) and not isinstance(y[0], string_types)): raise ValueError('You appear to be using a legacy multi-label data' ' representation. Sequence of sequences are no' ' longer supported; use a binary array or sparse' ' matrix instead.') except IndexError: pass # Invalid inputs if y.ndim > 2 or (y.dtype == object and len(y) and not isinstance(y.flat[0], string_types)): return 'unknown' # [[[1, 2]]] or [obj_1] and not ["label_1"] if y.ndim == 2 and y.shape[1] == 0: return 'unknown' # [[]] if y.ndim == 2 and y.shape[1] > 1: suffix = "-multioutput" # [[1, 2], [1, 2]] else: suffix = "" # [1, 2, 3] or [[1], [2], [3]] # check float and contains non-integer float values if y.dtype.kind == 'f' and np.any(y != y.astype(int)): # [.1, .2, 3] or [[.1, .2, 3]] or [[1., .2]] and not [1., 2., 3.] return 'continuous' + suffix if (len(np.unique(y)) > 2) or (y.ndim >= 2 and len(y[0]) > 1): return 'multiclass' + suffix # [1, 2, 3] or [[1., 2., 3]] or [[1, 2]] else: return 'binary' # [1, 2] or [["a"], ["b"]] def _check_partial_fit_first_call(clf, classes=None): """Private helper function for factorizing common classes param logic Estimators that implement the ``partial_fit`` API need to be provided with the list of possible classes at the first call to partial_fit. Subsequent calls to partial_fit should check that ``classes`` is still consistent with a previous value of ``clf.classes_`` when provided. This function returns True if it detects that this was the first call to ``partial_fit`` on ``clf``. In that case the ``classes_`` attribute is also set on ``clf``. """ if getattr(clf, 'classes_', None) is None and classes is None: raise ValueError("classes must be passed on the first call " "to partial_fit.") elif classes is not None: if getattr(clf, 'classes_', None) is not None: if not np.all(clf.classes_ == unique_labels(classes)): raise ValueError( "`classes=%r` is not the same as on last call " "to partial_fit, was: %r" % (classes, clf.classes_)) else: # This is the first call to partial_fit clf.classes_ = unique_labels(classes) return True # classes is None and clf.classes_ has already previously been set: # nothing to do return False def class_distribution(y, sample_weight=None): """Compute class priors from multioutput-multiclass target data Parameters ---------- y : array like or sparse matrix of size (n_samples, n_outputs) The labels for each example. sample_weight : array-like of shape = (n_samples,), optional Sample weights. Returns ------- classes : list of size n_outputs of arrays of size (n_classes,) List of classes for each column. n_classes : list of integrs of size n_outputs Number of classes in each column class_prior : list of size n_outputs of arrays of size (n_classes,) Class distribution of each column. """ classes = [] n_classes = [] class_prior = [] n_samples, n_outputs = y.shape if issparse(y): y = y.tocsc() y_nnz = np.diff(y.indptr) for k in range(n_outputs): col_nonzero = y.indices[y.indptr[k]:y.indptr[k + 1]] # separate sample weights for zero and non-zero elements if sample_weight is not None: nz_samp_weight = np.asarray(sample_weight)[col_nonzero] zeros_samp_weight_sum = (np.sum(sample_weight) - np.sum(nz_samp_weight)) else: nz_samp_weight = None zeros_samp_weight_sum = y.shape[0] - y_nnz[k] classes_k, y_k = np.unique(y.data[y.indptr[k]:y.indptr[k + 1]], return_inverse=True) class_prior_k = bincount(y_k, weights=nz_samp_weight) # An explicit zero was found, combine its wieght with the wieght # of the implicit zeros if 0 in classes_k: class_prior_k[classes_k == 0] += zeros_samp_weight_sum # If an there is an implict zero and it is not in classes and # class_prior, make an entry for it if 0 not in classes_k and y_nnz[k] < y.shape[0]: classes_k = np.insert(classes_k, 0, 0) class_prior_k = np.insert(class_prior_k, 0, zeros_samp_weight_sum) classes.append(classes_k) n_classes.append(classes_k.shape[0]) class_prior.append(class_prior_k / class_prior_k.sum()) else: for k in range(n_outputs): classes_k, y_k = np.unique(y[:, k], return_inverse=True) classes.append(classes_k) n_classes.append(classes_k.shape[0]) class_prior_k = bincount(y_k, weights=sample_weight) class_prior.append(class_prior_k / class_prior_k.sum()) return (classes, n_classes, class_prior)	bsd-3-clause
lehnertu/TEUFEL	scripts/plot_Screen_TD.py	1	4311	#!/usr/bin/env python # coding=UTF-8 import sys, time import os.path import argparse import numpy as np import h5py import matplotlib.pyplot as plt from matplotlib.ticker import NullFormatter from matplotlib.patches import Circle # magnetic field constant in N/A² mu0 = 4np.pi1e-7 parser = argparse.ArgumentParser() parser.add_argument('file', help='the file name of the screen output HDF5 file') parser.add_argument('-xy', help="indeces of plot point", dest="xy", type=int, nargs=2) print args = parser.parse_args() radfile = args.file radOK = os.path.isfile(radfile) if not radOK: print "file not found" sys.exit() # Open the file for reading print "reading ",radfile hdf = h5py.File(radfile, "r") print hdf print # Get the groups pos = hdf['ObservationPosition'] Nx = pos.attrs.get('Nx') Ny = pos.attrs.get('Ny') print "Nx=%d Ny=%d" % (Nx,Ny) print pos field = hdf['ElMagField'] print field t0 = field.attrs.get('t0') dt = field.attrs.get('dt') nots = field.attrs.get('NOTS') print "t0=%g dt=%g NOTS=%d" % (t0, dt, nots) pos = np.array(pos) a = np.array(field) hdf.close() print xcenter = (Nx-1)/2 ycenter = (Ny-1)/2 print "center = (",xcenter,",",ycenter,")" centerposition = pos[xcenter][ycenter] print "center position = ",centerposition onaxis = a[xcenter][ycenter] data = onaxis.transpose() Ex = data[0] Ey = data[1] Ez = data[2] Bx = data[3] By = data[4] Bz = data[5] EVec = np.array([Ex, Ey, Ez]).transpose() BVec = np.array([Bx, By, Bz]).transpose() # Poynting vector in V/m * (N/(A m)) / (N/A²) = W/m² SVec = np.cross(EVec, BVec) / mu0 # t = 1e9np.arange(t0,t0+(nots-1)dt,dt) t = 1e9np.linspace(t0,t0+(nots-1)dt,nots) print 'on axis energy flow density = ', 1e6SVec.sum(axis=0)dt, " µJ/m²" # first figure with the time-trace of the fields on axis left, width = 0.15, 0.80 rect1 = [left, 0.55, width, 0.40] #left, bottom, width, height rect2 = [left, 0.08, width, 0.40] fig = plt.figure(1,figsize=(12,9)) ax1 = fig.add_axes(rect1) ax4 = fig.add_axes(rect2, sharex=ax1) l1 = ax1.plot(t, Ex, "r-", label=r'$E_x$') l2 = ax1.plot(t, Ey, "b-", label=r'$E_y$') l3 = ax1.plot(t, Ez, "g-", label=r'$E_z$') ax1.set_ylabel(r'$E$ [V/m]') lines = l1 + l2 + l3 labels = [l.get_label() for l in lines] ax1.legend(lines,labels,loc='upper right') for label in ax1.get_xticklabels(): label.set_visible(False) ax1.grid(True) l4 = ax4.plot(t, Bx, "r-", label=r'$B_x$') l5 = ax4.plot(t, By, "b-", label=r'$B_y$') l6 = ax4.plot(t, Bz, "g-", label=r'$B_z$') ax4.set_ylabel(r'$B$ [T]') ax4.set_xlabel(r't [ns]') lines = l4 + l5 +l6 labels = [l.get_label() for l in lines] ax4.legend(lines,labels,loc='upper right') ax4.grid(True) if args.xy != None: xi = args.xy[0] yi = args.xy[1] print "index = (",xi,",",yi,")" position = pos[xi][yi] print "off-axis position = ",position offaxis = a[xi][yi] data = offaxis.transpose() Ex = data[0] Ey = data[1] Ez = data[2] Bx = data[3] By = data[4] Bz = data[5] EVec = np.array([Ex, Ey, Ez]).transpose() BVec = np.array([Bx, By, Bz]).transpose() # Poynting vector in V/m * (N/(A m)) / (N/A²) = W/m² SVec = np.cross(EVec, BVec) / mu0 # t = 1e9np.arange(t0,t0+(nots-1)dt,dt) t = 1e9np.linspace(t0,t0+(nots-1)dt,nots) print 'off axis energy flow density = ', 1e6SVec.sum(axis=0)dt, " µJ/m²" # second figure with the time-trace of the fields off axis fig2 = plt.figure(2,figsize=(12,9)) ax21 = fig2.add_axes(rect1) ax24 = fig2.add_axes(rect2, sharex=ax1) l21 = ax21.plot(t, Ex, "r-", label=r'$E_x$') l22 = ax21.plot(t, Ey, "b-", label=r'$E_y$') l23 = ax21.plot(t, Ez, "g-", label=r'$E_z$') ax21.set_ylabel(r'$E$ [V/m]') lines = l21 + l22 + l23 labels = [l.get_label() for l in lines] ax21.legend(lines,labels,loc='upper right') for label in ax21.get_xticklabels(): label.set_visible(False) ax21.grid(True) l24 = ax24.plot(t, Bx, "r-", label=r'$B_x$') l25 = ax24.plot(t, By, "b-", label=r'$B_y$') l26 = ax24.plot(t, Bz, "g-", label=r'$B_z$') ax24.set_ylabel(r'$B$ [T]') ax24.set_xlabel(r't [ns]') lines = l24 + l25 +l26 labels = [l.get_label() for l in lines] ax24.legend(lines,labels,loc='upper right') ax24.grid(True) plt.show()	gpl-3.0
henryre/shalo	shalo/model_search.py	1	5519	import numpy as np import pandas as pd import cPickle import datetime from itertools import product class Hyperparameter(object): """Base class for a grid search parameter""" def __init__(self, name): self.name = name def get_all_values(self): raise NotImplementedError() def draw_values(self, n): # Multidim parameters can't use choice directly v = self.get_all_values() return [v[int(i)] for i in np.random.choice(len(v), n)] class ListParameter(Hyperparameter): """List of parameter values for searching""" def __init__(self, name, parameter_list): self.parameter_list = np.array(parameter_list) super(ListParameter, self).__init__(name) def get_all_values(self): return self.parameter_list class RangeParameter(Hyperparameter): """ Range of parameter values for searching. min_value and max_value are the ends of the search range If log_base is specified, scale the search range in the log base step is range step size or exponent step size """ def __init__(self, name, min_value, max_value, step=1, log_base=None): self.min_value = min_value self.max_value = max_value self.step = step self.log_base = log_base super(RangeParameter, self).__init__(name) def get_all_values(self): if self.log_base: min_exp = math.log(self.min_value, self.log_base) max_exp = math.log(self.max_value, self.log_base) exps = np.arange(min_exp, max_exp + self.step, step=self.step) return np.power(self.log_base, exps) return np.arange( self.min_value, self.max_value + self.step, step=self.step ) class GridSearch(object): """ Runs hyperparameter grid search over a model object with train and score methods, training data (X), and training_marginals Selects based on maximizing F1 score on a supplied validation set Specify search space with Hyperparameter arguments """ def __init__(self, model, train_data, train_labels, parameters): self.model = model self.train_data = train_data self.train_labels = train_labels self.params = parameters self.param_names = [param.name for param in parameters] def search_space(self): return product(param.get_all_values() for param in self.params) def fit(self, dev_data, dev_labels, b=0.5, *model_hyperparams): """ Basic method to start grid search, returns DataFrame table of results b specifies the positive class threshold for calculating accuracy Non-search parameters are set using model_hyperparamters """ run_stats, score_opt, model_k = [], -1.0, 0 opt_params = None base_model_name = self.model.name # Iterate over the param values for k, param_vals in enumerate(self.search_space()): model_name = '{0}_{1}'.format(base_model_name, model_k) model_k += 1 # Set the new hyperparam configuration to test for pn, pv in zip(self.param_names, param_vals): model_hyperparams[pn] = pv print "=" 80 print "[%d] Testing %s" % (k+1, ', '.join([ "{0} = {1}".format(pn, pv) for pn, pv in zip(self.param_names, param_vals) ])) print "=" * 80 # Train the model self.model.train( self.train_data, self.train_labels, dev_sentence_data=dev_data, dev_labels=dev_labels, model_hyperparams ) # Test the model score = self.model.score(dev_data, dev_labels, b=b, verbose=True) run_stats.append(list(param_vals) + [score]) if score > score_opt: #self.model.save(model_name) opt_model = model_name score_opt = score opt_params = param_vals # Store optimal params optimal = {"name":opt_model, "params":param_vals} with open("optimal_params"+str(datetime.datetime.now()), 'wb') as f: cPickle.dump(optimal, f) # Set optimal parameter in the learner model #self.model.load(opt_model) for pn, pv in zip(self.param_names, opt_params): model_hyperparams[pn] = pv self.model.train( self.train_data, self.train_labels, dev_sentence_data=dev_data, dev_labels=dev_labels, model_hyperparams ) # Return DataFrame of scores self.results = pd.DataFrame.from_records( run_stats, columns=self.param_names + ['Accuracy'] ).sort_values(by='Accuracy', ascending=False) return self.results class RandomSearch(GridSearch): def __init__(self, model, train_data, train_labels, parameters, n=10): """Search a random sample of size n from a parameter grid""" self.n = n super(RandomSearch, self).__init__( model, train_data, train_labels, parameters ) print "Initialized RandomSearch of size {0} / {1}".format( self.n, np.product([len(w) for w in GridSearch.search_space(self)]) ) def search_space(self): return zip(*[param.draw_values(self.n) for param in self.params])	apache-2.0
andyraib/data-storage	python_scripts/env/lib/python3.6/site-packages/pandas/io/tests/test_clipboard.py	7	4897	# -- coding: utf-8 -- import numpy as np from numpy.random import randint import nose import pandas as pd from pandas import DataFrame from pandas import read_clipboard from pandas import get_option from pandas.util import testing as tm from pandas.util.testing import makeCustomDataframe as mkdf from pandas.util.clipboard.exceptions import PyperclipException try: DataFrame({'A': [1, 2]}).to_clipboard() except PyperclipException: raise nose.SkipTest("clipboard primitives not installed") class TestClipboard(tm.TestCase): @classmethod def setUpClass(cls): super(TestClipboard, cls).setUpClass() cls.data = {} cls.data['string'] = mkdf(5, 3, c_idx_type='s', r_idx_type='i', c_idx_names=[None], r_idx_names=[None]) cls.data['int'] = mkdf(5, 3, data_gen_f=lambda args: randint(2), c_idx_type='s', r_idx_type='i', c_idx_names=[None], r_idx_names=[None]) cls.data['float'] = mkdf(5, 3, data_gen_f=lambda r, c: float(r) + 0.01, c_idx_type='s', r_idx_type='i', c_idx_names=[None], r_idx_names=[None]) cls.data['mixed'] = DataFrame({'a': np.arange(1.0, 6.0) + 0.01, 'b': np.arange(1, 6), 'c': list('abcde')}) # Test columns exceeding "max_colwidth" (GH8305) _cw = get_option('display.max_colwidth') + 1 cls.data['colwidth'] = mkdf(5, 3, data_gen_f=lambda args: 'x' * _cw, c_idx_type='s', r_idx_type='i', c_idx_names=[None], r_idx_names=[None]) # Test GH-5346 max_rows = get_option('display.max_rows') cls.data['longdf'] = mkdf(max_rows + 1, 3, data_gen_f=lambda *args: randint(2), c_idx_type='s', r_idx_type='i', c_idx_names=[None], r_idx_names=[None]) # Test for non-ascii text: GH9263 cls.data['nonascii'] = pd.DataFrame({'en': 'in English'.split(), 'es': 'en español'.split()}) # unicode round trip test for GH 13747, GH 12529 cls.data['utf8'] = pd.DataFrame({'a': ['µasd', 'Ωœ∑´'], 'b': ['øπ∆˚¬', 'œ∑´®']}) cls.data_types = list(cls.data.keys()) @classmethod def tearDownClass(cls): super(TestClipboard, cls).tearDownClass() del cls.data_types, cls.data def check_round_trip_frame(self, data_type, excel=None, sep=None, encoding=None): data = self.data[data_type] data.to_clipboard(excel=excel, sep=sep, encoding=encoding) if sep is not None: result = read_clipboard(sep=sep, index_col=0, encoding=encoding) else: result = read_clipboard(encoding=encoding) tm.assert_frame_equal(data, result, check_dtype=False) def test_round_trip_frame_sep(self): for dt in self.data_types: self.check_round_trip_frame(dt, sep=',') def test_round_trip_frame_string(self): for dt in self.data_types: self.check_round_trip_frame(dt, excel=False) def test_round_trip_frame(self): for dt in self.data_types: self.check_round_trip_frame(dt) def test_read_clipboard_infer_excel(self): from textwrap import dedent from pandas.util.clipboard import clipboard_set text = dedent(""" John James Charlie Mingus 1 2 4 Harry Carney """.strip()) clipboard_set(text) df = pd.read_clipboard() # excel data is parsed correctly self.assertEqual(df.iloc[1][1], 'Harry Carney') # having diff tab counts doesn't trigger it text = dedent(""" a\t b 1 2 3 4 """.strip()) clipboard_set(text) res = pd.read_clipboard() text = dedent(""" a b 1 2 3 4 """.strip()) clipboard_set(text) exp = pd.read_clipboard() tm.assert_frame_equal(res, exp) def test_invalid_encoding(self): # test case for testing invalid encoding data = self.data['string'] with tm.assertRaises(ValueError): data.to_clipboard(encoding='ascii') with tm.assertRaises(NotImplementedError): pd.read_clipboard(encoding='ascii') def test_round_trip_valid_encodings(self): for enc in ['UTF-8', 'utf-8', 'utf8']: for dt in self.data_types: self.check_round_trip_frame(dt, encoding=enc)	apache-2.0
BorisJeremic/Real-ESSI-Examples	analytic_solution/test_cases/Contact/Stress_Based_Contact_Verification/SoftContact_NonLinHardShear/Area/A_1e-4/Normal_Stress_Plot.py	72	2800	#!/usr/bin/python import h5py import matplotlib.pylab as plt import matplotlib as mpl import sys import numpy as np; import matplotlib; import math; from matplotlib.ticker import MaxNLocator plt.rcParams.update({'font.size': 28}) # set tick width mpl.rcParams['xtick.major.size'] = 10 mpl.rcParams['xtick.major.width'] = 5 mpl.rcParams['xtick.minor.size'] = 10 mpl.rcParams['xtick.minor.width'] = 5 plt.rcParams['xtick.labelsize']=24 mpl.rcParams['ytick.major.size'] = 10 mpl.rcParams['ytick.major.width'] = 5 mpl.rcParams['ytick.minor.size'] = 10 mpl.rcParams['ytick.minor.width'] = 5 plt.rcParams['ytick.labelsize']=24 ############################################################### ## Analytical Solution ############################################################### # Go over each feioutput and plot each one. thefile = "Analytical_Solution_Normal_Stress.feioutput"; finput = h5py.File(thefile) # Read the time and displacement times = finput["time"][:] normal_stress = -finput["/Model/Elements/Element_Outputs"][9,:]; normal_strain = -finput["/Model/Elements/Element_Outputs"][6,:]; # Configure the figure filename, according to the input filename. outfig=thefile.replace("_","-") outfigname=outfig.replace("h5.feioutput","pdf") # Plot the figure. Add labels and titles. plt.figure(figsize=(12,10)) plt.plot(normal_strain100,normal_stress/1000,'-r',label='Analytical Solution', Linewidth=4, markersize=20) plt.xlabel(r"Interface Type #") plt.ylabel(r"Normal Stress $\sigma_n [kPa]$") plt.hold(True) ############################################################### ## Numerical Solution ############################################################### # Go over each feioutput and plot each one. thefile = "Monotonic_Contact_Behaviour_Adding_Normal_Load.h5.feioutput"; finput = h5py.File(thefile) # Read the time and displacement times = finput["time"][:] normal_stress = -finput["/Model/Elements/Element_Outputs"][9,:]; normal_strain = -finput["/Model/Elements/Element_Outputs"][6,:]; # Configure the figure filename, according to the input filename. outfig=thefile.replace("_","-") outfigname=outfig.replace("h5.feioutput","pdf") # Plot the figure. Add labels and titles. plt.plot(normal_strain100,normal_stress/1000,'-k',label='Numerical Solution', Linewidth=4, markersize=20) plt.xlabel(r"Normal Strain [%]") plt.ylabel(r"Normal Stress $\sigma_n [kPa]$") ############################################################# # # # axes = plt.gca() # # # axes.set_xlim([-7,7]) # # # axes.set_ylim([-1,1]) # outfigname = "Interface_Test_Normal_Stress.pdf"; # plt.axis([0, 5.5, 90, 101]) # legend = plt.legend() # legend.get_frame().set_linewidth(0.0) # legend.get_frame().set_facecolor('none') plt.legend() plt.savefig('Normal_Stress.pdf', bbox_inches='tight') # plt.show()	cc0-1.0
trmznt/genaf	genaf/__init__.py	1	5963	import logging log = logging.getLogger(__name__) import matplotlib matplotlib.use('Agg') log.info('Setting up matplotlib to use Agg') from pyramid.config import Configurator from rhombus import includeme as rho_includeme, init_app as rhombus_init_app, add_route_view from rhombus.lib.utils import cout, cerr, cexit, generic_userid_func from rhombus.lib.fsoverlay import fsomount from rhombus.models.core import set_func_userid from genaf.lib.procmgmt import init_queue from genaf.lib.configs import set_temp_path, get_temp_path, TEMP_TOOLS import os def includeme( config ): # GenAF configuration #config.add_static_view('genaf_assets', 'genaf:static/assets/') config.add_static_view(name='genaf_static', path="genaf:static/") add_route_view( config, 'genaf.views.marker', 'genaf.marker', '/marker', '/marker/@@action', '/marker/{id}@@edit', '/marker/{id}@@save', ('/marker/{id}', 'view') ) add_route_view( config, 'genaf.views.panel', 'genaf.panel', '/panel', '/panel/@@action', '/panel/{id}@@edit', '/panel/{id}@@save', ('/panel/{id}', 'view') ) add_route_view( config, 'genaf.views.batch', 'genaf.batch', '/batch', '/batch/@@action', '/batch/{id}@@edit', '/batch/{id}@@save', ('/batch/{id}', 'view') ) add_route_view( config, 'genaf.views.sample', 'genaf.sample', '/sample', '/sample/@@action', '/sample/{id}@@edit', '/sample/{id}@@save', ('/sample/{id}', 'view') ) add_route_view( config, 'genaf.views.location', 'genaf.location', '/location', '/location/@@action', '/location/{id}@@edit', '/location/{id}@@save', ('/location/{id}', 'view') ) add_route_view( config, 'genaf.views.assay', 'genaf.assay', '/assay', '/assay/@@action', '/assay/{id}@@drawchannels', '/assay/{id}@@edit', '/assay/{id}@@save', ('/assay/{id}', 'view') ) add_route_view( config, 'genaf.views.channel', 'genaf.channel', '/channel/@@action', ('/channel/{id}', 'view'), ) add_route_view( config, 'genaf.views.uploadmgr', 'genaf.uploadmgr', '/uploadmgr', '/uploadmgr/@@action', '/uploadmgr/{id}@@edit', '/uploadmgr/{id}@@save', ('/uploadmgr/{id}@@mainpanel', 'mainpanel', 'json'), ('/uploadmgr/{id}@@rpc', 'rpc', 'json'), ('/uploadmgr/{id}@@uploaddata', 'uploaddata', 'json'), ('/uploadmgr/{id}@@uploadinfo', 'uploadinfo', 'json'), '/uploadmgr/{id}@@template', ('/uploadmgr/{id}', 'view') ) add_route_view( config, 'genaf.views.famgr', 'genaf.famgr', '/famgr', '/famgr/{id}@@process', ('/famgr/{id}', 'view') ) add_route_view( config, 'genaf.views.task', 'genaf.task', '/task', ('/task/{id}', 'view'), ) add_route_view( config, 'rhombus.views.fso', 'rhombus.fso', '/fso{path:.}@@view', '/fso{path:.}@@edit', '/fso{path:.}@@save', '/fso{path:.}@@action', ('/fso{path:.}', 'index'), ) # tools and analysis config.add_route('tools-help', '/tools/help') config.add_view('genaf.views.tools.help.index', route_name='tools-help') config.add_route('tools-allele', '/tools/allele') config.add_view('genaf.views.tools.allele.index', route_name='tools-allele') config.add_route('tools-he', '/tools/he') config.add_view('genaf.views.tools.he.index', route_name='tools-he') config.add_route('tools-genotype', '/tools/genotype') config.add_view('genaf.views.tools.genotype.index', route_name='tools-genotype') config.add_route('tools-moi', '/tools/moi') config.add_view('genaf.views.tools.moi.index', route_name='tools-moi') config.add_route('tools-pcoa', '/tools/pcoa') config.add_view('genaf.views.tools.pcoa.index', route_name='tools-pcoa') config.add_route('tools-mca', '/tools/mca') config.add_view('genaf.views.tools.mca.index', route_name='tools-mca') config.add_route('tools-export', '/tools/export') config.add_view('genaf.views.tools.export.index', route_name='tools-export') config.add_route('tools-fst', '/tools/fst') config.add_view('genaf.views.tools.fst.index', route_name='tools-fst') config.add_route('tools-ld', '/tools/ld') config.add_view('genaf.views.tools.ld.index', route_name='tools-ld') config.add_route('tools-nj', '/tools/nj') config.add_view('genaf.views.tools.nj.index', route_name='tools-nj') config.add_route('tools-sample', '/tools/sample') config.add_view('genaf.views.tools.sample.index', route_name='tools-sample') config.add_route('tools-djost', '/tools/djost') config.add_view('genaf.views.tools.djost.index', route_name='tools-djost') # utilities config.add_route('utils-export', '/utils/export') config.add_view('genaf.views.utils.export.index', route_name='utils-export') config.add_route('utils-plot', '/utils/plot') config.add_view('genaf.views.utils.plot.index', route_name='utils-plot') def init_app( global_config, settings, prefix = '/mgr' ): # global, shared settings temp_path = settings['genaf.temp_directory'] set_temp_path( temp_path ) fsomount(TEMP_TOOLS, get_temp_path('', TEMP_TOOLS)) set_func_userid( generic_userid_func ) # preparing for multiprocessing init_queue(settings) config = rhombus_init_app( global_config, settings, prefix=prefix ) return config def main(global_config, *settings): """ This function returns a Pyramid WSGI application. """ config = Configurator(settings=settings) config.include('pyramid_chameleon') config.add_static_view('static', 'static', cache_max_age=3600) config.add_route('home', '/') config.scan() return config.make_wsgi_app()	lgpl-3.0
ChinaQuants/zipline	zipline/protocol.py	3	17052	# # Copyright 2013 Quantopian, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from copy import copy from six import iteritems, iterkeys import pandas as pd import numpy as np from . utils.protocol_utils import Enum from . utils.math_utils import nanstd, nanmean, nansum from zipline.utils.algo_instance import get_algo_instance from zipline.utils.serialization_utils import ( VERSION_LABEL ) # Datasource type should completely determine the other fields of a # message with its type. DATASOURCE_TYPE = Enum( 'AS_TRADED_EQUITY', 'MERGER', 'SPLIT', 'DIVIDEND', 'TRADE', 'TRANSACTION', 'ORDER', 'EMPTY', 'DONE', 'CUSTOM', 'BENCHMARK', 'COMMISSION', 'CLOSE_POSITION' ) # Expected fields/index values for a dividend Series. DIVIDEND_FIELDS = [ 'declared_date', 'ex_date', 'gross_amount', 'net_amount', 'pay_date', 'payment_sid', 'ratio', 'sid', ] # Expected fields/index values for a dividend payment Series. DIVIDEND_PAYMENT_FIELDS = [ 'id', 'payment_sid', 'cash_amount', 'share_count', ] def dividend_payment(data=None): """ Take a dictionary whose values are in DIVIDEND_PAYMENT_FIELDS and return a series representing the payment of a dividend. Ids are assigned to each historical dividend in PerformanceTracker.update_dividends. They are guaranteed to be unique integers with the context of a single simulation. If @data is non-empty, a id is required to identify the historical dividend associated with this payment. Additionally, if @data is non-empty, either data['cash_amount'] should be nonzero or data['payment_sid'] should be an asset identifier and data['share_count'] should be nonzero. The returned Series is given its id value as a name so that concatenating payments results in a DataFrame indexed by id. (Note, however, that the name value is not used to construct an index when this series is returned by function passed to `DataFrame.apply`. In such a case, pandas preserves the index of the DataFrame on which `apply` is being called.) """ return pd.Series( data=data, name=data['id'] if data is not None else None, index=DIVIDEND_PAYMENT_FIELDS, dtype=object, ) class Event(object): def __init__(self, initial_values=None): if initial_values: self.__dict__ = initial_values def __getitem__(self, name): return getattr(self, name) def __setitem__(self, name, value): setattr(self, name, value) def __delitem__(self, name): delattr(self, name) def keys(self): return self.__dict__.keys() def __eq__(self, other): return hasattr(other, '__dict__') and self.__dict__ == other.__dict__ def __contains__(self, name): return name in self.__dict__ def __repr__(self): return "Event({0})".format(self.__dict__) def to_series(self, index=None): return pd.Series(self.__dict__, index=index) class Order(Event): pass class Portfolio(object): def __init__(self): self.capital_used = 0.0 self.starting_cash = 0.0 self.portfolio_value = 0.0 self.pnl = 0.0 self.returns = 0.0 self.cash = 0.0 self.positions = Positions() self.start_date = None self.positions_value = 0.0 def __getitem__(self, key): return self.__dict__[key] def __repr__(self): return "Portfolio({0})".format(self.__dict__) def __getstate__(self): state_dict = copy(self.__dict__) # Have to convert to primitive dict state_dict['positions'] = dict(self.positions) STATE_VERSION = 1 state_dict[VERSION_LABEL] = STATE_VERSION return state_dict def __setstate__(self, state): OLDEST_SUPPORTED_STATE = 1 version = state.pop(VERSION_LABEL) if version < OLDEST_SUPPORTED_STATE: raise BaseException("Portfolio saved state is too old.") self.positions = Positions() self.positions.update(state.pop('positions')) self.__dict__.update(state) class Account(object): ''' The account object tracks information about the trading account. The values are updated as the algorithm runs and its keys remain unchanged. If connected to a broker, one can update these values with the trading account values as reported by the broker. ''' def __init__(self): self.settled_cash = 0.0 self.accrued_interest = 0.0 self.buying_power = float('inf') self.equity_with_loan = 0.0 self.total_positions_value = 0.0 self.regt_equity = 0.0 self.regt_margin = float('inf') self.initial_margin_requirement = 0.0 self.maintenance_margin_requirement = 0.0 self.available_funds = 0.0 self.excess_liquidity = 0.0 self.cushion = 0.0 self.day_trades_remaining = float('inf') self.leverage = 0.0 self.net_leverage = 0.0 self.net_liquidation = 0.0 def __getitem__(self, key): return self.__dict__[key] def __repr__(self): return "Account({0})".format(self.__dict__) def __getstate__(self): state_dict = copy(self.__dict__) STATE_VERSION = 1 state_dict[VERSION_LABEL] = STATE_VERSION return state_dict def __setstate__(self, state): OLDEST_SUPPORTED_STATE = 1 version = state.pop(VERSION_LABEL) if version < OLDEST_SUPPORTED_STATE: raise BaseException("Account saved state is too old.") self.__dict__.update(state) class Position(object): def __init__(self, sid): self.sid = sid self.amount = 0 self.cost_basis = 0.0 # per share self.last_sale_price = 0.0 def __getitem__(self, key): return self.__dict__[key] def __repr__(self): return "Position({0})".format(self.__dict__) def __getstate__(self): state_dict = copy(self.__dict__) STATE_VERSION = 1 state_dict[VERSION_LABEL] = STATE_VERSION return state_dict def __setstate__(self, state): OLDEST_SUPPORTED_STATE = 1 version = state.pop(VERSION_LABEL) if version < OLDEST_SUPPORTED_STATE: raise BaseException("Protocol Position saved state is too old.") self.__dict__.update(state) class Positions(dict): def __missing__(self, key): pos = Position(key) self[key] = pos return pos class SIDData(object): # Cache some data on the class so that this is shared for all instances of # siddata. # The dt where we cached the history. _history_cache_dt = None # _history_cache is a a dict mapping fields to pd.DataFrames. This is the # most data we have for a given field for the _history_cache_dt. _history_cache = {} # This is the cache that is used for returns. This will have a different # structure than the other history cache as this is always daily. _returns_cache_dt = None _returns_cache = None # The last dt that we needed to cache the number of minutes. _minute_bar_cache_dt = None # If we are in minute mode, there is some cost associated with computing # the number of minutes that we need to pass to the bar count of history. # This will remain constant for a given bar and day count. # This maps days to number of minutes. _minute_bar_cache = {} def __init__(self, sid, initial_values=None): self._sid = sid self._freqstr = None # To check if we have data, we use the __len__ which depends on the # __dict__. Because we are foward defining the attributes needed, we # need to account for their entrys in the __dict__. # We will add 1 because we need to account for the _initial_len entry # itself. self._initial_len = len(self.__dict__) + 1 if initial_values: self.__dict__.update(initial_values) @property def datetime(self): """ Provides an alias from data['foo'].datetime -> data['foo'].dt `datetime` was previously provided by adding a seperate `datetime` member of the SIDData object via a generator that wrapped the incoming data feed and added the field to each equity event. This alias is intended to be temporary, to provide backwards compatibility with existing algorithms, but should be considered deprecated, and may be removed in the future. """ return self.dt def get(self, name, default=None): return self.__dict__.get(name, default) def __getitem__(self, name): return self.__dict__[name] def __setitem__(self, name, value): self.__dict__[name] = value def __len__(self): return len(self.__dict__) - self._initial_len def __contains__(self, name): return name in self.__dict__ def __repr__(self): return "SIDData({0})".format(self.__dict__) def _get_buffer(self, bars, field='price', raw=False): """ Gets the result of history for the given number of bars and field. This will cache the results internally. """ cls = self.__class__ algo = get_algo_instance() now = algo.datetime if now != cls._history_cache_dt: # For a given dt, the history call for this field will not change. # We have a new dt, so we should reset the cache. cls._history_cache_dt = now cls._history_cache = {} if field not in self._history_cache \ or bars > len(cls._history_cache[field][0].index): # If we have never cached this field OR the amount of bars that we # need for this field is greater than the amount we have cached, # then we need to get more history. hst = algo.history( bars, self._freqstr, field, ffill=True, ) # Assert that the column holds ints, not security objects. if not isinstance(self._sid, str): hst.columns = hst.columns.astype(int) self._history_cache[field] = (hst, hst.values, hst.columns) # Slice of only the bars needed. This is because we strore the LARGEST # amount of history for the field, and we might request less than the # largest from the cache. buffer_, values, columns = cls._history_cache[field] if raw: sid_index = columns.get_loc(self._sid) return values[-bars:, sid_index] else: return buffer_[self._sid][-bars:] def _get_bars(self, days): """ Gets the number of bars needed for the current number of days. Figures this out based on the algo datafrequency and caches the result. This caches the result by replacing this function on the object. This means that after the first call to _get_bars, this method will point to a new function object. """ def daily_get_max_bars(days): return days def minute_get_max_bars(days): # max number of minute. regardless of current days or short # sessions return days * 390 def daily_get_bars(days): return days def minute_get_bars(days): cls = self.__class__ now = get_algo_instance().datetime if now != cls._minute_bar_cache_dt: cls._minute_bar_cache_dt = now cls._minute_bar_cache = {} if days not in cls._minute_bar_cache: # Cache this calculation to happen once per bar, even if we # use another transform with the same number of days. env = get_algo_instance().trading_environment prev = env.previous_trading_day(now) ds = env.days_in_range( env.add_trading_days(-days + 2, prev), prev, ) # compute the number of minutes in the (days - 1) days before # today. # 210 minutes in a an early close and 390 in a full day. ms = sum(210 if d in env.early_closes else 390 for d in ds) # Add the number of minutes for today. ms += int( (now - env.get_open_and_close(now)[0]).total_seconds() / 60 ) cls._minute_bar_cache[days] = ms + 1 # Account for this minute return cls._minute_bar_cache[days] if get_algo_instance().sim_params.data_frequency == 'daily': self._freqstr = '1d' # update this method to point to the daily variant. self._get_bars = daily_get_bars self._get_max_bars = daily_get_max_bars else: self._freqstr = '1m' # update this method to point to the minute variant. self._get_bars = minute_get_bars self._get_max_bars = minute_get_max_bars # Not actually recursive because we have already cached the new method. return self._get_bars(days) def mavg(self, days): bars = self._get_bars(days) max_bars = self._get_max_bars(days) prices = self._get_buffer(max_bars, raw=True)[-bars:] return nanmean(prices) def stddev(self, days): bars = self._get_bars(days) max_bars = self._get_max_bars(days) prices = self._get_buffer(max_bars, raw=True)[-bars:] return nanstd(prices, ddof=1) def vwap(self, days): bars = self._get_bars(days) max_bars = self._get_max_bars(days) prices = self._get_buffer(max_bars, raw=True)[-bars:] vols = self._get_buffer(max_bars, field='volume', raw=True)[-bars:] vol_sum = nansum(vols) try: ret = nansum(prices * vols) / vol_sum except ZeroDivisionError: ret = np.nan return ret def returns(self): algo = get_algo_instance() now = algo.datetime if now != self._returns_cache_dt: self._returns_cache_dt = now self._returns_cache = algo.history(2, '1d', 'price', ffill=True) hst = self._returns_cache[self._sid] return (hst.iloc[-1] - hst.iloc[0]) / hst.iloc[0] class BarData(object): """ Holds the event data for all sids for a given dt. This is what is passed as `data` to the `handle_data` function. Note: Many methods are analogues of dictionary because of historical usage of what this replaced as a dictionary subclass. """ def __init__(self, data=None): self._data = data or {} self._contains_override = None def __contains__(self, name): if self._contains_override: if self._contains_override(name): return name in self._data else: return False else: return name in self._data def has_key(self, name): """ DEPRECATED: __contains__ is preferred, but this method is for compatibility with existing algorithms. """ return name in self def __setitem__(self, name, value): self._data[name] = value def __getitem__(self, name): return self._data[name] def __delitem__(self, name): del self._data[name] def __iter__(self): for sid, data in iteritems(self._data): # Allow contains override to filter out sids. if sid in self: if len(data): yield sid def iterkeys(self): # Allow contains override to filter out sids. return (sid for sid in iterkeys(self._data) if sid in self) def keys(self): # Allow contains override to filter out sids. return list(self.iterkeys()) def itervalues(self): return (value for _sid, value in self.iteritems()) def values(self): return list(self.itervalues()) def iteritems(self): return ((sid, value) for sid, value in iteritems(self._data) if sid in self) def items(self): return list(self.iteritems()) def __len__(self): return len(self.keys()) def __repr__(self): return '{0}({1})'.format(self.__class__.__name__, self._data)	apache-2.0
eickenberg/scikit-learn	sklearn/decomposition/nmf.py	1	18931	""" Non-negative matrix factorization """ # Author: Vlad Niculae # Lars Buitinck <[email protected]> # Author: Chih-Jen Lin, National Taiwan University (original projected gradient # NMF implementation) # Author: Anthony Di Franco (original Python and NumPy port) # License: BSD 3 clause from __future__ import division from math import sqrt import warnings import numpy as np import scipy.sparse as sp from scipy.optimize import nnls from ..base import BaseEstimator, TransformerMixin from ..utils import check_random_state, check_array from ..utils.extmath import randomized_svd, safe_sparse_dot, squared_norm def safe_vstack(Xs): if any(sp.issparse(X) for X in Xs): return sp.vstack(Xs) else: return np.vstack(Xs) def norm(x): """Dot product-based Euclidean norm implementation See: http://fseoane.net/blog/2011/computing-the-vector-norm/ """ return sqrt(squared_norm(x)) def trace_dot(X, Y): """Trace of np.dot(X, Y.T).""" return np.dot(X.ravel(), Y.ravel()) def _sparseness(x): """Hoyer's measure of sparsity for a vector""" sqrt_n = np.sqrt(len(x)) return (sqrt_n - np.linalg.norm(x, 1) / norm(x)) / (sqrt_n - 1) def check_non_negative(X, whom): X = X.data if sp.issparse(X) else X if (X < 0).any(): raise ValueError("Negative values in data passed to %s" % whom) def _initialize_nmf(X, n_components, variant=None, eps=1e-6, random_state=None): """NNDSVD algorithm for NMF initialization. Computes a good initial guess for the non-negative rank k matrix approximation for X: X = WH Parameters ---------- X : array, [n_samples, n_features] The data matrix to be decomposed. n_components : array, [n_components, n_features] The number of components desired in the approximation. variant : None \| 'a' \| 'ar' The variant of the NNDSVD algorithm. Accepts None, 'a', 'ar' None: leaves the zero entries as zero 'a': Fills the zero entries with the average of X 'ar': Fills the zero entries with standard normal random variates. Default: None eps: float Truncate all values less then this in output to zero. random_state : numpy.RandomState \| int, optional The generator used to fill in the zeros, when using variant='ar' Default: numpy.random Returns ------- (W, H) : Initial guesses for solving X ~= WH such that the number of columns in W is n_components. Remarks ------- This implements the algorithm described in C. Boutsidis, E. Gallopoulos: SVD based initialization: A head start for nonnegative matrix factorization - Pattern Recognition, 2008 http://tinyurl.com/nndsvd """ check_non_negative(X, "NMF initialization") if variant not in (None, 'a', 'ar'): raise ValueError("Invalid variant name") U, S, V = randomized_svd(X, n_components) W, H = np.zeros(U.shape), np.zeros(V.shape) # The leading singular triplet is non-negative # so it can be used as is for initialization. W[:, 0] = np.sqrt(S[0]) * np.abs(U[:, 0]) H[0, :] = np.sqrt(S[0]) * np.abs(V[0, :]) for j in range(1, n_components): x, y = U[:, j], V[j, :] # extract positive and negative parts of column vectors x_p, y_p = np.maximum(x, 0), np.maximum(y, 0) x_n, y_n = np.abs(np.minimum(x, 0)), np.abs(np.minimum(y, 0)) # and their norms x_p_nrm, y_p_nrm = norm(x_p), norm(y_p) x_n_nrm, y_n_nrm = norm(x_n), norm(y_n) m_p, m_n = x_p_nrm * y_p_nrm, x_n_nrm * y_n_nrm # choose update if m_p > m_n: u = x_p / x_p_nrm v = y_p / y_p_nrm sigma = m_p else: u = x_n / x_n_nrm v = y_n / y_n_nrm sigma = m_n lbd = np.sqrt(S[j] * sigma) W[:, j] = lbd * u H[j, :] = lbd * v W[W < eps] = 0 H[H < eps] = 0 if variant == "a": avg = X.mean() W[W == 0] = avg H[H == 0] = avg elif variant == "ar": random_state = check_random_state(random_state) avg = X.mean() W[W == 0] = abs(avg * random_state.randn(len(W[W == 0])) / 100) H[H == 0] = abs(avg * random_state.randn(len(H[H == 0])) / 100) return W, H def _nls_subproblem(V, W, H, tol, max_iter, sigma=0.01, beta=0.1): """Non-negative least square solver Solves a non-negative least squares subproblem using the projected gradient descent algorithm. min \|\| WH - V \|\|_2 Parameters ---------- V, W : array-like Constant matrices. H : array-like Initial guess for the solution. tol : float Tolerance of the stopping condition. max_iter : int Maximum number of iterations before timing out. sigma : float Constant used in the sufficient decrease condition checked by the line search. Smaller values lead to a looser sufficient decrease condition, thus reducing the time taken by the line search, but potentially increasing the number of iterations of the projected gradient procedure. 0.01 is a commonly used value in the optimization literature. beta : float Factor by which the step size is decreased (resp. increased) until (resp. as long as) the sufficient decrease condition is satisfied. Larger values allow to find a better step size but lead to longer line search. 0.1 is a commonly used value in the optimization literature. Returns ------- H : array-like Solution to the non-negative least squares problem. grad : array-like The gradient. n_iter : int The number of iterations done by the algorithm. Reference --------- C.-J. Lin. Projected gradient methods for non-negative matrix factorization. Neural Computation, 19(2007), 2756-2779. http://www.csie.ntu.edu.tw/~cjlin/nmf/ """ WtV = safe_sparse_dot(W.T, V) WtW = np.dot(W.T, W) # values justified in the paper alpha = 1 for n_iter in range(1, max_iter + 1): grad = np.dot(WtW, H) - WtV # The following multiplication with a boolean array is more than twice # as fast as indexing into grad. if norm(grad * np.logical_or(grad < 0, H > 0)) < tol: break Hp = H for inner_iter in range(19): # Gradient step. Hn = H - alpha * grad # Projection step. Hn = Hn > 0 d = Hn - H gradd = np.dot(grad.ravel(), d.ravel()) dQd = np.dot(np.dot(WtW, d).ravel(), d.ravel()) suff_decr = (1 - sigma) gradd + 0.5 * dQd < 0 if inner_iter == 0: decr_alpha = not suff_decr if decr_alpha: if suff_decr: H = Hn break else: alpha = beta elif not suff_decr or (Hp == Hn).all(): H = Hp break else: alpha /= beta Hp = Hn if n_iter == max_iter: warnings.warn("Iteration limit reached in nls subproblem.") return H, grad, n_iter class ProjectedGradientNMF(BaseEstimator, TransformerMixin): """Non-Negative matrix factorization by Projected Gradient (NMF) Parameters ---------- n_components : int or None Number of components, if n_components is not set all components are kept init : 'nndsvd' \| 'nndsvda' \| 'nndsvdar' \| 'random' Method used to initialize the procedure. Default: 'nndsvdar' if n_components < n_features, otherwise random. Valid options:: 'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD) initialization (better for sparseness) 'nndsvda': NNDSVD with zeros filled with the average of X (better when sparsity is not desired) 'nndsvdar': NNDSVD with zeros filled with small random values (generally faster, less accurate alternative to NNDSVDa for when sparsity is not desired) 'random': non-negative random matrices sparseness : 'data' \| 'components' \| None, default: None Where to enforce sparsity in the model. beta : double, default: 1 Degree of sparseness, if sparseness is not None. Larger values mean more sparseness. eta : double, default: 0.1 Degree of correctness to maintain, if sparsity is not None. Smaller values mean larger error. tol : double, default: 1e-4 Tolerance value used in stopping conditions. max_iter : int, default: 200 Number of iterations to compute. nls_max_iter : int, default: 2000 Number of iterations in NLS subproblem. random_state : int or RandomState Random number generator seed control. Attributes ---------- `components_` : array, [n_components, n_features] Non-negative components of the data. `reconstruction_err_` : number Frobenius norm of the matrix difference between the training data and the reconstructed data from the fit produced by the model. ``\|\| X - WH \|\|_2`` Examples -------- >>> import numpy as np >>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]]) >>> from sklearn.decomposition import ProjectedGradientNMF >>> model = ProjectedGradientNMF(n_components=2, init='random', ... random_state=0) >>> model.fit(X) #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE ProjectedGradientNMF(beta=1, eta=0.1, init='random', max_iter=200, n_components=2, nls_max_iter=2000, random_state=0, sparseness=None, tol=0.0001) >>> model.components_ array([[ 0.77032744, 0.11118662], [ 0.38526873, 0.38228063]]) >>> model.reconstruction_err_ #doctest: +ELLIPSIS 0.00746... >>> model = ProjectedGradientNMF(n_components=2, ... sparseness='components', init='random', random_state=0) >>> model.fit(X) #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE ProjectedGradientNMF(beta=1, eta=0.1, init='random', max_iter=200, n_components=2, nls_max_iter=2000, random_state=0, sparseness='components', tol=0.0001) >>> model.components_ array([[ 1.67481991, 0.29614922], [ 0. , 0.4681982 ]]) >>> model.reconstruction_err_ #doctest: +ELLIPSIS 0.513... References ---------- This implements C.-J. Lin. Projected gradient methods for non-negative matrix factorization. Neural Computation, 19(2007), 2756-2779. http://www.csie.ntu.edu.tw/~cjlin/nmf/ P. Hoyer. Non-negative Matrix Factorization with Sparseness Constraints. Journal of Machine Learning Research 2004. NNDSVD is introduced in C. Boutsidis, E. Gallopoulos: SVD based initialization: A head start for nonnegative matrix factorization - Pattern Recognition, 2008 http://tinyurl.com/nndsvd """ def __init__(self, n_components=None, init=None, sparseness=None, beta=1, eta=0.1, tol=1e-4, max_iter=200, nls_max_iter=2000, random_state=None): self.n_components = n_components self.init = init self.tol = tol if sparseness not in (None, 'data', 'components'): raise ValueError( 'Invalid sparseness parameter: got %r instead of one of %r' % (sparseness, (None, 'data', 'components'))) self.sparseness = sparseness self.beta = beta self.eta = eta self.max_iter = max_iter self.nls_max_iter = nls_max_iter self.random_state = random_state def _init(self, X): n_samples, n_features = X.shape init = self.init if init is None: if self.n_components_ < n_features: init = 'nndsvd' else: init = 'random' random_state = self.random_state if init == 'nndsvd': W, H = _initialize_nmf(X, self.n_components_) elif init == 'nndsvda': W, H = _initialize_nmf(X, self.n_components_, variant='a') elif init == 'nndsvdar': W, H = _initialize_nmf(X, self.n_components_, variant='ar') elif init == "random": rng = check_random_state(random_state) W = rng.randn(n_samples, self.n_components_) # we do not write np.abs(W, out=W) to stay compatible with # numpy 1.5 and earlier where the 'out' keyword is not # supported as a kwarg on ufuncs np.abs(W, W) H = rng.randn(self.n_components_, n_features) np.abs(H, H) else: raise ValueError( 'Invalid init parameter: got %r instead of one of %r' % (init, (None, 'nndsvd', 'nndsvda', 'nndsvdar', 'random'))) return W, H def _update_W(self, X, H, W, tolW): n_samples, n_features = X.shape if self.sparseness is None: W, gradW, iterW = _nls_subproblem(X.T, H.T, W.T, tolW, self.nls_max_iter) elif self.sparseness == 'data': W, gradW, iterW = _nls_subproblem( safe_vstack([X.T, np.zeros((1, n_samples))]), safe_vstack([H.T, np.sqrt(self.beta) np.ones((1, self.n_components_))]), W.T, tolW, self.nls_max_iter) elif self.sparseness == 'components': W, gradW, iterW = _nls_subproblem( safe_vstack([X.T, np.zeros((self.n_components_, n_samples))]), safe_vstack([H.T, np.sqrt(self.eta) * np.eye(self.n_components_)]), W.T, tolW, self.nls_max_iter) return W.T, gradW.T, iterW def _update_H(self, X, H, W, tolH): n_samples, n_features = X.shape if self.sparseness is None: H, gradH, iterH = _nls_subproblem(X, W, H, tolH, self.nls_max_iter) elif self.sparseness == 'data': H, gradH, iterH = _nls_subproblem( safe_vstack([X, np.zeros((self.n_components_, n_features))]), safe_vstack([W, np.sqrt(self.eta) * np.eye(self.n_components_)]), H, tolH, self.nls_max_iter) elif self.sparseness == 'components': H, gradH, iterH = _nls_subproblem( safe_vstack([X, np.zeros((1, n_features))]), safe_vstack([W, np.sqrt(self.beta) * np.ones((1, self.n_components_))]), H, tolH, self.nls_max_iter) return H, gradH, iterH def fit_transform(self, X, y=None): """Learn a NMF model for the data X and returns the transformed data. This is more efficient than calling fit followed by transform. Parameters ---------- X: {array-like, sparse matrix}, shape = [n_samples, n_features] Data matrix to be decomposed Returns ------- data: array, [n_samples, n_components] Transformed data """ X = check_array(X, accept_sparse='csr') check_non_negative(X, "NMF.fit") n_samples, n_features = X.shape if not self.n_components: self.n_components_ = n_features else: self.n_components_ = self.n_components W, H = self._init(X) gradW = (np.dot(W, np.dot(H, H.T)) - safe_sparse_dot(X, H.T, dense_output=True)) gradH = (np.dot(np.dot(W.T, W), H) - safe_sparse_dot(W.T, X, dense_output=True)) init_grad = norm(np.r_[gradW, gradH.T]) tolW = max(0.001, self.tol) * init_grad # why max? tolH = tolW tol = self.tol * init_grad for n_iter in range(1, self.max_iter + 1): # stopping condition # as discussed in paper proj_norm = norm(np.r_[gradW[np.logical_or(gradW < 0, W > 0)], gradH[np.logical_or(gradH < 0, H > 0)]]) if proj_norm < tol: break # update W W, gradW, iterW = self._update_W(X, H, W, tolW) if iterW == 1: tolW = 0.1 * tolW # update H H, gradH, iterH = self._update_H(X, H, W, tolH) if iterH == 1: tolH = 0.1 * tolH if not sp.issparse(X): error = norm(X - np.dot(W, H)) else: sqnorm_X = np.dot(X.data, X.data) norm_WHT = trace_dot(np.dot(np.dot(W.T, W), H), H) cross_prod = trace_dot((X * H.T), W) error = sqrt(sqnorm_X + norm_WHT - 2. * cross_prod) self.reconstruction_err_ = error self.comp_sparseness_ = _sparseness(H.ravel()) self.data_sparseness_ = _sparseness(W.ravel()) H[H == 0] = 0 # fix up negative zeros self.components_ = H if n_iter == self.max_iter: warnings.warn("Iteration limit reached during fit. Solving for W exactly.") return self.transform(X) return W def fit(self, X, y=None, params): """Learn a NMF model for the data X. Parameters ---------- X: {array-like, sparse matrix}, shape = [n_samples, n_features] Data matrix to be decomposed Returns ------- self """ self.fit_transform(X, params) return self def transform(self, X): """Transform the data X according to the fitted NMF model Parameters ---------- X: {array-like, sparse matrix}, shape = [n_samples, n_features] Data matrix to be transformed by the model Returns ------- data: array, [n_samples, n_components] Transformed data """ X = check_array(X, accept_sparse='csc') Wt = np.zeros((self.n_components_, X.shape[0])) check_non_negative(X, "ProjectedGradientNMF.transform") if sp.issparse(X): Wt, _, _ = _nls_subproblem(X.T, self.components_.T, Wt, tol=self.tol, max_iter=self.nls_max_iter) else: for j in range(0, X.shape[0]): Wt[:, j], _ = nnls(self.components_.T, X[j, :]) return Wt.T class NMF(ProjectedGradientNMF): __doc__ = ProjectedGradientNMF.__doc__ pass	bsd-3-clause
ndingwall/scikit-learn	doc/tutorial/text_analytics/solutions/exercise_02_sentiment.py	19	3140	"""Build a sentiment analysis / polarity model Sentiment analysis can be casted as a binary text classification problem, that is fitting a linear classifier on features extracted from the text of the user messages so as to guess whether the opinion of the author is positive or negative. In this examples we will use a movie review dataset. """ # Author: Olivier Grisel <[email protected]> # License: Simplified BSD import sys from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.model_selection import GridSearchCV from sklearn.datasets import load_files from sklearn.model_selection import train_test_split from sklearn import metrics if __name__ == "__main__": # NOTE: we put the following in a 'if __name__ == "__main__"' protected # block to be able to use a multi-core grid search that also works under # Windows, see: http://docs.python.org/library/multiprocessing.html#windows # The multiprocessing module is used as the backend of joblib.Parallel # that is used when n_jobs != 1 in GridSearchCV # the training data folder must be passed as first argument movie_reviews_data_folder = sys.argv[1] dataset = load_files(movie_reviews_data_folder, shuffle=False) print("n_samples: %d" % len(dataset.data)) # split the dataset in training and test set: docs_train, docs_test, y_train, y_test = train_test_split( dataset.data, dataset.target, test_size=0.25, random_state=None) # TASK: Build a vectorizer / classifier pipeline that filters out tokens # that are too rare or too frequent pipeline = Pipeline([ ('vect', TfidfVectorizer(min_df=3, max_df=0.95)), ('clf', LinearSVC(C=1000)), ]) # TASK: Build a grid search to find out whether unigrams or bigrams are # more useful. # Fit the pipeline on the training set using grid search for the parameters parameters = { 'vect__ngram_range': [(1, 1), (1, 2)], } grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1) grid_search.fit(docs_train, y_train) # TASK: print the mean and std for each candidate along with the parameter # settings for all the candidates explored by grid search. n_candidates = len(grid_search.cv_results_['params']) for i in range(n_candidates): print(i, 'params - %s; mean - %0.2f; std - %0.2f' % (grid_search.cv_results_['params'][i], grid_search.cv_results_['mean_test_score'][i], grid_search.cv_results_['std_test_score'][i])) # TASK: Predict the outcome on the testing set and store it in a variable # named y_predicted y_predicted = grid_search.predict(docs_test) # Print the classification report print(metrics.classification_report(y_test, y_predicted, target_names=dataset.target_names)) # Print and plot the confusion matrix cm = metrics.confusion_matrix(y_test, y_predicted) print(cm) # import matplotlib.pyplot as plt # plt.matshow(cm) # plt.show()	bsd-3-clause
abhishekkrthakur/scikit-learn	sklearn/utils/estimator_checks.py	1	38107	from __future__ import print_function import warnings import sys import traceback import inspect import pickle import numpy as np from scipy import sparse import struct from sklearn.externals.six.moves import zip from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_false from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import META_ESTIMATORS from sklearn.utils.testing import set_random_state from sklearn.utils.testing import assert_greater from sklearn.utils.testing import SkipTest from sklearn.utils.testing import check_skip_travis from sklearn.utils.testing import ignore_warnings from sklearn.base import clone, ClassifierMixin from sklearn.metrics import accuracy_score, adjusted_rand_score, f1_score from sklearn.lda import LDA from sklearn.random_projection import BaseRandomProjection from sklearn.feature_selection import SelectKBest from sklearn.svm.base import BaseLibSVM from sklearn.pipeline import make_pipeline from sklearn.utils.validation import DataConversionWarning, NotFittedError from sklearn.cross_validation import train_test_split from sklearn.utils import shuffle from sklearn.preprocessing import StandardScaler from sklearn.datasets import load_iris, load_boston, make_blobs BOSTON = None CROSS_DECOMPOSITION = ['PLSCanonical', 'PLSRegression', 'CCA', 'PLSSVD'] def _boston_subset(n_samples=200): global BOSTON if BOSTON is None: boston = load_boston() X, y = boston.data, boston.target X, y = shuffle(X, y, random_state=0) X, y = X[:n_samples], y[:n_samples] X = StandardScaler().fit_transform(X) BOSTON = X, y return BOSTON def set_fast_parameters(estimator): # speed up some estimators params = estimator.get_params() if ("n_iter" in params and estimator.__class__.__name__ != "TSNE"): estimator.set_params(n_iter=5) if "max_iter" in params: # NMF if estimator.max_iter is not None: estimator.set_params(max_iter=min(5, estimator.max_iter)) # LinearSVR if estimator.__class__.__name__ == 'LinearSVR': estimator.set_params(max_iter=20) if "n_resampling" in params: # randomized lasso estimator.set_params(n_resampling=5) if "n_estimators" in params: # especially gradient boosting with default 100 estimator.set_params(n_estimators=min(5, estimator.n_estimators)) if "max_trials" in params: # RANSAC estimator.set_params(max_trials=10) if "n_init" in params: # K-Means estimator.set_params(n_init=2) if estimator.__class__.__name__ == "SelectFdr": # be tolerant of noisy datasets (not actually speed) estimator.set_params(alpha=.5) if isinstance(estimator, BaseRandomProjection): # Due to the jl lemma and often very few samples, the number # of components of the random matrix projection will be probably # greater than the number of features. # So we impose a smaller number (avoid "auto" mode) estimator.set_params(n_components=1) if isinstance(estimator, SelectKBest): # SelectKBest has a default of k=10 # which is more feature than we have in most case. estimator.set_params(k=1) class NotAnArray(object): " An object that is convertable to an array" def __init__(self, data): self.data = data def __array__(self, dtype=None): return self.data def _is_32bit(): """Detect if process is 32bit Python.""" return struct.calcsize('P') * 8 == 32 def check_estimator_sparse_data(name, Estimator): rng = np.random.RandomState(0) X = rng.rand(40, 10) X[X < .8] = 0 X = sparse.csr_matrix(X) y = (4 * rng.rand(40)).astype(np.int) # catch deprecation warnings with warnings.catch_warnings(): if name in ['Scaler', 'StandardScaler']: estimator = Estimator(with_mean=False) else: estimator = Estimator() set_fast_parameters(estimator) # fit and predict try: estimator.fit(X, y) if hasattr(estimator, "predict"): estimator.predict(X) if hasattr(estimator, 'predict_proba'): estimator.predict_proba(X) except TypeError as e: if 'sparse' not in repr(e): print("Estimator %s doesn't seem to fail gracefully on " "sparse data: error message state explicitly that " "sparse input is not supported if this is not the case." % name) raise except Exception: print("Estimator %s doesn't seem to fail gracefully on " "sparse data: it should raise a TypeError if sparse input " "is explicitly not supported." % name) raise def check_transformer(name, Transformer): X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) X = StandardScaler().fit_transform(X) X -= X.min() _check_transformer(name, Transformer, X, y) _check_transformer(name, Transformer, X.tolist(), y.tolist()) def check_transformer_data_not_an_array(name, Transformer): X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) X = StandardScaler().fit_transform(X) # We need to make sure that we have non negative data, for things # like NMF X -= X.min() - .1 this_X = NotAnArray(X) this_y = NotAnArray(np.asarray(y)) _check_transformer(name, Transformer, this_X, this_y) def check_transformers_unfitted(name, Transformer): X, y = _boston_subset() with warnings.catch_warnings(record=True): transformer = Transformer() assert_raises(NotFittedError, transformer.transform, X) def _check_transformer(name, Transformer, X, y): if name in ('CCA', 'LocallyLinearEmbedding', 'KernelPCA') and _is_32bit(): # Those transformers yield non-deterministic output when executed on # a 32bit Python. The same transformers are stable on 64bit Python. # FIXME: try to isolate a minimalistic reproduction case only depending # on numpy & scipy and/or maybe generate a test dataset that does not # cause such unstable behaviors. msg = name + ' is non deterministic on 32bit Python' raise SkipTest(msg) n_samples, n_features = np.asarray(X).shape # catch deprecation warnings with warnings.catch_warnings(record=True): transformer = Transformer() set_random_state(transformer) if name == "KernelPCA": transformer.remove_zero_eig = False set_fast_parameters(transformer) # fit if name in CROSS_DECOMPOSITION: y_ = np.c_[y, y] y_[::2, 1] = 2 else: y_ = y transformer.fit(X, y_) X_pred = transformer.fit_transform(X, y=y_) if isinstance(X_pred, tuple): for x_pred in X_pred: assert_equal(x_pred.shape[0], n_samples) else: assert_equal(X_pred.shape[0], n_samples) if hasattr(transformer, 'transform'): if name in CROSS_DECOMPOSITION: X_pred2 = transformer.transform(X, y_) X_pred3 = transformer.fit_transform(X, y=y_) else: X_pred2 = transformer.transform(X) X_pred3 = transformer.fit_transform(X, y=y_) if isinstance(X_pred, tuple) and isinstance(X_pred2, tuple): for x_pred, x_pred2, x_pred3 in zip(X_pred, X_pred2, X_pred3): assert_array_almost_equal( x_pred, x_pred2, 2, "fit_transform and transform outcomes not consistent in %s" % Transformer) assert_array_almost_equal( x_pred, x_pred3, 2, "consecutive fit_transform outcomes not consistent in %s" % Transformer) else: assert_array_almost_equal( X_pred, X_pred2, 2, "fit_transform and transform outcomes not consistent in %s" % Transformer) assert_array_almost_equal( X_pred, X_pred3, 2, "consecutive fit_transform outcomes not consistent in %s" % Transformer) # raises error on malformed input for transform if hasattr(X, 'T'): # If it's not an array, it does not have a 'T' property assert_raises(ValueError, transformer.transform, X.T) @ignore_warnings def check_pipeline_consistency(name, Estimator): # check that make_pipeline(est) gives same score as est X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) X -= X.min() y = multioutput_estimator_convert_y_2d(name, y) estimator = Estimator() set_fast_parameters(estimator) set_random_state(estimator) pipeline = make_pipeline(estimator) estimator.fit(X, y) pipeline.fit(X, y) funcs = ["score", "fit_transform"] for func_name in funcs: func = getattr(estimator, func_name, None) if func is not None: func_pipeline = getattr(pipeline, func_name) result = func(X, y) result_pipe = func_pipeline(X, y) assert_array_almost_equal(result, result_pipe) @ignore_warnings def check_fit_score_takes_y(name, Estimator): # check that all estimators accept an optional y # in fit and score so they can be used in pipelines rnd = np.random.RandomState(0) X = rnd.uniform(size=(10, 3)) y = (X[:, 0] 4).astype(np.int) y = multioutput_estimator_convert_y_2d(name, y) estimator = Estimator() set_fast_parameters(estimator) set_random_state(estimator) funcs = ["fit", "score", "partial_fit", "fit_predict", "fit_transform"] for func_name in funcs: func = getattr(estimator, func_name, None) if func is not None: func(X, y) args = inspect.getargspec(func).args assert_true(args[2] in ["y", "Y"]) def check_estimators_dtypes(name, Estimator): rnd = np.random.RandomState(0) X_train_32 = 4 * rnd.uniform(size=(10, 3)).astype(np.float32) X_train_64 = X_train_32.astype(np.float64) X_train_int_64 = X_train_32.astype(np.int64) X_train_int_32 = X_train_32.astype(np.int32) y = X_train_int_64[:, 0] y = multioutput_estimator_convert_y_2d(name, y) for X_train in [X_train_32, X_train_64, X_train_int_64, X_train_int_32]: with warnings.catch_warnings(record=True): estimator = Estimator() set_fast_parameters(estimator) set_random_state(estimator, 1) estimator.fit(X_train, y) for method in ["predict", "transform", "decision_function", "predict_proba"]: try: if hasattr(estimator, method): getattr(estimator, method)(X_train) except NotImplementedError: # FIXME # non-standard handling of ducktyping in BaggingEstimator pass def check_estimators_nan_inf(name, Estimator): rnd = np.random.RandomState(0) X_train_finite = rnd.uniform(size=(10, 3)) X_train_nan = rnd.uniform(size=(10, 3)) X_train_nan[0, 0] = np.nan X_train_inf = rnd.uniform(size=(10, 3)) X_train_inf[0, 0] = np.inf y = np.ones(10) y[:5] = 0 y = multioutput_estimator_convert_y_2d(name, y) error_string_fit = "Estimator doesn't check for NaN and inf in fit." error_string_predict = ("Estimator doesn't check for NaN and inf in" " predict.") error_string_transform = ("Estimator doesn't check for NaN and inf in" " transform.") for X_train in [X_train_nan, X_train_inf]: # catch deprecation warnings with warnings.catch_warnings(record=True): estimator = Estimator() set_fast_parameters(estimator) set_random_state(estimator, 1) # try to fit try: estimator.fit(X_train, y) except ValueError as e: if 'inf' not in repr(e) and 'NaN' not in repr(e): print(error_string_fit, Estimator, e) traceback.print_exc(file=sys.stdout) raise e except Exception as exc: print(error_string_fit, Estimator, exc) traceback.print_exc(file=sys.stdout) raise exc else: raise AssertionError(error_string_fit, Estimator) # actually fit estimator.fit(X_train_finite, y) # predict if hasattr(estimator, "predict"): try: estimator.predict(X_train) except ValueError as e: if 'inf' not in repr(e) and 'NaN' not in repr(e): print(error_string_predict, Estimator, e) traceback.print_exc(file=sys.stdout) raise e except Exception as exc: print(error_string_predict, Estimator, exc) traceback.print_exc(file=sys.stdout) else: raise AssertionError(error_string_predict, Estimator) # transform if hasattr(estimator, "transform"): try: estimator.transform(X_train) except ValueError as e: if 'inf' not in repr(e) and 'NaN' not in repr(e): print(error_string_transform, Estimator, e) traceback.print_exc(file=sys.stdout) raise e except Exception as exc: print(error_string_transform, Estimator, exc) traceback.print_exc(file=sys.stdout) else: raise AssertionError(error_string_transform, Estimator) def check_transformer_pickle(name, Transformer): X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) n_samples, n_features = X.shape X = StandardScaler().fit_transform(X) X -= X.min() # catch deprecation warnings with warnings.catch_warnings(record=True): transformer = Transformer() if not hasattr(transformer, 'transform'): return set_random_state(transformer) set_fast_parameters(transformer) # fit if name in CROSS_DECOMPOSITION: random_state = np.random.RandomState(seed=12345) y_ = np.vstack([y, 2 * y + random_state.randint(2, size=len(y))]) y_ = y_.T else: y_ = y transformer.fit(X, y_) X_pred = transformer.fit(X, y_).transform(X) pickled_transformer = pickle.dumps(transformer) unpickled_transformer = pickle.loads(pickled_transformer) pickled_X_pred = unpickled_transformer.transform(X) assert_array_almost_equal(pickled_X_pred, X_pred) def check_estimators_partial_fit_n_features(name, Alg): # check if number of features changes between calls to partial_fit. if not hasattr(Alg, 'partial_fit'): return X, y = make_blobs(n_samples=50, random_state=1) X -= X.min() with warnings.catch_warnings(record=True): alg = Alg() set_fast_parameters(alg) if isinstance(alg, ClassifierMixin): classes = np.unique(y) alg.partial_fit(X, y, classes=classes) else: alg.partial_fit(X, y) assert_raises(ValueError, alg.partial_fit, X[:, :-1], y) def check_clustering(name, Alg): X, y = make_blobs(n_samples=50, random_state=1) X, y = shuffle(X, y, random_state=7) X = StandardScaler().fit_transform(X) n_samples, n_features = X.shape # catch deprecation and neighbors warnings with warnings.catch_warnings(record=True): alg = Alg() set_fast_parameters(alg) if hasattr(alg, "n_clusters"): alg.set_params(n_clusters=3) set_random_state(alg) if name == 'AffinityPropagation': alg.set_params(preference=-100) alg.set_params(max_iter=100) # fit alg.fit(X) # with lists alg.fit(X.tolist()) assert_equal(alg.labels_.shape, (n_samples,)) pred = alg.labels_ assert_greater(adjusted_rand_score(pred, y), 0.4) # fit another time with ``fit_predict`` and compare results if name is 'SpectralClustering': # there is no way to make Spectral clustering deterministic :( return set_random_state(alg) with warnings.catch_warnings(record=True): pred2 = alg.fit_predict(X) assert_array_equal(pred, pred2) def check_clusterer_compute_labels_predict(name, Clusterer): """Check that predict is invariant of compute_labels""" X, y = make_blobs(n_samples=20, random_state=0) clusterer = Clusterer() if hasattr(clusterer, "compute_labels"): # MiniBatchKMeans if hasattr(clusterer, "random_state"): clusterer.set_params(random_state=0) X_pred1 = clusterer.fit(X).predict(X) clusterer.set_params(compute_labels=False) X_pred2 = clusterer.fit(X).predict(X) assert_array_equal(X_pred1, X_pred2) def check_classifiers_one_label(name, Classifier): error_string_fit = "Classifier can't train when only one class is present." error_string_predict = ("Classifier can't predict when only one class is " "present.") rnd = np.random.RandomState(0) X_train = rnd.uniform(size=(10, 3)) X_test = rnd.uniform(size=(10, 3)) y = np.ones(10) # catch deprecation warnings with warnings.catch_warnings(record=True): classifier = Classifier() set_fast_parameters(classifier) # try to fit try: classifier.fit(X_train, y) except ValueError as e: if 'class' not in repr(e): print(error_string_fit, Classifier, e) traceback.print_exc(file=sys.stdout) raise e else: return except Exception as exc: print(error_string_fit, Classifier, exc) traceback.print_exc(file=sys.stdout) raise exc # predict try: assert_array_equal(classifier.predict(X_test), y) except Exception as exc: print(error_string_predict, Classifier, exc) raise exc def check_classifiers_train(name, Classifier): X_m, y_m = make_blobs(random_state=0) X_m, y_m = shuffle(X_m, y_m, random_state=7) X_m = StandardScaler().fit_transform(X_m) # generate binary problem from multi-class one y_b = y_m[y_m != 2] X_b = X_m[y_m != 2] for (X, y) in [(X_m, y_m), (X_b, y_b)]: # catch deprecation warnings classes = np.unique(y) n_classes = len(classes) n_samples, n_features = X.shape with warnings.catch_warnings(record=True): classifier = Classifier() if name in ['BernoulliNB', 'MultinomialNB']: X -= X.min() set_fast_parameters(classifier) set_random_state(classifier) # raises error on malformed input for fit assert_raises(ValueError, classifier.fit, X, y[:-1]) # fit classifier.fit(X, y) # with lists classifier.fit(X.tolist(), y.tolist()) assert_true(hasattr(classifier, "classes_")) y_pred = classifier.predict(X) assert_equal(y_pred.shape, (n_samples,)) # training set performance if name not in ['BernoulliNB', 'MultinomialNB']: assert_greater(accuracy_score(y, y_pred), 0.85) # raises error on malformed input for predict assert_raises(ValueError, classifier.predict, X.T) if hasattr(classifier, "decision_function"): try: # decision_function agrees with predict decision = classifier.decision_function(X) if n_classes is 2: assert_equal(decision.shape, (n_samples,)) dec_pred = (decision.ravel() > 0).astype(np.int) assert_array_equal(dec_pred, y_pred) if (n_classes is 3 and not isinstance(classifier, BaseLibSVM)): # 1on1 of LibSVM works differently assert_equal(decision.shape, (n_samples, n_classes)) assert_array_equal(np.argmax(decision, axis=1), y_pred) # raises error on malformed input assert_raises(ValueError, classifier.decision_function, X.T) # raises error on malformed input for decision_function assert_raises(ValueError, classifier.decision_function, X.T) except NotImplementedError: pass if hasattr(classifier, "predict_proba"): # predict_proba agrees with predict y_prob = classifier.predict_proba(X) assert_equal(y_prob.shape, (n_samples, n_classes)) assert_array_equal(np.argmax(y_prob, axis=1), y_pred) # check that probas for all classes sum to one assert_array_almost_equal(np.sum(y_prob, axis=1), np.ones(n_samples)) # raises error on malformed input assert_raises(ValueError, classifier.predict_proba, X.T) # raises error on malformed input for predict_proba assert_raises(ValueError, classifier.predict_proba, X.T) def check_estimators_unfitted(name, Estimator): """Check if NotFittedError is raised when calling predict and related functions""" # Common test for Regressors as well as Classifiers X, y = _boston_subset() with warnings.catch_warnings(record=True): est = Estimator() assert_raises(NotFittedError, est.predict, X) if hasattr(est, 'predict'): assert_raises(NotFittedError, est.predict, X) if hasattr(est, 'decision_function'): assert_raises(NotFittedError, est.decision_function, X) if hasattr(est, 'predict_proba'): assert_raises(NotFittedError, est.predict_proba, X) if hasattr(est, 'predict_log_proba'): assert_raises(NotFittedError, est.predict_log_proba, X) def check_classifiers_input_shapes(name, Classifier): iris = load_iris() X, y = iris.data, iris.target X, y = shuffle(X, y, random_state=1) X = StandardScaler().fit_transform(X) # catch deprecation warnings with warnings.catch_warnings(record=True): classifier = Classifier() set_fast_parameters(classifier) set_random_state(classifier) # fit classifier.fit(X, y) y_pred = classifier.predict(X) set_random_state(classifier) # Check that when a 2D y is given, a DataConversionWarning is # raised with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always", DataConversionWarning) classifier.fit(X, y[:, np.newaxis]) msg = "expected 1 DataConversionWarning, got: %s" % ( ", ".join([str(w_x) for w_x in w])) assert_equal(len(w), 1, msg) assert_array_equal(y_pred, classifier.predict(X)) def check_classifiers_classes(name, Classifier): X, y = make_blobs(n_samples=30, random_state=0, cluster_std=0.1) X, y = shuffle(X, y, random_state=7) X = StandardScaler().fit_transform(X) # We need to make sure that we have non negative data, for things # like NMF X -= X.min() - .1 y_names = np.array(["one", "two", "three"])[y] for y_names in [y_names, y_names.astype('O')]: if name in ["LabelPropagation", "LabelSpreading"]: # TODO some complication with -1 label y_ = y else: y_ = y_names classes = np.unique(y_) # catch deprecation warnings with warnings.catch_warnings(record=True): classifier = Classifier() if name == 'BernoulliNB': classifier.set_params(binarize=X.mean()) set_fast_parameters(classifier) # fit classifier.fit(X, y_) y_pred = classifier.predict(X) # training set performance assert_array_equal(np.unique(y_), np.unique(y_pred)) if np.any(classifier.classes_ != classes): print("Unexpected classes_ attribute for %r: " "expected %s, got %s" % (classifier, classes, classifier.classes_)) def check_classifiers_pickle(name, Classifier): X, y = make_blobs(random_state=0) X, y = shuffle(X, y, random_state=7) X -= X.min() # catch deprecation warnings with warnings.catch_warnings(record=True): classifier = Classifier() set_fast_parameters(classifier) # raises error on malformed input for fit assert_raises(ValueError, classifier.fit, X, y[:-1]) # fit classifier.fit(X, y) y_pred = classifier.predict(X) pickled_classifier = pickle.dumps(classifier) unpickled_classifier = pickle.loads(pickled_classifier) pickled_y_pred = unpickled_classifier.predict(X) assert_array_almost_equal(pickled_y_pred, y_pred) def check_regressors_int(name, Regressor): X, _ = _boston_subset() X = X[:50] rnd = np.random.RandomState(0) y = rnd.randint(3, size=X.shape[0]) y = multioutput_estimator_convert_y_2d(name, y) if name == 'OrthogonalMatchingPursuitCV': # FIXME: This test is unstable on Travis, see issue #3190. check_skip_travis() rnd = np.random.RandomState(0) # catch deprecation warnings with warnings.catch_warnings(record=True): # separate estimators to control random seeds regressor_1 = Regressor() regressor_2 = Regressor() set_fast_parameters(regressor_1) set_fast_parameters(regressor_2) set_random_state(regressor_1) set_random_state(regressor_2) if name in CROSS_DECOMPOSITION: y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))]) y_ = y_.T else: y_ = y # fit regressor_1.fit(X, y_) pred1 = regressor_1.predict(X) regressor_2.fit(X, y_.astype(np.float)) pred2 = regressor_2.predict(X) assert_array_almost_equal(pred1, pred2, 2, name) def check_regressors_train(name, Regressor): X, y = _boston_subset() y = StandardScaler().fit_transform(y) # X is already scaled y = multioutput_estimator_convert_y_2d(name, y) if name == 'OrthogonalMatchingPursuitCV': # FIXME: This test is unstable on Travis, see issue #3190. check_skip_travis() rnd = np.random.RandomState(0) # catch deprecation warnings with warnings.catch_warnings(record=True): regressor = Regressor() set_fast_parameters(regressor) if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'): # linear regressors need to set alpha, but not generalized CV ones regressor.alpha = 0.01 # raises error on malformed input for fit assert_raises(ValueError, regressor.fit, X, y[:-1]) # fit if name in CROSS_DECOMPOSITION: y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))]) y_ = y_.T else: y_ = y set_random_state(regressor) regressor.fit(X, y_) regressor.fit(X.tolist(), y_.tolist()) regressor.predict(X) # TODO: find out why PLS and CCA fail. RANSAC is random # and furthermore assumes the presence of outliers, hence # skipped if name not in ('PLSCanonical', 'CCA', 'RANSACRegressor'): assert_greater(regressor.score(X, y_), 0.5) def check_regressors_pickle(name, Regressor): X, y = _boston_subset() y = StandardScaler().fit_transform(y) # X is already scaled y = multioutput_estimator_convert_y_2d(name, y) if name == 'OrthogonalMatchingPursuitCV': # FIXME: This test is unstable on Travis, see issue #3190. check_skip_travis() rnd = np.random.RandomState(0) # catch deprecation warnings with warnings.catch_warnings(record=True): regressor = Regressor() set_fast_parameters(regressor) if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'): # linear regressors need to set alpha, but not generalized CV ones regressor.alpha = 0.01 if name in CROSS_DECOMPOSITION: y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))]) y_ = y_.T else: y_ = y regressor.fit(X, y_) y_pred = regressor.predict(X) # store old predictions pickled_regressor = pickle.dumps(regressor) unpickled_regressor = pickle.loads(pickled_regressor) pickled_y_pred = unpickled_regressor.predict(X) assert_array_almost_equal(pickled_y_pred, y_pred) def check_class_weight_classifiers(name, Classifier): for n_centers in [2, 3]: # create a very noisy dataset X, y = make_blobs(centers=n_centers, random_state=0, cluster_std=20) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0) n_centers = len(np.unique(y_train)) if n_centers == 2: class_weight = {0: 1000, 1: 0.0001} else: class_weight = {0: 1000, 1: 0.0001, 2: 0.0001} with warnings.catch_warnings(record=True): classifier = Classifier(class_weight=class_weight) if hasattr(classifier, "n_iter"): classifier.set_params(n_iter=100) if hasattr(classifier, "min_weight_fraction_leaf"): classifier.set_params(min_weight_fraction_leaf=0.01) set_random_state(classifier) classifier.fit(X_train, y_train) y_pred = classifier.predict(X_test) assert_greater(np.mean(y_pred == 0), 0.9) def check_class_weight_auto_classifiers(name, Classifier, X_train, y_train, X_test, y_test, weights): with warnings.catch_warnings(record=True): classifier = Classifier() if hasattr(classifier, "n_iter"): classifier.set_params(n_iter=100) set_random_state(classifier) classifier.fit(X_train, y_train) y_pred = classifier.predict(X_test) classifier.set_params(class_weight='auto') classifier.fit(X_train, y_train) y_pred_auto = classifier.predict(X_test) assert_greater(f1_score(y_test, y_pred_auto, average='weighted'), f1_score(y_test, y_pred, average='weighted')) def check_class_weight_auto_linear_classifier(name, Classifier): """Test class weights with non-contiguous class labels.""" X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0], [1.0, 1.0], [1.0, 0.0]]) y = [1, 1, 1, -1, -1] with warnings.catch_warnings(record=True): classifier = Classifier() if hasattr(classifier, "n_iter"): # This is a very small dataset, default n_iter are likely to prevent # convergence classifier.set_params(n_iter=1000) set_random_state(classifier) # Let the model compute the class frequencies classifier.set_params(class_weight='auto') coef_auto = classifier.fit(X, y).coef_.copy() # Count each label occurrence to reweight manually mean_weight = (1. / 3 + 1. / 2) / 2 class_weight = { 1: 1. / 3 / mean_weight, -1: 1. / 2 / mean_weight, } classifier.set_params(class_weight=class_weight) coef_manual = classifier.fit(X, y).coef_.copy() assert_array_almost_equal(coef_auto, coef_manual) def check_estimators_overwrite_params(name, Estimator): X, y = make_blobs(random_state=0, n_samples=9) y = multioutput_estimator_convert_y_2d(name, y) # some want non-negative input X -= X.min() with warnings.catch_warnings(record=True): # catch deprecation warnings estimator = Estimator() if name == 'MiniBatchDictLearning' or name == 'MiniBatchSparsePCA': # FIXME # for MiniBatchDictLearning and MiniBatchSparsePCA estimator.batch_size = 1 set_fast_parameters(estimator) set_random_state(estimator) params = estimator.get_params() estimator.fit(X, y) new_params = estimator.get_params() for k, v in params.items(): assert_false(np.any(new_params[k] != v), "Estimator %s changes its parameter %s" " from %s to %s during fit." % (name, k, v, new_params[k])) def check_sparsify_coefficients(name, Estimator): X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [-1, -2], [2, 2], [-2, -2]]) y = [1, 1, 1, 2, 2, 2, 3, 3, 3] est = Estimator() est.fit(X, y) pred_orig = est.predict(X) # test sparsify with dense inputs est.sparsify() assert_true(sparse.issparse(est.coef_)) pred = est.predict(X) assert_array_equal(pred, pred_orig) # pickle and unpickle with sparse coef_ est = pickle.loads(pickle.dumps(est)) assert_true(sparse.issparse(est.coef_)) pred = est.predict(X) assert_array_equal(pred, pred_orig) def check_classifier_data_not_an_array(name, Estimator): X = np.array([[3, 0], [0, 1], [0, 2], [1, 1], [1, 2], [2, 1]]) y = [1, 1, 1, 2, 2, 2] y = multioutput_estimator_convert_y_2d(name, y) check_estimators_data_not_an_array(name, Estimator, X, y) def check_regressor_data_not_an_array(name, Estimator): X, y = _boston_subset(n_samples=50) y = multioutput_estimator_convert_y_2d(name, y) check_estimators_data_not_an_array(name, Estimator, X, y) def check_estimators_data_not_an_array(name, Estimator, X, y): if name in CROSS_DECOMPOSITION: raise SkipTest # catch deprecation warnings with warnings.catch_warnings(record=True): # separate estimators to control random seeds estimator_1 = Estimator() estimator_2 = Estimator() set_fast_parameters(estimator_1) set_fast_parameters(estimator_2) set_random_state(estimator_1) set_random_state(estimator_2) y_ = NotAnArray(np.asarray(y)) X_ = NotAnArray(np.asarray(X)) # fit estimator_1.fit(X_, y_) pred1 = estimator_1.predict(X_) estimator_2.fit(X, y) pred2 = estimator_2.predict(X) assert_array_almost_equal(pred1, pred2, 2, name) def check_parameters_default_constructible(name, Estimator): classifier = LDA() # test default-constructibility # get rid of deprecation warnings with warnings.catch_warnings(record=True): if name in META_ESTIMATORS: estimator = Estimator(classifier) else: estimator = Estimator() # test cloning clone(estimator) # test __repr__ repr(estimator) # test that set_params returns self assert_true(isinstance(estimator.set_params(), Estimator)) # test if init does nothing but set parameters # this is important for grid_search etc. # We get the default parameters from init and then # compare these against the actual values of the attributes. # this comes from getattr. Gets rid of deprecation decorator. init = getattr(estimator.__init__, 'deprecated_original', estimator.__init__) try: args, varargs, kws, defaults = inspect.getargspec(init) except TypeError: # init is not a python function. # true for mixins return params = estimator.get_params() if name in META_ESTIMATORS: # they need a non-default argument args = args[2:] else: args = args[1:] if args: # non-empty list assert_equal(len(args), len(defaults)) else: return for arg, default in zip(args, defaults): if arg not in params.keys(): # deprecated parameter, not in get_params assert_true(default is None) continue if isinstance(params[arg], np.ndarray): assert_array_equal(params[arg], default) else: assert_equal(params[arg], default) def multioutput_estimator_convert_y_2d(name, y): # Estimators in mono_output_task_error raise ValueError if y is of 1-D # Convert into a 2-D y for those estimators. if name in (['MultiTaskElasticNetCV', 'MultiTaskLassoCV', 'MultiTaskLasso', 'MultiTaskElasticNet']): return y[:, np.newaxis] return y def check_non_transformer_estimators_n_iter(name, estimator, multi_output=False): # Check if all iterative solvers, run for more than one iteratiom iris = load_iris() X, y_ = iris.data, iris.target if multi_output: y_ = y_[:, np.newaxis] set_random_state(estimator, 0) if name == 'AffinityPropagation': estimator.fit(X) else: estimator.fit(X, y_) assert_greater(estimator.n_iter_, 0) def check_transformer_n_iter(name, estimator): if name in CROSS_DECOMPOSITION: # Check using default data X = [[0., 0., 1.], [1., 0., 0.], [2., 2., 2.], [2., 5., 4.]] y_ = [[0.1, -0.2], [0.9, 1.1], [0.1, -0.5], [0.3, -0.2]] else: X, y_ = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) X -= X.min() - 0.1 set_random_state(estimator, 0) estimator.fit(X, y_) # These return a n_iter per component. if name in CROSS_DECOMPOSITION: for iter_ in estimator.n_iter_: assert_greater(iter_, 1) else: assert_greater(estimator.n_iter_, 1)	bsd-3-clause
mailhexu/pyDFTutils	build/lib/pyDFTutils/wannier90/pythtb.py	2	153556	from __future__ import print_function # PythTB python tight binding module. # December 22, 2016 __version__='1.7.1' # Copyright 2010, 2012, 2016 by Sinisa Coh and David Vanderbilt # # This file is part of PythTB. PythTB is free software: you can # redistribute it and/or modify it under the terms of the GNU General # Public License as published by the Free Software Foundation, either # version 3 of the License, or (at your option) any later version. # # PythTB is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY # or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public # License for more details. # # A copy of the GNU General Public License should be available # alongside this source in a file named gpl-3.0.txt. If not, # see <http://www.gnu.org/licenses/>. # # PythTB is availabe at http://www.physics.rutgers.edu/pythtb/ import numpy as np # numerics for matrices import sys # for exiting import copy # for deepcopying class tb_model(object): r""" This is the main class of the PythTB package which contains all information for the tight-binding model. :param dim_k: Dimensionality of reciprocal space, i.e., specifies how many directions are considered to be periodic. :param dim_r: Dimensionality of real space, i.e., specifies how many real space lattice vectors there are and how many coordinates are needed to specify the orbital coordinates. .. note:: Parameter dim_r can be larger than dim_k! For example, a polymer is a three-dimensional molecule (one needs three coordinates to specify orbital positions), but it is periodic along only one direction. For a polymer, therefore, we should have dim_k equal to 1 and dim_r equal to 3. See similar example here: :ref:`trestle-example`. :param lat: Array containing lattice vectors in Cartesian coordinates (in arbitrary units). In example the below, the first lattice vector has coordinates [1.0,0.5] while the second one has coordinates [0.0,2.0]. By default, lattice vectors are an identity matrix. :param orb: Array containing reduced coordinates of all tight-binding orbitals. In the example below, the first orbital is defined with reduced coordinates [0.2,0.3]. Its Cartesian coordinates are therefore 0.2 times the first lattice vector plus 0.3 times the second lattice vector. If orb is an integer code will assume that there are these many orbitals all at the origin of the unit cell. By default the code will assume a single orbital at the origin. :param per: This is an optional parameter giving a list of lattice vectors which are considered to be periodic. In the example below, only the vector [0.0,2.0] is considered to be periodic (since per=[1]). By default, all lattice vectors are assumed to be periodic. If dim_k is smaller than dim_r, then by default the first dim_k vectors are considered to be periodic. :param nspin: Number of explicit spin components assumed for each orbital in orb. Allowed values of nspin are 1 and 2. If nspin is 1 then the model is spinless, if nspin is 2 then it is explicitly a spinfull model and each orbital is assumed to have two spin components. Default value of this parameter is 1. Of course one can make spinfull calculation even with nspin set to 1, but then the user must keep track of which orbital corresponds to which spin component. Example usage:: # Creates model that is two-dimensional in real space but only # one-dimensional in reciprocal space. Second lattice vector is # chosen to be periodic (since per=[1]). Three orbital # coordinates are specified. tb = tb_model(1, 2, lat=[[1.0, 0.5], [0.0, 2.0]], orb=[[0.2, 0.3], [0.1, 0.1], [0.2, 0.2]], per=[1]) """ def __init__(self,dim_k,dim_r,lat=None,orb=None,per=None,nspin=1): # initialize _dim_k = dimensionality of k-space (integer) if type(dim_k).__name__!='int': raise Exception("\n\nArgument dim_k not an integer") if dim_k < 0 or dim_k > 4: raise Exception("\n\nArgument dim_k out of range. Must be between 0 and 4.") self._dim_k=dim_k # initialize _dim_r = dimensionality of r-space (integer) if type(dim_r).__name__!='int': raise Exception("\n\nArgument dim_r not an integer") if dim_r < dim_k or dim_r > 4: raise Exception("\n\nArgument dim_r out of range. Must be dim_r>=dim_k and dim_r<=4.") self._dim_r=dim_r # initialize _lat = lattice vectors, array of dim_rdim_r # format is _lat(lat_vec_index,cartesian_index) # special option: 'unit' implies unit matrix, also default value if lat is 'unit' or lat is None: self._lat=np.identity(dim_r,float) print(" Lattice vectors not specified! I will use identity matrix.") elif type(lat).__name__ not in ['list','ndarray']: raise Exception("\n\nArgument lat is not a list.") else: self._lat=np.array(lat,dtype=float) if self._lat.shape!=(dim_r,dim_r): raise Exception("\n\nWrong lat array dimensions") # check that volume is not zero and that have right handed system if dim_r>0: if np.abs(np.linalg.det(self._lat))<1.0E-6: raise Exception("\n\nLattice vectors length/area/volume too close to zero, or zero.") if np.linalg.det(self._lat)<0.0: raise Exception("\n\nLattice vectors need to form right handed system.") # initialize _norb = number of basis orbitals per cell # and _orb = orbital locations, in reduced coordinates # format is _orb(orb_index,lat_vec_index) # special option: 'bravais' implies one atom at origin if orb is 'bravais' or orb is None: self._norb=1 self._orb=np.zeros((1,dim_r)) print(" Orbital positions not specified. I will assume a single orbital at the origin.") elif type(orb).__name__=='int': self._norb=orb self._orb=np.zeros((orb,dim_r)) print(" Orbital positions not specified. I will assume ",orb," orbitals at the origin") elif type(orb).__name__ not in ['list','ndarray']: raise Exception("\n\nArgument orb is not a list or an integer") else: self._orb=np.array(orb,dtype=float) if len(self._orb.shape)!=2: raise Exception("\n\nWrong orb array rank") self._norb=self._orb.shape[0] # number of orbitals if self._orb.shape[1]!=dim_r: raise Exception("\n\nWrong orb array dimensions") # choose which self._dim_k out of self._dim_r dimensions are # to be considered periodic. if per==None: # by default first _dim_k dimensions are periodic self._per=list(range(self._dim_k)) else: if len(per)!=self._dim_k: raise Exception("\n\nWrong choice of periodic/infinite direction!") # store which directions are the periodic ones self._per=per # remember number of spin components if nspin not in [1,2]: raise Exception("\n\nWrong value of nspin, must be 1 or 2!") self._nspin=nspin # by default, assume model did not come from w90 object and that # position operator is diagonal self._assume_position_operator_diagonal=True # compute number of electronic states at each k-point self._nsta=self._norbself._nspin # Initialize onsite energies to zero if self._nspin==1: self._site_energies=np.zeros((self._norb),dtype=float) elif self._nspin==2: self._site_energies=np.zeros((self._norb,2,2),dtype=complex) # remember which onsite energies user has specified self._site_energies_specified=np.zeros(self._norb,dtype=bool) self._site_energies_specified[:]=False # Initialize hoppings to empty list self._hoppings=[] # The onsite energies and hoppings are not specified # when creating a 'tb_model' object. They are speficied # subsequently by separate function calls defined below. def set_onsite(self,onsite_en,ind_i=None,mode="set"): r""" Defines on-site energies for tight-binding orbitals. One can either set energy for one tight-binding orbital, or all at once. .. warning:: In previous version of PythTB this function was called set_sites. For backwards compatibility one can still use that name but that feature will be removed in future releases. :param onsite_en: Either a list of on-site energies (in arbitrary units) for each orbital, or a single on-site energy (in this case ind_i parameter must be given). In the case when nspin is 1 (spinless) then each on-site energy is a single number. If nspin is 2 then on-site energy can be given either as a single number, or as an array of four numbers, or 2x2 matrix. If a single number is given, it is interpreted as on-site energy for both up and down spin component. If an array of four numbers is given, these are the coefficients of I, sigma_x, sigma_y, and sigma_z (that is, the 2x2 identity and the three Pauli spin matrices) respectively. Finally, full 2x2 matrix can be given as well. If this function is never called, on-site energy is assumed to be zero. :param ind_i: Index of tight-binding orbital whose on-site energy you wish to change. This parameter should be specified only when onsite_en is a single number (not a list). :param mode: Similar to parameter mode in function set_hop. Speficies way in which parameter onsite_en* is used. It can either set value of on-site energy from scratch, reset it, or add to it. * "set" -- Default value. On-site energy is set to value of onsite_en parameter. One can use "set" on each tight-binding orbital only once. * "reset" -- Specifies on-site energy to given value. This function can be called multiple times for the same orbital(s). * "add" -- Adds to the previous value of on-site energy. This function can be called multiple times for the same orbital(s). Example usage:: # Defines on-site energy of first orbital to be 0.0, # second 1.0, and third 2.0 tb.set_onsite([0.0, 1.0, 2.0]) # Increases value of on-site energy for second orbital tb.set_onsite(100.0, 1, mode="add") # Changes on-site energy of second orbital to zero tb.set_onsite(0.0, 1, mode="reset") # Sets all three on-site energies at once tb.set_onsite([2.0, 3.0, 4.0], mode="reset") """ if ind_i==None: if (len(onsite_en)!=self._norb): raise Exception("\n\nWrong number of site energies") # make sure ind_i is not out of scope if ind_i!=None: if ind_i<0 or ind_i>=self._norb: raise Exception("\n\nIndex ind_i out of scope.") # make sure that onsite terms are real/hermitian if ind_i!=None: to_check=[onsite_en] else: to_check=onsite_en for ons in to_check: if np.array(ons).shape==(): if np.abs(np.array(ons)-np.array(ons).conjugate())>1.0E-8: raise Exception("\n\nOnsite energy should not have imaginary part!") elif np.array(ons).shape==(4,): if np.max(np.abs(np.array(ons)-np.array(ons).conjugate()))>1.0E-8: raise Exception("\n\nOnsite energy or Zeeman field should not have imaginary part!") elif np.array(ons).shape==(2,2): if np.max(np.abs(np.array(ons)-np.array(ons).T.conjugate()))>1.0E-8: raise Exception("\n\nOnsite matrix should be Hermitian!") # specifying onsite energies from scratch, can be called only once if mode.lower()=="set": # specifying only one site at a time if ind_i!=None: # make sure we specify things only once if self._site_energies_specified[ind_i]==True: raise Exception("\n\nOnsite energy for this site was already specified! Use mode=\"reset\" or mode=\"add\".") else: self._site_energies[ind_i]=self._val_to_block(onsite_en) self._site_energies_specified[ind_i]=True # specifying all sites at once else: # make sure we specify things only once if True in self._site_energies_specified[ind_i]: raise Exception("\n\nSome or all onsite energies were already specified! Use mode=\"reset\" or mode=\"add\".") else: for i in range(self._norb): self._site_energies[i]=self._val_to_block(onsite_en[i]) self._site_energies_specified[:]=True # reset values of onsite terms, without adding to previous value elif mode.lower()=="reset": # specifying only one site at a time if ind_i!=None: self._site_energies[ind_i]=self._val_to_block(onsite_en) self._site_energies_specified[ind_i]=True # specifying all sites at once else: for i in range(self._norb): self._site_energies[i]=self._val_to_block(onsite_en[i]) self._site_energies_specified[:]=True # add to previous value elif mode.lower()=="add": # specifying only one site at a time if ind_i!=None: self._site_energies[ind_i]+=self._val_to_block(onsite_en) self._site_energies_specified[ind_i]=True # specifying all sites at once else: for i in range(self._norb): self._site_energies[i]+=self._val_to_block(onsite_en[i]) self._site_energies_specified[:]=True else: raise Exception("\n\nWrong value of mode parameter") def set_hop(self,hop_amp,ind_i,ind_j,ind_R=None,mode="set",allow_conjugate_pair=False): r""" Defines hopping parameters between tight-binding orbitals. In the notation used in section 3.1 equation 3.6 of :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>` this function specifies the following object .. math:: H_{ij}({\bf R})= \langle \phi_{{\bf 0} i} \vert H \vert \phi_{{\bf R},j} \rangle Where :math:`\langle \phi_{{\bf 0} i} \vert` is i-th tight-binding orbital in the home unit cell and :math:`\vert \phi_{{\bf R},j} \rangle` is j-th tight-binding orbital in unit cell shifted by lattice vector :math:`{\bf R}`. :math:`H` is the Hamiltonian. (Strictly speaking, this term specifies hopping amplitude for hopping from site j+R to site i, not vice-versa.) Hopping in the opposite direction is automatically included by the code since .. math:: H_{ji}(-{\bf R})= \left[ H_{ij}({\bf R}) \right]^{} .. warning:: There is no need to specify hoppings in both :math:`i \rightarrow j+R` direction and opposite :math:`j \rightarrow i-R` direction since that is done automatically. If you want to specifiy hoppings in both directions, see description of parameter allow_conjugate_pair. .. warning:: In previous version of PythTB this function was called add_hop. For backwards compatibility one can still use that name but that feature will be removed in future releases. :param hop_amp: Hopping amplitude; can be real or complex number, equals :math:`H_{ij}({\bf R})`. If nspin* is 2 then hopping amplitude can be given either as a single number, or as an array of four numbers, or as 2x2 matrix. If a single number is given, it is interpreted as hopping amplitude for both up and down spin component. If an array of four numbers is given, these are the coefficients of I, sigma_x, sigma_y, and sigma_z (that is, the 2x2 identity and the three Pauli spin matrices) respectively. Finally, full 2x2 matrix can be given as well. :param ind_i: Index of bra orbital from the bracket :math:`\langle \phi_{{\bf 0} i} \vert H \vert \phi_{{\bf R},j} \rangle`. This orbital is assumed to be in the home unit cell. :param ind_j: Index of ket orbital from the bracket :math:`\langle \phi_{{\bf 0} i} \vert H \vert \phi_{{\bf R},j} \rangle`. This orbital does not have to be in the home unit cell; its unit cell position is determined by parameter ind_R. :param ind_R: Specifies, in reduced coordinates, the shift of the ket orbital. The number of coordinates must equal the dimensionality in real space (dim_r parameter) for consistency, but only the periodic directions of ind_R will be considered. If reciprocal space is zero-dimensional (as in a molecule), this parameter does not need to be specified. :param mode: Similar to parameter mode in function set_onsite. Speficies way in which parameter hop_amp is used. It can either set value of hopping term from scratch, reset it, or add to it. * "set" -- Default value. Hopping term is set to value of hop_amp parameter. One can use "set" for each triplet of ind_i, ind_j, ind_R only once. * "reset" -- Specifies on-site energy to given value. This function can be called multiple times for the same triplet ind_i, ind_j, ind_R. * "add" -- Adds to the previous value of hopping term This function can be called multiple times for the same triplet ind_i, ind_j, ind_R. If set_hop was ever called with allow_conjugate_pair set to True, then it is possible that user has specified both :math:`i \rightarrow j+R` and conjugate pair :math:`j \rightarrow i-R`. In this case, "set", "reset", and "add" parameters will treat triplet ind_i, ind_j, ind_R and conjugate triplet ind_j, ind_i, -ind_R as distinct. :param allow_conjugate_pair: Default value is False. If set to True code will allow user to specify hopping :math:`i \rightarrow j+R` even if conjugate-pair hopping :math:`j \rightarrow i-R` has been specified. If both terms are specified, code will still count each term two times. Example usage:: # Specifies complex hopping amplitude between first orbital in home # unit cell and third orbital in neigbouring unit cell. tb.set_hop(0.3+0.4j, 0, 2, [0, 1]) # change value of this hopping tb.set_hop(0.1+0.2j, 0, 2, [0, 1], mode="reset") # add to previous value (after this function call below, # hopping term amplitude is 100.1+0.2j) tb.set_hop(100.0, 0, 2, [0, 1], mode="add") """ # if self._dim_k!=0 and (ind_R is None): raise Exception("\n\nNeed to specify ind_R!") # if necessary convert from integer to array if self._dim_k==1 and type(ind_R).__name__=='int': tmpR=np.zeros(self._dim_r,dtype=int) tmpR[self._per]=ind_R ind_R=tmpR # check length of ind_R if self._dim_k!=0: if len(ind_R)!=self._dim_r: raise Exception("\n\nLength of input ind_R vector must equal dim_r! Even if dim_k<dim_r.") # make sure ind_i and ind_j are not out of scope if ind_i<0 or ind_i>=self._norb: raise Exception("\n\nIndex ind_i out of scope.") if ind_j<0 or ind_j>=self._norb: raise Exception("\n\nIndex ind_j out of scope.") # do not allow onsite hoppings to be specified here because then they # will be double-counted if self._dim_k==0: if ind_i==ind_j: raise Exception("\n\nDo not use set_hop for onsite terms. Use set_onsite instead!") else: if ind_i==ind_j: all_zer=True for k in self._per: if int(ind_R[k])!=0: all_zer=False if all_zer==True: raise Exception("\n\nDo not use set_hop for onsite terms. Use set_onsite instead!") # # make sure that if <i\|H\|j+R> is specified that <j\|H\|i-R> is not! if allow_conjugate_pair==False: for h in self._hoppings: if ind_i==h[2] and ind_j==h[1]: if self._dim_k==0: raise Exception(\ """\n Following matrix element was already implicitely specified: i="""+str(ind_i)+" j="+str(ind_j)+""" Remember, specifying <i\|H\|j> automatically specifies <j\|H\|i>. For consistency, specify all hoppings for a given bond in the same direction. (Or, alternatively, see the documentation on the 'allow_conjugate_pair' flag.) """) elif False not in (np.array(ind_R)[self._per]==(-1)np.array(h[3])[self._per]): raise Exception(\ """\n Following matrix element was already implicitely specified: i="""+str(ind_i)+" j="+str(ind_j)+" R="+str(ind_R)+""" Remember,specifying <i\|H\|j+R> automatically specifies <j\|H\|i-R>. For consistency, specify all hoppings for a given bond in the same direction. (Or, alternatively, see the documentation on the 'allow_conjugate_pair' flag.) """) # convert to 2by2 matrix if needed hop_use=self._val_to_block(hop_amp) # hopping term parameters to be stored if self._dim_k==0: new_hop=[hop_use,int(ind_i),int(ind_j)] else: new_hop=[hop_use,int(ind_i),int(ind_j),np.array(ind_R)] # # see if there is a hopping term with same i,j,R use_index=None for iih,h in enumerate(self._hoppings): # check if the same same_ijR=False if ind_i==h[1] and ind_j==h[2]: if self._dim_k==0: same_ijR=True else: if False not in (np.array(ind_R)[self._per]==np.array(h[3])[self._per]): same_ijR=True # if they are the same then store index of site at which they are the same if same_ijR==True: use_index=iih # # specifying hopping terms from scratch, can be called only once if mode.lower()=="set": # make sure we specify things only once if use_index!=None: raise Exception("\n\nHopping energy for this site was already specified! Use mode=\"reset\" or mode=\"add\".") else: self._hoppings.append(new_hop) # reset value of hopping term, without adding to previous value elif mode.lower()=="reset": if use_index!=None: self._hoppings[use_index]=new_hop else: self._hoppings.append(new_hop) # add to previous value elif mode.lower()=="add": if use_index!=None: self._hoppings[use_index][0]+=new_hop[0] else: self._hoppings.append(new_hop) else: raise Exception("\n\nWrong value of mode parameter") def _val_to_block(self,val): """If nspin=2 then returns a 2 by 2 matrix from the input parameters. If only one real number is given in the input then assume that this is the diagonal term. If array with four elements is given then first one is the diagonal term, and other three are Zeeman field direction. If given a 2 by 2 matrix, just return it. If nspin=1 then just returns val.""" # spinless case if self._nspin==1: return val # spinfull case elif self._nspin==2: # matrix to return ret=np.zeros((2,2),dtype=complex) # use_val=np.array(val) # only one number is given if use_val.shape==(): ret[0,0]+=use_val ret[1,1]+=use_val # if four numbers are given elif use_val.shape==(4,): # diagonal ret[0,0]+=use_val[0] ret[1,1]+=use_val[0] # sigma_x ret[0,1]+=use_val[1] ret[1,0]+=use_val[1] # sigma_y ret[0,1]+=use_val[2](-1.0j) ret[1,0]+=use_val[2]( 1.0j) # sigma_z ret[0,0]+=use_val[3] ret[1,1]+=use_val[3](-1.0) # if 2 by 2 matrix is given elif use_val.shape==(2,2): return use_val else: raise Exception(\ """\n Wrong format of the on-site or hopping term. Must be single number, or in the case of a spinfull model can be array of four numbers or 2x2 matrix.""") return ret def display(self): r""" Prints on the screen some information about this tight-binding model. This function doesn't take any parameters. """ print('---------------------------------------') print('report of tight-binding model') print('---------------------------------------') print('k-space dimension =',self._dim_k) print('r-space dimension =',self._dim_r) print('number of spin components =',self._nspin) print('periodic directions =',self._per) print('number of orbitals =',self._norb) print('number of electronic states =',self._nsta) print('lattice vectors:') for i,o in enumerate(self._lat): print(" #",_nice_int(i,2)," ===> [", end=' ') for j,v in enumerate(o): print(_nice_float(v,7,4), end=' ') if j!=len(o)-1: print(",", end=' ') print("]") print('positions of orbitals:') for i,o in enumerate(self._orb): print(" #",_nice_int(i,2)," ===> [", end=' ') for j,v in enumerate(o): print(_nice_float(v,7,4), end=' ') if j!=len(o)-1: print(",", end=' ') print("]") print('site energies:') for i,site in enumerate(self._site_energies): print(" #",_nice_int(i,2)," ===> ", end=' ') if self._nspin==1: print(_nice_float(site,7,4)) elif self._nspin==2: print(str(site).replace("\n"," ")) print('hoppings:') for i,hopping in enumerate(self._hoppings): print("<",_nice_int(hopping[1],2),"\| H \|",_nice_int(hopping[2],2), end=' ') if len(hopping)==4: print("+ [", end=' ') for j,v in enumerate(hopping[3]): print(_nice_int(v,2), end=' ') if j!=len(hopping[3])-1: print(",", end=' ') else: print("]", end=' ') print("> ===> ", end=' ') if self._nspin==1: print(_nice_complex(hopping[0],7,4)) elif self._nspin==2: print(str(hopping[0]).replace("\n"," ")) print() def visualize(self,dir_first,dir_second=None,eig_dr=None,draw_hoppings=True,ph_color="black"): r""" Rudimentary function for visualizing tight-binding model geometry, hopping between tight-binding orbitals, and electron eigenstates. If eigenvector is not drawn, then orbitals in home cell are drawn as red circles, and those in neighboring cells are drawn with different shade of red. Hopping term directions are drawn with green lines connecting two orbitals. Origin of unit cell is indicated with blue dot, while real space unit vectors are drawn with blue lines. If eigenvector is drawn, then electron eigenstate on each orbital is drawn with a circle whose size is proportional to wavefunction amplitude while its color depends on the phase. There are various coloring schemes for the phase factor; see more details under ph_color parameter. If eigenvector is drawn and coloring scheme is "red-blue" or "wheel", all other elements of the picture are drawn in gray or black. :param dir_first: First index of Cartesian coordinates used for plotting. :param dir_second: Second index of Cartesian coordinates used for plotting. For example if dir_first=0 and dir_second=2, and Cartesian coordinates of some orbital is [2.0,4.0,6.0] then it will be drawn at coordinate [2.0,6.0]. If dimensionality of real space (dim_r) is zero or one then dir_second should not be specified. :param eig_dr: Optional parameter specifying eigenstate to plot. If specified, this should be one-dimensional array of complex numbers specifying wavefunction at each orbital in the tight-binding basis. If not specified, eigenstate is not drawn. :param draw_hoppings: Optional parameter specifying whether to draw all allowed hopping terms in the tight-binding model. Default value is True. :param ph_color: Optional parameter determining the way eigenvector phase factors are translated into color. Default value is "black". Convention of the wavefunction phase is as in convention 1 in section 3.1 of :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>`. In other words, these wavefunction phases are in correspondence with cell-periodic functions :math:`u_{n {\bf k}} ({\bf r})` not :math:`\Psi_{n {\bf k}} ({\bf r})`. * "black" -- phase of eigenvectors are ignored and wavefunction is always colored in black. * "red-blue" -- zero phase is drawn red, while phases or pi or -pi are drawn blue. Phases in between are interpolated between red and blue. Some phase information is lost in this coloring becase phase of +phi and -phi have same color. * "wheel" -- each phase is given unique color. In steps of pi/3 starting from 0, colors are assigned (in increasing hue) as: red, yellow, green, cyan, blue, magenta, red. :returns: * fig -- Figure object from matplotlib.pyplot module that can be used to save the figure in PDF, EPS or similar format, for example using fig.savefig("name.pdf") command. * ax -- Axes object from matplotlib.pyplot module that can be used to tweak the plot, for example by adding a plot title ax.set_title("Title goes here"). Example usage:: # Draws x-y projection of tight-binding model # tweaks figure and saves it as a PDF. (fig, ax) = tb.visualize(0, 1) ax.set_title("Title goes here") fig.savefig("model.pdf") See also these examples: :ref:`edge-example`, :ref:`visualize-example`. """ # check the format of eig_dr if not (eig_dr is None): if eig_dr.shape!=(self._norb,): raise Exception("\n\nWrong format of eig_dr! Must be array of size norb.") # check that ph_color is correct if ph_color not in ["black","red-blue","wheel"]: raise Exception("\n\nWrong value of ph_color parameter!") # check if dir_second had to be specified if dir_second==None and self._dim_r>1: raise Exception("\n\nNeed to specify index of second coordinate for projection!") # start a new figure import pylab as plt fig=plt.figure(figsize=[plt.rcParams["figure.figsize"][0], plt.rcParams["figure.figsize"][0]]) ax=fig.add_subplot(111, aspect='equal') def proj(v): "Project vector onto drawing plane" coord_x=v[dir_first] if dir_second==None: coord_y=0.0 else: coord_y=v[dir_second] return [coord_x,coord_y] def to_cart(red): "Convert reduced to Cartesian coordinates" return np.dot(red,self._lat) # define colors to be used in plotting everything # except eigenvectors if (eig_dr is None) or ph_color=="black": c_cell="b" c_orb="r" c_nei=[0.85,0.65,0.65] c_hop="g" else: c_cell=[0.4,0.4,0.4] c_orb=[0.0,0.0,0.0] c_nei=[0.6,0.6,0.6] c_hop=[0.0,0.0,0.0] # determine color scheme for eigenvectors def color_to_phase(ph): if ph_color=="black": return "k" if ph_color=="red-blue": ph=np.abs(ph/np.pi) return [1.0-ph,0.0,ph] if ph_color=="wheel": if ph<0.0: ph=ph+2.0np.pi ph=6.0ph/(2.0np.pi) x_ph=1.0-np.abs(ph%2.0-1.0) if ph>=0.0 and ph<1.0: ret_col=[1.0 ,x_ph,0.0 ] if ph>=1.0 and ph<2.0: ret_col=[x_ph,1.0 ,0.0 ] if ph>=2.0 and ph<3.0: ret_col=[0.0 ,1.0 ,x_ph] if ph>=3.0 and ph<4.0: ret_col=[0.0 ,x_ph,1.0 ] if ph>=4.0 and ph<5.0: ret_col=[x_ph,0.0 ,1.0 ] if ph>=5.0 and ph<=6.0: ret_col=[1.0 ,0.0 ,x_ph] return ret_col # draw origin ax.plot([0.0],[0.0],"o",c=c_cell,mec="w",mew=0.0,zorder=7,ms=4.5) # first draw unit cell vectors which are considered to be periodic for i in self._per: # pick a unit cell vector and project it down to the drawing plane vec=proj(self._lat[i]) ax.plot([0.0,vec[0]],[0.0,vec[1]],"-",c=c_cell,lw=1.5,zorder=7) # now draw all orbitals for i in range(self._norb): # find position of orbital in cartesian coordinates pos=to_cart(self._orb[i]) pos=proj(pos) ax.plot([pos[0]],[pos[1]],"o",c=c_orb,mec="w",mew=0.0,zorder=10,ms=4.0) # draw hopping terms if draw_hoppings==True: for h in self._hoppings: # draw both i->j+R and i-R->j hop for s in range(2): # get "from" and "to" coordinates pos_i=np.copy(self._orb[h[1]]) pos_j=np.copy(self._orb[h[2]]) # add also lattice vector if not 0-dim if self._dim_k!=0: if s==0: pos_j[self._per]=pos_j[self._per]+h[3][self._per] if s==1: pos_i[self._per]=pos_i[self._per]-h[3][self._per] # project down vector to the plane pos_i=np.array(proj(to_cart(pos_i))) pos_j=np.array(proj(to_cart(pos_j))) # add also one point in the middle to bend the curve prcnt=0.05 # bend always by this ammount pos_mid=(pos_i+pos_j)0.5 dif=pos_j-pos_i # difference vector orth=np.array([dif[1],-1.0dif[0]]) # orthogonal to difference vector orth=orth/np.sqrt(np.dot(orth,orth)) # normalize pos_mid=pos_mid+orthprcntnp.sqrt(np.dot(dif,dif)) # shift mid point in orthogonal direction # draw hopping all_pnts=np.array([pos_i,pos_mid,pos_j]).T ax.plot(all_pnts[0],all_pnts[1],"-",c=c_hop,lw=0.75,zorder=8) # draw "from" and "to" sites ax.plot([pos_i[0]],[pos_i[1]],"o",c=c_nei,zorder=9,mew=0.0,ms=4.0,mec="w") ax.plot([pos_j[0]],[pos_j[1]],"o",c=c_nei,zorder=9,mew=0.0,ms=4.0,mec="w") # now draw the eigenstate if not (eig_dr is None): for i in range(self._norb): # find position of orbital in cartesian coordinates pos=to_cart(self._orb[i]) pos=proj(pos) # find norm of eigenfunction at this point nrm=(eig_dr[i]eig_dr[i].conjugate()).real # rescale and get size of circle nrm_rad=2.0nrmfloat(self._norb) # get color based on the phase of the eigenstate phase=np.angle(eig_dr[i]) c_ph=color_to_phase(phase) ax.plot([pos[0]],[pos[1]],"o",c=c_ph,mec="w",mew=0.0,ms=nrm_rad,zorder=11,alpha=0.8) # center the image # first get the current limit, which is probably tight xl=ax.set_xlim() yl=ax.set_ylim() # now get the center of current limit centx=(xl[1]+xl[0])0.5 centy=(yl[1]+yl[0])0.5 # now get the maximal size (lengthwise or heightwise) mx=max([xl[1]-xl[0],yl[1]-yl[0]]) # set new limits extr=0.05 # add some boundary as well ax.set_xlim(centx-mx(0.5+extr),centx+mx(0.5+extr)) ax.set_ylim(centy-mx(0.5+extr),centy+mx(0.5+extr)) # return a figure and axes to the user return (fig,ax) def get_num_orbitals(self): "Returns number of orbitals in the model." return self._norb def get_orb(self): "Returns reduced coordinates of orbitals in format [orbital,coordinate.]" return self._orb.copy() def get_lat(self): "Returns lattice vectors in format [vector,coordinate]." return self._lat.copy() def _gen_ham(self,k_input=None): """Generate Hamiltonian for a certain k-point, K-point is given in reduced coordinates!""" kpnt=np.array(k_input) if not (k_input is None): # if kpnt is just a number then convert it to an array if len(kpnt.shape)==0: kpnt=np.array([kpnt]) # check that k-vector is of corect size if kpnt.shape!=(self._dim_k,): raise Exception("\n\nk-vector of wrong shape!") else: if self._dim_k!=0: raise Exception("\n\nHave to provide a k-vector!") # zero the Hamiltonian matrix if self._nspin==1: ham=np.zeros((self._norb,self._norb),dtype=complex) elif self._nspin==2: ham=np.zeros((self._norb,2,self._norb,2),dtype=complex) # modify diagonal elements for i in range(self._norb): if self._nspin==1: ham[i,i]=self._site_energies[i] elif self._nspin==2: ham[i,:,i,:]=self._site_energies[i] # go over all hoppings for hopping in self._hoppings: # get all data for the hopping parameter if self._nspin==1: amp=complex(hopping[0]) elif self._nspin==2: amp=np.array(hopping[0],dtype=complex) i=hopping[1] j=hopping[2] # in 0-dim case there is no phase factor if self._dim_k>0: ind_R=np.array(hopping[3],dtype=float) # vector from one site to another rv=-self._orb[i,:]+self._orb[j,:]+ind_R # Take only components of vector which are periodic rv=rv[self._per] # Calculate the hopping, see details in info/tb/tb.pdf phase=np.exp((2.0j)np.pinp.dot(kpnt,rv)) amp=ampphase # add this hopping into a matrix and also its conjugate if self._nspin==1: ham[i,j]+=amp ham[j,i]+=amp.conjugate() elif self._nspin==2: ham[i,:,j,:]+=amp ham[j,:,i,:]+=amp.T.conjugate() return ham def _sol_ham(self,ham,eig_vectors=False): """Solves Hamiltonian and returns eigenvectors, eigenvalues""" # reshape matrix first if self._nspin==1: ham_use=ham elif self._nspin==2: ham_use=ham.reshape((2self._norb,2self._norb)) # check that matrix is hermitian if np.max(ham_use-ham_use.T.conj())>1.0E-9: raise Exception("\n\nHamiltonian matrix is not hermitian?!") #solve matrix if eig_vectors==False: # only find eigenvalues eval=np.linalg.eigvalsh(ham_use) # sort eigenvalues and convert to real numbers eval=_nicefy_eig(eval) return np.array(eval,dtype=float) else: # find eigenvalues and eigenvectors (eval,eig)=np.linalg.eigh(ham_use) # transpose matrix eig since otherwise it is confusing # now eig[i,:] is eigenvector for eval[i]-th eigenvalue eig=eig.T # sort evectors, eigenvalues and convert to real numbers (eval,eig)=_nicefy_eig(eval,eig) # reshape eigenvectors if doing a spinfull calculation if self._nspin==2: eig=eig.reshape((self._nsta,self._norb,2)) return (eval,eig) def solve_all(self,k_list=None,eig_vectors=False): r""" Solves for eigenvalues and (optionally) eigenvectors of the tight-binding model on a given one-dimensional list of k-vectors. .. note:: Eigenvectors (wavefunctions) returned by this function and used throughout the code are exclusively given in convention 1 as described in section 3.1 of :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>`. In other words, they are in correspondence with cell-periodic functions :math:`u_{n {\bf k}} ({\bf r})` not :math:`\Psi_{n {\bf k}} ({\bf r})`. .. note:: In some cases class :class:`pythtb.wf_array` provides a more elegant way to deal with eigensolutions on a regular mesh of k-vectors. :param k_list: One-dimensional array of k-vectors. Each k-vector is given in reduced coordinates of the reciprocal space unit cell. For example, for real space unit cell vectors [1.0,0.0] and [0.0,2.0] and associated reciprocal space unit vectors [2.0pi,0.0] and [0.0,pi], k-vector with reduced coordinates [0.25,0.25] corresponds to k-vector [0.5pi,0.25pi]. Dimensionality of each vector must equal to the number of periodic directions (i.e. dimensionality of reciprocal space, dim_k). This parameter shouldn't be specified for system with zero-dimensional k-space (dim_k =0). :param eig_vectors: Optional boolean parameter, specifying whether eigenvectors should be returned. If eig_vectors is True, then both eigenvalues and eigenvectors are returned, otherwise only eigenvalues are returned. :returns: * eval -- Two dimensional array of eigenvalues for all bands for all kpoints. Format is eval[band,kpoint] where first index (band) corresponds to the electron band in question and second index (kpoint) corresponds to the k-point as listed in the input parameter k_list. Eigenvalues are sorted from smallest to largest at each k-point seperately. In the case when reciprocal space is zero-dimensional (as in a molecule) kpoint index is dropped and eval is of the format eval[band]. * evec -- Three dimensional array of eigenvectors for all bands and all kpoints. If nspin equals 1 the format of evec is evec[band,kpoint,orbital] where "band" is the electron band in question, "kpoint" is index of k-vector as given in input parameter k_list. Finally, "orbital" refers to the tight-binding orbital basis function. Ordering of bands is the same as in eval. Eigenvectors evec[n,k,j] correspond to :math:`C^{n {\bf k}}_{j}` from section 3.1 equation 3.5 and 3.7 of the :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>`. In the case when reciprocal space is zero-dimensional (as in a molecule) kpoint index is dropped and evec is of the format evec[band,orbital]. In the spinfull calculation (nspin equals 2) evec has additional component evec[...,spin] corresponding to the spin component of the wavefunction. Example usage:: # Returns eigenvalues for three k-vectors eval = tb.solve_all([[0.0, 0.0], [0.0, 0.2], [0.0, 0.5]]) # Returns eigenvalues and eigenvectors for two k-vectors (eval, evec) = tb.solve_all([[0.0, 0.0], [0.0, 0.2]], eig_vectors=True) """ # if not 0-dim case if not (k_list is None): nkp=len(k_list) # number of k points # first initialize matrices for all return data # indices are [band,kpoint] ret_eval=np.zeros((self._nsta,nkp),dtype=float) # indices are [band,kpoint,orbital,spin] if self._nspin==1: ret_evec=np.zeros((self._nsta,nkp,self._norb),dtype=complex) elif self._nspin==2: ret_evec=np.zeros((self._nsta,nkp,self._norb,2),dtype=complex) # go over all kpoints for i,k in enumerate(k_list): # generate Hamiltonian at that point ham=self._gen_ham(k) # solve Hamiltonian if eig_vectors==False: eval=self._sol_ham(ham,eig_vectors=eig_vectors) ret_eval[:,i]=eval[:] else: (eval,evec)=self._sol_ham(ham,eig_vectors=eig_vectors) ret_eval[:,i]=eval[:] if self._nspin==1: ret_evec[:,i,:]=evec[:,:] elif self._nspin==2: ret_evec[:,i,:,:]=evec[:,:,:] # return stuff if eig_vectors==False: # indices of eval are [band,kpoint] return ret_eval else: # indices of eval are [band,kpoint] for evec are [band,kpoint,orbital,(spin)] return (ret_eval,ret_evec) else: # 0 dim case # generate Hamiltonian ham=self._gen_ham() # solve if eig_vectors==False: eval=self._sol_ham(ham,eig_vectors=eig_vectors) # indices of eval are [band] return eval else: (eval,evec)=self._sol_ham(ham,eig_vectors=eig_vectors) # indices of eval are [band] and of evec are [band,orbital,spin] return (eval,evec) def solve_one(self,k_point=None,eig_vectors=False): r""" Similar to :func:`pythtb.tb_model.solve_all` but solves tight-binding model for only one k-vector. """ # if not 0-dim case if not (k_point is None): if eig_vectors==False: eval=self.solve_all([k_point],eig_vectors=eig_vectors) # indices of eval are [band] return eval[:,0] else: (eval,evec)=self.solve_all([k_point],eig_vectors=eig_vectors) # indices of eval are [band] for evec are [band,orbital,spin] if self._nspin==1: return (eval[:,0],evec[:,0,:]) elif self._nspin==2: return (eval[:,0],evec[:,0,:,:]) else: # do the same as solve_all return self.solve_all(eig_vectors=eig_vectors) def cut_piece(self,num,fin_dir,glue_edgs=False): r""" Constructs a (d-1)-dimensional tight-binding model out of a d-dimensional one by repeating the unit cell a given number of times along one of the periodic lattice vectors. The real-space lattice vectors of the returned model are the same as those of the original model; only the dimensionality of reciprocal space is reduced. :param num: How many times to repeat the unit cell. :param fin_dir: Index of the real space lattice vector along which you no longer wish to maintain periodicity. :param glue_edgs: Optional boolean parameter specifying whether to allow hoppings from one edge to the other of a cut model. :returns: * fin_model -- Object of type :class:`pythtb.tb_model` representing a cutout tight-binding model. Orbitals in fin_model are numbered so that the i-th orbital of the n-th unit cell has index i+norbn (here norb is the number of orbitals in the original model). Example usage:: A = tb_model(3, 3, ...) # Construct two-dimensional model B out of three-dimensional # model A by repeating model along second lattice vector ten times B = A.cut_piece(10, 1) # Further cut two-dimensional model B into one-dimensional model # A by repeating unit cell twenty times along third lattice # vector and allow hoppings from one edge to the other C = B.cut_piece(20, 2, glue_edgs=True) See also these examples: :ref:`haldane_fin-example`, :ref:`edge-example`. """ if self._dim_k ==0: raise Exception("\n\nModel is already finite") if type(num).__name__!='int': raise Exception("\n\nArgument num not an integer") # check value of num if num<1: raise Exception("\n\nArgument num must be positive!") if num==1 and glue_edgs==True: raise Exception("\n\nCan't have num==1 and glueing of the edges!") # generate orbitals of a finite model fin_orb=[] onsite=[] # store also onsite energies for i in range(num): # go over all cells in finite direction for j in range(self._norb): # go over all orbitals in one cell # make a copy of j-th orbital orb_tmp=np.copy(self._orb[j,:]) # change coordinate along finite direction orb_tmp[fin_dir]+=float(i) # add to the list fin_orb.append(orb_tmp) # do the onsite energies at the same time onsite.append(self._site_energies[j]) onsite=np.array(onsite) fin_orb=np.array(fin_orb) # generate periodic directions of a finite model fin_per=copy.deepcopy(self._per) # find if list of periodic directions contains the one you # want to make finite if fin_per.count(fin_dir)!=1: raise Exception("\n\nCan not make model finite along this direction!") # remove index which is no longer periodic fin_per.remove(fin_dir) # generate object of tb_model type that will correspond to a cutout fin_model=tb_model(self._dim_k-1, self._dim_r, copy.deepcopy(self._lat), fin_orb, fin_per, self._nspin) # remember if came from w90 fin_model._assume_position_operator_diagonal=self._assume_position_operator_diagonal # now put all onsite terms for the finite model fin_model.set_onsite(onsite,mode="reset") # put all hopping terms for c in range(num): # go over all cells in finite direction for h in range(len(self._hoppings)): # go over all hoppings in one cell # amplitude of the hop is the same amp=self._hoppings[h][0] # lattice vector of the hopping ind_R=copy.deepcopy(self._hoppings[h][3]) jump_fin=ind_R[fin_dir] # store by how many cells is the hopping in finite direction if fin_model._dim_k!=0: ind_R[fin_dir]=0 # one of the directions now becomes finite # index of "from" and "to" hopping indices hi=self._hoppings[h][1] + cself._norb # have to compensate for the fact that ind_R in finite direction # will not be used in the finite model hj=self._hoppings[h][2] + (c + jump_fin)self._norb # decide whether this hopping should be added or not to_add=True # if edges are not glued then neglect all jumps that spill out if glue_edgs==False: if hj<0 or hj>=self._norbnum: to_add=False # if edges are glued then do mod division to wrap up the hopping else: hj=int(hj)%int(self._norbnum) # add hopping to a finite model if to_add==True: if fin_model._dim_k==0: fin_model.set_hop(amp,hi,hj,mode="add",allow_conjugate_pair=True) else: fin_model.set_hop(amp,hi,hj,ind_R,mode="add",allow_conjugate_pair=True) return fin_model def reduce_dim(self,remove_k,value_k): r""" Reduces dimensionality of the model by taking a reciprocal-space slice of the Bloch Hamiltonian :math:`{\cal H}_{\bf k}`. The Bloch Hamiltonian (defined in :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>` in section 3.1 equation 3.7) of a d-dimensional model is a function of d-dimensional k-vector. This function returns a d-1 dimensional tight-binding model obtained by constraining one of k-vector components in :math:`{\cal H}_{\bf k}` to be a constant. :param remove_k: Which reciprocal space unit vector component you wish to keep constant. :param value_k: Value of the k-vector component to which you are constraining this model. Must be given in reduced coordinates. :returns: red_tb -- Object of type :class:`pythtb.tb_model` representing a reduced tight-binding model. Example usage:: # Constrains second k-vector component to equal 0.3 red_tb = tb.reduce_dim(1, 0.3) """ # if self._dim_k==0: raise Exception("\n\nCan not reduce dimensionality even further!") # make a copy red_tb=copy.deepcopy(self) # make one of the directions not periodic red_tb._per.remove(remove_k) red_tb._dim_k=len(red_tb._per) # check that really removed one and only one direction if red_tb._dim_k!=self._dim_k-1: raise Exception("\n\nSpecified wrong dimension to reduce!") # specify hopping terms from scratch red_tb._hoppings=[] # set all hopping parameters for this value of value_k for h in range(len(self._hoppings)): hop=self._hoppings[h] if self._nspin==1: amp=complex(hop[0]) elif self._nspin==2: amp=np.array(hop[0],dtype=complex) i=hop[1]; j=hop[2] ind_R=np.array(hop[3],dtype=int) # vector from one site to another rv=-red_tb._orb[i,:]+red_tb._orb[j,:]+np.array(ind_R,dtype=float) # take only r-vector component along direction you are not making periodic rv=rv[remove_k] # Calculate the part of hopping phase, only for this direction phase=np.exp((2.0j)np.pi(value_krv)) # store modified version of the hop # Since we are getting rid of one dimension, it could be that now # one of the hopping terms became onsite term because one direction # is no longer periodic if i==j and (False not in (np.array(ind_R[red_tb._per],dtype=int)==0)): if ind_R[remove_k]==0: # in this case this is really an onsite term red_tb.set_onsite(ampphase,i,mode="add") else: # in this case must treat both R and -R because that term would # have been counted twice without dimensional reduction if self._nspin==1: red_tb.set_onsite(ampphase+(ampphase).conj(),i,mode="add") elif self._nspin==2: red_tb.set_onsite(ampphase+(amp.Tphase).conj(),i,mode="add") else: # just in case make the R vector zero along the reduction dimension ind_R[remove_k]=0 # add hopping term red_tb.set_hop(ampphase,i,j,ind_R,mode="add",allow_conjugate_pair=True) return red_tb def make_supercell(self, sc_red_lat, return_sc_vectors=False, to_home=True): r""" Returns tight-binding model :class:`pythtb.tb_model` representing a super-cell of a current object. This function can be used together with cut_piece* in order to create slabs with arbitrary surfaces. By default all orbitals will be shifted to the home cell after unit cell has been created. That way all orbitals will have reduced coordinates between 0 and 1. If you wish to avoid this behavior, you need to set, to_home argument to False. :param sc_red_lat: Array of integers with size dim_rdim_r* defining a super-cell lattice vectors in terms of reduced coordinates of the original tight-binding model. First index in the array specifies super-cell vector, while second index specifies coordinate of that super-cell vector. If dim_k<dim_r then still need to specify full array with size dim_rdim_r* for consistency, but non-periodic directions must have 0 on off-diagonal elemets s and 1 on diagonal. :param return_sc_vectors: Optional parameter. Default value is False. If True returns also lattice vectors inside the super-cell. Internally, super-cell tight-binding model will have orbitals repeated in the same order in which these super-cell vectors are given, but if argument to_home is set True (which it is by default) then additionally, orbitals will be shifted to the home cell. :param to_home: Optional parameter, if True will shift all orbitals to the home cell. Default value is True. :returns: * sc_tb -- Object of type :class:`pythtb.tb_model` representing a tight-binding model in a super-cell. * sc_vectors -- Super-cell vectors, returned only if return_sc_vectors is set to True (default value is False). Example usage:: # Creates super-cell out of 2d tight-binding model tb sc_tb = tb.make_supercell([[2, 1], [-1, 2]]) """ # Can't make super cell for model without periodic directions if self._dim_r==0: raise Exception("\n\nMust have at least one periodic direction to make a super-cell") # convert array to numpy array use_sc_red_lat=np.array(sc_red_lat) # checks on super-lattice array if use_sc_red_lat.shape!=(self._dim_r,self._dim_r): raise Exception("\n\nDimension of sc_red_lat array must be dim_rdim_r") if use_sc_red_lat.dtype!=int: raise Exception("\n\nsc_red_lat array elements must be integers") for i in range(self._dim_r): for j in range(self._dim_r): if (i==j) and (i not in self._per) and use_sc_red_lat[i,j]!=1: raise Exception("\n\nDiagonal elements of sc_red_lat for non-periodic directions must equal 1.") if (i!=j) and ((i not in self._per) or (j not in self._per)) and use_sc_red_lat[i,j]!=0: raise Exception("\n\nOff-diagonal elements of sc_red_lat for non-periodic directions must equal 0.") if np.abs(np.linalg.det(use_sc_red_lat))<1.0E-6: raise Exception("\n\nSuper-cell lattice vectors length/area/volume too close to zero, or zero.") if np.linalg.det(use_sc_red_lat)<0.0: raise Exception("\n\nSuper-cell lattice vectors need to form right handed system.") # converts reduced vector in original lattice to reduced vector in super-cell lattice def to_red_sc(red_vec_orig): return np.linalg.solve(np.array(use_sc_red_lat.T,dtype=float), np.array(red_vec_orig,dtype=float)) # conservative estimate on range of search for super-cell vectors max_R=np.max(np.abs(use_sc_red_lat))self._dim_r # candidates for super-cell vectors # this is hard-coded and can be improved! sc_cands=[] if self._dim_r==1: for i in range(-max_R,max_R+1): sc_cands.append(np.array([i])) elif self._dim_r==2: for i in range(-max_R,max_R+1): for j in range(-max_R,max_R+1): sc_cands.append(np.array([i,j])) elif self._dim_r==3: for i in range(-max_R,max_R+1): for j in range(-max_R,max_R+1): for k in range(-max_R,max_R+1): sc_cands.append(np.array([i,j,k])) elif self._dim_r==4: for i in range(-max_R,max_R+1): for j in range(-max_R,max_R+1): for k in range(-max_R,max_R+1): for l in range(-max_R,max_R+1): sc_cands.append(np.array([i,j,k,l])) else: raise Exception("\n\nWrong dimensionality of dim_r!") # find all vectors inside super-cell # store them here sc_vec=[] eps_shift=np.sqrt(2.0)1.0E-8 # shift of the grid, so to avoid double counting # for vec in sc_cands: # compute reduced coordinates of this candidate vector in the super-cell frame tmp_red=to_red_sc(vec).tolist() # check if in the interior inside=True for t in tmp_red: if t<=-1.0eps_shift or t>1.0-eps_shift: inside=False if inside==True: sc_vec.append(np.array(vec)) # number of times unit cell is repeated in the super-cell num_sc=len(sc_vec) # check that found enough super-cell vectors if int(round(np.abs(np.linalg.det(use_sc_red_lat))))!=num_sc: raise Exception("\n\nSuper-cell generation failed! Wrong number of super-cell vectors found.") # cartesian vectors of the super lattice sc_cart_lat=np.dot(use_sc_red_lat,self._lat) # orbitals of the super-cell tight-binding model sc_orb=[] for cur_sc_vec in sc_vec: # go over all super-cell vectors for orb in self._orb: # go over all orbitals # shift orbital and compute coordinates in # reduced coordinates of super-cell sc_orb.append(to_red_sc(orb+cur_sc_vec)) # create super-cell tb_model object to be returned sc_tb=tb_model(self._dim_k,self._dim_r,sc_cart_lat,sc_orb,per=self._per,nspin=self._nspin) # remember if came from w90 sc_tb._assume_position_operator_diagonal=self._assume_position_operator_diagonal # repeat onsite energies for i in range(num_sc): for j in range(self._norb): sc_tb.set_onsite(self._site_energies[j],iself._norb+j) # set hopping terms for c,cur_sc_vec in enumerate(sc_vec): # go over all super-cell vectors for h in range(len(self._hoppings)): # go over all hopping terms of the original model # amplitude of the hop is the same amp=self._hoppings[h][0] # lattice vector of the hopping ind_R=copy.deepcopy(self._hoppings[h][3]) # super-cell component of hopping lattice vector # shift also by current super cell vector sc_part=np.floor(to_red_sc(ind_R+cur_sc_vec)) # round down! sc_part=np.array(sc_part,dtype=int) # find remaining vector in the original reduced coordinates orig_part=ind_R+cur_sc_vec-np.dot(sc_part,use_sc_red_lat) # remaining vector must equal one of the super-cell vectors pair_ind=None for p,pair_sc_vec in enumerate(sc_vec): if False not in (pair_sc_vec==orig_part): if pair_ind!=None: raise Exception("\n\nFound duplicate super cell vector!") pair_ind=p if pair_ind==None: raise Exception("\n\nDid not find super cell vector!") # index of "from" and "to" hopping indices hi=self._hoppings[h][1] + cself._norb hj=self._hoppings[h][2] + pair_indself._norb # add hopping term sc_tb.set_hop(amp,hi,hj,sc_part,mode="add",allow_conjugate_pair=True) # put orbitals to home cell if asked for if to_home==True: sc_tb._shift_to_home() # return new tb model and vectors if needed if return_sc_vectors==False: return sc_tb else: return (sc_tb,sc_vec) def _shift_to_home(self): """Shifts all orbital positions to the home unit cell. After this function is called all reduced coordiantes of orbitals will be between 0 and 1. It may be useful to call this function after using make_supercell.""" # go over all orbitals for i in range(self._norb): cur_orb=self._orb[i] # compute orbital in the home cell round_orb=(np.array(cur_orb)+1.0E-6)%1.0 # find displacement vector needed to bring back to home cell disp_vec=np.array(np.round(cur_orb-round_orb),dtype=int) # check if have at least one non-zero component if True in (disp_vec!=0): # shift orbital self._orb[i]-=np.array(disp_vec,dtype=float) # shift also hoppings if self._dim_k!=0: for h in range(len(self._hoppings)): if self._hoppings[h][1]==i: self._hoppings[h][3]-=disp_vec if self._hoppings[h][2]==i: self._hoppings[h][3]+=disp_vec def k_uniform_mesh(self,mesh_size): r""" Returns a uniform grid of k-points that can be passed to passed to function :func:`pythtb.tb_model.solve_all`. This function is useful for plotting density of states histogram and similar. Returned uniform grid of k-points always contains the origin. :param mesh_size: Number of k-points in the mesh in each periodic direction of the model. :returns: k_vec -- Array of k-vectors on the mesh that can be directly passed to function :func:`pythtb.tb_model.solve_all`. Example usage:: # returns a 10x20x30 mesh of a tight binding model # with three periodic directions k_vec = my_model.k_uniform_mesh([10,20,30]) # solve model on the uniform mesh my_model.solve_all(k_vec) """ # get the mesh size and checks for consistency use_mesh=np.array(list(map(round,mesh_size)),dtype=int) if use_mesh.shape!=(self._dim_k,): print(use_mesh.shape) raise Exception("\n\nIncorrect size of the specified k-mesh!") if np.min(use_mesh)<=0: raise Exception("\n\nMesh must have positive non-zero number of elements.") # construct the mesh if self._dim_k==1: # get a mesh k_vec=np.mgrid[0:use_mesh[0]] # normalize the mesh norm=np.tile(np.array(use_mesh,dtype=float),use_mesh) norm=norm.reshape(use_mesh.tolist()+[1]) norm=norm.transpose([1,0]) k_vec=k_vec/norm # final reshape k_vec=k_vec.transpose([1,0]).reshape([use_mesh[0],1]) elif self._dim_k==2: # get a mesh k_vec=np.mgrid[0:use_mesh[0],0:use_mesh[1]] # normalize the mesh norm=np.tile(np.array(use_mesh,dtype=float),use_mesh) norm=norm.reshape(use_mesh.tolist()+[2]) norm=norm.transpose([2,0,1]) k_vec=k_vec/norm # final reshape k_vec=k_vec.transpose([1,2,0]).reshape([use_mesh[0]use_mesh[1],2]) elif self._dim_k==3: # get a mesh k_vec=np.mgrid[0:use_mesh[0],0:use_mesh[1],0:use_mesh[2]] # normalize the mesh norm=np.tile(np.array(use_mesh,dtype=float),use_mesh) norm=norm.reshape(use_mesh.tolist()+[3]) norm=norm.transpose([3,0,1,2]) k_vec=k_vec/norm # final reshape k_vec=k_vec.transpose([1,2,3,0]).reshape([use_mesh[0]use_mesh[1]use_mesh[2],3]) else: raise Exception("\n\nUnsupported dim_k!") return k_vec def k_path(self,kpts,nk,report=True): r""" Interpolates a path in reciprocal space between specified k-points. In 2D or 3D the k-path can consist of several straight segments connecting high-symmetry points ("nodes"), and the results can be used to plot the bands along this path. The interpolated path that is returned contains as equidistant k-points as possible. :param kpts: Array of k-vectors in reciprocal space between which interpolated path should be constructed. These k-vectors must be given in reduced coordinates. As a special case, in 1D k-space kpts may be a string: "full" -- Implies [ 0.0, 0.5, 1.0] (full BZ) * "fullc" -- Implies [-0.5, 0.0, 0.5] (full BZ, centered) * "half" -- Implies [ 0.0, 0.5] (half BZ) :param nk: Total number of k-points to be used in making the plot. :param report: Optional parameter specifying whether printout is desired (default is True). :returns: * k_vec -- Array of (nearly) equidistant interpolated k-points. The distance between the points is calculated in the Cartesian frame, however coordinates themselves are given in dimensionless reduced coordinates! This is done so that this array can be directly passed to function :func:`pythtb.tb_model.solve_all`. * k_dist -- Array giving accumulated k-distance to each k-point in the path. Unlike array k_vec this one has dimensions! (Units are defined here so that for an one-dimensional crystal with lattice constant equal to for example 10 the length of the Brillouin zone would equal 1/10=0.1. In other words factors of :math:`2\pi` are absorbed into k.) This array can be used to plot path in the k-space so that the distances between the k-points in the plot are exact. * k_node -- Array giving accumulated k-distance to each node on the path in Cartesian coordinates. This array is typically used to plot nodes (typically special points) on the path in k-space. Example usage:: # Construct a path connecting four nodal points in k-space # Path will contain 401 k-points, roughly equally spaced path = [[0.0, 0.0], [0.0, 0.5], [0.5, 0.5], [0.0, 0.0]] (k_vec,k_dist,k_node) = my_model.k_path(path,401) # solve for eigenvalues on that path evals = tb.solve_all(k_vec) # then use evals, k_dist, and k_node to plot bandstructure # (see examples) """ # processing of special cases for kpts if kpts=='full': # full Brillouin zone for 1D case k_list=np.array([[0.],[0.5],[1.]]) elif kpts=='fullc': # centered full Brillouin zone for 1D case k_list=np.array([[-0.5],[0.],[0.5]]) elif kpts=='half': # half Brillouin zone for 1D case k_list=np.array([[0.],[0.5]]) else: k_list=np.array(kpts) # in 1D case if path is specified as a vector, convert it to an (n,1) array if len(k_list.shape)==1 and self._dim_k==1: k_list=np.array([k_list]).T # make sure that k-points in the path have correct dimension if k_list.shape[1]!=self._dim_k: print('input k-space dimension is',k_list.shape[1]) print('k-space dimension taken from model is',self._dim_k) raise Exception("\n\nk-space dimensions do not match") # must have more k-points in the path than number of nodes if nk<k_list.shape[0]: raise Exception("\n\nMust have more points in the path than number of nodes.") # number of nodes n_nodes=k_list.shape[0] # extract the lattice vectors from the TB model lat_per=np.copy(self._lat) # choose only those that correspond to periodic directions lat_per=lat_per[self._per] # compute k_space metric tensor k_metric = np.linalg.inv(np.dot(lat_per,lat_per.T)) # Find distances between nodes and set k_node, which is # accumulated distance since the start of the path # initialize array k_node k_node=np.zeros(n_nodes,dtype=float) for n in range(1,n_nodes): dk = k_list[n]-k_list[n-1] dklen = np.sqrt(np.dot(dk,np.dot(k_metric,dk))) k_node[n]=k_node[n-1]+dklen # Find indices of nodes in interpolated list node_index=[0] for n in range(1,n_nodes-1): frac=k_node[n]/k_node[-1] node_index.append(int(round(frac(nk-1)))) node_index.append(nk-1) # initialize two arrays temporarily with zeros # array giving accumulated k-distance to each k-point k_dist=np.zeros(nk,dtype=float) # array listing the interpolated k-points k_vec=np.zeros((nk,self._dim_k),dtype=float) # go over all kpoints k_vec[0]=k_list[0] for n in range(1,n_nodes): n_i=node_index[n-1] n_f=node_index[n] kd_i=k_node[n-1] kd_f=k_node[n] k_i=k_list[n-1] k_f=k_list[n] for j in range(n_i,n_f+1): frac=float(j-n_i)/float(n_f-n_i) k_dist[j]=kd_i+frac(kd_f-kd_i) k_vec[j]=k_i+frac(k_f-k_i) if report==True: if self._dim_k==1: print(' Path in 1D BZ defined by nodes at '+str(k_list.flatten())) else: print('----- k_path report begin ----------') original=np.get_printoptions() np.set_printoptions(precision=5) print('real-space lattice vectors\n', lat_per) print('k-space metric tensor\n', k_metric) print('internal coordinates of nodes\n', k_list) if (lat_per.shape[0]==lat_per.shape[1]): # lat_per is invertible lat_per_inv=np.linalg.inv(lat_per).T print('reciprocal-space lattice vectors\n', lat_per_inv) # cartesian coordinates of nodes kpts_cart=np.tensordot(k_list,lat_per_inv,axes=1) print('cartesian coordinates of nodes\n',kpts_cart) print('list of segments:') for n in range(1,n_nodes): dk=k_node[n]-k_node[n-1] dk_str=_nice_float(dk,7,5) print(' length = '+dk_str+' from ',k_list[n-1],' to ',k_list[n]) print('node distance list:', k_node) print('node index list: ', np.array(node_index)) np.set_printoptions(precision=original["precision"]) print('----- k_path report end ------------') print() return (k_vec,k_dist,k_node) def ignore_position_operator_offdiagonal(self): """Call to this function enables one to approximately compute Berry-like objects from tight-binding models that were obtained from Wannier90.""" self._assume_position_operator_diagonal=True def position_matrix(self, evec, dir): r""" Returns matrix elements of the position operator along direction dir* for eigenvectors evec at a single k-point. Position operator is defined in reduced coordinates. The returned object :math:`X` is .. math:: X_{m n {\bf k}}^{\alpha} = \langle u_{m {\bf k}} \vert r^{\alpha} \vert u_{n {\bf k}} \rangle Here :math:`r^{\alpha}` is the position operator along direction :math:`\alpha` that is selected by dir. :param evec: Eigenvectors for which we are computing matrix elements of the position operator. The shape of this array is evec[band,orbital] if nspin equals 1 and evec[band,orbital,spin] if nspin equals 2. :param dir: Direction along which we are computing the center. This integer must not be one of the periodic directions since position operator matrix element in that case is not well defined. :returns: * pos_mat -- Position operator matrix :math:`X_{m n}` as defined above. This is a square matrix with size determined by number of bands given in evec input array. First index of pos_mat corresponds to bra vector (m) and second index to ket (n). Example usage:: # diagonalizes Hamiltonian at some k-points (evals, evecs) = my_model.solve_all(k_vec,eig_vectors=True) # computes position operator matrix elements for 3-rd kpoint # and bottom five bands along first coordinate pos_mat = my_model.position_matrix(evecs[:5,2], 0) See also this example: :ref:`haldane_hwf-example`, """ # make sure specified direction is not periodic! if dir in self._per: raise Exception("Can not compute position matrix elements along periodic direction!") # make sure direction is not out of range if dir<0 or dir>=self._dim_r: raise Exception("Direction out of range!") # check if model came from w90 if self._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() # get coordinates of orbitals along the specified direction pos_tmp=self._orb[:,dir] # reshape arrays in the case of spinfull calculation if self._nspin==2: # tile along spin direction if needed pos_use=np.tile(pos_tmp,(2,1)).transpose().flatten() # also flatten the state along the spin index evec_use=evec.reshape((evec.shape[0],evec.shape[1]evec.shape[2])) else: pos_use=pos_tmp evec_use=evec # position matrix elements pos_mat=np.zeros((evec_use.shape[0],evec_use.shape[0]),dtype=complex) # go over all bands for i in range(evec_use.shape[0]): for j in range(evec_use.shape[0]): pos_mat[i,j]=np.dot(evec_use[i].conj(),pos_useevec_use[j]) # make sure matrix is hermitian if np.max(pos_mat-pos_mat.T.conj())>1.0E-9: raise Exception("\n\n Position matrix is not hermitian?!") return pos_mat def position_expectation(self,evec,dir): r""" Returns diagonal matrix elements of the position operator. These elements :math:`X_{n n}` can be interpreted as an average position of n-th Bloch state evec[n] along direction dir. Generally speaking these centers are not hybrid Wannier function centers (which are instead returned by :func:`pythtb.tb_model.position_hwf`). See function :func:`pythtb.tb_model.position_matrix` for definition of matrix :math:`X`. :param evec: Eigenvectors for which we are computing matrix elements of the position operator. The shape of this array is evec[band,orbital] if nspin equals 1 and evec[band,orbital,spin] if nspin equals 2. :param dir: Direction along which we are computing matrix elements. This integer must not be one of the periodic directions since position operator matrix element in that case is not well defined. :returns: * pos_exp -- Diagonal elements of the position operator matrix :math:`X`. Length of this vector is determined by number of bands given in evec input array. Example usage:: # diagonalizes Hamiltonian at some k-points (evals, evecs) = my_model.solve_all(k_vec,eig_vectors=True) # computes average position for 3-rd kpoint # and bottom five bands along first coordinate pos_exp = my_model.position_expectation(evecs[:5,2], 0) See also this example: :ref:`haldane_hwf-example`. """ # check if model came from w90 if self._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() pos_exp=self.position_matrix(evec,dir).diagonal() return np.array(np.real(pos_exp),dtype=float) def position_hwf(self,evec,dir,hwf_evec=False,basis="orbital"): r""" Returns eigenvalues and optionally eigenvectors of the position operator matrix :math:`X` in either Bloch or orbital basis. These eigenvectors can be interpreted as linear combinations of Bloch states evec that have minimal extent (or spread :math:`\Omega` in the sense of maximally localized Wannier functions) along direction dir. The eigenvalues are average positions of these localized states. Note that these eigenvectors are not maximally localized Wannier functions in the usual sense because they are localized only along one direction. They are also not the average positions of the Bloch states evec, which are instead computed by :func:`pythtb.tb_model.position_expectation`. See function :func:`pythtb.tb_model.position_matrix` for the definition of the matrix :math:`X`. See also Fig. 3 in Phys. Rev. Lett. 102, 107603 (2009) for a discussion of the hybrid Wannier function centers in the context of a Chern insulator. :param evec: Eigenvectors for which we are computing matrix elements of the position operator. The shape of this array is evec[band,orbital] if nspin equals 1 and evec[band,orbital,spin] if nspin equals 2. :param dir: Direction along which we are computing matrix elements. This integer must not be one of the periodic directions since position operator matrix element in that case is not well defined. :param hwf_evec: Optional boolean variable. If set to True this function will return not only eigenvalues but also eigenvectors of :math:`X`. Default value is False. :param basis: Optional parameter. If basis="bloch" then hybrid Wannier function hwf_evec is written in the Bloch basis. I.e. hwf[i,j] correspond to the weight of j-th Bloch state from evec in the i-th hybrid Wannier function. If basis="orbital" and nspin=1 then hwf[i,orb] correspond to the weight of orb-th orbital in the i-th hybrid Wannier function. If basis="orbital" and nspin=2 then hwf[i,orb,spin] correspond to the weight of orb-th orbital, spin-th spin component in the i-th hybrid Wannier function. Default value is "orbital". :returns: * hwfc -- Eigenvalues of the position operator matrix :math:`X` (also called hybrid Wannier function centers). Length of this vector equals number of bands given in evec input array. Hybrid Wannier function centers are ordered in ascending order. Note that in general n-th hwfc does not correspond to n-th electronic state evec. * hwf -- Eigenvectors of the position operator matrix :math:`X`. (also called hybrid Wannier functions). These are returned only if parameter hwf_evec is set to True. The shape of this array is [h,x] or [h,x,s] depending on value of basis and nspin. If basis is "bloch" then x refers to indices of Bloch states evec. If basis is "orbital" then x (or x and s) correspond to orbital index (or orbital and spin index if nspin is 2). Example usage:: # diagonalizes Hamiltonian at some k-points (evals, evecs) = my_model.solve_all(k_vec,eig_vectors=True) # computes hybrid Wannier centers (and functions) for 3-rd kpoint # and bottom five bands along first coordinate (hwfc, hwf) = my_model.position_hwf(evecs[:5,2], 0, hwf_evec=True, basis="orbital") See also this example: :ref:`haldane_hwf-example`, """ # check if model came from w90 if self._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() # get position matrix pos_mat=self.position_matrix(evec,dir) # diagonalize if hwf_evec==False: hwfc=np.linalg.eigvalsh(pos_mat) # sort eigenvalues and convert to real numbers hwfc=_nicefy_eig(hwfc) return np.array(hwfc,dtype=float) else: # find eigenvalues and eigenvectors (hwfc,hwf)=np.linalg.eigh(pos_mat) # transpose matrix eig since otherwise it is confusing # now eig[i,:] is eigenvector for eval[i]-th eigenvalue hwf=hwf.T # sort evectors, eigenvalues and convert to real numbers (hwfc,hwf)=_nicefy_eig(hwfc,hwf) # convert to right basis if basis.lower().strip()=="bloch": return (hwfc,hwf) elif basis.lower().strip()=="orbital": if self._nspin==1: ret_hwf=np.zeros((hwf.shape[0],self._norb),dtype=complex) # sum over bloch states to get hwf in orbital basis for i in range(ret_hwf.shape[0]): ret_hwf[i]=np.dot(hwf[i],evec) hwf=ret_hwf else: ret_hwf=np.zeros((hwf.shape[0],self._norb2),dtype=complex) # get rid of spin indices evec_use=evec.reshape([hwf.shape[0],self._norb2]) # sum over states for i in range(ret_hwf.shape[0]): ret_hwf[i]=np.dot(hwf[i],evec_use) # restore spin indices hwf=ret_hwf.reshape([hwf.shape[0],self._norb,2]) return (hwfc,hwf) else: raise Exception("\n\nBasis must be either bloch or orbital!") # keeping old name for backwards compatibility # will be removed in future tb_model.set_sites=tb_model.set_onsite tb_model.add_hop=tb_model.set_hop tbmodel=tb_model class wf_array(object): r""" This class is used to solve a tight-binding model :class:`pythtb.tb_model` on a regular or non-regular grid of points in reciprocal space and/or parameter space, and perform on it various calculations. For example it can be used to calculate the Berry phase, Berry curvature, 1st Chern number, etc. Regular k-space grid: If the grid is a regular k-mesh (no parametric dimensions), a single call to the function :func:`pythtb.wf_array.solve_on_grid` will both construct a k-mesh that uniformly covers the Brillouin zone, and populate it with wavefunctions (eigenvectors) computed on this grid. The last point in each k-dimension is set so that it represents the same Bloch function as the first one (this involves the insertion of some orbital-position-dependent phase factors). Example :ref:`haldane_bp-example` shows how to use wf_array on a regular grid of points in k-space. Examples :ref:`cone-example` and :ref:`3site_cycle-example` show how to use non-regular grid of points. Parametric or irregular k-space grid grid: An irregular grid of points, or a grid that includes also one or more parametric dimensions, can be populated manually with the help of the [] operator. For example, to copy eeigenvectors evec into coordinate (2,3) in the wf_array object wf one can simply do:: wf[2,3]=evec The eigenvectors (wavefunctions) evec in the example above are expected to be in the format evec[band,orbital] (or evec[band,orbital,spin] for the spinfull calculation). This is the same format as returned by :func:`pythtb.tb_model.solve_one` or :func:`pythtb.tb_model.solve_all` (in the latter case one needs to restrict it to a single k-point as evec[:,kpt,:] if the model has dim_k>=1). If wf_array is used for closed paths, either in a reciprocal-space or parametric direction, then one needs to include both the starting and ending eigenfunctions even though they are physically equivalent. If the array dimension in question is a k-vector direction and the path traverses the Brillouin zone in a primitive reciprocal-lattice direction, :func:`pythtb.wf_array.impose_pbc` can be used to associate the starting and ending points with each other; if it is a non-winding loop in k-space or a loop in parameter space, then :func:`pythtb.wf_array.impose_loop` can be used instead. (These may not be necessary if only Berry fluxes are needed.) Example :ref:`3site_cycle-example` shows how one of the directions of wf_array object need not be a k-vector direction, but can instead be a Hamiltonian parameter :math:`\lambda` (see also discussion after equation 4.1 in :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>`). :param model: Object of type :class:`pythtb.tb_model` representing tight-binding model associated with this array of eigenvectors. :param mesh_arr: Array giving a dimension of the grid of points in each reciprocal-space or parametric direction. Example usage:: # Construct wf_array capable of storing an 11x21 array of # wavefunctions wf = wf_array(tb, [11, 21]) # populate this wf_array with regular grid of points in # Brillouin zone wf.solve_on_grid([0.0, 0.0]) # Compute set of eigenvectors at one k-point (eval, evec) = tb.solve_one([kx, ky], eig_vectors = True) # Store it manually into a specified location in the array wf[3, 4] = evec # To access the eigenvectors from the same position print wf[3, 4] """ def __init__(self,model,mesh_arr): # number of electronic states for each k-point self._nsta=model._nsta # number of spin components self._nspin=model._nspin # number of orbitals self._norb=model._norb # store orbitals from the model self._orb=np.copy(model._orb) # store entire model as well self._model=copy.deepcopy(model) # store dimension of array of points on which to keep wavefunctions self._mesh_arr=np.array(mesh_arr) self._dim_arr=len(self._mesh_arr) # all dimensions should be 2 or larger, because pbc can be used if True in (self._mesh_arr<=1).tolist(): raise Exception("\n\nDimension of wf_array object in each direction must be 2 or larger.") # generate temporary array used later to generate object ._wfs wfs_dim=np.copy(self._mesh_arr) wfs_dim=np.append(wfs_dim,self._nsta) wfs_dim=np.append(wfs_dim,self._norb) if self._nspin==2: wfs_dim=np.append(wfs_dim,self._nspin) # store wavefunctions here in the form _wfs[kx_index,ky_index, ... ,band,orb,spin] self._wfs=np.zeros(wfs_dim,dtype=complex) def solve_on_grid(self,start_k): r""" Solve a tight-binding model on a regular mesh of k-points covering the entire reciprocal-space unit cell. Both points at the opposite sides of reciprocal-space unit cell are included in the array. This function also automatically imposes periodic boundary conditions on the eigenfunctions. See also the discussion in :func:`pythtb.wf_array.impose_pbc`. :param start_k: Origin of a regular grid of points in the reciprocal space. :returns: * gaps -- returns minimal direct bandgap between n-th and n+1-th band on all the k-points in the mesh. Note that in the case of band crossings one may have to use very dense k-meshes to resolve the crossing. Example usage:: # Solve eigenvectors on a regular grid anchored # at a given point wf.solve_on_grid([-0.5, -0.5]) """ # check dimensionality if self._dim_arr!=self._model._dim_k: raise Exception("\n\nIf using solve_on_grid method, dimension of wf_array must equal dim_k of the tight-binding model!") # to return gaps at all k-points if self._norb<=1: all_gaps=None # trivial case since there is only one band else: gap_dim=np.copy(self._mesh_arr)-1 gap_dim=np.append(gap_dim,self._norbself._nspin-1) all_gaps=np.zeros(gap_dim,dtype=float) # if self._dim_arr==1: # don't need to go over the last point because that will be # computed in the impose_pbc call for i in range(self._mesh_arr[0]-1): # generate a kpoint kpt=[start_k[0]+float(i)/float(self._mesh_arr[0]-1)] # solve at that point (eval,evec)=self._model.solve_one(kpt,eig_vectors=True) # store wavefunctions self[i]=evec # store gaps if all_gaps is not None: all_gaps[i,:]=eval[1:]-eval[:-1] # impose boundary conditions self.impose_pbc(0,self._model._per[0]) elif self._dim_arr==2: for i in range(self._mesh_arr[0]-1): for j in range(self._mesh_arr[1]-1): kpt=[start_k[0]+float(i)/float(self._mesh_arr[0]-1),\ start_k[1]+float(j)/float(self._mesh_arr[1]-1)] (eval,evec)=self._model.solve_one(kpt,eig_vectors=True) self[i,j]=evec if all_gaps is not None: all_gaps[i,j,:]=eval[1:]-eval[:-1] for dir in range(2): self.impose_pbc(dir,self._model._per[dir]) elif self._dim_arr==3: for i in range(self._mesh_arr[0]-1): for j in range(self._mesh_arr[1]-1): for k in range(self._mesh_arr[2]-1): kpt=[start_k[0]+float(i)/float(self._mesh_arr[0]-1),\ start_k[1]+float(j)/float(self._mesh_arr[1]-1),\ start_k[2]+float(k)/float(self._mesh_arr[2]-1)] (eval,evec)=self._model.solve_one(kpt,eig_vectors=True) self[i,j,k]=evec if all_gaps is not None: all_gaps[i,j,k,:]=eval[1:]-eval[:-1] for dir in range(3): self.impose_pbc(dir,self._model._per[dir]) elif self._dim_arr==4: for i in range(self._mesh_arr[0]-1): for j in range(self._mesh_arr[1]-1): for k in range(self._mesh_arr[2]-1): for l in range(self._mesh_arr[3]-1): kpt=[start_k[0]+float(i)/float(self._mesh_arr[0]-1),\ start_k[1]+float(j)/float(self._mesh_arr[1]-1),\ start_k[2]+float(k)/float(self._mesh_arr[2]-1),\ start_k[3]+float(l)/float(self._mesh_arr[3]-1)] (eval,evec)=self._model.solve_one(kpt,eig_vectors=True) self[i,j,k,l]=evec if all_gaps is not None: all_gaps[i,j,k,l,:]=eval[1:]-eval[:-1] for dir in range(4): self.impose_pbc(dir,self._model._per[dir]) else: raise Exception("\n\nWrong dimensionality!") return all_gaps.min(axis=tuple(range(self._dim_arr))) def __check_key(self,key): # do some checks for 1D if self._dim_arr==1: if type(key).__name__!='int': raise TypeError("Key should be an integer!") if key<(-1)self._mesh_arr[0] or key>=self._mesh_arr[0]: raise IndexError("Key outside the range!") # do checks for higher dimension else: if len(key)!=self._dim_arr: raise TypeError("Wrong dimensionality of key!") for i,k in enumerate(key): if type(k).__name__!='int': raise TypeError("Key should be set of integers!") if k<(-1)self._mesh_arr[i] or k>=self._mesh_arr[i]: raise IndexError("Key outside the range!") def __getitem__(self,key): # check that key is in the correct range self.__check_key(key) # return wavefunction return self._wfs[key] def __setitem__(self,key,value): # check that key is in the correct range self.__check_key(key) # store wavefunction self._wfs[key]=np.array(value,dtype=complex) def impose_pbc(self,mesh_dir,k_dir): r""" If the wf_array* object was populated using the :func:`pythtb.wf_array.solve_on_grid` method, this function should not be used since it will be called automatically by the code. The eigenfunctions :math:`\Psi_{n {\bf k}}` are by convention chosen to obey a periodic gauge, i.e., :math:`\Psi_{n,{\bf k+G}}=\Psi_{n {\bf k}}` not only up to a phase, but they are also equal in phase. It follows that the cell-periodic Bloch functions are related by :math:`u_{n,{\bf k+G}}=e^{-i{\bf G}\cdot{\bf r}}\Psi_{n {\bf k}}`. See :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>` section 4.4 and equation 4.18 for more detail. This routine sets the cell-periodic Bloch function at the end of the string in direction :math:`{\bf G}` according to this formula, overwriting the previous value. This function will impose these periodic boundary conditions along one direction of the array. We are assuming that the k-point mesh increases by exactly one reciprocal lattice vector along this direction. This is currently not checked by the code; it is the responsibility of the user. Currently wf_array does not store the k-vectors on which the model was solved; it only stores the eigenvectors (wavefunctions). :param mesh_dir: Direction of wf_array along which you wish to impose periodic boundary conditions. :param k_dir: Corresponding to the periodic k-vector direction in the Brillouin zone of the underlying tb_model. Since version 1.7.0 this parameter is defined so that it is specified between 0 and dim_r-1. See example :ref:`3site_cycle-example`, where the periodic boundary condition is applied only along one direction of wf_array. Example usage:: # Imposes periodic boundary conditions along the mesh_dir=0 # direction of the wf_array object, assuming that along that # direction the k_dir=1 component of the k-vector is increased # by one reciprocal lattice vector. This could happen, for # example, if the underlying tb_model is two dimensional but # wf_array is a one-dimensional path along k_y direction. wf.impose_pbc(mesh_dir=0,k_dir=1) """ if k_dir not in self._model._per: raise Exception("Periodic boundary condition can be specified only along periodic directions!") # Compute phase factors ffac=np.exp(-2.jnp.piself._orb[:,k_dir]) if self._nspin==1: phase=ffac else: # for spinors, same phase multiplies both components phase=np.zeros((self._norb,2),dtype=complex) phase[:,0]=ffac phase[:,1]=ffac # Copy first eigenvector onto last one, multiplying by phase factors # We can use numpy broadcasting since the orbital index is last if mesh_dir==0: self._wfs[-1,...]=self._wfs[0,...]phase elif mesh_dir==1: self._wfs[:,-1,...]=self._wfs[:,0,...]phase elif mesh_dir==2: self._wfs[:,:,-1,...]=self._wfs[:,:,0,...]phase elif mesh_dir==3: self._wfs[:,:,:,-1,...]=self._wfs[:,:,:,0,...]phase else: raise Exception("\n\nWrong value of mesh_dir.") def impose_loop(self,mesh_dir): r""" If the user knows that the first and last points along the mesh_dir direction correspond to the same Hamiltonian (this is not checked), then this routine can be used to set the eigenvectors equal (with equal phase), by replacing the last eigenvector with the first one (for each band, and for each other mesh direction, if any). This routine should not be used if the first and last points are related by a reciprocal lattice vector; in that case, :func:`pythtb.wf_array.impose_pbc` should be used instead. :param mesh_dir: Direction of wf_array along which you wish to impose periodic boundary conditions. Example usage:: # Suppose the wf_array object is three-dimensional # corresponding to (kx,ky,lambda) where (kx,ky) are # wavevectors of a 2D insulator and lambda is an # adiabatic parameter that goes around a closed loop. # Then to insure that the states at the ends of the lambda # path are equal (with equal phase) in preparation for # computing Berry phases in lambda for given (kx,ky), # do wf.impose_loop(mesh_dir=2) """ # Copy first eigenvector onto last one if mesh_dir==0: self._wfs[-1,...]=self._wfs[0,...] elif mesh_dir==1: self._wfs[:,-1,...]=self._wfs[:,0,...] elif mesh_dir==2: self._wfs[:,:,-1,...]=self._wfs[:,:,0,...] elif mesh_dir==3: self._wfs[:,:,:,-1,...]=self._wfs[:,:,:,0,...] else: raise Exception("\n\nWrong value of mesh_dir.") def berry_phase(self,occ,dir=None,contin=True,berry_evals=False): r""" Computes the Berry phase along a given array direction and for a given set of occupied states. This assumes that the occupied bands are well separated in energy from unoccupied bands. It is the responsibility of the user to check that this is satisfied. By default, the Berry phase traced over occupied bands is returned, but optionally the individual phases of the eigenvalues of the global unitary rotation matrix (corresponding to "maximally localized Wannier centers" or "Wilson loop eigenvalues") can be requested (see parameter berry_evals for more details). For an array of size N in direction $dir$, the Berry phase is computed from the N-1 inner products of neighboring eigenfunctions. This corresponds to an "open-path Berry phase" if the first and last points have no special relation. If they correspond to the same physical Hamiltonian, and have been properly aligned in phase using :func:`pythtb.wf_array.impose_pbc` or :func:`pythtb.wf_array.impose_loop`, then a closed-path Berry phase will be computed. For a one-dimensional wf_array (i.e., a single string), the computed Berry phases are always chosen to be between -pi and pi. For a higher dimensional wf_array, the Berry phase is computed for each one-dimensional string of points, and an array of Berry phases is returned. The Berry phase for the first string (with lowest index) is always constrained to be between -pi and pi. The range of the remaining phases depends on the value of the input parameter contin. The discretized formula used to compute Berry phase is described in Sec. 4.5 of :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>`. :param occ: Array of indices of energy bands which are considered to be occupied. :param dir: Index of wf_array direction along which Berry phase is computed. This parameters needs not be specified for a one-dimensional wf_array. :param contin: Optional boolean parameter. If True then the branch choice of the Berry phase (which is indeterminate modulo 2pi) is made so that neighboring strings (in the direction of increasing index value) have as close as possible phases. The phase of the first string (with lowest index) is always constrained to be between -pi and pi. If False, the Berry phase for every string is constrained to be between -pi and pi. The default value is True. :param berry_evals: Optional boolean parameter. If True then will compute and return the phases of the eigenvalues of the product of overlap matrices. (These numbers correspond also to hybrid Wannier function centers.) These phases are either forced to be between -pi and pi (if contin* is False) or they are made to be continuous (if contin is True). :returns: * pha -- If berry_evals is False (default value) then returns the Berry phase for each string. For a one-dimensional wf_array this is just one number. For a higher-dimensional wf_array pha contains one phase for each one-dimensional string in the following format. For example, if wf_array contains k-points on mesh with indices [i,j,k] and if direction along which Berry phase is computed is dir=1 then pha will be two dimensional array with indices [i,k], since Berry phase is computed along second direction. If berry_evals is True then for each string returns phases of all eigenvalues of the product of overlap matrices. In the convention used for previous example, pha in this case would have indices [i,k,n] where n refers to index of individual phase of the product matrix eigenvalue. Example usage:: # Computes Berry phases along second direction for three lowest # occupied states. For example, if wf is threedimensional, then # pha[2,3] would correspond to Berry phase of string of states # along wf[2,:,3] pha = wf.berry_phase([0, 1, 2], 1) See also these examples: :ref:`haldane_bp-example`, :ref:`cone-example`, :ref:`3site_cycle-example`, """ # check if model came from w90 if self._model._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() #if dir<0 or dir>self._dim_arr-1: # raise Exception("\n\nDirection key out of range") # # This could be coded more efficiently, but it is hard-coded for now. # # 1D case if self._dim_arr==1: # pick which wavefunctions to use wf_use=self._wfs[:,occ,:] # calculate berry phase ret=_one_berry_loop(wf_use,berry_evals) # 2D case elif self._dim_arr==2: # choice along which direction you wish to calculate berry phase if dir==0: ret=[] for i in range(self._mesh_arr[1]): wf_use=self._wfs[:,i,:,:][:,occ,:] ret.append(_one_berry_loop(wf_use,berry_evals)) elif dir==1: ret=[] for i in range(self._mesh_arr[0]): wf_use=self._wfs[i,:,:,:][:,occ,:] ret.append(_one_berry_loop(wf_use,berry_evals)) else: raise Exception("\n\nWrong direction for Berry phase calculation!") # 3D case elif self._dim_arr==3: # choice along which direction you wish to calculate berry phase if dir==0: ret=[] for i in range(self._mesh_arr[1]): ret_t=[] for j in range(self._mesh_arr[2]): wf_use=self._wfs[:,i,j,:,:][:,occ,:] ret_t.append(_one_berry_loop(wf_use,berry_evals)) ret.append(ret_t) elif dir==1: ret=[] for i in range(self._mesh_arr[0]): ret_t=[] for j in range(self._mesh_arr[2]): wf_use=self._wfs[i,:,j,:,:][:,occ,:] ret_t.append(_one_berry_loop(wf_use,berry_evals)) ret.append(ret_t) elif dir==2: ret=[] for i in range(self._mesh_arr[0]): ret_t=[] for j in range(self._mesh_arr[1]): wf_use=self._wfs[i,j,:,:,:][:,occ,:] ret_t.append(_one_berry_loop(wf_use,berry_evals)) ret.append(ret_t) else: raise Exception("\n\nWrong direction for Berry phase calculation!") else: raise Exception("\n\nWrong dimensionality!") # convert phases to numpy array if self._dim_arr>1 or berry_evals==True: ret=np.array(ret,dtype=float) # make phases of eigenvalues continuous if contin==True: # iron out 2pi jumps, make the gauge choice such that first phase in the # list is fixed, others are then made continuous. if berry_evals==False: # 2D case if self._dim_arr==2: ret=_one_phase_cont(ret,ret[0]) # 3D case elif self._dim_arr==3: for i in range(ret.shape[1]): if i==0: clos=ret[0,0] else: clos=ret[0,i-1] ret[:,i]=_one_phase_cont(ret[:,i],clos) elif self._dim_arr!=1: raise Exception("\n\nWrong dimensionality!") # make eigenvalues continuous. This does not take care of band-character # at band crossing for example it will just connect pairs that are closest # at neighboring points. else: # 2D case if self._dim_arr==2: ret=_array_phases_cont(ret,ret[0,:]) # 3D case elif self._dim_arr==3: for i in range(ret.shape[1]): if i==0: clos=ret[0,0,:] else: clos=ret[0,i-1,:] ret[:,i]=_array_phases_cont(ret[:,i],clos) elif self._dim_arr!=1: raise Exception("\n\nWrong dimensionality!") return ret def position_matrix(self, key, occ, dir): """Similar to :func:`pythtb.tb_model.position_matrix`. Only difference is that states are now specified with key in the mesh key and indices of bands occ.""" # check if model came from w90 if self._model._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() # evec=self._wfs[tuple(key)][occ] return self._model.position_matrix(evec,dir) def position_expectation(self, key, occ, dir): """Similar to :func:`pythtb.tb_model.position_expectation`. Only difference is that states are now specified with key in the mesh key and indices of bands occ.""" # check if model came from w90 if self._model._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() # evec=self._wfs[tuple(key)][occ] return self._model.position_expectation(evec,dir) def position_hwf(self, key, occ, dir, hwf_evec=False, basis="bloch"): """Similar to :func:`pythtb.tb_model.position_hwf`. Only difference is that states are now specified with key in the mesh key and indices of bands occ.""" # check if model came from w90 if self._model._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() # evec=self._wfs[tuple(key)][occ] return self._model.position_hwf(evec,dir,hwf_evec,basis) def berry_flux(self,occ,dirs=None,individual_phases=False): r""" In the case of a 2-dimensional wf_array array calculates the integral of Berry curvature over the entire plane. In higher dimensional case (3 or 4) it will compute integrated curvature over all 2-dimensional slices of a higher-dimensional wf_array. :param occ: Array of indices of energy bands which are considered to be occupied. :param dirs: Array of indices of two wf_array directions on which the Berry flux is computed. This parameter needs not be specified for a two-dimensional wf_array. By default dirs takes first two directions in the array. :param individual_phases: If True then returns Berry phase for each plaquette (small square) in the array. Default value is False. :returns: * flux -- In a 2-dimensional case returns and integral of Berry curvature (if individual_phases is True then returns integral of Berry phase around each plaquette). In higher dimensional case returns integral of Berry curvature over all slices defined with directions dirs. Returned value is an array over the remaining indices of wf_array. (If individual_phases is True then it returns again phases around each plaquette for each slice. First indices define the slice, last two indices index the plaquette.) Example usage:: # Computes integral of Berry curvature of first three bands flux = wf.berry_flux([0, 1, 2]) """ # check if model came from w90 if self._model._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() # default case is to take first two directions for flux calculation if dirs==None: dirs=[0,1] # consistency checks if dirs[0]==dirs[1]: raise Exception("Need to specify two different directions for Berry flux calculation.") if dirs[0]>=self._dim_arr or dirs[1]>=self._dim_arr or dirs[0]<0 or dirs[1]<0: raise Exception("Direction for Berry flux calculation out of bounds.") # 2D case if self._dim_arr==2: # compute the fluxes through all plaquettes on the entire plane ord=list(range(len(self._wfs.shape))) # select two directions from dirs ord[0]=dirs[0] ord[1]=dirs[1] plane_wfs=self._wfs.transpose(ord) # take bands of choice plane_wfs=plane_wfs[:,:,occ] # compute fluxes all_phases=_one_flux_plane(plane_wfs) # return either total flux or individual phase for each plaquete if individual_phases==False: return all_phases.sum() else: return all_phases # 3D or 4D case elif self._dim_arr in [3,4]: # compute the fluxes through all plaquettes on the entire plane ord=list(range(len(self._wfs.shape))) # select two directions from dirs ord[0]=dirs[0] ord[1]=dirs[1] # find directions over which we wish to loop ld=list(range(self._dim_arr)) ld.remove(dirs[0]) ld.remove(dirs[1]) if len(ld)!=self._dim_arr-2: raise Exception("Hm, this should not happen? Inconsistency with the mesh size.") # add remaining indices if self._dim_arr==3: ord[2]=ld[0] if self._dim_arr==4: ord[2]=ld[0] ord[3]=ld[1] # reorder wavefunctions use_wfs=self._wfs.transpose(ord) # loop over the the remaining direction if self._dim_arr==3: slice_phases=np.zeros((self._mesh_arr[ord[2]],self._mesh_arr[dirs[0]]-1,self._mesh_arr[dirs[1]]-1),dtype=float) for i in range(self._mesh_arr[ord[2]]): # take a 2d slice plane_wfs=use_wfs[:,:,i] # take bands of choice plane_wfs=plane_wfs[:,:,occ] # compute fluxes on the slice slice_phases[i,:,:]=_one_flux_plane(plane_wfs) elif self._dim_arr==4: slice_phases=np.zeros((self._mesh_arr[ord[2]],self._mesh_arr[ord[3]],self._mesh_arr[dirs[0]]-1,self._mesh_arr[dirs[1]]-1),dtype=float) for i in range(self._mesh_arr[ord[2]]): for j in range(self._mesh_arr[ord[3]]): # take a 2d slice plane_wfs=use_wfs[:,:,i,j] # take bands of choice plane_wfs=plane_wfs[:,:,occ] # compute fluxes on the slice slice_phases[i,j,:,:]=_one_flux_plane(plane_wfs) # return either total flux or individual phase for each plaquete if individual_phases==False: return slice_phases.sum(axis=(-2,-1)) else: return slice_phases else: raise Exception("\n\nWrong dimensionality!") def berry_curv(self,occ,individual_phases=False): r""" .. warning:: This function has been renamed as :func:`pythtb.berry_flux` and is provided here only for backwards compatibility with versions of pythtb prior to 1.7.0. Please use related :func:`pythtb.berry_flux` as this function may not exist in future releases. """ print(""" Warning: Usage of function berry_curv is discouraged. It has been renamed as berry_flux, which should be used instead. """) return self.berry_flux(occ,individual_phases) def k_path(kpts,nk,endpoint=True): r""" .. warning:: This function is here only for backwards compatibility with version of pythtb prior to 1.7.0. Please use related :func:`pythtb.tb_model.k_path` function as this function might not exist in the future releases of the code. """ print(""" Warning: Usage of function k_path is discouraged. Instead of the following code: k_vec=k_path(...) please use the following code: (k_vec,k_dist,k_node)=my_model.k_path(...) Note that this k_path function is a member of the tb_model class. """) if kpts=='full': # this means the full Brillouin zone for 1D case if endpoint==True: return np.arange(nk+1,dtype=float)/float(nk) else: return np.arange(nk,dtype=float)/float(nk) elif kpts=='half': # this means the half Brillouin zone for 1D case if endpoint==True: return np.arange(nk+1,dtype=float)/float(2.nk) else: return np.arange(nk,dtype=float)/float(2.nk) else: # general case kint=[] k_list=np.array(kpts) # go over all kpoints for i in range(len(k_list)-1): # go over all steps for j in range(nk): cur=k_list[i]+(k_list[i+1]-k_list[i])float(j)/float(nk) kint.append(cur) # add last point if endpoint==True: kint.append(k_list[-1]) # kint=np.array(kint) return kint def _nicefy_eig(eval,eig=None): "Sort eigenvaules and eigenvectors, if given, and convert to real numbers" # first take only real parts of the eigenvalues eval=np.array(eval.real,dtype=float) # sort energies args=eval.argsort() eval=eval[args] if not (eig is None): eig=eig[args] return (eval,eig) return eval # for nice justified printout def _nice_float(x,just,rnd): return str(round(x,rnd)).rjust(just) def _nice_int(x,just): return str(x).rjust(just) def _nice_complex(x,just,rnd): ret="" ret+=_nice_float(complex(x).real,just,rnd) if complex(x).imag<0.0: ret+=" - " else: ret+=" + " ret+=_nice_float(abs(complex(x).imag),just,rnd) ret+=" i" return ret def _wf_dpr(wf1,wf2): """calculate dot product between two wavefunctions. wf1 and wf2 are of the form [orbital,spin]""" return np.dot(wf1.flatten().conjugate(),wf2.flatten()) def _one_berry_loop(wf,berry_evals=False): """Do one Berry phase calculation (also returns a product of M matrices). Always returns numbers between -pi and pi. wf has format [kpnt,band,orbital,spin] and kpnt has to be one dimensional. Assumes that first and last k-point are the same. Therefore if there are n wavefunctions in total, will calculate phase along n-1 links only! If berry_evals is True then will compute phases for individual states, these corresponds to 1d hybrid Wannier function centers. Otherwise just return one number, Berry phase.""" # number of occupied states nocc=wf.shape[1] # temporary matrices prd=np.identity(nocc,dtype=complex) ovr=np.zeros([nocc,nocc],dtype=complex) # go over all pairs of k-points, assuming that last point is overcounted! for i in range(wf.shape[0]-1): # generate overlap matrix, go over all bands for j in range(nocc): for k in range(nocc): ovr[j,k]=_wf_dpr(wf[i,j,:],wf[i+1,k,:]) # only find Berry phase if berry_evals==False: # multiply overlap matrices prd=np.dot(prd,ovr) # also find phases of individual eigenvalues else: # cleanup matrices with SVD then take product matU,sing,matV=np.linalg.svd(ovr) prd=np.dot(prd,np.dot(matU,matV)) # calculate Berry phase if berry_evals==False: det=np.linalg.det(prd) pha=(-1.0)np.angle(det) return pha # calculate phases of all eigenvalues else: evals=np.linalg.eigvals(prd) eval_pha=(-1.0)np.angle(evals) # sort these numbers as well eval_pha=np.sort(eval_pha) return eval_pha def _one_flux_plane(wfs2d): "Compute fluxes on a two-dimensional plane of states." # size of the mesh nk0=wfs2d.shape[0] nk1=wfs2d.shape[1] # number of bands (will compute flux of all bands taken together) nbnd=wfs2d.shape[2] # here store flux through each plaquette of the mesh all_phases=np.zeros((nk0-1,nk1-1),dtype=float) # go over all plaquettes for i in range(nk0-1): for j in range(nk1-1): # generate a small loop made out of four pieces wf_use=[] wf_use.append(wfs2d[i,j]) wf_use.append(wfs2d[i+1,j]) wf_use.append(wfs2d[i+1,j+1]) wf_use.append(wfs2d[i,j+1]) wf_use.append(wfs2d[i,j]) wf_use=np.array(wf_use,dtype=complex) # calculate phase around one plaquette all_phases[i,j]=_one_berry_loop(wf_use) return all_phases def no_2pi(x,clos): "Make x as close to clos by adding or removing 2pi" while abs(clos-x)>np.pi: if clos-x>np.pi: x+=2.0np.pi elif clos-x<-1.0np.pi: x-=2.0np.pi return x def _one_phase_cont(pha,clos): """Reads in 1d array of numbers pha and makes sure that they are continuous, i.e., that there are no jumps of 2pi. First number is made as close to clos as possible.""" ret=np.copy(pha) # go through entire list and "iron out" 2pi jumps for i in range(len(ret)): # which number to compare to if i==0: cmpr=clos else: cmpr=ret[i-1] # make sure there are no 2pi jumps ret[i]=no_2pi(ret[i],cmpr) return ret def _array_phases_cont(arr_pha,clos): """Reads in 2d array of phases arr_pha and makes sure that they are continuous along first index, i.e., that there are no jumps of 2pi. First array of phasese is made as close to clos as possible.""" ret=np.zeros_like(arr_pha) # go over all points for i in range(arr_pha.shape[0]): # which phases to compare to if i==0: cmpr=clos else: cmpr=ret[i-1,:] # remember which indices are still available to be matched avail=list(range(arr_pha.shape[1])) # go over all phases in cmpr[:] for j in range(cmpr.shape[0]): # minimal distance between pairs min_dist=1.0E10 # closest index best_k=None # go over each phase in arr_pha[i,:] for k in avail: cur_dist=np.abs(np.exp(1.0jcmpr[j])-np.exp(1.0jarr_pha[i,k])) if cur_dist<=min_dist: min_dist=cur_dist best_k=k # remove this index from being possible pair later avail.pop(avail.index(best_k)) # store phase in correct place ret[i,j]=arr_pha[i,best_k] # make sure there are no 2pi jumps ret[i,j]=no_2pi(ret[i,j],cmpr[j]) return ret class w90(object): r""" This class of the PythTB package imports tight-binding model parameters from an output of a `Wannier90 <http://www.wannier.org>`_ code. The `Wannier90 <http://www.wannier.org>`_ code is a post-processing tool that takes as an input electron wavefunctions and energies computed from first-principles using any of the following codes: Quantum-Espresso (PWscf), AbInit, SIESTA, FLEUR, Wien2k, VASP. As an output Wannier90 will create files that contain parameters for a tight-binding model that exactly reproduces the first-principles calculated electron band structure. The interface from Wannier90 to PythTB will use only the following files created by Wannier90: - prefix.win - prefix\_hr.dat - prefix\_centres.xyz - prefix\_band.kpt (optional) - prefix\_band.dat (optional) The first file (prefix.win) is an input file to Wannier90 itself. This file is needed so that PythTB can read in the unit cell vectors. To correctly create the second and the third file (prefix\_hr.dat and prefix\_centres.dat) one needs to include the following flags in the win file:: hr_plot = True write_xyz = True translate_home_cell = False These lines ensure that prefix\_hr.dat and prefix\_centres.dat are written and that the centers of the Wannier functions written in the prefix\_centres.dat file are not translated to the home cell. The prefix\_hr.dat file contains the onsite and hopping terms. The final two files (prefix\_band.kpt and prefix\_band.dat) are optional. Please see documentation of function :func:`pythtb.w90.w90_bands_consistency` for more detail. So far we tested only Wannier90 version 2.0.1. .. warning:: For the time being PythTB is not optimized to be used with very large tight-binding models. Therefore it is not advisable to use the interface to Wannier90 with large first-principles calculations that contain many k-points and/or electron bands. One way to reduce the computational cost is to wannierize with Wannier90 only the bands of interest (for example, bands near the Fermi level). Units used throught this interface with Wannier90 are electron-volts (eV) and Angstroms. .. warning:: User needs to make sure that the Wannier functions computed using Wannier90 code are well localized. Otherwise the tight-binding model might not interpolate well the band structure. To ensure that the Wannier functions are well localized it is often enough to check that the total spread at the beginning of the minimization procedure (first total spread printed in .wout file) is not more than 20% larger than the total spread at the end of the minimization procedure. If those spreads differ by much more than 20% user needs to specify better initial projection functions. In addition, please note that the interpolation is valid only within the frozen energy window of the disentanglement procedure. .. warning:: So far PythTB assumes that the position operator is diagonal in the tight-binding basis. This is discussed in the :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>` in Eq. 2.7., :math:`\langle\phi_{{\bf R} i} \vert {\bf r} \vert \phi_{{\bf R}' j} \rangle = ({\bf R} + {\bf t}_j) \delta_{{\bf R} {\bf R}'} \delta_{ij}`. However, this relation does not hold for Wannier functions! Therefore, if you use tight-binding model derived from this class in computing Berry-like objects that involve position operator such as Berry phase or Berry flux, you would not get the same result as if you computed those objects directly from the first-principles code! Nevertheless, this approximation does not affect other properties such as band structure dispersion. For the testing purposes user can download the following :download:`wannier90 output example <misc/wannier90_example.tar.gz>` and use the following :ref:`script <w90_quick>` to test the functionality of the interface to PythTB. Run the following command in unix terminal to decompress the tarball:: tar -zxf wannier90_example.tar.gz and then run the following :ref:`script <w90_quick>` in the same folder. :param path: Relative path to the folder that contains Wannier90 files. These are prefix.win, prefix\_hr.dat, prefix\_centres.dat and optionally prefix\_band.kpt and prefix\_band.dat. :param prefix: This is the prefix used by Wannier90 code. Typically the input to the Wannier90 code is name prefix.win. Initially this function will read in the entire Wannier90 output. To create :class:`pythtb.tb_model` object user needs to call :func:`pythtb.w90.model`. Example usage:: # reads Wannier90 from folder called example_a # it assumes that that folder contains files "silicon.win" and so on silicon=w90("example_a", "silicon") """ def __init__(self,path,prefix): # store path and prefix self.path=path self.prefix=prefix # read in lattice_vectors f=open(self.path+"/"+self.prefix+".win","r") ln=f.readlines() f.close() # get lattice vector self.lat=np.zeros((3,3),dtype=float) found=False for i in range(len(ln)): sp=ln[i].split() if len(sp)>=2: if sp[0].lower()=="begin" and sp[1].lower()=="unit_cell_cart": # get units right if ln[i+1].strip().lower()=="bohr": pref=0.5291772108 skip=1 elif ln[i+1].strip().lower() in ["ang","angstrom"]: pref=1.0 skip=1 else: pref=1.0 skip=0 # now get vectors for j in range(3): sp=ln[i+skip+1+j].split() for k in range(3): self.lat[j,k]=float(sp[k])pref found=True break if found==False: raise Exception("Unable to find unit_cell_cart block in the .win file.") # read in hamiltonian matrix, in eV f=open(self.path+"/"+self.prefix+"_hr.dat","r") ln=f.readlines() f.close() # # get number of wannier functions self.num_wan=int(ln[1]) # get number of Wigner-Seitz points num_ws=int(ln[2]) # get degenereacies of Wigner-Seitz points deg_ws=[] for j in range(3,len(ln)): sp=ln[j].split() for s in sp: deg_ws.append(int(s)) if len(deg_ws)==num_ws: last_j=j break if len(deg_ws)>num_ws: raise Exception("Too many degeneracies for WS points!") deg_ws=np.array(deg_ws,dtype=int) # now read in matrix elements # Convention used in w90 is to write out: # R1, R2, R3, i, j, ham_r(i,j,R) # where ham_r(i,j,R) corresponds to matrix element < i \| H \| j+R > self.ham_r={} # format is ham_r[(R1,R2,R3)]["h"][i,j] for < i \| H \| j+R > ind_R=0 # which R vector in line is this? for j in range(last_j+1,len(ln)): sp=ln[j].split() # get reduced lattice vector components ham_R1=int(sp[0]) ham_R2=int(sp[1]) ham_R3=int(sp[2]) # get Wannier indices ham_i=int(sp[3])-1 ham_j=int(sp[4])-1 # get matrix element ham_val=float(sp[5])+1.0jfloat(sp[6]) # store stuff, for each R store hamiltonian and degeneracy ham_key=(ham_R1,ham_R2,ham_R3) if (ham_key in self.ham_r)==False: self.ham_r[ham_key]={ "h":np.zeros((self.num_wan,self.num_wan),dtype=complex), "deg":deg_ws[ind_R] } ind_R+=1 self.ham_r[ham_key]["h"][ham_i,ham_j]=ham_val # check if for every non-zero R there is also -R for R in self.ham_r: if not (R[0]==0 and R[1]==0 and R[2]==0): found_pair=False for P in self.ham_r: if not (R[0]==0 and R[1]==0 and R[2]==0): # check if they are opposite if R[0]==-P[0] and R[1]==-P[1] and R[2]==-P[2]: if found_pair==True: raise Exception("Found duplicate negative R!") found_pair=True if found_pair==False: raise Exception("Did not find negative R for R = "+R+"!") # read in wannier centers f=open(self.path+"/"+self.prefix+"_centres.xyz","r") ln=f.readlines() f.close() # Wannier centers in Cartesian, Angstroms xyz_cen=[] for i in range(2,2+self.num_wan): sp=ln[i].split() if sp[0]=="X": tmp=[] for j in range(3): tmp.append(float(sp[j+1])) xyz_cen.append(tmp) else: raise Exception("Inconsistency in the centres file.") self.xyz_cen=np.array(xyz_cen,dtype=float) # get orbital positions in reduced coordinates self.red_cen=_cart_to_red((self.lat[0],self.lat[1],self.lat[2]),self.xyz_cen) def model(self,zero_energy=0.0,min_hopping_norm=None,max_distance=None,ignorable_imaginary_part=None): """ This function returns :class:`pythtb.tb_model` object that can be used to interpolate the band structure at arbitrary k-point, analyze the wavefunction character, etc. The tight-binding basis orbitals in the returned object are maximally localized Wannier functions as computed by Wannier90. The orbital character of these functions can be inferred either from the projections block in the prefix.win or from the prefix.nnkp file. Please note that the character of the maximally localized Wannier functions is not exactly the same as that specified by the initial projections. One way to ensure that the Wannier functions are as close to the initial projections as possible is to first choose a good set of initial projections (for these initial and final spread should not differ more than 20%) and then perform another Wannier90 run setting num_iter=0 in the prefix.win file. Number of spin components is always set to 1, even if the underlying DFT calculation includes spin. Please refer to the projections block or the prefix.nnkp file to see which orbitals correspond to which spin. Locations of the orbitals in the returned :class:`pythtb.tb_model` object are equal to the centers of the Wannier functions computed by Wannier90. :param zero_energy: Sets the zero of the energy in the band structure. This value is typically set to the Fermi level computed by the density-functional code (or to the top of the valence band). Units are electron-volts. :param min_hopping_norm: Hopping terms read from Wannier90 with complex norm less than min_hopping_norm will not be included in the returned tight-binding model. This parameters is specified in electron-volts. By default all terms regardless of their norm are included. :param max_distance: Hopping terms from site i to site j+R will be ignored if the distance from orbital i to j+R is larger than max_distance. This parameter is given in Angstroms. By default all terms regardless of the distance are included. :param ignorable_imaginary_part: The hopping term will be assumed to be exactly real if the absolute value of the imaginary part as computed by Wannier90 is less than ignorable_imaginary_part. By default imaginary terms are not ignored. Units are again eV. :returns: * tb -- The object of type :class:`pythtb.tb_model` that can be used to interpolate Wannier90 band structure to an arbitrary k-point as well as to analyze the character of the wavefunctions. Please note Example usage:: # returns tb_model with all hopping parameters my_model=silicon.model() # simplified model that contains only hopping terms above 0.01 eV my_model_simple=silicon.model(min_hopping_norm=0.01) my_model_simple.display() """ # make the model object tb=tb_model(3,3,self.lat,self.red_cen) # remember that this model was computed from w90 tb._assume_position_operator_diagonal=False # add onsite energies onsite=np.zeros(self.num_wan,dtype=float) for i in range(self.num_wan): tmp_ham=self.ham_r[(0,0,0)]["h"][i,i]/float(self.ham_r[(0,0,0)]["deg"]) onsite[i]=tmp_ham.real if np.abs(tmp_ham.imag)>1.0E-9: raise Exception("Onsite terms should be real!") tb.set_onsite(onsite-zero_energy) # add hopping terms for R in self.ham_r: # avoid double counting use_this_R=True # avoid onsite terms if R[0]==0 and R[1]==0 and R[2]==0: avoid_diagonal=True else: avoid_diagonal=False # avoid taking both R and -R if R[0]!=0: if R[0]<0: use_this_R=False else: if R[1]!=0: if R[1]<0: use_this_R=False else: if R[2]<0: use_this_R=False # get R vector vecR=_red_to_cart((self.lat[0],self.lat[1],self.lat[2]),[R])[0] # scan through unique R if use_this_R==True: for i in range(self.num_wan): vec_i=self.xyz_cen[i] for j in range(self.num_wan): vec_j=self.xyz_cen[j] # get distance between orbitals dist_ijR=np.sqrt(np.dot(-vec_i+vec_j+vecR, -vec_i+vec_j+vecR)) # to prevent double counting if not (avoid_diagonal==True and j<=i): # only if distance between orbitals is small enough if max_distance is not None: if dist_ijR>max_distance: continue # divide the matrix element from w90 with the degeneracy tmp_ham=self.ham_r[R]["h"][i,j]/float(self.ham_r[R]["deg"]) # only if big enough matrix element if min_hopping_norm is not None: if np.abs(tmp_ham)<min_hopping_norm: continue # remove imaginary part if needed if ignorable_imaginary_part is not None: if np.abs(tmp_ham.imag)<ignorable_imaginary_part: tmp_ham=tmp_ham.real+0.0j # set the hopping term tb.set_hop(tmp_ham,i,j,list(R)) return tb def dist_hop(self): """ This is one of the diagnostic tools that can be used to help in determining min_hopping_norm and max_distance parameter in :func:`pythtb.w90.model` function call. This function returns all hopping terms (from orbital i to j+R) as well as the distances between the i and j+R orbitals. For well localized Wannier functions hopping term should decay exponentially with distance. :returns: * dist -- Distances between Wannier function centers (i and j+R) in Angstroms. * ham -- Corresponding hopping terms in eV. Example usage:: # get distances and hopping terms (dist,ham)=silicon.dist_hop() # plot logarithm of the hopping term as a function of distance import pylab as plt fig, ax = plt.subplots() ax.scatter(dist,np.log(np.abs(ham))) fig.savefig("localization.pdf") """ ret_ham=[] ret_dist=[] for R in self.ham_r: # treat diagonal terms differently if R[0]==0 and R[1]==0 and R[2]==0: avoid_diagonal=True else: avoid_diagonal=False # get R vector vecR=_red_to_cart((self.lat[0],self.lat[1],self.lat[2]),[R])[0] for i in range(self.num_wan): vec_i=self.xyz_cen[i] for j in range(self.num_wan): vec_j=self.xyz_cen[j] # diagonal terms if not (avoid_diagonal==True and i==j): # divide the matrix element from w90 with the degeneracy ret_ham.append(self.ham_r[R]["h"][i,j]/float(self.ham_r[R]["deg"])) # get distance between orbitals ret_dist.append(np.sqrt(np.dot(-vec_i+vec_j+vecR,-vec_i+vec_j+vecR))) return (np.array(ret_dist),np.array(ret_ham)) def shells(self,num_digits=2): """ This is one of the diagnostic tools that can be used to help in determining max_distance parameter in :func:`pythtb.w90.model` function call. :param num_digits: Distances will be rounded up to these many digits. Default value is 2. :returns: * shells -- All distances between all Wannier function centers (i and j+R) in Angstroms. Example usage:: # prints on screen all shells print silicon.shells() """ shells=[] for R in self.ham_r: # get R vector vecR=_red_to_cart((self.lat[0],self.lat[1],self.lat[2]),[R])[0] for i in range(self.num_wan): vec_i=self.xyz_cen[i] for j in range(self.num_wan): vec_j=self.xyz_cen[j] # get distance between orbitals dist_ijR=np.sqrt(np.dot(-vec_i+vec_j+vecR, -vec_i+vec_j+vecR)) # round it up shells.append(round(dist_ijR,num_digits)) # remove duplicates and sort shells=np.sort(list(set(shells))) return shells def w90_bands_consistency(self): """ This function reads in band structure as interpolated by Wannier90. Please note that this is not the same as the band structure calculated by the underlying DFT code. The two will agree only on the coarse set of k-points that were used in Wannier90 generation. The purpose of this function is to compare the interpolation in Wannier90 with that in PythTB. If no terms were ignored in the call to :func:`pythtb.w90.model` then the two should be exactly the same (up to numerical precision). Otherwise one should expect deviations. However, if one carefully chooses the cutoff parameters in :func:`pythtb.w90.model` it is likely that one could reproduce the full band-structure with only few dominant hopping terms. Please note that this tests only the eigenenergies, not eigenvalues (wavefunctions). The code assumes that the following files were generated by Wannier90, - prefix\_band.kpt - prefix\_band.dat These files will be generated only if the prefix.win file contains the kpoint_path block. :returns: * kpts -- k-points in reduced coordinates used in the interpolation in Wannier90 code. The format of kpts is the same as the one used by the input to :func:`pythtb.tb_model.solve_all`. * ene -- energies interpolated by Wannier90 in eV. Format is ene[band,kpoint]. Example usage:: # get band structure from wannier90 (w90_kpt,w90_evals)=silicon.w90_bands_consistency() # get simplified model my_model_simple=silicon.model(min_hopping_norm=0.01) # solve simplified model on the same k-path as in wannier90 evals=my_model.solve_all(w90_kpt) # plot comparison of the two import pylab as plt fig, ax = plt.subplots() for i in range(evals.shape[0]): ax.plot(range(evals.shape[1]),evals[i],"r-",zorder=-50) for i in range(w90_evals.shape[0]): ax.plot(range(w90_evals.shape[1]),w90_evals[i],"k-",zorder=-100) fig.savefig("comparison.pdf") """ # read in kpoints in reduced coordinates kpts=np.loadtxt(self.path+"/"+self.prefix+"_band.kpt",skiprows=1) # ignore weights kpts=kpts[:,:3] # read in energies ene=np.loadtxt(self.path+"/"+self.prefix+"_band.dat") # ignore kpath distance ene=ene[:,1] # correct shape ene=ene.reshape((self.num_wan,kpts.shape[0])) return (kpts,ene) def _cart_to_red(tmp,cart): "Convert cartesian vectors cart to reduced coordinates of a1,a2,a3 vectors" (a1,a2,a3)=tmp # matrix with lattice vectors cnv=np.array([a1,a2,a3]) # transpose a matrix cnv=cnv.T # invert a matrix cnv=np.linalg.inv(cnv) # reduced coordinates red=np.zeros_like(cart,dtype=float) for i in range(0,len(cart)): red[i]=np.dot(cnv,cart[i]) return red def _red_to_cart(tmp,red): "Convert reduced to cartesian vectors." (a1,a2,a3)=tmp # cartesian coordinates cart=np.zeros_like(red,dtype=float) for i in range(0,len(cart)): cart[i,:]=a1red[i][0]+a2red[i][1]+a3*red[i][2] return cart def _offdiag_approximation_warning_and_stop(): raise Exception(""" ---------------------------------------------------------------------- It looks like you are trying to calculate Berry-like object that involves position operator. However, you are using a tight-binding model that was generated from Wannier90. This procedure introduces approximation as it ignores off-diagonal elements of the position operator in the Wannier basis. This is discussed here in more detail: http://physics.rutgers.edu/pythtb/usage.html#pythtb.w90 If you know what you are doing and wish to continue with the calculation despite this approximation, please call the following function on your tb_model object my_model.ignore_position_operator_offdiagonal() ---------------------------------------------------------------------- """)	lgpl-3.0
JosmanPS/scikit-learn	examples/manifold/plot_compare_methods.py	259	4031	""" ========================================= Comparison of Manifold Learning methods ========================================= An illustration of dimensionality reduction on the S-curve dataset with various manifold learning methods. For a discussion and comparison of these algorithms, see the :ref:`manifold module page <manifold>` For a similar example, where the methods are applied to a sphere dataset, see :ref:`example_manifold_plot_manifold_sphere.py` Note that the purpose of the MDS is to find a low-dimensional representation of the data (here 2D) in which the distances respect well the distances in the original high-dimensional space, unlike other manifold-learning algorithms, it does not seeks an isotropic representation of the data in the low-dimensional space. """ # Author: Jake Vanderplas -- <[email protected]> print(__doc__) from time import time import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib.ticker import NullFormatter from sklearn import manifold, datasets # Next line to silence pyflakes. This import is needed. Axes3D n_points = 1000 X, color = datasets.samples_generator.make_s_curve(n_points, random_state=0) n_neighbors = 10 n_components = 2 fig = plt.figure(figsize=(15, 8)) plt.suptitle("Manifold Learning with %i points, %i neighbors" % (1000, n_neighbors), fontsize=14) try: # compatibility matplotlib < 1.0 ax = fig.add_subplot(251, projection='3d') ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral) ax.view_init(4, -72) except: ax = fig.add_subplot(251, projection='3d') plt.scatter(X[:, 0], X[:, 2], c=color, cmap=plt.cm.Spectral) methods = ['standard', 'ltsa', 'hessian', 'modified'] labels = ['LLE', 'LTSA', 'Hessian LLE', 'Modified LLE'] for i, method in enumerate(methods): t0 = time() Y = manifold.LocallyLinearEmbedding(n_neighbors, n_components, eigen_solver='auto', method=method).fit_transform(X) t1 = time() print("%s: %.2g sec" % (methods[i], t1 - t0)) ax = fig.add_subplot(252 + i) plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral) plt.title("%s (%.2g sec)" % (labels[i], t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') t0 = time() Y = manifold.Isomap(n_neighbors, n_components).fit_transform(X) t1 = time() print("Isomap: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(257) plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral) plt.title("Isomap (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') t0 = time() mds = manifold.MDS(n_components, max_iter=100, n_init=1) Y = mds.fit_transform(X) t1 = time() print("MDS: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(258) plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral) plt.title("MDS (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') t0 = time() se = manifold.SpectralEmbedding(n_components=n_components, n_neighbors=n_neighbors) Y = se.fit_transform(X) t1 = time() print("SpectralEmbedding: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(259) plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral) plt.title("SpectralEmbedding (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') t0 = time() tsne = manifold.TSNE(n_components=n_components, init='pca', random_state=0) Y = tsne.fit_transform(X) t1 = time() print("t-SNE: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(250) plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral) plt.title("t-SNE (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') plt.show()	bsd-3-clause
gdhungana/corrLSS	py/corrLSS/correlation.py	1	9562	import numpy as np import matplotlib.pyplot as plt import treecorr as tc import astropy.table import healpy as hp from corrLSS.util import apply_mask,radec2thetaphi # Cosmology def set_cosmology(): Omega_matter = 0.140247/0.68002322 Omega_baryon = 0.022337/0.68002322 Omega_curvature = 0 H0 = 68.002320 sigma_8 = 0.811322 n_s = 0.963180 from astropy.cosmology import FlatLambdaCDM cosmo=FlatLambdaCDM(H0=H0,Om0=Omega_matter) return cosmo def arrange_catalog(catfile,rndfile=None,zmin=None,zmax=None,objtype=None,truthfile=None): """ Use treecorr to evaluate two point correlation given a data catalog and a random catalog """ print("Reading data catalog") #datatab=astropy.table.Table.read(catfile) cat=astropy.io.fits.open(catfile) datacat=cat[1].data try: z_data=datacat['Z_COSMO'] print("Using Z_COSMO for z") except: try: z_data=datacat['TRUEZ'] print("Using TRUEZ for z") except: try: z_data=datacat['Z'] print("Using Z for z") except: raise ValueError("None of the specified z-types match. Check fits header") if truthfile is not None: #- required to match targetid for ra,dec tru=astropy.io.fits.open(truthfile) trucat=tru[1].data truid=trucat['TARGETID'] dataid=datacat['TARGETID'] #- map targetid sorted as in dataid tt=np.argsort(truid) ss=np.searchsorted(truid[tt],dataid) srt_idx=tt[ss] np.testing.assert_array_equal(truid[srt_idx],dataid) print("100% targets matched for data catalog") ra_data=trucat['RA'][srt_idx] dec_data=trucat['DEC'][srt_idx] else: ra_data=datacat['ra'] dec_data=datacat['dec'] if objtype is not None: try: kk=np.where(datacat['SOURCETYPE']==objtype)[0] print("Using sourcetype {}".format(objtype)) except: try: kk=np.where(datacat['SPECTYPE']==objtype)[0] print("Using spectype {}".format(objtype)) except: print("Objtype doesn't match header key. Check fits header") print("Total {} in the data: {}".format(objtype,len(kk))) print("Total {} in the data: {}".format(objtype,len(kk))) ra_data=ra_data[kk] dec_data=dec_data[kk] z_data=z_data[kk] cosmo=set_cosmology() if zmin is None: zmin=np.min(z_data) if zmax is None: zmax=np.max(z_data) print("zmin:{} to zmax: {}".format(zmin,zmax)) #TODO make this loop for differnt redshift bins to avoid reading catalogs each time wh=np.logical_and(z_data>zmin,z_data<zmax) ngal=np.count_nonzero(wh) print("Bin contains: {} galaxies".format(np.count_nonzero(wh))) print(cosmo.H0) cmvr_data=cosmo.comoving_distance(z_data[wh])cosmo.H0.value/100. dmin,dmax=cosmo.comoving_distance([zmin,zmax])cosmo.H0.value/100. print("Dmin to Dmax: {} to {}".format(dmin,dmax)) print("Organizing data catalog to use") datacat=make_catalog(ra_data[wh],dec_data[wh],cmvr_data) if rndfile is not None: print("Reading random catalog") #rndtab=astropy.table.Table.read(rndfile) rnd=astropy.io.fits.open(rndfile) rndtab=rnd[1].data z_rnd=rndtab['z'] ra_rnd=rndtab['ra'] dec_rnd=rndtab['dec'] whr=np.logical_and(z_rnd>zmin,z_rnd<zmax) nran=np.count_nonzero(whr) print("Bin Contains: {} random objects".format( np.count_nonzero(whr))) cmvr_rnd=cosmo.comoving_distance(z_rnd[whr])cosmo.H0.value/100. print("Organizing random catalog to use") rndcat=make_catalog(ra_rnd[whr],dec_rnd[whr],cmvr_rnd) return datacat, rndcat else: return datacat def correlate_tc(datacat,rndcat,outfile,cutoff=None): """ datacat and randcat are tc.catalog object """ print("Auto correlating data") dd=tc.NNCorrelation(min_sep=0.1,bin_size=0.025,max_sep=180.) dd.process(datacat) print("Auto correlating random") rr=tc.NNCorrelation(min_sep=0.1,bin_size=0.025,max_sep=180.) rr.process(rndcat) print("Cross Correlating") dr=tc.NNCorrelation(min_sep=0.1,bin_size=0.025,max_sep=180.) dr.process(datacat,rndcat) print("Calculating 2-pt. correlation") xi,xivar=dd.calculateXi(rr,dr) tab=astropy.table.Table([np.exp(dd.logr),xi,xivar],names=('r','xi','xivar')) tab.write(outfile,overwrite=True) def random_data_xyz(datacat,bandwidth=0.2,format='xyz'): """ data cat is treecorr catalog object and should have x, y, and z random is created here in xyz """ from scipy.stats import gaussian_kde if format=='xyz': values=np.vstack([datacat.x,datacat.y,datacat.z]) kde=gaussian_kde(values,bw_method=bandwidth/values.std(ddof=1)) nx,ny,nz=kde.resample(2len(datacat.z)) randcat=tc.Catalog(x=nx,y=ny,z=nz) elif format=='radecr': values=np.vstack([datacat.ra/datacat.ra_units,datacat.dec/datacat.dec_units,datacat.r]) kde=gaussian_kde(values,bw_method=bandwidth/values.std(ddof=1)) nra,ndec,nr=kde.resample(2len(datacat.r)) randcat=tc.Catalog(ra=nra,dec=ndec,ra_units='deg',dec_units='deg',r=nr) return randcat def make_catalog(ra,dec,cmvr=None): #- ra, dec in degrees cat=tc.Catalog(ra=ra,dec=dec,r=cmvr,ra_units='deg',dec_units='deg') return cat def two_point(data,data_R,bins,method='landy-szalay',seed=1234,saverandom=False): """ Uses nearest neighbors KDtree to evaluate two point correlation args: data: n samples x m features data array, eg. x,y,z positions bins: 1d bins array return: two - pt correlation correlation give the method. Errors are not returned. A bootstrap sampling can be run N times to evaluate errors. """ from sklearn.neighbors import KDTree data = np.asarray(data) bins = np.asarray(bins) rng = np.random.RandomState(seed) if method not in ['standard', 'landy-szalay']: raise ValueError("method must be 'standard' or 'landy-szalay'") if bins.ndim != 1: raise ValueError("bins must be a 1D array") if data.ndim == 1: data = data[:, np.newaxis] elif data.ndim != 2: raise ValueError("data should be 1D or 2D") n_samples, n_features = data.shape Nbins = len(bins) - 1 # shuffle all but one axis to get background distribution if data_R is None: data_R = data.copy() for i in range(n_features - 1): rng.shuffle(data_R[:, i]) else: data_R = np.asarray(data_R) if (data_R.ndim != 2) or (data_R.shape[-1] != n_features): raise ValueError('data_R must have same n_features as data') factor = len(data_R) 1. / len(data) KDT_D=KDTree(data) KDT_R=KDTree(data_R) print("Correlating Data, data size: {}".format(len(data))) counts_DD=KDT_D.two_point_correlation(data,bins) print('Correlating Random, random size: {}'.format(len(data_R))) counts_RR=KDT_R.two_point_correlation(data_R,bins) DD=np.diff(counts_DD) RR=np.diff(counts_RR) #- Check for zero in RR RR_zero = (RR == 0) RR[RR_zero]=1 if method == 'standard': corr = factor*2DD/RR - 1 elif method == 'landy-szalay': print("Cross Correlating") counts_DR=KDT_R.two_point_correlation(data,bins) DR=np.diff(counts_DR) print("Evaluating correlation using {}".format(method)) corr = (factor*2 DD - 2 * factor * DR + RR)/RR corr[RR_zero] = np.nan return corr def extract_catalog(catalog,zmin=None,zmax=None): print("Reading catalog.") tab = astropy.table.Table.read(catalog) ra = tab['RA'] dec = tab['DEC'] z = tab['Z'] print("Objects in catalog: {}".format(len(z))) if zmin is None: zmin=np.min(z) if zmax is None: zmax=np.max(z) sel=np.where((z >= zmin) & (z < zmax)) ra = ra[sel] dec = dec[sel] z = z[sel] print("Objects in this redshift bin".format(z.shape[0])) #- set cosmology print("Setting Fiducial Cosmology") cosmo = set_cosmology() cmv_r = cosmo.comoving_distance(z)cosmo.H0.value/100. #- Coordinates: carx,cary,carz = ra_dec_to_xyz(ra,dec) cmv_r #- set data: data=np.transpose([carx,cary,carz]) return data def make_data_R_catalog(datacat,outfile='random_from_datacat.fits',seed=1234): """ Make random background from shuffling data """ print("Reading data catalog") datatab=astropy.table.Table.read(datacat) ra = datatab['ra'] dec = datatab['dec'] z = datatab['z'] data=np.transpose([ra,dec,z]) #- create random by shuffling all but 1 axis print("Making random catalog from data") data_R = data.copy() n_samples, n_features = data.shape rng = np.random.RandomState(seed) for i in range(n_features - 1): rng.shuffle(data_R[:, i]) randdata=astropy.table.Table([data_R[:,0],data_R[:,1],data_R[:,2]],names=('RA','DEC','Z')) randdata.write(outfile,format='fits') print("Written Random file from data shuffling: {}".format(outfile)) def est_correlation(data,bins,data_R=None,method='landy-szalay'): #- correlation print("Evaluating 2-pt Correlation.") corr=two_point(data,bins,method=method,data_R=data_R) return bins,corr	mit
has2k1/plotnine	plotnine/tests/test_position.py	1	5061	import string import numpy as np import pandas as pd import pytest from plotnine import (ggplot, aes, geom_point, geom_jitter, geom_bar, geom_col, geom_boxplot, geom_text, geom_rect, after_stat, position_dodge, position_dodge2, position_jitter, position_jitterdodge, position_nudge, position_stack, theme) from plotnine.positions.position import position from plotnine.exceptions import PlotnineError n = 6 m = 10 random_state = np.random.RandomState(1234567890) df1 = pd.DataFrame({'x': [1, 2, 1, 2], 'y': [1, 1, 2, 2]}) df2 = pd.DataFrame({'x': np.repeat(range(n+1), range(n+1)), 'z': np.repeat(range(n//2), range(3, n2, 4))}) df3 = pd.DataFrame({ 'x': random_state.choice(['A', 'B'], nm), 'y': random_state.randint(0, 20, nm), 'c': random_state.choice([False, False, True, False], nm) }) random_state.seed(1234567890) _theme = theme(subplots_adjust={'right': 0.85}) def test_jitter(): df1 = pd.DataFrame({'x': [1, 2, 1, 2], 'y': [1, 1, 2, 2]}) p = (ggplot(df1, aes('x', 'y')) + geom_point(size=10) + geom_jitter(size=10, color='red', random_state=random_state) + geom_jitter(size=10, color='blue', width=0.1, height=0.1, random_state=random_state)) assert p + _theme == 'jitter' with pytest.raises(PlotnineError): geom_jitter(position=position_jitter(), width=0.1) def test_nudge(): p = (ggplot(df1, aes('x', 'y')) + geom_point(size=10) + geom_point(size=10, color='red', position=position_nudge(.25, .25))) assert p + _theme == 'nudge' def test_stack(): p = (ggplot(df2, aes('factor(z)')) + geom_bar(aes(fill='factor(x)'), position='stack')) assert p + _theme == 'stack' def test_stack_negative(): df = df1.copy() _loc = df.columns.get_loc df.iloc[0, _loc('y')] = -1 df.iloc[len(df)-1, _loc('y')] = -1 p = (ggplot(df) + geom_col(aes('factor(x)', 'y', fill='factor(y)'), position='stack') + geom_text(aes('factor(x)', 'y', label='y'), position=position_stack(vjust=0.5)) ) assert p + _theme == 'stack-negative' def test_fill(): p = (ggplot(df2, aes('factor(z)')) + geom_bar(aes(fill='factor(x)'), position='fill')) assert p + _theme == 'fill' def test_dodge(): p = (ggplot(df2, aes('factor(z)')) + geom_bar(aes(fill='factor(x)'), position='dodge')) assert p + _theme == 'dodge' def test_dodge_preserve_single(): df1 = pd.DataFrame({'x': ['a', 'b', 'b'], 'y': ['a', 'a', 'b']}) p = (ggplot(df1, aes('x', fill='y')) + geom_bar(position=position_dodge(preserve='single'))) assert p + _theme == 'dodge_preserve_single' def test_dodge_preserve_single_text(): df1 = pd.DataFrame({'x': ['a', 'b', 'b', 'b'], 'y': ['a', 'a', 'b', 'b']}) d = position_dodge(preserve='single', width=0.9) p = (ggplot(df1, aes('x', fill='y')) + geom_bar(position=d) + geom_text( aes(y=after_stat('count'), label=after_stat('count')), stat='count', position=d, va='bottom') ) assert p + _theme == 'dodge_preserve_single_text' def test_dodge2(): p = (ggplot(df3, aes('x', 'y', color='c')) + geom_boxplot(position='dodge2', size=2)) assert p + _theme == 'dodge2' def test_dodge2_varwidth(): p = (ggplot(df3, aes('x', 'y', color='c')) + geom_boxplot( position=position_dodge2(preserve='single'), varwidth=True, size=2) ) assert p + _theme == 'dodge2_varwidth' def test_jitterdodge(): df = pd.DataFrame({ 'x': np.ones(n*2), 'y': np.repeat(np.arange(n), 2), 'letters': np.repeat(list(string.ascii_lowercase[:n]), 2)}) position = position_jitterdodge(random_state=random_state) p = (ggplot(df, aes('x', 'y', fill='letters')) + geom_point(size=10, fill='black') + geom_point(size=10, position=position)) assert p + _theme == 'jitterdodge' def test_position_from_geom(): geom = geom_point(position='jitter') assert isinstance(position.from_geom(geom), position_jitter) geom = geom_point(position='position_jitter') assert isinstance(position.from_geom(geom), position_jitter) geom = geom_point(position=position_jitter()) assert isinstance(position.from_geom(geom), position_jitter) geom = geom_point(position=position_jitter) assert isinstance(position.from_geom(geom), position_jitter) def test_dodge_empty_data(): empty_df = pd.DataFrame({'x': [], 'y': []}) p = (ggplot(df1, aes('x', 'y')) + geom_point() + geom_rect( empty_df, aes(xmin='x', xmax='x+1', ymin='y', ymax='y+1'), position='dodge') ) p.draw_test()	gpl-2.0
poryfly/scikit-learn	examples/semi_supervised/plot_label_propagation_digits.py	268	2723	""" =================================================== Label Propagation digits: Demonstrating performance =================================================== This example demonstrates the power of semisupervised learning by training a Label Spreading model to classify handwritten digits with sets of very few labels. The handwritten digit dataset has 1797 total points. The model will be trained using all points, but only 30 will be labeled. Results in the form of a confusion matrix and a series of metrics over each class will be very good. At the end, the top 10 most uncertain predictions will be shown. """ print(__doc__) # Authors: Clay Woolam <[email protected]> # Licence: BSD import numpy as np import matplotlib.pyplot as plt from scipy import stats from sklearn import datasets from sklearn.semi_supervised import label_propagation from sklearn.metrics import confusion_matrix, classification_report digits = datasets.load_digits() rng = np.random.RandomState(0) indices = np.arange(len(digits.data)) rng.shuffle(indices) X = digits.data[indices[:330]] y = digits.target[indices[:330]] images = digits.images[indices[:330]] n_total_samples = len(y) n_labeled_points = 30 indices = np.arange(n_total_samples) unlabeled_set = indices[n_labeled_points:] # shuffle everything around y_train = np.copy(y) y_train[unlabeled_set] = -1 ############################################################################### # Learn with LabelSpreading lp_model = label_propagation.LabelSpreading(gamma=0.25, max_iter=5) lp_model.fit(X, y_train) predicted_labels = lp_model.transduction_[unlabeled_set] true_labels = y[unlabeled_set] cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_) print("Label Spreading model: %d labeled & %d unlabeled points (%d total)" % (n_labeled_points, n_total_samples - n_labeled_points, n_total_samples)) print(classification_report(true_labels, predicted_labels)) print("Confusion matrix") print(cm) # calculate uncertainty values for each transduced distribution pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T) # pick the top 10 most uncertain labels uncertainty_index = np.argsort(pred_entropies)[-10:] ############################################################################### # plot f = plt.figure(figsize=(7, 5)) for index, image_index in enumerate(uncertainty_index): image = images[image_index] sub = f.add_subplot(2, 5, index + 1) sub.imshow(image, cmap=plt.cm.gray_r) plt.xticks([]) plt.yticks([]) sub.set_title('predict: %i\ntrue: %i' % ( lp_model.transduction_[image_index], y[image_index])) f.suptitle('Learning with small amount of labeled data') plt.show()	bsd-3-clause
JoseGuzman/myIPythonNotebooks	pub/nerst.py	1	2413	""" nerst.py Jose Guzman, [email protected] Created: Fri Apr 29 08:04:57 CEST 2016 Solves the Nerst equation for different chloride conditions. """ from math import log from terminaltables import AsciiTable import numpy as np def nerst(t, oution, inion, z): """ Solves the following equation: .. math:: E = \frac{R T}{z F} \ln\frac{out}{in}\\ Arguments --------- oution : float Extracellular ionic concentration intion : float Intracellular ionic concentration z : int Valence of the ion t : temp Temperature in celsius Returns ------- voltage : float the potential (in mV) at which the net flux of current is zero. Examples -------- >>> nerst( t = 30, oution = 126.5, inion = 10, z=-1) >>> -66.294937 """ K = 273.15 + t # transform in kelvin volt = ((8.31451K)/(z96485.3))log(oution/inion) return(volt1000) # in mV if __name__ == '__main__': data = [['Reference', 'E_rev (Cl-) mV']] # data from Pavlidis % Madison 1999 ECl = nerst(t = 30, oution=126.5, inion=10, z=-1) data.append(['Pavlidis & Madison, 1999', ECl]) # data from Sasaki et al ECl = nerst(t = 30, oution=133.3, inion=4, z=-1) data.append(['Sasaki et al., 2012', ECl]) # data from Mitra et al, 2011 ECl = nerst(t = 20, oution=129, inion=9, z=-1) data.append(['Mitra et al., 2011', ECl]) # data from Kraushaar and Jonas, 2000 ECl = nerst(t = 20, oution=126.5, inion=149, z=-1) data.append(['Krausharr and Jonas, 2000',ECl]) # data from Espinoza ECl = nerst(t = 20, oution=133.5, inion=44, z=-1) data.append(['Espinoza et al., ', ECl]) # data for brain organoids (for Cl) ECl = nerst(t = 20, oution=133.5, inion=28, z=-1) data.append(['Guzman et al., ', ECl]) table = AsciiTable(data) print table.table import matplotlib.pyplot as plt x = np.arange(0.1, 50.0, 0.01) k = lambda x:58*np.log10(x/100.) # K-nerst equation y = k(x) plt.semilogx(x,y, color='royalblue') plt.vlines(2.5, -120, k(2.5), linestyle=':', color='brown') plt.hlines(k(2.5), 0.01, 2.5, linestyle=':', color='brown') plt.ylim(ymin=-120) plt.xlim(xmin=0.1) plt.ylabel('Resting membrane potential (mV)') plt.xlabel('Log [K$^+$]') plt.show()	gpl-2.0
josephcslater/scipy	scipy/signal/filter_design.py	14	135076	"""Filter design. """ from __future__ import division, print_function, absolute_import import warnings import math import numpy import numpy as np from numpy import (atleast_1d, poly, polyval, roots, real, asarray, resize, pi, absolute, logspace, r_, sqrt, tan, log10, arctan, arcsinh, sin, exp, cosh, arccosh, ceil, conjugate, zeros, sinh, append, concatenate, prod, ones, array, mintypecode) from numpy.polynomial.polynomial import polyval as npp_polyval from scipy import special, optimize from scipy.special import comb, factorial from scipy._lib._numpy_compat import polyvalfromroots __all__ = ['findfreqs', 'freqs', 'freqz', 'tf2zpk', 'zpk2tf', 'normalize', 'lp2lp', 'lp2hp', 'lp2bp', 'lp2bs', 'bilinear', 'iirdesign', 'iirfilter', 'butter', 'cheby1', 'cheby2', 'ellip', 'bessel', 'band_stop_obj', 'buttord', 'cheb1ord', 'cheb2ord', 'ellipord', 'buttap', 'cheb1ap', 'cheb2ap', 'ellipap', 'besselap', 'BadCoefficients', 'freqs_zpk', 'freqz_zpk', 'tf2sos', 'sos2tf', 'zpk2sos', 'sos2zpk', 'group_delay', 'sosfreqz', 'iirnotch', 'iirpeak'] class BadCoefficients(UserWarning): """Warning about badly conditioned filter coefficients""" pass abs = absolute def findfreqs(num, den, N, kind='ba'): """ Find array of frequencies for computing the response of an analog filter. Parameters ---------- num, den : array_like, 1-D The polynomial coefficients of the numerator and denominator of the transfer function of the filter or LTI system, where the coefficients are ordered from highest to lowest degree. Or, the roots of the transfer function numerator and denominator (i.e. zeroes and poles). N : int The length of the array to be computed. kind : str {'ba', 'zp'}, optional Specifies whether the numerator and denominator are specified by their polynomial coefficients ('ba'), or their roots ('zp'). Returns ------- w : (N,) ndarray A 1-D array of frequencies, logarithmically spaced. Examples -------- Find a set of nine frequencies that span the "interesting part" of the frequency response for the filter with the transfer function H(s) = s / (s^2 + 8s + 25) >>> from scipy import signal >>> signal.findfreqs([1, 0], [1, 8, 25], N=9) array([ 1.00000000e-02, 3.16227766e-02, 1.00000000e-01, 3.16227766e-01, 1.00000000e+00, 3.16227766e+00, 1.00000000e+01, 3.16227766e+01, 1.00000000e+02]) """ if kind == 'ba': ep = atleast_1d(roots(den)) + 0j tz = atleast_1d(roots(num)) + 0j elif kind == 'zp': ep = atleast_1d(den) + 0j tz = atleast_1d(num) + 0j else: raise ValueError("input must be one of {'ba', 'zp'}") if len(ep) == 0: ep = atleast_1d(-1000) + 0j ez = r_['-1', numpy.compress(ep.imag >= 0, ep, axis=-1), numpy.compress((abs(tz) < 1e5) & (tz.imag >= 0), tz, axis=-1)] integ = abs(ez) < 1e-10 hfreq = numpy.around(numpy.log10(numpy.max(3 * abs(ez.real + integ) + 1.5 * ez.imag)) + 0.5) lfreq = numpy.around(numpy.log10(0.1 * numpy.min(abs(real(ez + integ)) + 2 * ez.imag)) - 0.5) w = logspace(lfreq, hfreq, N) return w def freqs(b, a, worN=None, plot=None): """ Compute frequency response of analog filter. Given the M-order numerator `b` and N-order denominator `a` of an analog filter, compute its frequency response:: b[0](jw)M + b[1](jw)*(M-1) + ... + b[M] H(w) = ---------------------------------------------- a[0](jw)*N + a[1](jw)*(N-1) + ... + a[N] Parameters ---------- b : array_like Numerator of a linear filter. a : array_like Denominator of a linear filter. worN : {None, int, array_like}, optional If None, then compute at 200 frequencies around the interesting parts of the response curve (determined by pole-zero locations). If a single integer, then compute at that many frequencies. Otherwise, compute the response at the angular frequencies (e.g. rad/s) given in `worN`. plot : callable, optional A callable that takes two arguments. If given, the return parameters `w` and `h` are passed to plot. Useful for plotting the frequency response inside `freqs`. Returns ------- w : ndarray The angular frequencies at which `h` was computed. h : ndarray The frequency response. See Also -------- freqz : Compute the frequency response of a digital filter. Notes ----- Using Matplotlib's "plot" function as the callable for `plot` produces unexpected results, this plots the real part of the complex transfer function, not the magnitude. Try ``lambda w, h: plot(w, abs(h))``. Examples -------- >>> from scipy.signal import freqs, iirfilter >>> b, a = iirfilter(4, [1, 10], 1, 60, analog=True, ftype='cheby1') >>> w, h = freqs(b, a, worN=np.logspace(-1, 2, 1000)) >>> import matplotlib.pyplot as plt >>> plt.semilogx(w, 20 np.log10(abs(h))) >>> plt.xlabel('Frequency') >>> plt.ylabel('Amplitude response [dB]') >>> plt.grid() >>> plt.show() """ if worN is None: w = findfreqs(b, a, 200) elif isinstance(worN, int): N = worN w = findfreqs(b, a, N) else: w = worN w = atleast_1d(w) s = 1j * w h = polyval(b, s) / polyval(a, s) if plot is not None: plot(w, h) return w, h def freqs_zpk(z, p, k, worN=None): """ Compute frequency response of analog filter. Given the zeros `z`, poles `p`, and gain `k` of a filter, compute its frequency response:: (jw-z[0]) * (jw-z[1]) * ... * (jw-z[-1]) H(w) = k * ---------------------------------------- (jw-p[0]) * (jw-p[1]) * ... * (jw-p[-1]) Parameters ---------- z : array_like Zeroes of a linear filter p : array_like Poles of a linear filter k : scalar Gain of a linear filter worN : {None, int, array_like}, optional If None, then compute at 200 frequencies around the interesting parts of the response curve (determined by pole-zero locations). If a single integer, then compute at that many frequencies. Otherwise, compute the response at the angular frequencies (e.g. rad/s) given in `worN`. Returns ------- w : ndarray The angular frequencies at which `h` was computed. h : ndarray The frequency response. See Also -------- freqs : Compute the frequency response of an analog filter in TF form freqz : Compute the frequency response of a digital filter in TF form freqz_zpk : Compute the frequency response of a digital filter in ZPK form Notes ----- .. versionadded: 0.19.0 Examples -------- >>> from scipy.signal import freqs_zpk, iirfilter >>> z, p, k = iirfilter(4, [1, 10], 1, 60, analog=True, ftype='cheby1', ... output='zpk') >>> w, h = freqs_zpk(z, p, k, worN=np.logspace(-1, 2, 1000)) >>> import matplotlib.pyplot as plt >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.xlabel('Frequency') >>> plt.ylabel('Amplitude response [dB]') >>> plt.grid() >>> plt.show() """ k = np.asarray(k) if k.size > 1: raise ValueError('k must be a single scalar gain') if worN is None: w = findfreqs(z, p, 200, kind='zp') elif isinstance(worN, int): N = worN w = findfreqs(z, p, N, kind='zp') else: w = worN w = atleast_1d(w) s = 1j * w num = polyvalfromroots(s, z) den = polyvalfromroots(s, p) h = k * num/den return w, h def freqz(b, a=1, worN=None, whole=False, plot=None): """ Compute the frequency response of a digital filter. Given the M-order numerator `b` and N-order denominator `a` of a digital filter, compute its frequency response:: jw -jw -jwM jw B(e ) b[0] + b[1]e + .... + b[M]e H(e ) = ---- = ----------------------------------- jw -jw -jwN A(e ) a[0] + a[1]e + .... + a[N]e Parameters ---------- b : array_like numerator of a linear filter a : array_like denominator of a linear filter worN : {None, int, array_like}, optional If None (default), then compute at 512 frequencies equally spaced around the unit circle. If a single integer, then compute at that many frequencies. If an array_like, compute the response at the frequencies given (in radians/sample). whole : bool, optional Normally, frequencies are computed from 0 to the Nyquist frequency, pi radians/sample (upper-half of unit-circle). If `whole` is True, compute frequencies from 0 to 2pi radians/sample. plot : callable A callable that takes two arguments. If given, the return parameters `w` and `h` are passed to plot. Useful for plotting the frequency response inside `freqz`. Returns ------- w : ndarray The normalized frequencies at which `h` was computed, in radians/sample. h : ndarray The frequency response, as complex numbers. See Also -------- sosfreqz Notes ----- Using Matplotlib's "plot" function as the callable for `plot` produces unexpected results, this plots the real part of the complex transfer function, not the magnitude. Try ``lambda w, h: plot(w, abs(h))``. Examples -------- >>> from scipy import signal >>> b = signal.firwin(80, 0.5, window=('kaiser', 8)) >>> w, h = signal.freqz(b) >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> plt.title('Digital filter frequency response') >>> ax1 = fig.add_subplot(111) >>> plt.plot(w, 20 np.log10(abs(h)), 'b') >>> plt.ylabel('Amplitude [dB]', color='b') >>> plt.xlabel('Frequency [rad/sample]') >>> ax2 = ax1.twinx() >>> angles = np.unwrap(np.angle(h)) >>> plt.plot(w, angles, 'g') >>> plt.ylabel('Angle (radians)', color='g') >>> plt.grid() >>> plt.axis('tight') >>> plt.show() """ b, a = map(atleast_1d, (b, a)) if whole: lastpoint = 2 * pi else: lastpoint = pi if worN is None: N = 512 w = numpy.linspace(0, lastpoint, N, endpoint=False) elif isinstance(worN, int): N = worN w = numpy.linspace(0, lastpoint, N, endpoint=False) else: w = worN w = atleast_1d(w) zm1 = exp(-1j * w) h = polyval(b[::-1], zm1) / polyval(a[::-1], zm1) if plot is not None: plot(w, h) return w, h def freqz_zpk(z, p, k, worN=None, whole=False): """ Compute the frequency response of a digital filter in ZPK form. Given the Zeros, Poles and Gain of a digital filter, compute its frequency response:: :math:`H(z)=k \prod_i (z - Z[i]) / \prod_j (z - P[j])` where :math:`k` is the `gain`, :math:`Z` are the `zeros` and :math:`P` are the `poles`. Parameters ---------- z : array_like Zeroes of a linear filter p : array_like Poles of a linear filter k : scalar Gain of a linear filter worN : {None, int, array_like}, optional If None (default), then compute at 512 frequencies equally spaced around the unit circle. If a single integer, then compute at that many frequencies. If an array_like, compute the response at the frequencies given (in radians/sample). whole : bool, optional Normally, frequencies are computed from 0 to the Nyquist frequency, pi radians/sample (upper-half of unit-circle). If `whole` is True, compute frequencies from 0 to 2pi radians/sample. Returns ------- w : ndarray The normalized frequencies at which `h` was computed, in radians/sample. h : ndarray The frequency response. See Also -------- freqs : Compute the frequency response of an analog filter in TF form freqs_zpk : Compute the frequency response of an analog filter in ZPK form freqz : Compute the frequency response of a digital filter in TF form Notes ----- .. versionadded: 0.19.0 Examples -------- >>> from scipy import signal >>> z, p, k = signal.butter(4, 0.2, output='zpk') >>> w, h = signal.freqz_zpk(z, p, k) >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> plt.title('Digital filter frequency response') >>> ax1 = fig.add_subplot(111) >>> plt.plot(w, 20 np.log10(abs(h)), 'b') >>> plt.ylabel('Amplitude [dB]', color='b') >>> plt.xlabel('Frequency [rad/sample]') >>> ax2 = ax1.twinx() >>> angles = np.unwrap(np.angle(h)) >>> plt.plot(w, angles, 'g') >>> plt.ylabel('Angle (radians)', color='g') >>> plt.grid() >>> plt.axis('tight') >>> plt.show() """ z, p = map(atleast_1d, (z, p)) if whole: lastpoint = 2 * pi else: lastpoint = pi if worN is None: N = 512 w = numpy.linspace(0, lastpoint, N, endpoint=False) elif isinstance(worN, int): N = worN w = numpy.linspace(0, lastpoint, N, endpoint=False) else: w = worN w = atleast_1d(w) zm1 = exp(1j * w) h = k * polyvalfromroots(zm1, z) / polyvalfromroots(zm1, p) return w, h def group_delay(system, w=None, whole=False): r"""Compute the group delay of a digital filter. The group delay measures by how many samples amplitude envelopes of various spectral components of a signal are delayed by a filter. It is formally defined as the derivative of continuous (unwrapped) phase:: d jw D(w) = - -- arg H(e) dw Parameters ---------- system : tuple of array_like (b, a) Numerator and denominator coefficients of a filter transfer function. w : {None, int, array-like}, optional If None (default), then compute at 512 frequencies equally spaced around the unit circle. If a single integer, then compute at that many frequencies. If array, compute the delay at the frequencies given (in radians/sample). whole : bool, optional Normally, frequencies are computed from 0 to the Nyquist frequency, pi radians/sample (upper-half of unit-circle). If `whole` is True, compute frequencies from 0 to ``2pi`` radians/sample. Returns ------- w : ndarray The normalized frequencies at which the group delay was computed, in radians/sample. gd : ndarray The group delay. Notes ----- The similar function in MATLAB is called `grpdelay`. If the transfer function :math:`H(z)` has zeros or poles on the unit circle, the group delay at corresponding frequencies is undefined. When such a case arises the warning is raised and the group delay is set to 0 at those frequencies. For the details of numerical computation of the group delay refer to [1]_. .. versionadded: 0.16.0 See Also -------- freqz : Frequency response of a digital filter References ---------- .. [1] Richard G. Lyons, "Understanding Digital Signal Processing, 3rd edition", p. 830. Examples -------- >>> from scipy import signal >>> b, a = signal.iirdesign(0.1, 0.3, 5, 50, ftype='cheby1') >>> w, gd = signal.group_delay((b, a)) >>> import matplotlib.pyplot as plt >>> plt.title('Digital filter group delay') >>> plt.plot(w, gd) >>> plt.ylabel('Group delay [samples]') >>> plt.xlabel('Frequency [rad/sample]') >>> plt.show() """ if w is None: w = 512 if isinstance(w, int): if whole: w = np.linspace(0, 2 pi, w, endpoint=False) else: w = np.linspace(0, pi, w, endpoint=False) w = np.atleast_1d(w) b, a = map(np.atleast_1d, system) c = np.convolve(b, a[::-1]) cr = c * np.arange(c.size) z = np.exp(-1j * w) num = np.polyval(cr[::-1], z) den = np.polyval(c[::-1], z) singular = np.absolute(den) < 10 * EPSILON if np.any(singular): warnings.warn( "The group delay is singular at frequencies [{0}], setting to 0". format(", ".join("{0:.3f}".format(ws) for ws in w[singular])) ) gd = np.zeros_like(w) gd[~singular] = np.real(num[~singular] / den[~singular]) - a.size + 1 return w, gd def _validate_sos(sos): """Helper to validate a SOS input""" sos = np.atleast_2d(sos) if sos.ndim != 2: raise ValueError('sos array must be 2D') n_sections, m = sos.shape if m != 6: raise ValueError('sos array must be shape (n_sections, 6)') if not (sos[:, 3] == 1).all(): raise ValueError('sos[:, 3] should be all ones') return sos, n_sections def sosfreqz(sos, worN=None, whole=False): """ Compute the frequency response of a digital filter in SOS format. Given `sos`, an array with shape (n, 6) of second order sections of a digital filter, compute the frequency response of the system function:: B0(z) B1(z) B{n-1}(z) H(z) = ----- * ----- * ... * --------- A0(z) A1(z) A{n-1}(z) for z = exp(omega1j), where B{k}(z) and A{k}(z) are numerator and denominator of the transfer function of the k-th second order section. Parameters ---------- sos : array_like Array of second-order filter coefficients, must have shape ``(n_sections, 6)``. Each row corresponds to a second-order section, with the first three columns providing the numerator coefficients and the last three providing the denominator coefficients. worN : {None, int, array_like}, optional If None (default), then compute at 512 frequencies equally spaced around the unit circle. If a single integer, then compute at that many frequencies. If an array_like, compute the response at the frequencies given (in radians/sample). whole : bool, optional Normally, frequencies are computed from 0 to the Nyquist frequency, pi radians/sample (upper-half of unit-circle). If `whole` is True, compute frequencies from 0 to 2pi radians/sample. Returns ------- w : ndarray The normalized frequencies at which `h` was computed, in radians/sample. h : ndarray The frequency response, as complex numbers. See Also -------- freqz, sosfilt Notes ----- .. versionadded:: 0.19.0 Examples -------- Design a 15th-order bandpass filter in SOS format. >>> from scipy import signal >>> sos = signal.ellip(15, 0.5, 60, (0.2, 0.4), btype='bandpass', ... output='sos') Compute the frequency response at 1500 points from DC to Nyquist. >>> w, h = signal.sosfreqz(sos, worN=1500) Plot the response. >>> import matplotlib.pyplot as plt >>> plt.subplot(2, 1, 1) >>> db = 20np.log10(np.abs(h)) >>> plt.plot(w/np.pi, db) >>> plt.ylim(-75, 5) >>> plt.grid(True) >>> plt.yticks([0, -20, -40, -60]) >>> plt.ylabel('Gain [dB]') >>> plt.title('Frequency Response') >>> plt.subplot(2, 1, 2) >>> plt.plot(w/np.pi, np.angle(h)) >>> plt.grid(True) >>> plt.yticks([-np.pi, -0.5np.pi, 0, 0.5np.pi, np.pi], ... [r'$-\\pi$', r'$-\\pi/2$', '0', r'$\\pi/2$', r'$\\pi$']) >>> plt.ylabel('Phase [rad]') >>> plt.xlabel('Normalized frequency (1.0 = Nyquist)') >>> plt.show() If the same filter is implemented as a single transfer function, numerical error corrupts the frequency response: >>> b, a = signal.ellip(15, 0.5, 60, (0.2, 0.4), btype='bandpass', ... output='ba') >>> w, h = signal.freqz(b, a, worN=1500) >>> plt.subplot(2, 1, 1) >>> db = 20np.log10(np.abs(h)) >>> plt.plot(w/np.pi, db) >>> plt.subplot(2, 1, 2) >>> plt.plot(w/np.pi, np.angle(h)) >>> plt.show() """ sos, n_sections = _validate_sos(sos) if n_sections == 0: raise ValueError('Cannot compute frequencies with no sections') h = 1. for row in sos: w, rowh = freqz(row[:3], row[3:], worN=worN, whole=whole) h = rowh return w, h def _cplxreal(z, tol=None): """ Split into complex and real parts, combining conjugate pairs. The 1D input vector `z` is split up into its complex (`zc`) and real (`zr`) elements. Every complex element must be part of a complex-conjugate pair, which are combined into a single number (with positive imaginary part) in the output. Two complex numbers are considered a conjugate pair if their real and imaginary parts differ in magnitude by less than ``tol abs(z)``. Parameters ---------- z : array_like Vector of complex numbers to be sorted and split tol : float, optional Relative tolerance for testing realness and conjugate equality. Default is ``100 * spacing(1)`` of `z`'s data type (i.e. 2e-14 for float64) Returns ------- zc : ndarray Complex elements of `z`, with each pair represented by a single value having positive imaginary part, sorted first by real part, and then by magnitude of imaginary part. The pairs are averaged when combined to reduce error. zr : ndarray Real elements of `z` (those having imaginary part less than `tol` times their magnitude), sorted by value. Raises ------ ValueError If there are any complex numbers in `z` for which a conjugate cannot be found. See Also -------- _cplxpair Examples -------- >>> a = [4, 3, 1, 2-2j, 2+2j, 2-1j, 2+1j, 2-1j, 2+1j, 1+1j, 1-1j] >>> zc, zr = _cplxreal(a) >>> print zc [ 1.+1.j 2.+1.j 2.+1.j 2.+2.j] >>> print zr [ 1. 3. 4.] """ z = atleast_1d(z) if z.size == 0: return z, z elif z.ndim != 1: raise ValueError('_cplxreal only accepts 1D input') if tol is None: # Get tolerance from dtype of input tol = 100 * np.finfo((1.0 * z).dtype).eps # Sort by real part, magnitude of imaginary part (speed up further sorting) z = z[np.lexsort((abs(z.imag), z.real))] # Split reals from conjugate pairs real_indices = abs(z.imag) <= tol * abs(z) zr = z[real_indices].real if len(zr) == len(z): # Input is entirely real return array([]), zr # Split positive and negative halves of conjugates z = z[~real_indices] zp = z[z.imag > 0] zn = z[z.imag < 0] if len(zp) != len(zn): raise ValueError('Array contains complex value with no matching ' 'conjugate.') # Find runs of (approximately) the same real part same_real = np.diff(zp.real) <= tol * abs(zp[:-1]) diffs = numpy.diff(concatenate(([0], same_real, [0]))) run_starts = numpy.where(diffs > 0)[0] run_stops = numpy.where(diffs < 0)[0] # Sort each run by their imaginary parts for i in range(len(run_starts)): start = run_starts[i] stop = run_stops[i] + 1 for chunk in (zp[start:stop], zn[start:stop]): chunk[...] = chunk[np.lexsort([abs(chunk.imag)])] # Check that negatives match positives if any(abs(zp - zn.conj()) > tol * abs(zn)): raise ValueError('Array contains complex value with no matching ' 'conjugate.') # Average out numerical inaccuracy in real vs imag parts of pairs zc = (zp + zn.conj()) / 2 return zc, zr def _cplxpair(z, tol=None): """ Sort into pairs of complex conjugates. Complex conjugates in `z` are sorted by increasing real part. In each pair, the number with negative imaginary part appears first. If pairs have identical real parts, they are sorted by increasing imaginary magnitude. Two complex numbers are considered a conjugate pair if their real and imaginary parts differ in magnitude by less than ``tol * abs(z)``. The pairs are forced to be exact complex conjugates by averaging the positive and negative values. Purely real numbers are also sorted, but placed after the complex conjugate pairs. A number is considered real if its imaginary part is smaller than `tol` times the magnitude of the number. Parameters ---------- z : array_like 1-dimensional input array to be sorted. tol : float, optional Relative tolerance for testing realness and conjugate equality. Default is ``100 * spacing(1)`` of `z`'s data type (i.e. 2e-14 for float64) Returns ------- y : ndarray Complex conjugate pairs followed by real numbers. Raises ------ ValueError If there are any complex numbers in `z` for which a conjugate cannot be found. See Also -------- _cplxreal Examples -------- >>> a = [4, 3, 1, 2-2j, 2+2j, 2-1j, 2+1j, 2-1j, 2+1j, 1+1j, 1-1j] >>> z = _cplxpair(a) >>> print(z) [ 1.-1.j 1.+1.j 2.-1.j 2.+1.j 2.-1.j 2.+1.j 2.-2.j 2.+2.j 1.+0.j 3.+0.j 4.+0.j] """ z = atleast_1d(z) if z.size == 0 or np.isrealobj(z): return np.sort(z) if z.ndim != 1: raise ValueError('z must be 1-dimensional') zc, zr = _cplxreal(z, tol) # Interleave complex values and their conjugates, with negative imaginary # parts first in each pair zc = np.dstack((zc.conj(), zc)).flatten() z = np.append(zc, zr) return z def tf2zpk(b, a): r"""Return zero, pole, gain (z, p, k) representation from a numerator, denominator representation of a linear filter. Parameters ---------- b : array_like Numerator polynomial coefficients. a : array_like Denominator polynomial coefficients. Returns ------- z : ndarray Zeros of the transfer function. p : ndarray Poles of the transfer function. k : float System gain. Notes ----- If some values of `b` are too close to 0, they are removed. In that case, a BadCoefficients warning is emitted. The `b` and `a` arrays are interpreted as coefficients for positive, descending powers of the transfer function variable. So the inputs :math:`b = [b_0, b_1, ..., b_M]` and :math:`a =[a_0, a_1, ..., a_N]` can represent an analog filter of the form: .. math:: H(s) = \frac {b_0 s^M + b_1 s^{(M-1)} + \cdots + b_M} {a_0 s^N + a_1 s^{(N-1)} + \cdots + a_N} or a discrete-time filter of the form: .. math:: H(z) = \frac {b_0 z^M + b_1 z^{(M-1)} + \cdots + b_M} {a_0 z^N + a_1 z^{(N-1)} + \cdots + a_N} This "positive powers" form is found more commonly in controls engineering. If `M` and `N` are equal (which is true for all filters generated by the bilinear transform), then this happens to be equivalent to the "negative powers" discrete-time form preferred in DSP: .. math:: H(z) = \frac {b_0 + b_1 z^{-1} + \cdots + b_M z^{-M}} {a_0 + a_1 z^{-1} + \cdots + a_N z^{-N}} Although this is true for common filters, remember that this is not true in the general case. If `M` and `N` are not equal, the discrete-time transfer function coefficients must first be converted to the "positive powers" form before finding the poles and zeros. """ b, a = normalize(b, a) b = (b + 0.0) / a[0] a = (a + 0.0) / a[0] k = b[0] b /= b[0] z = roots(b) p = roots(a) return z, p, k def zpk2tf(z, p, k): """ Return polynomial transfer function representation from zeros and poles Parameters ---------- z : array_like Zeros of the transfer function. p : array_like Poles of the transfer function. k : float System gain. Returns ------- b : ndarray Numerator polynomial coefficients. a : ndarray Denominator polynomial coefficients. """ z = atleast_1d(z) k = atleast_1d(k) if len(z.shape) > 1: temp = poly(z[0]) b = zeros((z.shape[0], z.shape[1] + 1), temp.dtype.char) if len(k) == 1: k = [k[0]] * z.shape[0] for i in range(z.shape[0]): b[i] = k[i] * poly(z[i]) else: b = k * poly(z) a = atleast_1d(poly(p)) # Use real output if possible. Copied from numpy.poly, since # we can't depend on a specific version of numpy. if issubclass(b.dtype.type, numpy.complexfloating): # if complex roots are all complex conjugates, the roots are real. roots = numpy.asarray(z, complex) pos_roots = numpy.compress(roots.imag > 0, roots) neg_roots = numpy.conjugate(numpy.compress(roots.imag < 0, roots)) if len(pos_roots) == len(neg_roots): if numpy.all(numpy.sort_complex(neg_roots) == numpy.sort_complex(pos_roots)): b = b.real.copy() if issubclass(a.dtype.type, numpy.complexfloating): # if complex roots are all complex conjugates, the roots are real. roots = numpy.asarray(p, complex) pos_roots = numpy.compress(roots.imag > 0, roots) neg_roots = numpy.conjugate(numpy.compress(roots.imag < 0, roots)) if len(pos_roots) == len(neg_roots): if numpy.all(numpy.sort_complex(neg_roots) == numpy.sort_complex(pos_roots)): a = a.real.copy() return b, a def tf2sos(b, a, pairing='nearest'): """ Return second-order sections from transfer function representation Parameters ---------- b : array_like Numerator polynomial coefficients. a : array_like Denominator polynomial coefficients. pairing : {'nearest', 'keep_odd'}, optional The method to use to combine pairs of poles and zeros into sections. See `zpk2sos`. Returns ------- sos : ndarray Array of second-order filter coefficients, with shape ``(n_sections, 6)``. See `sosfilt` for the SOS filter format specification. See Also -------- zpk2sos, sosfilt Notes ----- It is generally discouraged to convert from TF to SOS format, since doing so usually will not improve numerical precision errors. Instead, consider designing filters in ZPK format and converting directly to SOS. TF is converted to SOS by first converting to ZPK format, then converting ZPK to SOS. .. versionadded:: 0.16.0 """ return zpk2sos(tf2zpk(b, a), pairing=pairing) def sos2tf(sos): """ Return a single transfer function from a series of second-order sections Parameters ---------- sos : array_like Array of second-order filter coefficients, must have shape ``(n_sections, 6)``. See `sosfilt` for the SOS filter format specification. Returns ------- b : ndarray Numerator polynomial coefficients. a : ndarray Denominator polynomial coefficients. Notes ----- .. versionadded:: 0.16.0 """ sos = np.asarray(sos) b = [1.] a = [1.] n_sections = sos.shape[0] for section in range(n_sections): b = np.polymul(b, sos[section, :3]) a = np.polymul(a, sos[section, 3:]) return b, a def sos2zpk(sos): """ Return zeros, poles, and gain of a series of second-order sections Parameters ---------- sos : array_like Array of second-order filter coefficients, must have shape ``(n_sections, 6)``. See `sosfilt` for the SOS filter format specification. Returns ------- z : ndarray Zeros of the transfer function. p : ndarray Poles of the transfer function. k : float System gain. Notes ----- .. versionadded:: 0.16.0 """ sos = np.asarray(sos) n_sections = sos.shape[0] z = np.empty(n_sections2, np.complex128) p = np.empty(n_sections2, np.complex128) k = 1. for section in range(n_sections): zpk = tf2zpk(sos[section, :3], sos[section, 3:]) z[2section:2(section+1)] = zpk[0] p[2section:2(section+1)] = zpk[1] k = zpk[2] return z, p, k def _nearest_real_complex_idx(fro, to, which): """Get the next closest real or complex element based on distance""" assert which in ('real', 'complex') order = np.argsort(np.abs(fro - to)) mask = np.isreal(fro[order]) if which == 'complex': mask = ~mask return order[np.where(mask)[0][0]] def zpk2sos(z, p, k, pairing='nearest'): """ Return second-order sections from zeros, poles, and gain of a system Parameters ---------- z : array_like Zeros of the transfer function. p : array_like Poles of the transfer function. k : float System gain. pairing : {'nearest', 'keep_odd'}, optional The method to use to combine pairs of poles and zeros into sections. See Notes below. Returns ------- sos : ndarray Array of second-order filter coefficients, with shape ``(n_sections, 6)``. See `sosfilt` for the SOS filter format specification. See Also -------- sosfilt Notes ----- The algorithm used to convert ZPK to SOS format is designed to minimize errors due to numerical precision issues. The pairing algorithm attempts to minimize the peak gain of each biquadratic section. This is done by pairing poles with the nearest zeros, starting with the poles closest to the unit circle. Algorithms The current algorithms are designed specifically for use with digital filters. (The output coefficents are not correct for analog filters.) The steps in the ``pairing='nearest'`` and ``pairing='keep_odd'`` algorithms are mostly shared. The ``nearest`` algorithm attempts to minimize the peak gain, while ``'keep_odd'`` minimizes peak gain under the constraint that odd-order systems should retain one section as first order. The algorithm steps and are as follows: As a pre-processing step, add poles or zeros to the origin as necessary to obtain the same number of poles and zeros for pairing. If ``pairing == 'nearest'`` and there are an odd number of poles, add an additional pole and a zero at the origin. The following steps are then iterated over until no more poles or zeros remain: 1. Take the (next remaining) pole (complex or real) closest to the unit circle to begin a new filter section. 2. If the pole is real and there are no other remaining real poles [#]_, add the closest real zero to the section and leave it as a first order section. Note that after this step we are guaranteed to be left with an even number of real poles, complex poles, real zeros, and complex zeros for subsequent pairing iterations. 3. Else: 1. If the pole is complex and the zero is the only remaining real zero, then pair the pole with the next* closest zero (guaranteed to be complex). This is necessary to ensure that there will be a real zero remaining to eventually create a first-order section (thus keeping the odd order). 2. Else pair the pole with the closest remaining zero (complex or real). 3. Proceed to complete the second-order section by adding another pole and zero to the current pole and zero in the section: 1. If the current pole and zero are both complex, add their conjugates. 2. Else if the pole is complex and the zero is real, add the conjugate pole and the next closest real zero. 3. Else if the pole is real and the zero is complex, add the conjugate zero and the real pole closest to those zeros. 4. Else (we must have a real pole and real zero) add the next real pole closest to the unit circle, and then add the real zero closest to that pole. .. [#] This conditional can only be met for specific odd-order inputs with the ``pairing == 'keep_odd'`` method. .. versionadded:: 0.16.0 Examples -------- Design a 6th order low-pass elliptic digital filter for a system with a sampling rate of 8000 Hz that has a pass-band corner frequency of 1000 Hz. The ripple in the pass-band should not exceed 0.087 dB, and the attenuation in the stop-band should be at least 90 dB. In the following call to `signal.ellip`, we could use ``output='sos'``, but for this example, we'll use ``output='zpk'``, and then convert to SOS format with `zpk2sos`: >>> from scipy import signal >>> z, p, k = signal.ellip(6, 0.087, 90, 1000/(0.58000), output='zpk') Now convert to SOS format. >>> sos = signal.zpk2sos(z, p, k) The coefficients of the numerators of the sections: >>> sos[:, :3] array([[ 0.0014154 , 0.00248707, 0.0014154 ], [ 1. , 0.72965193, 1. ], [ 1. , 0.17594966, 1. ]]) The symmetry in the coefficients occurs because all the zeros are on the unit circle. The coefficients of the denominators of the sections: >>> sos[:, 3:] array([[ 1. , -1.32543251, 0.46989499], [ 1. , -1.26117915, 0.6262586 ], [ 1. , -1.25707217, 0.86199667]]) The next example shows the effect of the `pairing` option. We have a system with three poles and three zeros, so the SOS array will have shape (2, 6). The means there is, in effect, an extra pole and an extra zero at the origin in the SOS representation. >>> z1 = np.array([-1, -0.5-0.5j, -0.5+0.5j]) >>> p1 = np.array([0.75, 0.8+0.1j, 0.8-0.1j]) With ``pairing='nearest'`` (the default), we obtain >>> signal.zpk2sos(z1, p1, 1) array([[ 1. , 1. , 0.5 , 1. , -0.75, 0. ], [ 1. , 1. , 0. , 1. , -1.6 , 0.65]]) The first section has the zeros {-0.5-0.05j, -0.5+0.5j} and the poles {0, 0.75}, and the second section has the zeros {-1, 0} and poles {0.8+0.1j, 0.8-0.1j}. Note that the extra pole and zero at the origin have been assigned to different sections. With ``pairing='keep_odd'``, we obtain: >>> signal.zpk2sos(z1, p1, 1, pairing='keep_odd') array([[ 1. , 1. , 0. , 1. , -0.75, 0. ], [ 1. , 1. , 0.5 , 1. , -1.6 , 0.65]]) The extra pole and zero at the origin are in the same section. The first section is, in effect, a first-order section. """ # TODO in the near future: # 1. Add SOS capability to `filtfilt`, `freqz`, etc. somehow (#3259). # 2. Make `decimate` use `sosfilt` instead of `lfilter`. # 3. Make sosfilt automatically simplify sections to first order # when possible. Note this might make `sosfiltfilt` a bit harder (ICs). # 4. Further optimizations of the section ordering / pole-zero pairing. # See the wiki for other potential issues. valid_pairings = ['nearest', 'keep_odd'] if pairing not in valid_pairings: raise ValueError('pairing must be one of %s, not %s' % (valid_pairings, pairing)) if len(z) == len(p) == 0: return array([[k, 0., 0., 1., 0., 0.]]) # ensure we have the same number of poles and zeros, and make copies p = np.concatenate((p, np.zeros(max(len(z) - len(p), 0)))) z = np.concatenate((z, np.zeros(max(len(p) - len(z), 0)))) n_sections = (max(len(p), len(z)) + 1) // 2 sos = zeros((n_sections, 6)) if len(p) % 2 == 1 and pairing == 'nearest': p = np.concatenate((p, [0.])) z = np.concatenate((z, [0.])) assert len(p) == len(z) # Ensure we have complex conjugate pairs # (note that _cplxreal only gives us one element of each complex pair): z = np.concatenate(_cplxreal(z)) p = np.concatenate(_cplxreal(p)) p_sos = np.zeros((n_sections, 2), np.complex128) z_sos = np.zeros_like(p_sos) for si in range(n_sections): # Select the next "worst" pole p1_idx = np.argmin(np.abs(1 - np.abs(p))) p1 = p[p1_idx] p = np.delete(p, p1_idx) # Pair that pole with a zero if np.isreal(p1) and np.isreal(p).sum() == 0: # Special case to set a first-order section z1_idx = _nearest_real_complex_idx(z, p1, 'real') z1 = z[z1_idx] z = np.delete(z, z1_idx) p2 = z2 = 0 else: if not np.isreal(p1) and np.isreal(z).sum() == 1: # Special case to ensure we choose a complex zero to pair # with so later (setting up a first-order section) z1_idx = _nearest_real_complex_idx(z, p1, 'complex') assert not np.isreal(z[z1_idx]) else: # Pair the pole with the closest zero (real or complex) z1_idx = np.argmin(np.abs(p1 - z)) z1 = z[z1_idx] z = np.delete(z, z1_idx) # Now that we have p1 and z1, figure out what p2 and z2 need to be if not np.isreal(p1): if not np.isreal(z1): # complex pole, complex zero p2 = p1.conj() z2 = z1.conj() else: # complex pole, real zero p2 = p1.conj() z2_idx = _nearest_real_complex_idx(z, p1, 'real') z2 = z[z2_idx] assert np.isreal(z2) z = np.delete(z, z2_idx) else: if not np.isreal(z1): # real pole, complex zero z2 = z1.conj() p2_idx = _nearest_real_complex_idx(p, z1, 'real') p2 = p[p2_idx] assert np.isreal(p2) else: # real pole, real zero # pick the next "worst" pole to use idx = np.where(np.isreal(p))[0] assert len(idx) > 0 p2_idx = idx[np.argmin(np.abs(np.abs(p[idx]) - 1))] p2 = p[p2_idx] # find a real zero to match the added pole assert np.isreal(p2) z2_idx = _nearest_real_complex_idx(z, p2, 'real') z2 = z[z2_idx] assert np.isreal(z2) z = np.delete(z, z2_idx) p = np.delete(p, p2_idx) p_sos[si] = [p1, p2] z_sos[si] = [z1, z2] assert len(p) == len(z) == 0 # we've consumed all poles and zeros del p, z # Construct the system, reversing order so the "worst" are last p_sos = np.reshape(p_sos[::-1], (n_sections, 2)) z_sos = np.reshape(z_sos[::-1], (n_sections, 2)) gains = np.ones(n_sections) gains[0] = k for si in range(n_sections): x = zpk2tf(z_sos[si], p_sos[si], gains[si]) sos[si] = np.concatenate(x) return sos def _align_nums(nums): """Aligns the shapes of multiple numerators. Given an array of numerator coefficient arrays [[a_1, a_2,..., a_n],..., [b_1, b_2,..., b_m]], this function pads shorter numerator arrays with zero's so that all numerators have the same length. Such alignment is necessary for functions like 'tf2ss', which needs the alignment when dealing with SIMO transfer functions. Parameters ---------- nums: array_like Numerator or list of numerators. Not necessarily with same length. Returns ------- nums: array The numerator. If `nums` input was a list of numerators then a 2d array with padded zeros for shorter numerators is returned. Otherwise returns ``np.asarray(nums)``. """ try: # The statement can throw a ValueError if one # of the numerators is a single digit and another # is array-like e.g. if nums = [5, [1, 2, 3]] nums = asarray(nums) if not np.issubdtype(nums.dtype, np.number): raise ValueError("dtype of numerator is non-numeric") return nums except ValueError: nums = [np.atleast_1d(num) for num in nums] max_width = max(num.size for num in nums) # pre-allocate aligned_nums = np.zeros((len(nums), max_width)) # Create numerators with padded zeros for index, num in enumerate(nums): aligned_nums[index, -num.size:] = num return aligned_nums def normalize(b, a): """Normalize numerator/denominator of a continuous-time transfer function. If values of `b` are too close to 0, they are removed. In that case, a BadCoefficients warning is emitted. Parameters ---------- b: array_like Numerator of the transfer function. Can be a 2d array to normalize multiple transfer functions. a: array_like Denominator of the transfer function. At most 1d. Returns ------- num: array The numerator of the normalized transfer function. At least a 1d array. A 2d-array if the input `num` is a 2d array. den: 1d-array The denominator of the normalized transfer function. Notes ----- Coefficients for both the numerator and denominator should be specified in descending exponent order (e.g., ``s^2 + 3s + 5`` would be represented as ``[1, 3, 5]``). """ num, den = b, a den = np.atleast_1d(den) num = np.atleast_2d(_align_nums(num)) if den.ndim != 1: raise ValueError("Denominator polynomial must be rank-1 array.") if num.ndim > 2: raise ValueError("Numerator polynomial must be rank-1 or" " rank-2 array.") if np.all(den == 0): raise ValueError("Denominator must have at least on nonzero element.") # Trim leading zeros in denominator, leave at least one. den = np.trim_zeros(den, 'f') # Normalize transfer function num, den = num / den[0], den / den[0] # Count numerator columns that are all zero leading_zeros = 0 for col in num.T: if np.allclose(col, 0, atol=1e-14): leading_zeros += 1 else: break # Trim leading zeros of numerator if leading_zeros > 0: warnings.warn("Badly conditioned filter coefficients (numerator): the " "results may be meaningless", BadCoefficients) # Make sure at least one column remains if leading_zeros == num.shape[1]: leading_zeros -= 1 num = num[:, leading_zeros:] # Squeeze first dimension if singular if num.shape[0] == 1: num = num[0, :] return num, den def lp2lp(b, a, wo=1.0): """ Transform a lowpass filter prototype to a different frequency. Return an analog low-pass filter with cutoff frequency `wo` from an analog low-pass filter prototype with unity cutoff frequency, in transfer function ('ba') representation. """ a, b = map(atleast_1d, (a, b)) try: wo = float(wo) except TypeError: wo = float(wo[0]) d = len(a) n = len(b) M = max((d, n)) pwo = pow(wo, numpy.arange(M - 1, -1, -1)) start1 = max((n - d, 0)) start2 = max((d - n, 0)) b = b pwo[start1] / pwo[start2:] a = a * pwo[start1] / pwo[start1:] return normalize(b, a) def lp2hp(b, a, wo=1.0): """ Transform a lowpass filter prototype to a highpass filter. Return an analog high-pass filter with cutoff frequency `wo` from an analog low-pass filter prototype with unity cutoff frequency, in transfer function ('ba') representation. """ a, b = map(atleast_1d, (a, b)) try: wo = float(wo) except TypeError: wo = float(wo[0]) d = len(a) n = len(b) if wo != 1: pwo = pow(wo, numpy.arange(max((d, n)))) else: pwo = numpy.ones(max((d, n)), b.dtype.char) if d >= n: outa = a[::-1] * pwo outb = resize(b, (d,)) outb[n:] = 0.0 outb[:n] = b[::-1] * pwo[:n] else: outb = b[::-1] * pwo outa = resize(a, (n,)) outa[d:] = 0.0 outa[:d] = a[::-1] * pwo[:d] return normalize(outb, outa) def lp2bp(b, a, wo=1.0, bw=1.0): """ Transform a lowpass filter prototype to a bandpass filter. Return an analog band-pass filter with center frequency `wo` and bandwidth `bw` from an analog low-pass filter prototype with unity cutoff frequency, in transfer function ('ba') representation. """ a, b = map(atleast_1d, (a, b)) D = len(a) - 1 N = len(b) - 1 artype = mintypecode((a, b)) ma = max([N, D]) Np = N + ma Dp = D + ma bprime = numpy.zeros(Np + 1, artype) aprime = numpy.zeros(Dp + 1, artype) wosq = wo * wo for j in range(Np + 1): val = 0.0 for i in range(0, N + 1): for k in range(0, i + 1): if ma - i + 2 * k == j: val += comb(i, k) * b[N - i] * (wosq) (i - k) / bw i bprime[Np - j] = val for j in range(Dp + 1): val = 0.0 for i in range(0, D + 1): for k in range(0, i + 1): if ma - i + 2 * k == j: val += comb(i, k) * a[D - i] * (wosq) (i - k) / bw i aprime[Dp - j] = val return normalize(bprime, aprime) def lp2bs(b, a, wo=1.0, bw=1.0): """ Transform a lowpass filter prototype to a bandstop filter. Return an analog band-stop filter with center frequency `wo` and bandwidth `bw` from an analog low-pass filter prototype with unity cutoff frequency, in transfer function ('ba') representation. """ a, b = map(atleast_1d, (a, b)) D = len(a) - 1 N = len(b) - 1 artype = mintypecode((a, b)) M = max([N, D]) Np = M + M Dp = M + M bprime = numpy.zeros(Np + 1, artype) aprime = numpy.zeros(Dp + 1, artype) wosq = wo * wo for j in range(Np + 1): val = 0.0 for i in range(0, N + 1): for k in range(0, M - i + 1): if i + 2 * k == j: val += (comb(M - i, k) * b[N - i] * (wosq) ** (M - i - k) * bw ** i) bprime[Np - j] = val for j in range(Dp + 1): val = 0.0 for i in range(0, D + 1): for k in range(0, M - i + 1): if i + 2 * k == j: val += (comb(M - i, k) * a[D - i] * (wosq) ** (M - i - k) * bw ** i) aprime[Dp - j] = val return normalize(bprime, aprime) def bilinear(b, a, fs=1.0): """Return a digital filter from an analog one using a bilinear transform. The bilinear transform substitutes ``(z-1) / (z+1)`` for ``s``. """ fs = float(fs) a, b = map(atleast_1d, (a, b)) D = len(a) - 1 N = len(b) - 1 artype = float M = max([N, D]) Np = M Dp = M bprime = numpy.zeros(Np + 1, artype) aprime = numpy.zeros(Dp + 1, artype) for j in range(Np + 1): val = 0.0 for i in range(N + 1): for k in range(i + 1): for l in range(M - i + 1): if k + l == j: val += (comb(i, k) * comb(M - i, l) * b[N - i] * pow(2 * fs, i) * (-1) ** k) bprime[j] = real(val) for j in range(Dp + 1): val = 0.0 for i in range(D + 1): for k in range(i + 1): for l in range(M - i + 1): if k + l == j: val += (comb(i, k) * comb(M - i, l) * a[D - i] * pow(2 * fs, i) * (-1) ** k) aprime[j] = real(val) return normalize(bprime, aprime) def iirdesign(wp, ws, gpass, gstop, analog=False, ftype='ellip', output='ba'): """Complete IIR digital and analog filter design. Given passband and stopband frequencies and gains, construct an analog or digital IIR filter of minimum order for a given basic type. Return the output in numerator, denominator ('ba'), pole-zero ('zpk') or second order sections ('sos') form. Parameters ---------- wp, ws : float Passband and stopband edge frequencies. For digital filters, these are normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`wp` and `ws` are thus in half-cycles / sample.) For example: - Lowpass: wp = 0.2, ws = 0.3 - Highpass: wp = 0.3, ws = 0.2 - Bandpass: wp = [0.2, 0.5], ws = [0.1, 0.6] - Bandstop: wp = [0.1, 0.6], ws = [0.2, 0.5] For analog filters, `wp` and `ws` are angular frequencies (e.g. rad/s). gpass : float The maximum loss in the passband (dB). gstop : float The minimum attenuation in the stopband (dB). analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. ftype : str, optional The type of IIR filter to design: - Butterworth : 'butter' - Chebyshev I : 'cheby1' - Chebyshev II : 'cheby2' - Cauer/elliptic: 'ellip' - Bessel/Thomson: 'bessel' output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. See Also -------- butter : Filter design using order and critical points cheby1, cheby2, ellip, bessel buttord : Find order and critical points from passband and stopband spec cheb1ord, cheb2ord, ellipord iirfilter : General filter design using order and critical frequencies Notes ----- The ``'sos'`` output parameter was added in 0.16.0. """ try: ordfunc = filter_dict[ftype][1] except KeyError: raise ValueError("Invalid IIR filter type: %s" % ftype) except IndexError: raise ValueError(("%s does not have order selection. Use " "iirfilter function.") % ftype) wp = atleast_1d(wp) ws = atleast_1d(ws) band_type = 2 * (len(wp) - 1) band_type += 1 if wp[0] >= ws[0]: band_type += 1 btype = {1: 'lowpass', 2: 'highpass', 3: 'bandstop', 4: 'bandpass'}[band_type] N, Wn = ordfunc(wp, ws, gpass, gstop, analog=analog) return iirfilter(N, Wn, rp=gpass, rs=gstop, analog=analog, btype=btype, ftype=ftype, output=output) def iirfilter(N, Wn, rp=None, rs=None, btype='band', analog=False, ftype='butter', output='ba'): """ IIR digital and analog filter design given order and critical points. Design an Nth-order digital or analog filter and return the filter coefficients. Parameters ---------- N : int The order of the filter. Wn : array_like A scalar or length-2 sequence giving the critical frequencies. For digital filters, `Wn` is normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`Wn` is thus in half-cycles / sample.) For analog filters, `Wn` is an angular frequency (e.g. rad/s). rp : float, optional For Chebyshev and elliptic filters, provides the maximum ripple in the passband. (dB) rs : float, optional For Chebyshev and elliptic filters, provides the minimum attenuation in the stop band. (dB) btype : {'bandpass', 'lowpass', 'highpass', 'bandstop'}, optional The type of filter. Default is 'bandpass'. analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. ftype : str, optional The type of IIR filter to design: - Butterworth : 'butter' - Chebyshev I : 'cheby1' - Chebyshev II : 'cheby2' - Cauer/elliptic: 'ellip' - Bessel/Thomson: 'bessel' output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. See Also -------- butter : Filter design using order and critical points cheby1, cheby2, ellip, bessel buttord : Find order and critical points from passband and stopband spec cheb1ord, cheb2ord, ellipord iirdesign : General filter design using passband and stopband spec Notes ----- The ``'sos'`` output parameter was added in 0.16.0. Examples -------- Generate a 17th-order Chebyshev II bandpass filter and plot the frequency response: >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> b, a = signal.iirfilter(17, [50, 200], rs=60, btype='band', ... analog=True, ftype='cheby2') >>> w, h = signal.freqs(b, a, 1000) >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> ax.semilogx(w, 20 * np.log10(abs(h))) >>> ax.set_title('Chebyshev Type II bandpass frequency response') >>> ax.set_xlabel('Frequency [radians / second]') >>> ax.set_ylabel('Amplitude [dB]') >>> ax.axis((10, 1000, -100, 10)) >>> ax.grid(which='both', axis='both') >>> plt.show() """ ftype, btype, output = [x.lower() for x in (ftype, btype, output)] Wn = asarray(Wn) try: btype = band_dict[btype] except KeyError: raise ValueError("'%s' is an invalid bandtype for filter." % btype) try: typefunc = filter_dict[ftype][0] except KeyError: raise ValueError("'%s' is not a valid basic IIR filter." % ftype) if output not in ['ba', 'zpk', 'sos']: raise ValueError("'%s' is not a valid output form." % output) if rp is not None and rp < 0: raise ValueError("passband ripple (rp) must be positive") if rs is not None and rs < 0: raise ValueError("stopband attenuation (rs) must be positive") # Get analog lowpass prototype if typefunc == buttap: z, p, k = typefunc(N) elif typefunc == besselap: z, p, k = typefunc(N, norm=bessel_norms[ftype]) elif typefunc == cheb1ap: if rp is None: raise ValueError("passband ripple (rp) must be provided to " "design a Chebyshev I filter.") z, p, k = typefunc(N, rp) elif typefunc == cheb2ap: if rs is None: raise ValueError("stopband attenuation (rs) must be provided to " "design an Chebyshev II filter.") z, p, k = typefunc(N, rs) elif typefunc == ellipap: if rs is None or rp is None: raise ValueError("Both rp and rs must be provided to design an " "elliptic filter.") z, p, k = typefunc(N, rp, rs) else: raise NotImplementedError("'%s' not implemented in iirfilter." % ftype) # Pre-warp frequencies for digital filter design if not analog: if numpy.any(Wn < 0) or numpy.any(Wn > 1): raise ValueError("Digital filter critical frequencies " "must be 0 <= Wn <= 1") fs = 2.0 warped = 2 * fs * tan(pi * Wn / fs) else: warped = Wn # transform to lowpass, bandpass, highpass, or bandstop if btype in ('lowpass', 'highpass'): if numpy.size(Wn) != 1: raise ValueError('Must specify a single critical frequency Wn') if btype == 'lowpass': z, p, k = _zpklp2lp(z, p, k, wo=warped) elif btype == 'highpass': z, p, k = _zpklp2hp(z, p, k, wo=warped) elif btype in ('bandpass', 'bandstop'): try: bw = warped[1] - warped[0] wo = sqrt(warped[0] * warped[1]) except IndexError: raise ValueError('Wn must specify start and stop frequencies') if btype == 'bandpass': z, p, k = _zpklp2bp(z, p, k, wo=wo, bw=bw) elif btype == 'bandstop': z, p, k = _zpklp2bs(z, p, k, wo=wo, bw=bw) else: raise NotImplementedError("'%s' not implemented in iirfilter." % btype) # Find discrete equivalent if necessary if not analog: z, p, k = _zpkbilinear(z, p, k, fs=fs) # Transform to proper out type (pole-zero, state-space, numer-denom) if output == 'zpk': return z, p, k elif output == 'ba': return zpk2tf(z, p, k) elif output == 'sos': return zpk2sos(z, p, k) def _relative_degree(z, p): """ Return relative degree of transfer function from zeros and poles """ degree = len(p) - len(z) if degree < 0: raise ValueError("Improper transfer function. " "Must have at least as many poles as zeros.") else: return degree # TODO: merge these into existing functions or make public versions def _zpkbilinear(z, p, k, fs): """ Return a digital filter from an analog one using a bilinear transform. Transform a set of poles and zeros from the analog s-plane to the digital z-plane using Tustin's method, which substitutes ``(z-1) / (z+1)`` for ``s``, maintaining the shape of the frequency response. Parameters ---------- z : array_like Zeros of the analog IIR filter transfer function. p : array_like Poles of the analog IIR filter transfer function. k : float System gain of the analog IIR filter transfer function. fs : float Sample rate, as ordinary frequency (e.g. hertz). No prewarping is done in this function. Returns ------- z : ndarray Zeros of the transformed digital filter transfer function. p : ndarray Poles of the transformed digital filter transfer function. k : float System gain of the transformed digital filter. """ z = atleast_1d(z) p = atleast_1d(p) degree = _relative_degree(z, p) fs2 = 2fs # Bilinear transform the poles and zeros z_z = (fs2 + z) / (fs2 - z) p_z = (fs2 + p) / (fs2 - p) # Any zeros that were at infinity get moved to the Nyquist frequency z_z = append(z_z, -ones(degree)) # Compensate for gain change k_z = k real(prod(fs2 - z) / prod(fs2 - p)) return z_z, p_z, k_z def _zpklp2lp(z, p, k, wo=1.0): r""" Transform a lowpass filter prototype to a different frequency. Return an analog low-pass filter with cutoff frequency `wo` from an analog low-pass filter prototype with unity cutoff frequency, using zeros, poles, and gain ('zpk') representation. Parameters ---------- z : array_like Zeros of the analog IIR filter transfer function. p : array_like Poles of the analog IIR filter transfer function. k : float System gain of the analog IIR filter transfer function. wo : float Desired cutoff, as angular frequency (e.g. rad/s). Defaults to no change. Returns ------- z : ndarray Zeros of the transformed low-pass filter transfer function. p : ndarray Poles of the transformed low-pass filter transfer function. k : float System gain of the transformed low-pass filter. Notes ----- This is derived from the s-plane substitution .. math:: s \rightarrow \frac{s}{\omega_0} """ z = atleast_1d(z) p = atleast_1d(p) wo = float(wo) # Avoid int wraparound degree = _relative_degree(z, p) # Scale all points radially from origin to shift cutoff frequency z_lp = wo * z p_lp = wo * p # Each shifted pole decreases gain by wo, each shifted zero increases it. # Cancel out the net change to keep overall gain the same k_lp = k * wo*degree return z_lp, p_lp, k_lp def _zpklp2hp(z, p, k, wo=1.0): r""" Transform a lowpass filter prototype to a highpass filter. Return an analog high-pass filter with cutoff frequency `wo` from an analog low-pass filter prototype with unity cutoff frequency, using zeros, poles, and gain ('zpk') representation. Parameters ---------- z : array_like Zeros of the analog IIR filter transfer function. p : array_like Poles of the analog IIR filter transfer function. k : float System gain of the analog IIR filter transfer function. wo : float Desired cutoff, as angular frequency (e.g. rad/s). Defaults to no change. Returns ------- z : ndarray Zeros of the transformed high-pass filter transfer function. p : ndarray Poles of the transformed high-pass filter transfer function. k : float System gain of the transformed high-pass filter. Notes ----- This is derived from the s-plane substitution .. math:: s \rightarrow \frac{\omega_0}{s} This maintains symmetry of the lowpass and highpass responses on a logarithmic scale. """ z = atleast_1d(z) p = atleast_1d(p) wo = float(wo) degree = _relative_degree(z, p) # Invert positions radially about unit circle to convert LPF to HPF # Scale all points radially from origin to shift cutoff frequency z_hp = wo / z p_hp = wo / p # If lowpass had zeros at infinity, inverting moves them to origin. z_hp = append(z_hp, zeros(degree)) # Cancel out gain change caused by inversion k_hp = k real(prod(-z) / prod(-p)) return z_hp, p_hp, k_hp def _zpklp2bp(z, p, k, wo=1.0, bw=1.0): r""" Transform a lowpass filter prototype to a bandpass filter. Return an analog band-pass filter with center frequency `wo` and bandwidth `bw` from an analog low-pass filter prototype with unity cutoff frequency, using zeros, poles, and gain ('zpk') representation. Parameters ---------- z : array_like Zeros of the analog IIR filter transfer function. p : array_like Poles of the analog IIR filter transfer function. k : float System gain of the analog IIR filter transfer function. wo : float Desired passband center, as angular frequency (e.g. rad/s). Defaults to no change. bw : float Desired passband width, as angular frequency (e.g. rad/s). Defaults to 1. Returns ------- z : ndarray Zeros of the transformed band-pass filter transfer function. p : ndarray Poles of the transformed band-pass filter transfer function. k : float System gain of the transformed band-pass filter. Notes ----- This is derived from the s-plane substitution .. math:: s \rightarrow \frac{s^2 + {\omega_0}^2}{s \cdot \mathrm{BW}} This is the "wideband" transformation, producing a passband with geometric (log frequency) symmetry about `wo`. """ z = atleast_1d(z) p = atleast_1d(p) wo = float(wo) bw = float(bw) degree = _relative_degree(z, p) # Scale poles and zeros to desired bandwidth z_lp = z * bw/2 p_lp = p * bw/2 # Square root needs to produce complex result, not NaN z_lp = z_lp.astype(complex) p_lp = p_lp.astype(complex) # Duplicate poles and zeros and shift from baseband to +wo and -wo z_bp = concatenate((z_lp + sqrt(z_lp2 - wo2), z_lp - sqrt(z_lp2 - wo2))) p_bp = concatenate((p_lp + sqrt(p_lp2 - wo2), p_lp - sqrt(p_lp2 - wo2))) # Move degree zeros to origin, leaving degree zeros at infinity for BPF z_bp = append(z_bp, zeros(degree)) # Cancel out gain change from frequency scaling k_bp = k * bwdegree return z_bp, p_bp, k_bp def _zpklp2bs(z, p, k, wo=1.0, bw=1.0): r""" Transform a lowpass filter prototype to a bandstop filter. Return an analog band-stop filter with center frequency `wo` and stopband width `bw` from an analog low-pass filter prototype with unity cutoff frequency, using zeros, poles, and gain ('zpk') representation. Parameters ---------- z : array_like Zeros of the analog IIR filter transfer function. p : array_like Poles of the analog IIR filter transfer function. k : float System gain of the analog IIR filter transfer function. wo : float Desired stopband center, as angular frequency (e.g. rad/s). Defaults to no change. bw : float Desired stopband width, as angular frequency (e.g. rad/s). Defaults to 1. Returns ------- z : ndarray Zeros of the transformed band-stop filter transfer function. p : ndarray Poles of the transformed band-stop filter transfer function. k : float System gain of the transformed band-stop filter. Notes ----- This is derived from the s-plane substitution .. math:: s \rightarrow \frac{s \cdot \mathrm{BW}}{s^2 + {\omega_0}^2} This is the "wideband" transformation, producing a stopband with geometric (log frequency) symmetry about `wo`. """ z = atleast_1d(z) p = atleast_1d(p) wo = float(wo) bw = float(bw) degree = _relative_degree(z, p) # Invert to a highpass filter with desired bandwidth z_hp = (bw/2) / z p_hp = (bw/2) / p # Square root needs to produce complex result, not NaN z_hp = z_hp.astype(complex) p_hp = p_hp.astype(complex) # Duplicate poles and zeros and shift from baseband to +wo and -wo z_bs = concatenate((z_hp + sqrt(z_hp2 - wo2), z_hp - sqrt(z_hp2 - wo2))) p_bs = concatenate((p_hp + sqrt(p_hp2 - wo2), p_hp - sqrt(p_hp2 - wo*2))) # Move any zeros that were at infinity to the center of the stopband z_bs = append(z_bs, +1jwo * ones(degree)) z_bs = append(z_bs, -1jwo ones(degree)) # Cancel out gain change caused by inversion k_bs = k * real(prod(-z) / prod(-p)) return z_bs, p_bs, k_bs def butter(N, Wn, btype='low', analog=False, output='ba'): """ Butterworth digital and analog filter design. Design an Nth-order digital or analog Butterworth filter and return the filter coefficients. Parameters ---------- N : int The order of the filter. Wn : array_like A scalar or length-2 sequence giving the critical frequencies. For a Butterworth filter, this is the point at which the gain drops to 1/sqrt(2) that of the passband (the "-3 dB point"). For digital filters, `Wn` is normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`Wn` is thus in half-cycles / sample.) For analog filters, `Wn` is an angular frequency (e.g. rad/s). btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional The type of filter. Default is 'lowpass'. analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. See Also -------- buttord, buttap Notes ----- The Butterworth filter has maximally flat frequency response in the passband. The ``'sos'`` output parameter was added in 0.16.0. Examples -------- Plot the filter's frequency response, showing the critical points: >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> b, a = signal.butter(4, 100, 'low', analog=True) >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.title('Butterworth filter frequency response') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.axvline(100, color='green') # cutoff frequency >>> plt.show() """ return iirfilter(N, Wn, btype=btype, analog=analog, output=output, ftype='butter') def cheby1(N, rp, Wn, btype='low', analog=False, output='ba'): """ Chebyshev type I digital and analog filter design. Design an Nth-order digital or analog Chebyshev type I filter and return the filter coefficients. Parameters ---------- N : int The order of the filter. rp : float The maximum ripple allowed below unity gain in the passband. Specified in decibels, as a positive number. Wn : array_like A scalar or length-2 sequence giving the critical frequencies. For Type I filters, this is the point in the transition band at which the gain first drops below -`rp`. For digital filters, `Wn` is normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`Wn` is thus in half-cycles / sample.) For analog filters, `Wn` is an angular frequency (e.g. rad/s). btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional The type of filter. Default is 'lowpass'. analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. See Also -------- cheb1ord, cheb1ap Notes ----- The Chebyshev type I filter maximizes the rate of cutoff between the frequency response's passband and stopband, at the expense of ripple in the passband and increased ringing in the step response. Type I filters roll off faster than Type II (`cheby2`), but Type II filters do not have any ripple in the passband. The equiripple passband has N maxima or minima (for example, a 5th-order filter has 3 maxima and 2 minima). Consequently, the DC gain is unity for odd-order filters, or -rp dB for even-order filters. The ``'sos'`` output parameter was added in 0.16.0. Examples -------- Plot the filter's frequency response, showing the critical points: >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> b, a = signal.cheby1(4, 5, 100, 'low', analog=True) >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.title('Chebyshev Type I frequency response (rp=5)') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.axvline(100, color='green') # cutoff frequency >>> plt.axhline(-5, color='green') # rp >>> plt.show() """ return iirfilter(N, Wn, rp=rp, btype=btype, analog=analog, output=output, ftype='cheby1') def cheby2(N, rs, Wn, btype='low', analog=False, output='ba'): """ Chebyshev type II digital and analog filter design. Design an Nth-order digital or analog Chebyshev type II filter and return the filter coefficients. Parameters ---------- N : int The order of the filter. rs : float The minimum attenuation required in the stop band. Specified in decibels, as a positive number. Wn : array_like A scalar or length-2 sequence giving the critical frequencies. For Type II filters, this is the point in the transition band at which the gain first reaches -`rs`. For digital filters, `Wn` is normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`Wn` is thus in half-cycles / sample.) For analog filters, `Wn` is an angular frequency (e.g. rad/s). btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional The type of filter. Default is 'lowpass'. analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. See Also -------- cheb2ord, cheb2ap Notes ----- The Chebyshev type II filter maximizes the rate of cutoff between the frequency response's passband and stopband, at the expense of ripple in the stopband and increased ringing in the step response. Type II filters do not roll off as fast as Type I (`cheby1`). The ``'sos'`` output parameter was added in 0.16.0. Examples -------- Plot the filter's frequency response, showing the critical points: >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> b, a = signal.cheby2(4, 40, 100, 'low', analog=True) >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.title('Chebyshev Type II frequency response (rs=40)') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.axvline(100, color='green') # cutoff frequency >>> plt.axhline(-40, color='green') # rs >>> plt.show() """ return iirfilter(N, Wn, rs=rs, btype=btype, analog=analog, output=output, ftype='cheby2') def ellip(N, rp, rs, Wn, btype='low', analog=False, output='ba'): """ Elliptic (Cauer) digital and analog filter design. Design an Nth-order digital or analog elliptic filter and return the filter coefficients. Parameters ---------- N : int The order of the filter. rp : float The maximum ripple allowed below unity gain in the passband. Specified in decibels, as a positive number. rs : float The minimum attenuation required in the stop band. Specified in decibels, as a positive number. Wn : array_like A scalar or length-2 sequence giving the critical frequencies. For elliptic filters, this is the point in the transition band at which the gain first drops below -`rp`. For digital filters, `Wn` is normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`Wn` is thus in half-cycles / sample.) For analog filters, `Wn` is an angular frequency (e.g. rad/s). btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional The type of filter. Default is 'lowpass'. analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. See Also -------- ellipord, ellipap Notes ----- Also known as Cauer or Zolotarev filters, the elliptical filter maximizes the rate of transition between the frequency response's passband and stopband, at the expense of ripple in both, and increased ringing in the step response. As `rp` approaches 0, the elliptical filter becomes a Chebyshev type II filter (`cheby2`). As `rs` approaches 0, it becomes a Chebyshev type I filter (`cheby1`). As both approach 0, it becomes a Butterworth filter (`butter`). The equiripple passband has N maxima or minima (for example, a 5th-order filter has 3 maxima and 2 minima). Consequently, the DC gain is unity for odd-order filters, or -rp dB for even-order filters. The ``'sos'`` output parameter was added in 0.16.0. Examples -------- Plot the filter's frequency response, showing the critical points: >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> b, a = signal.ellip(4, 5, 40, 100, 'low', analog=True) >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.title('Elliptic filter frequency response (rp=5, rs=40)') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.axvline(100, color='green') # cutoff frequency >>> plt.axhline(-40, color='green') # rs >>> plt.axhline(-5, color='green') # rp >>> plt.show() """ return iirfilter(N, Wn, rs=rs, rp=rp, btype=btype, analog=analog, output=output, ftype='elliptic') def bessel(N, Wn, btype='low', analog=False, output='ba', norm='phase'): """ Bessel/Thomson digital and analog filter design. Design an Nth-order digital or analog Bessel filter and return the filter coefficients. Parameters ---------- N : int The order of the filter. Wn : array_like A scalar or length-2 sequence giving the critical frequencies (defined by the `norm` parameter). For analog filters, `Wn` is an angular frequency (e.g. rad/s). For digital filters, `Wn` is normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`Wn` is thus in half-cycles / sample.) btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional The type of filter. Default is 'lowpass'. analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. (See Notes.) output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. norm : {'phase', 'delay', 'mag'}, optional Critical frequency normalization: ``phase`` The filter is normalized such that the phase response reaches its midpoint at angular (e.g. rad/s) frequency `Wn`. This happens for both low-pass and high-pass filters, so this is the "phase-matched" case. The magnitude response asymptotes are the same as a Butterworth filter of the same order with a cutoff of `Wn`. This is the default, and matches MATLAB's implementation. ``delay`` The filter is normalized such that the group delay in the passband is 1/`Wn` (e.g. seconds). This is the "natural" type obtained by solving Bessel polynomials. ``mag`` The filter is normalized such that the gain magnitude is -3 dB at angular frequency `Wn`. .. versionadded:: 0.18.0 Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. Notes ----- Also known as a Thomson filter, the analog Bessel filter has maximally flat group delay and maximally linear phase response, with very little ringing in the step response. [1]_ The Bessel is inherently an analog filter. This function generates digital Bessel filters using the bilinear transform, which does not preserve the phase response of the analog filter. As such, it is only approximately correct at frequencies below about fs/4. To get maximally-flat group delay at higher frequencies, the analog Bessel filter must be transformed using phase-preserving techniques. See `besselap` for implementation details and references. The ``'sos'`` output parameter was added in 0.16.0. Examples -------- Plot the phase-normalized frequency response, showing the relationship to the Butterworth's cutoff frequency (green): >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> b, a = signal.butter(4, 100, 'low', analog=True) >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(np.abs(h)), color='silver', ls='dashed') >>> b, a = signal.bessel(4, 100, 'low', analog=True, norm='phase') >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(np.abs(h))) >>> plt.title('Bessel filter magnitude response (with Butterworth)') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.axvline(100, color='green') # cutoff frequency >>> plt.show() and the phase midpoint: >>> plt.figure() >>> plt.semilogx(w, np.unwrap(np.angle(h))) >>> plt.axvline(100, color='green') # cutoff frequency >>> plt.axhline(-np.pi, color='red') # phase midpoint >>> plt.title('Bessel filter phase response') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Phase [radians]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.show() Plot the magnitude-normalized frequency response, showing the -3 dB cutoff: >>> b, a = signal.bessel(3, 10, 'low', analog=True, norm='mag') >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(np.abs(h))) >>> plt.axhline(-3, color='red') # -3 dB magnitude >>> plt.axvline(10, color='green') # cutoff frequency >>> plt.title('Magnitude-normalized Bessel filter frequency response') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.show() Plot the delay-normalized filter, showing the maximally-flat group delay at 0.1 seconds: >>> b, a = signal.bessel(5, 1/0.1, 'low', analog=True, norm='delay') >>> w, h = signal.freqs(b, a) >>> plt.figure() >>> plt.semilogx(w[1:], -np.diff(np.unwrap(np.angle(h)))/np.diff(w)) >>> plt.axhline(0.1, color='red') # 0.1 seconds group delay >>> plt.title('Bessel filter group delay') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Group delay [seconds]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.show() References ---------- .. [1] Thomson, W.E., "Delay Networks having Maximally Flat Frequency Characteristics", Proceedings of the Institution of Electrical Engineers, Part III, November 1949, Vol. 96, No. 44, pp. 487-490. """ return iirfilter(N, Wn, btype=btype, analog=analog, output=output, ftype='bessel_'+norm) def maxflat(): pass def yulewalk(): pass def band_stop_obj(wp, ind, passb, stopb, gpass, gstop, type): """ Band Stop Objective Function for order minimization. Returns the non-integer order for an analog band stop filter. Parameters ---------- wp : scalar Edge of passband `passb`. ind : int, {0, 1} Index specifying which `passb` edge to vary (0 or 1). passb : ndarray Two element sequence of fixed passband edges. stopb : ndarray Two element sequence of fixed stopband edges. gstop : float Amount of attenuation in stopband in dB. gpass : float Amount of ripple in the passband in dB. type : {'butter', 'cheby', 'ellip'} Type of filter. Returns ------- n : scalar Filter order (possibly non-integer). """ passbC = passb.copy() passbC[ind] = wp nat = (stopb * (passbC[0] - passbC[1]) / (stopb ** 2 - passbC[0] * passbC[1])) nat = min(abs(nat)) if type == 'butter': GSTOP = 10 ** (0.1 * abs(gstop)) GPASS = 10 ** (0.1 * abs(gpass)) n = (log10((GSTOP - 1.0) / (GPASS - 1.0)) / (2 * log10(nat))) elif type == 'cheby': GSTOP = 10 ** (0.1 * abs(gstop)) GPASS = 10 ** (0.1 * abs(gpass)) n = arccosh(sqrt((GSTOP - 1.0) / (GPASS - 1.0))) / arccosh(nat) elif type == 'ellip': GSTOP = 10 ** (0.1 * gstop) GPASS = 10 ** (0.1 * gpass) arg1 = sqrt((GPASS - 1.0) / (GSTOP - 1.0)) arg0 = 1.0 / nat d0 = special.ellipk([arg0 2, 1 - arg0 2]) d1 = special.ellipk([arg1 2, 1 - arg1 2]) n = (d0[0] * d1[1] / (d0[1] * d1[0])) else: raise ValueError("Incorrect type: %s" % type) return n def buttord(wp, ws, gpass, gstop, analog=False): """Butterworth filter order selection. Return the order of the lowest order digital or analog Butterworth filter that loses no more than `gpass` dB in the passband and has at least `gstop` dB attenuation in the stopband. Parameters ---------- wp, ws : float Passband and stopband edge frequencies. For digital filters, these are normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`wp` and `ws` are thus in half-cycles / sample.) For example: - Lowpass: wp = 0.2, ws = 0.3 - Highpass: wp = 0.3, ws = 0.2 - Bandpass: wp = [0.2, 0.5], ws = [0.1, 0.6] - Bandstop: wp = [0.1, 0.6], ws = [0.2, 0.5] For analog filters, `wp` and `ws` are angular frequencies (e.g. rad/s). gpass : float The maximum loss in the passband (dB). gstop : float The minimum attenuation in the stopband (dB). analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. Returns ------- ord : int The lowest order for a Butterworth filter which meets specs. wn : ndarray or float The Butterworth natural frequency (i.e. the "3dB frequency"). Should be used with `butter` to give filter results. See Also -------- butter : Filter design using order and critical points cheb1ord : Find order and critical points from passband and stopband spec cheb2ord, ellipord iirfilter : General filter design using order and critical frequencies iirdesign : General filter design using passband and stopband spec Examples -------- Design an analog bandpass filter with passband within 3 dB from 20 to 50 rad/s, while rejecting at least -40 dB below 14 and above 60 rad/s. Plot its frequency response, showing the passband and stopband constraints in gray. >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> N, Wn = signal.buttord([20, 50], [14, 60], 3, 40, True) >>> b, a = signal.butter(N, Wn, 'band', True) >>> w, h = signal.freqs(b, a, np.logspace(1, 2, 500)) >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.title('Butterworth bandpass filter fit to constraints') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.grid(which='both', axis='both') >>> plt.fill([1, 14, 14, 1], [-40, -40, 99, 99], '0.9', lw=0) # stop >>> plt.fill([20, 20, 50, 50], [-99, -3, -3, -99], '0.9', lw=0) # pass >>> plt.fill([60, 60, 1e9, 1e9], [99, -40, -40, 99], '0.9', lw=0) # stop >>> plt.axis([10, 100, -60, 3]) >>> plt.show() """ wp = atleast_1d(wp) ws = atleast_1d(ws) filter_type = 2 * (len(wp) - 1) filter_type += 1 if wp[0] >= ws[0]: filter_type += 1 # Pre-warp frequencies for digital filter design if not analog: passb = tan(pi * wp / 2.0) stopb = tan(pi * ws / 2.0) else: passb = wp * 1.0 stopb = ws * 1.0 if filter_type == 1: # low nat = stopb / passb elif filter_type == 2: # high nat = passb / stopb elif filter_type == 3: # stop wp0 = optimize.fminbound(band_stop_obj, passb[0], stopb[0] - 1e-12, args=(0, passb, stopb, gpass, gstop, 'butter'), disp=0) passb[0] = wp0 wp1 = optimize.fminbound(band_stop_obj, stopb[1] + 1e-12, passb[1], args=(1, passb, stopb, gpass, gstop, 'butter'), disp=0) passb[1] = wp1 nat = ((stopb * (passb[0] - passb[1])) / (stopb ** 2 - passb[0] * passb[1])) elif filter_type == 4: # pass nat = ((stopb ** 2 - passb[0] * passb[1]) / (stopb * (passb[0] - passb[1]))) nat = min(abs(nat)) GSTOP = 10 ** (0.1 * abs(gstop)) GPASS = 10 ** (0.1 * abs(gpass)) ord = int(ceil(log10((GSTOP - 1.0) / (GPASS - 1.0)) / (2 * log10(nat)))) # Find the Butterworth natural frequency WN (or the "3dB" frequency") # to give exactly gpass at passb. try: W0 = (GPASS - 1.0) ** (-1.0 / (2.0 * ord)) except ZeroDivisionError: W0 = 1.0 print("Warning, order is zero...check input parameters.") # now convert this frequency back from lowpass prototype # to the original analog filter if filter_type == 1: # low WN = W0 * passb elif filter_type == 2: # high WN = passb / W0 elif filter_type == 3: # stop WN = numpy.zeros(2, float) discr = sqrt((passb[1] - passb[0]) ** 2 + 4 * W0 ** 2 * passb[0] * passb[1]) WN[0] = ((passb[1] - passb[0]) + discr) / (2 * W0) WN[1] = ((passb[1] - passb[0]) - discr) / (2 * W0) WN = numpy.sort(abs(WN)) elif filter_type == 4: # pass W0 = numpy.array([-W0, W0], float) WN = (-W0 * (passb[1] - passb[0]) / 2.0 + sqrt(W0 ** 2 / 4.0 * (passb[1] - passb[0]) ** 2 + passb[0] * passb[1])) WN = numpy.sort(abs(WN)) else: raise ValueError("Bad type: %s" % filter_type) if not analog: wn = (2.0 / pi) * arctan(WN) else: wn = WN if len(wn) == 1: wn = wn[0] return ord, wn def cheb1ord(wp, ws, gpass, gstop, analog=False): """Chebyshev type I filter order selection. Return the order of the lowest order digital or analog Chebyshev Type I filter that loses no more than `gpass` dB in the passband and has at least `gstop` dB attenuation in the stopband. Parameters ---------- wp, ws : float Passband and stopband edge frequencies. For digital filters, these are normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`wp` and `ws` are thus in half-cycles / sample.) For example: - Lowpass: wp = 0.2, ws = 0.3 - Highpass: wp = 0.3, ws = 0.2 - Bandpass: wp = [0.2, 0.5], ws = [0.1, 0.6] - Bandstop: wp = [0.1, 0.6], ws = [0.2, 0.5] For analog filters, `wp` and `ws` are angular frequencies (e.g. rad/s). gpass : float The maximum loss in the passband (dB). gstop : float The minimum attenuation in the stopband (dB). analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. Returns ------- ord : int The lowest order for a Chebyshev type I filter that meets specs. wn : ndarray or float The Chebyshev natural frequency (the "3dB frequency") for use with `cheby1` to give filter results. See Also -------- cheby1 : Filter design using order and critical points buttord : Find order and critical points from passband and stopband spec cheb2ord, ellipord iirfilter : General filter design using order and critical frequencies iirdesign : General filter design using passband and stopband spec Examples -------- Design a digital lowpass filter such that the passband is within 3 dB up to 0.2(fs/2), while rejecting at least -40 dB above 0.3(fs/2). Plot its frequency response, showing the passband and stopband constraints in gray. >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> N, Wn = signal.cheb1ord(0.2, 0.3, 3, 40) >>> b, a = signal.cheby1(N, 3, Wn, 'low') >>> w, h = signal.freqz(b, a) >>> plt.semilogx(w / np.pi, 20 * np.log10(abs(h))) >>> plt.title('Chebyshev I lowpass filter fit to constraints') >>> plt.xlabel('Normalized frequency') >>> plt.ylabel('Amplitude [dB]') >>> plt.grid(which='both', axis='both') >>> plt.fill([.01, 0.2, 0.2, .01], [-3, -3, -99, -99], '0.9', lw=0) # stop >>> plt.fill([0.3, 0.3, 2, 2], [ 9, -40, -40, 9], '0.9', lw=0) # pass >>> plt.axis([0.08, 1, -60, 3]) >>> plt.show() """ wp = atleast_1d(wp) ws = atleast_1d(ws) filter_type = 2 * (len(wp) - 1) if wp[0] < ws[0]: filter_type += 1 else: filter_type += 2 # Pre-warp frequencies for digital filter design if not analog: passb = tan(pi * wp / 2.0) stopb = tan(pi * ws / 2.0) else: passb = wp * 1.0 stopb = ws * 1.0 if filter_type == 1: # low nat = stopb / passb elif filter_type == 2: # high nat = passb / stopb elif filter_type == 3: # stop wp0 = optimize.fminbound(band_stop_obj, passb[0], stopb[0] - 1e-12, args=(0, passb, stopb, gpass, gstop, 'cheby'), disp=0) passb[0] = wp0 wp1 = optimize.fminbound(band_stop_obj, stopb[1] + 1e-12, passb[1], args=(1, passb, stopb, gpass, gstop, 'cheby'), disp=0) passb[1] = wp1 nat = ((stopb * (passb[0] - passb[1])) / (stopb ** 2 - passb[0] * passb[1])) elif filter_type == 4: # pass nat = ((stopb ** 2 - passb[0] * passb[1]) / (stopb * (passb[0] - passb[1]))) nat = min(abs(nat)) GSTOP = 10 ** (0.1 * abs(gstop)) GPASS = 10 ** (0.1 * abs(gpass)) ord = int(ceil(arccosh(sqrt((GSTOP - 1.0) / (GPASS - 1.0))) / arccosh(nat))) # Natural frequencies are just the passband edges if not analog: wn = (2.0 / pi) * arctan(passb) else: wn = passb if len(wn) == 1: wn = wn[0] return ord, wn def cheb2ord(wp, ws, gpass, gstop, analog=False): """Chebyshev type II filter order selection. Return the order of the lowest order digital or analog Chebyshev Type II filter that loses no more than `gpass` dB in the passband and has at least `gstop` dB attenuation in the stopband. Parameters ---------- wp, ws : float Passband and stopband edge frequencies. For digital filters, these are normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`wp` and `ws` are thus in half-cycles / sample.) For example: - Lowpass: wp = 0.2, ws = 0.3 - Highpass: wp = 0.3, ws = 0.2 - Bandpass: wp = [0.2, 0.5], ws = [0.1, 0.6] - Bandstop: wp = [0.1, 0.6], ws = [0.2, 0.5] For analog filters, `wp` and `ws` are angular frequencies (e.g. rad/s). gpass : float The maximum loss in the passband (dB). gstop : float The minimum attenuation in the stopband (dB). analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. Returns ------- ord : int The lowest order for a Chebyshev type II filter that meets specs. wn : ndarray or float The Chebyshev natural frequency (the "3dB frequency") for use with `cheby2` to give filter results. See Also -------- cheby2 : Filter design using order and critical points buttord : Find order and critical points from passband and stopband spec cheb1ord, ellipord iirfilter : General filter design using order and critical frequencies iirdesign : General filter design using passband and stopband spec Examples -------- Design a digital bandstop filter which rejects -60 dB from 0.2(fs/2) to 0.5(fs/2), while staying within 3 dB below 0.1(fs/2) or above 0.6(fs/2). Plot its frequency response, showing the passband and stopband constraints in gray. >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> N, Wn = signal.cheb2ord([0.1, 0.6], [0.2, 0.5], 3, 60) >>> b, a = signal.cheby2(N, 60, Wn, 'stop') >>> w, h = signal.freqz(b, a) >>> plt.semilogx(w / np.pi, 20 * np.log10(abs(h))) >>> plt.title('Chebyshev II bandstop filter fit to constraints') >>> plt.xlabel('Normalized frequency') >>> plt.ylabel('Amplitude [dB]') >>> plt.grid(which='both', axis='both') >>> plt.fill([.01, .1, .1, .01], [-3, -3, -99, -99], '0.9', lw=0) # stop >>> plt.fill([.2, .2, .5, .5], [ 9, -60, -60, 9], '0.9', lw=0) # pass >>> plt.fill([.6, .6, 2, 2], [-99, -3, -3, -99], '0.9', lw=0) # stop >>> plt.axis([0.06, 1, -80, 3]) >>> plt.show() """ wp = atleast_1d(wp) ws = atleast_1d(ws) filter_type = 2 * (len(wp) - 1) if wp[0] < ws[0]: filter_type += 1 else: filter_type += 2 # Pre-warp frequencies for digital filter design if not analog: passb = tan(pi * wp / 2.0) stopb = tan(pi * ws / 2.0) else: passb = wp * 1.0 stopb = ws * 1.0 if filter_type == 1: # low nat = stopb / passb elif filter_type == 2: # high nat = passb / stopb elif filter_type == 3: # stop wp0 = optimize.fminbound(band_stop_obj, passb[0], stopb[0] - 1e-12, args=(0, passb, stopb, gpass, gstop, 'cheby'), disp=0) passb[0] = wp0 wp1 = optimize.fminbound(band_stop_obj, stopb[1] + 1e-12, passb[1], args=(1, passb, stopb, gpass, gstop, 'cheby'), disp=0) passb[1] = wp1 nat = ((stopb * (passb[0] - passb[1])) / (stopb ** 2 - passb[0] * passb[1])) elif filter_type == 4: # pass nat = ((stopb ** 2 - passb[0] * passb[1]) / (stopb * (passb[0] - passb[1]))) nat = min(abs(nat)) GSTOP = 10 ** (0.1 * abs(gstop)) GPASS = 10 ** (0.1 * abs(gpass)) ord = int(ceil(arccosh(sqrt((GSTOP - 1.0) / (GPASS - 1.0))) / arccosh(nat))) # Find frequency where analog response is -gpass dB. # Then convert back from low-pass prototype to the original filter. new_freq = cosh(1.0 / ord * arccosh(sqrt((GSTOP - 1.0) / (GPASS - 1.0)))) new_freq = 1.0 / new_freq if filter_type == 1: nat = passb / new_freq elif filter_type == 2: nat = passb * new_freq elif filter_type == 3: nat = numpy.zeros(2, float) nat[0] = (new_freq / 2.0 * (passb[0] - passb[1]) + sqrt(new_freq ** 2 * (passb[1] - passb[0]) ** 2 / 4.0 + passb[1] * passb[0])) nat[1] = passb[1] * passb[0] / nat[0] elif filter_type == 4: nat = numpy.zeros(2, float) nat[0] = (1.0 / (2.0 * new_freq) * (passb[0] - passb[1]) + sqrt((passb[1] - passb[0]) ** 2 / (4.0 * new_freq ** 2) + passb[1] * passb[0])) nat[1] = passb[0] * passb[1] / nat[0] if not analog: wn = (2.0 / pi) * arctan(nat) else: wn = nat if len(wn) == 1: wn = wn[0] return ord, wn def ellipord(wp, ws, gpass, gstop, analog=False): """Elliptic (Cauer) filter order selection. Return the order of the lowest order digital or analog elliptic filter that loses no more than `gpass` dB in the passband and has at least `gstop` dB attenuation in the stopband. Parameters ---------- wp, ws : float Passband and stopband edge frequencies. For digital filters, these are normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`wp` and `ws` are thus in half-cycles / sample.) For example: - Lowpass: wp = 0.2, ws = 0.3 - Highpass: wp = 0.3, ws = 0.2 - Bandpass: wp = [0.2, 0.5], ws = [0.1, 0.6] - Bandstop: wp = [0.1, 0.6], ws = [0.2, 0.5] For analog filters, `wp` and `ws` are angular frequencies (e.g. rad/s). gpass : float The maximum loss in the passband (dB). gstop : float The minimum attenuation in the stopband (dB). analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. Returns ------- ord : int The lowest order for an Elliptic (Cauer) filter that meets specs. wn : ndarray or float The Chebyshev natural frequency (the "3dB frequency") for use with `ellip` to give filter results. See Also -------- ellip : Filter design using order and critical points buttord : Find order and critical points from passband and stopband spec cheb1ord, cheb2ord iirfilter : General filter design using order and critical frequencies iirdesign : General filter design using passband and stopband spec Examples -------- Design an analog highpass filter such that the passband is within 3 dB above 30 rad/s, while rejecting -60 dB at 10 rad/s. Plot its frequency response, showing the passband and stopband constraints in gray. >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> N, Wn = signal.ellipord(30, 10, 3, 60, True) >>> b, a = signal.ellip(N, 3, 60, Wn, 'high', True) >>> w, h = signal.freqs(b, a, np.logspace(0, 3, 500)) >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.title('Elliptical highpass filter fit to constraints') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.grid(which='both', axis='both') >>> plt.fill([.1, 10, 10, .1], [1e4, 1e4, -60, -60], '0.9', lw=0) # stop >>> plt.fill([30, 30, 1e9, 1e9], [-99, -3, -3, -99], '0.9', lw=0) # pass >>> plt.axis([1, 300, -80, 3]) >>> plt.show() """ wp = atleast_1d(wp) ws = atleast_1d(ws) filter_type = 2 * (len(wp) - 1) filter_type += 1 if wp[0] >= ws[0]: filter_type += 1 # Pre-warp frequencies for digital filter design if not analog: passb = tan(pi * wp / 2.0) stopb = tan(pi * ws / 2.0) else: passb = wp * 1.0 stopb = ws * 1.0 if filter_type == 1: # low nat = stopb / passb elif filter_type == 2: # high nat = passb / stopb elif filter_type == 3: # stop wp0 = optimize.fminbound(band_stop_obj, passb[0], stopb[0] - 1e-12, args=(0, passb, stopb, gpass, gstop, 'ellip'), disp=0) passb[0] = wp0 wp1 = optimize.fminbound(band_stop_obj, stopb[1] + 1e-12, passb[1], args=(1, passb, stopb, gpass, gstop, 'ellip'), disp=0) passb[1] = wp1 nat = ((stopb * (passb[0] - passb[1])) / (stopb ** 2 - passb[0] * passb[1])) elif filter_type == 4: # pass nat = ((stopb ** 2 - passb[0] * passb[1]) / (stopb * (passb[0] - passb[1]))) nat = min(abs(nat)) GSTOP = 10 ** (0.1 * gstop) GPASS = 10 ** (0.1 * gpass) arg1 = sqrt((GPASS - 1.0) / (GSTOP - 1.0)) arg0 = 1.0 / nat d0 = special.ellipk([arg0 2, 1 - arg0 2]) d1 = special.ellipk([arg1 2, 1 - arg1 2]) ord = int(ceil(d0[0] * d1[1] / (d0[1] * d1[0]))) if not analog: wn = arctan(passb) * 2.0 / pi else: wn = passb if len(wn) == 1: wn = wn[0] return ord, wn def buttap(N): """Return (z,p,k) for analog prototype of Nth-order Butterworth filter. The filter will have an angular (e.g. rad/s) cutoff frequency of 1. See Also -------- butter : Filter design function using this prototype """ if abs(int(N)) != N: raise ValueError("Filter order must be a nonnegative integer") z = numpy.array([]) m = numpy.arange(-N+1, N, 2) # Middle value is 0 to ensure an exactly real pole p = -numpy.exp(1j * pi * m / (2 * N)) k = 1 return z, p, k def cheb1ap(N, rp): """ Return (z,p,k) for Nth-order Chebyshev type I analog lowpass filter. The returned filter prototype has `rp` decibels of ripple in the passband. The filter's angular (e.g. rad/s) cutoff frequency is normalized to 1, defined as the point at which the gain first drops below ``-rp``. See Also -------- cheby1 : Filter design function using this prototype """ if abs(int(N)) != N: raise ValueError("Filter order must be a nonnegative integer") elif N == 0: # Avoid divide-by-zero error # Even order filters have DC gain of -rp dB return numpy.array([]), numpy.array([]), 10(-rp/20) z = numpy.array([]) # Ripple factor (epsilon) eps = numpy.sqrt(10 (0.1 * rp) - 1.0) mu = 1.0 / N * arcsinh(1 / eps) # Arrange poles in an ellipse on the left half of the S-plane m = numpy.arange(-N+1, N, 2) theta = pi * m / (2N) p = -sinh(mu + 1jtheta) k = numpy.prod(-p, axis=0).real if N % 2 == 0: k = k / sqrt((1 + eps * eps)) return z, p, k def cheb2ap(N, rs): """ Return (z,p,k) for Nth-order Chebyshev type I analog lowpass filter. The returned filter prototype has `rs` decibels of ripple in the stopband. The filter's angular (e.g. rad/s) cutoff frequency is normalized to 1, defined as the point at which the gain first reaches ``-rs``. See Also -------- cheby2 : Filter design function using this prototype """ if abs(int(N)) != N: raise ValueError("Filter order must be a nonnegative integer") elif N == 0: # Avoid divide-by-zero warning return numpy.array([]), numpy.array([]), 1 # Ripple factor (epsilon) de = 1.0 / sqrt(10 ** (0.1 * rs) - 1) mu = arcsinh(1.0 / de) / N if N % 2: m = numpy.concatenate((numpy.arange(-N+1, 0, 2), numpy.arange(2, N, 2))) else: m = numpy.arange(-N+1, N, 2) z = -conjugate(1j / sin(m * pi / (2.0 * N))) # Poles around the unit circle like Butterworth p = -exp(1j * pi * numpy.arange(-N+1, N, 2) / (2 * N)) # Warp into Chebyshev II p = sinh(mu) * p.real + 1j * cosh(mu) * p.imag p = 1.0 / p k = (numpy.prod(-p, axis=0) / numpy.prod(-z, axis=0)).real return z, p, k EPSILON = 2e-16 def _vratio(u, ineps, mp): [s, c, d, phi] = special.ellipj(u, mp) ret = abs(ineps - s / c) return ret def _kratio(m, k_ratio): m = float(m) if m < 0: m = 0.0 if m > 1: m = 1.0 if abs(m) > EPSILON and (abs(m) + EPSILON) < 1: k = special.ellipk([m, 1 - m]) r = k[0] / k[1] - k_ratio elif abs(m) > EPSILON: r = -k_ratio else: r = 1e20 return abs(r) def ellipap(N, rp, rs): """Return (z,p,k) of Nth-order elliptic analog lowpass filter. The filter is a normalized prototype that has `rp` decibels of ripple in the passband and a stopband `rs` decibels down. The filter's angular (e.g. rad/s) cutoff frequency is normalized to 1, defined as the point at which the gain first drops below ``-rp``. See Also -------- ellip : Filter design function using this prototype References ---------- .. [1] Lutova, Tosic, and Evans, "Filter Design for Signal Processing", Chapters 5 and 12. """ if abs(int(N)) != N: raise ValueError("Filter order must be a nonnegative integer") elif N == 0: # Avoid divide-by-zero warning # Even order filters have DC gain of -rp dB return numpy.array([]), numpy.array([]), 10(-rp/20) elif N == 1: p = -sqrt(1.0 / (10 (0.1 * rp) - 1.0)) k = -p z = [] return asarray(z), asarray(p), k eps = numpy.sqrt(10 ** (0.1 * rp) - 1) ck1 = eps / numpy.sqrt(10 ** (0.1 * rs) - 1) ck1p = numpy.sqrt(1 - ck1 * ck1) if ck1p == 1: raise ValueError("Cannot design a filter with given rp and rs" " specifications.") val = special.ellipk([ck1 * ck1, ck1p * ck1p]) if abs(1 - ck1p * ck1p) < EPSILON: krat = 0 else: krat = N * val[0] / val[1] m = optimize.fmin(_kratio, [0.5], args=(krat,), maxfun=250, maxiter=250, disp=0) if m < 0 or m > 1: m = optimize.fminbound(_kratio, 0, 1, args=(krat,), maxfun=250, maxiter=250, disp=0) capk = special.ellipk(m) j = numpy.arange(1 - N % 2, N, 2) jj = len(j) [s, c, d, phi] = special.ellipj(j * capk / N, m * numpy.ones(jj)) snew = numpy.compress(abs(s) > EPSILON, s, axis=-1) z = 1.0 / (sqrt(m) * snew) z = 1j * z z = numpy.concatenate((z, conjugate(z))) r = optimize.fmin(_vratio, special.ellipk(m), args=(1. / eps, ck1p * ck1p), maxfun=250, maxiter=250, disp=0) v0 = capk * r / (N * val[0]) [sv, cv, dv, phi] = special.ellipj(v0, 1 - m) p = -(c * d * sv * cv + 1j * s * dv) / (1 - (d * sv) ** 2.0) if N % 2: newp = numpy.compress(abs(p.imag) > EPSILON * numpy.sqrt(numpy.sum(p * numpy.conjugate(p), axis=0).real), p, axis=-1) p = numpy.concatenate((p, conjugate(newp))) else: p = numpy.concatenate((p, conjugate(p))) k = (numpy.prod(-p, axis=0) / numpy.prod(-z, axis=0)).real if N % 2 == 0: k = k / numpy.sqrt((1 + eps * eps)) return z, p, k # TODO: Make this a real public function scipy.misc.ff def _falling_factorial(x, n): r""" Return the factorial of `x` to the `n` falling. This is defined as: .. math:: x^\underline n = (x)_n = x (x-1) \cdots (x-n+1) This can more efficiently calculate ratios of factorials, since: n!/m! == falling_factorial(n, n-m) where n >= m skipping the factors that cancel out the usual factorial n! == ff(n, n) """ val = 1 for k in range(x - n + 1, x + 1): val = k return val def _bessel_poly(n, reverse=False): """ Return the coefficients of Bessel polynomial of degree `n` If `reverse` is true, a reverse Bessel polynomial is output. Output is a list of coefficients: [1] = 1 [1, 1] = 1s + 1 [1, 3, 3] = 1s^2 + 3s + 3 [1, 6, 15, 15] = 1s^3 + 6s^2 + 15s + 15 [1, 10, 45, 105, 105] = 1s^4 + 10s^3 + 45s^2 + 105s + 105 etc. Output is a Python list of arbitrary precision long ints, so n is only limited by your hardware's memory. Sequence is http://oeis.org/A001498 , and output can be confirmed to match http://oeis.org/A001498/b001498.txt : >>> i = 0 >>> for n in range(51): ... for x in _bessel_poly(n, reverse=True): ... print(i, x) ... i += 1 """ if abs(int(n)) != n: raise ValueError("Polynomial order must be a nonnegative integer") else: n = int(n) # np.int32 doesn't work, for instance out = [] for k in range(n + 1): num = _falling_factorial(2n - k, n) den = 2*(n - k) factorial(k, exact=True) out.append(num // den) if reverse: return out[::-1] else: return out def _campos_zeros(n): """ Return approximate zero locations of Bessel polynomials y_n(x) for order `n` using polynomial fit (Campos-Calderon 2011) """ if n == 1: return asarray([-1+0j]) s = npp_polyval(n, [0, 0, 2, 0, -3, 1]) b3 = npp_polyval(n, [16, -8]) / s b2 = npp_polyval(n, [-24, -12, 12]) / s b1 = npp_polyval(n, [8, 24, -12, -2]) / s b0 = npp_polyval(n, [0, -6, 0, 5, -1]) / s r = npp_polyval(n, [0, 0, 2, 1]) a1 = npp_polyval(n, [-6, -6]) / r a2 = 6 / r k = np.arange(1, n+1) x = npp_polyval(k, [0, a1, a2]) y = npp_polyval(k, [b0, b1, b2, b3]) return x + 1jy def _aberth(f, fp, x0, tol=1e-15, maxiter=50): """ Given a function `f`, its first derivative `fp`, and a set of initial guesses `x0`, simultaneously find the roots of the polynomial using the Aberth-Ehrlich method. ``len(x0)`` should equal the number of roots of `f`. (This is not a complete implementation of Bini's algorithm.) """ N = len(x0) x = array(x0, complex) beta = np.empty_like(x0) for iteration in range(maxiter): alpha = -f(x) / fp(x) # Newton's method # Model "repulsion" between zeros for k in range(N): beta[k] = np.sum(1/(x[k] - x[k+1:])) beta[k] += np.sum(1/(x[k] - x[:k])) x += alpha / (1 + alpha beta) if not all(np.isfinite(x)): raise RuntimeError('Root-finding calculation failed') # Mekwi: The iterative process can be stopped when \|hn\| has become # less than the largest error one is willing to permit in the root. if all(abs(alpha) <= tol): break else: raise Exception('Zeros failed to converge') return x def _bessel_zeros(N): """ Find zeros of ordinary Bessel polynomial of order `N`, by root-finding of modified Bessel function of the second kind """ if N == 0: return asarray([]) # Generate starting points x0 = _campos_zeros(N) # Zeros are the same for exp(1/x)K_{N+0.5}(1/x) and Nth-order ordinary # Bessel polynomial y_N(x) def f(x): return special.kve(N+0.5, 1/x) # First derivative of above def fp(x): return (special.kve(N-0.5, 1/x)/(2x2) - special.kve(N+0.5, 1/x)/(x2) + special.kve(N+1.5, 1/x)/(2x2)) # Starting points converge to true zeros x = _aberth(f, fp, x0) # Improve precision using Newton's method on each for i in range(len(x)): x[i] = optimize.newton(f, x[i], fp, tol=1e-15) # Average complex conjugates to make them exactly symmetrical x = np.mean((x, x[::-1].conj()), 0) # Zeros should sum to -1 if abs(np.sum(x) + 1) > 1e-15: raise RuntimeError('Generated zeros are inaccurate') return x def _norm_factor(p, k): """ Numerically find frequency shift to apply to delay-normalized filter such that -3 dB point is at 1 rad/sec. `p` is an array_like of polynomial poles `k` is a float gain First 10 values are listed in "Bessel Scale Factors" table, "Bessel Filters Polynomials, Poles and Circuit Elements 2003, C. Bond." """ p = asarray(p, dtype=complex) def G(w): """ Gain of filter """ return abs(k / prod(1jw - p)) def cutoff(w): """ When gain = -3 dB, return 0 """ return G(w) - 1/np.sqrt(2) return optimize.newton(cutoff, 1.5) def besselap(N, norm='phase'): """ Return (z,p,k) for analog prototype of an Nth-order Bessel filter. Parameters ---------- N : int The order of the filter. norm : {'phase', 'delay', 'mag'}, optional Frequency normalization: ``phase`` The filter is normalized such that the phase response reaches its midpoint at an angular (e.g. rad/s) cutoff frequency of 1. This happens for both low-pass and high-pass filters, so this is the "phase-matched" case. [6]_ The magnitude response asymptotes are the same as a Butterworth filter of the same order with a cutoff of `Wn`. This is the default, and matches MATLAB's implementation. ``delay`` The filter is normalized such that the group delay in the passband is 1 (e.g. 1 second). This is the "natural" type obtained by solving Bessel polynomials ``mag`` The filter is normalized such that the gain magnitude is -3 dB at angular frequency 1. This is called "frequency normalization" by Bond. [1]_ .. versionadded:: 0.18.0 Returns ------- z : ndarray Zeros of the transfer function. Is always an empty array. p : ndarray Poles of the transfer function. k : scalar Gain of the transfer function. For phase-normalized, this is always 1. See Also -------- bessel : Filter design function using this prototype Notes ----- To find the pole locations, approximate starting points are generated [2]_ for the zeros of the ordinary Bessel polynomial [3]_, then the Aberth-Ehrlich method [4]_ [5]_ is used on the Kv(x) Bessel function to calculate more accurate zeros, and these locations are then inverted about the unit circle. References ---------- .. [1] C.R. Bond, "Bessel Filter Constants", http://www.crbond.com/papers/bsf.pdf .. [2] Campos and Calderon, "Approximate closed-form formulas for the zeros of the Bessel Polynomials", :arXiv:`1105.0957`. .. [3] Thomson, W.E., "Delay Networks having Maximally Flat Frequency Characteristics", Proceedings of the Institution of Electrical Engineers, Part III, November 1949, Vol. 96, No. 44, pp. 487-490. .. [4] Aberth, "Iteration Methods for Finding all Zeros of a Polynomial Simultaneously", Mathematics of Computation, Vol. 27, No. 122, April 1973 .. [5] Ehrlich, "A modified Newton method for polynomials", Communications of the ACM, Vol. 10, Issue 2, pp. 107-108, Feb. 1967, :DOI:`10.1145/363067.363115` .. [6] Miller and Bohn, "A Bessel Filter Crossover, and Its Relation to Others", RaneNote 147, 1998, http://www.rane.com/note147.html """ if abs(int(N)) != N: raise ValueError("Filter order must be a nonnegative integer") if N == 0: p = [] k = 1 else: # Find roots of reverse Bessel polynomial p = 1/_bessel_zeros(N) a_last = _falling_factorial(2N, N) // 2N # Shift them to a different normalization if required if norm in ('delay', 'mag'): # Normalized for group delay of 1 k = a_last if norm == 'mag': # -3 dB magnitude point is at 1 rad/sec norm_factor = _norm_factor(p, k) p /= norm_factor k = norm_factor-N a_last elif norm == 'phase': # Phase-matched (1/2 max phase shift at 1 rad/sec) # Asymptotes are same as Butterworth filter p = 10(-math.log10(a_last)/N) k = 1 else: raise ValueError('normalization not understood') return asarray([]), asarray(p, dtype=complex), float(k) def iirnotch(w0, Q): """ Design second-order IIR notch digital filter. A notch filter is a band-stop filter with a narrow bandwidth (high quality factor). It rejects a narrow frequency band and leaves the rest of the spectrum little changed. Parameters ---------- w0 : float Normalized frequency to remove from a signal. It is a scalar that must satisfy ``0 < w0 < 1``, with ``w0 = 1`` corresponding to half of the sampling frequency. Q : float Quality factor. Dimensionless parameter that characterizes notch filter -3 dB bandwidth ``bw`` relative to its center frequency, ``Q = w0/bw``. Returns ------- b, a : ndarray, ndarray Numerator (``b``) and denominator (``a``) polynomials of the IIR filter. See Also -------- iirpeak Notes ----- .. versionadded: 0.19.0 References ---------- .. [1] Sophocles J. Orfanidis, "Introduction To Signal Processing", Prentice-Hall, 1996 Examples -------- Design and plot filter to remove the 60Hz component from a signal sampled at 200Hz, using a quality factor Q = 30 >>> from scipy import signal >>> import numpy as np >>> import matplotlib.pyplot as plt >>> fs = 200.0 # Sample frequency (Hz) >>> f0 = 60.0 # Frequency to be removed from signal (Hz) >>> Q = 30.0 # Quality factor >>> w0 = f0/(fs/2) # Normalized Frequency >>> # Design notch filter >>> b, a = signal.iirnotch(w0, Q) >>> # Frequency response >>> w, h = signal.freqz(b, a) >>> # Generate frequency axis >>> freq = wfs/(2np.pi) >>> # Plot >>> fig, ax = plt.subplots(2, 1, figsize=(8, 6)) >>> ax[0].plot(freq, 20np.log10(abs(h)), color='blue') >>> ax[0].set_title("Frequency Response") >>> ax[0].set_ylabel("Amplitude (dB)", color='blue') >>> ax[0].set_xlim([0, 100]) >>> ax[0].set_ylim([-25, 10]) >>> ax[0].grid() >>> ax[1].plot(freq, np.unwrap(np.angle(h))180/np.pi, color='green') >>> ax[1].set_ylabel("Angle (degrees)", color='green') >>> ax[1].set_xlabel("Frequency (Hz)") >>> ax[1].set_xlim([0, 100]) >>> ax[1].set_yticks([-90, -60, -30, 0, 30, 60, 90]) >>> ax[1].set_ylim([-90, 90]) >>> ax[1].grid() >>> plt.show() """ return _design_notch_peak_filter(w0, Q, "notch") def iirpeak(w0, Q): """ Design second-order IIR peak (resonant) digital filter. A peak filter is a band-pass filter with a narrow bandwidth (high quality factor). It rejects components outside a narrow frequency band. Parameters ---------- w0 : float Normalized frequency to be retained in a signal. It is a scalar that must satisfy ``0 < w0 < 1``, with ``w0 = 1`` corresponding to half of the sampling frequency. Q : float Quality factor. Dimensionless parameter that characterizes peak filter -3 dB bandwidth ``bw`` relative to its center frequency, ``Q = w0/bw``. Returns ------- b, a : ndarray, ndarray Numerator (``b``) and denominator (``a``) polynomials of the IIR filter. See Also -------- iirnotch Notes ----- .. versionadded: 0.19.0 References ---------- .. [1] Sophocles J. Orfanidis, "Introduction To Signal Processing", Prentice-Hall, 1996 Examples -------- Design and plot filter to remove the frequencies other than the 300Hz component from a signal sampled at 1000Hz, using a quality factor Q = 30 >>> from scipy import signal >>> import numpy as np >>> import matplotlib.pyplot as plt >>> fs = 1000.0 # Sample frequency (Hz) >>> f0 = 300.0 # Frequency to be retained (Hz) >>> Q = 30.0 # Quality factor >>> w0 = f0/(fs/2) # Normalized Frequency >>> # Design peak filter >>> b, a = signal.iirpeak(w0, Q) >>> # Frequency response >>> w, h = signal.freqz(b, a) >>> # Generate frequency axis >>> freq = wfs/(2np.pi) >>> # Plot >>> fig, ax = plt.subplots(2, 1, figsize=(8, 6)) >>> ax[0].plot(freq, 20np.log10(abs(h)), color='blue') >>> ax[0].set_title("Frequency Response") >>> ax[0].set_ylabel("Amplitude (dB)", color='blue') >>> ax[0].set_xlim([0, 500]) >>> ax[0].set_ylim([-50, 10]) >>> ax[0].grid() >>> ax[1].plot(freq, np.unwrap(np.angle(h))180/np.pi, color='green') >>> ax[1].set_ylabel("Angle (degrees)", color='green') >>> ax[1].set_xlabel("Frequency (Hz)") >>> ax[1].set_xlim([0, 500]) >>> ax[1].set_yticks([-90, -60, -30, 0, 30, 60, 90]) >>> ax[1].set_ylim([-90, 90]) >>> ax[1].grid() >>> plt.show() """ return _design_notch_peak_filter(w0, Q, "peak") def _design_notch_peak_filter(w0, Q, ftype): """ Design notch or peak digital filter. Parameters ---------- w0 : float Normalized frequency to remove from a signal. It is a scalar that must satisfy ``0 < w0 < 1``, with ``w0 = 1`` corresponding to half of the sampling frequency. Q : float Quality factor. Dimensionless parameter that characterizes notch filter -3 dB bandwidth ``bw`` relative to its center frequency, ``Q = w0/bw``. ftype : str The type of IIR filter to design: - notch filter : ``notch`` - peak filter : ``peak`` Returns ------- b, a : ndarray, ndarray Numerator (``b``) and denominator (``a``) polynomials of the IIR filter. """ # Guarantee that the inputs are floats w0 = float(w0) Q = float(Q) # Checks if w0 is within the range if w0 > 1.0 or w0 < 0.0: raise ValueError("w0 should be such that 0 < w0 < 1") # Get bandwidth bw = w0/Q # Normalize inputs bw = bwnp.pi w0 = w0np.pi # Compute -3dB atenuation gb = 1/np.sqrt(2) if ftype == "notch": # Compute beta: formula 11.3.4 (p.575) from reference [1] beta = (np.sqrt(1.0-gb2.0)/gb)np.tan(bw/2.0) elif ftype == "peak": # Compute beta: formula 11.3.19 (p.579) from reference [1] beta = (gb/np.sqrt(1.0-gb*2.0))np.tan(bw/2.0) else: raise ValueError("Unknown ftype.") # Compute gain: formula 11.3.6 (p.575) from reference [1] gain = 1.0/(1.0+beta) # Compute numerator b and denominator a # formulas 11.3.7 (p.575) and 11.3.21 (p.579) # from reference [1] if ftype == "notch": b = gainnp.array([1.0, -2.0np.cos(w0), 1.0]) else: b = (1.0-gain)np.array([1.0, 0.0, -1.0]) a = np.array([1.0, -2.0gainnp.cos(w0), (2.0gain-1.0)]) return b, a filter_dict = {'butter': [buttap, buttord], 'butterworth': [buttap, buttord], 'cauer': [ellipap, ellipord], 'elliptic': [ellipap, ellipord], 'ellip': [ellipap, ellipord], 'bessel': [besselap], 'bessel_phase': [besselap], 'bessel_delay': [besselap], 'bessel_mag': [besselap], 'cheby1': [cheb1ap, cheb1ord], 'chebyshev1': [cheb1ap, cheb1ord], 'chebyshevi': [cheb1ap, cheb1ord], 'cheby2': [cheb2ap, cheb2ord], 'chebyshev2': [cheb2ap, cheb2ord], 'chebyshevii': [cheb2ap, cheb2ord], } band_dict = {'band': 'bandpass', 'bandpass': 'bandpass', 'pass': 'bandpass', 'bp': 'bandpass', 'bs': 'bandstop', 'bandstop': 'bandstop', 'bands': 'bandstop', 'stop': 'bandstop', 'l': 'lowpass', 'low': 'lowpass', 'lowpass': 'lowpass', 'lp': 'lowpass', 'high': 'highpass', 'highpass': 'highpass', 'h': 'highpass', 'hp': 'highpass', } bessel_norms = {'bessel': 'phase', 'bessel_phase': 'phase', 'bessel_delay': 'delay', 'bessel_mag': 'mag'}	bsd-3-clause
QudevETH/PycQED_py3	pycqed/tests/test_unit_conversions.py	1	1978	import unittest import numpy as np import matplotlib.pyplot as plt from pycqed.analysis.tools.plotting import SI_prefix_and_scale_factor from pycqed.analysis.tools.plotting import set_xlabel, set_ylabel from pycqed.analysis.tools.plotting import SI_val_to_msg_str class Test_SI_prefix_scale_factor(unittest.TestCase): def test_non_SI(self): unit = 'arb.unit.' scale_factor, post_unit = SI_prefix_and_scale_factor(val=5, unit=unit) self.assertEqual(scale_factor, 1) self.assertEqual(unit, post_unit) def test_SI_scale_factors(self): unit = 'V' scale_factor, post_unit = SI_prefix_and_scale_factor(val=5, unit=unit) self.assertEqual(scale_factor, 1) self.assertEqual(' '+unit, post_unit) scale_factor, post_unit = SI_prefix_and_scale_factor(val=5000, unit=unit) self.assertEqual(scale_factor, 1/1000) self.assertEqual('k'+unit, post_unit) scale_factor, post_unit = SI_prefix_and_scale_factor(val=0.05, unit=unit) self.assertEqual(scale_factor, 1000) self.assertEqual('m'+unit, post_unit) class test_SI_unit_aware_labels(unittest.TestCase): def test_label_scaling(self): """ This test creates a dummy plot and checks if the tick labels are rescaled correctly """ f, ax = plt.subplots() x = np.linspace(-6, 6, 101) y = np.cos(x) ax.plot(x*1000, y/1e5) set_xlabel(ax, 'Distance', 'm') set_ylabel(ax, 'Amplitude', 'V') xlab = ax.get_xlabel() ylab = ax.get_ylabel() self.assertEqual(xlab, 'Distance (km)') self.assertEqual(ylab, 'Amplitude (μV)') def test_SI_val_to_msg_str(self): val, unit = SI_val_to_msg_str(1030, 'm') self.assertEqual(val, str(1.03)) self.assertEqual(unit, 'km')	mit
ishay2b/tensorflow	tensorflow/examples/learn/wide_n_deep_tutorial.py	18	8111	# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Example code for TensorFlow Wide & Deep Tutorial using TF.Learn API.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import shutil import sys import tempfile import pandas as pd from six.moves import urllib import tensorflow as tf CSV_COLUMNS = [ "age", "workclass", "fnlwgt", "education", "education_num", "marital_status", "occupation", "relationship", "race", "gender", "capital_gain", "capital_loss", "hours_per_week", "native_country", "income_bracket" ] gender = tf.feature_column.categorical_column_with_vocabulary_list( "gender", ["Female", "Male"]) education = tf.feature_column.categorical_column_with_vocabulary_list( "education", [ "Bachelors", "HS-grad", "11th", "Masters", "9th", "Some-college", "Assoc-acdm", "Assoc-voc", "7th-8th", "Doctorate", "Prof-school", "5th-6th", "10th", "1st-4th", "Preschool", "12th" ]) marital_status = tf.feature_column.categorical_column_with_vocabulary_list( "marital_status", [ "Married-civ-spouse", "Divorced", "Married-spouse-absent", "Never-married", "Separated", "Married-AF-spouse", "Widowed" ]) relationship = tf.feature_column.categorical_column_with_vocabulary_list( "relationship", [ "Husband", "Not-in-family", "Wife", "Own-child", "Unmarried", "Other-relative" ]) workclass = tf.feature_column.categorical_column_with_vocabulary_list( "workclass", [ "Self-emp-not-inc", "Private", "State-gov", "Federal-gov", "Local-gov", "?", "Self-emp-inc", "Without-pay", "Never-worked" ]) # To show an example of hashing: occupation = tf.feature_column.categorical_column_with_hash_bucket( "occupation", hash_bucket_size=1000) native_country = tf.feature_column.categorical_column_with_hash_bucket( "native_country", hash_bucket_size=1000) # Continuous base columns. age = tf.feature_column.numeric_column("age") education_num = tf.feature_column.numeric_column("education_num") capital_gain = tf.feature_column.numeric_column("capital_gain") capital_loss = tf.feature_column.numeric_column("capital_loss") hours_per_week = tf.feature_column.numeric_column("hours_per_week") # Transformations. age_buckets = tf.feature_column.bucketized_column( age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65]) # Wide columns and deep columns. base_columns = [ gender, education, marital_status, relationship, workclass, occupation, native_country, age_buckets, ] crossed_columns = [ tf.feature_column.crossed_column( ["education", "occupation"], hash_bucket_size=1000), tf.feature_column.crossed_column( [age_buckets, "education", "occupation"], hash_bucket_size=1000), tf.feature_column.crossed_column( ["native_country", "occupation"], hash_bucket_size=1000) ] deep_columns = [ tf.feature_column.indicator_column(workclass), tf.feature_column.indicator_column(education), tf.feature_column.indicator_column(gender), tf.feature_column.indicator_column(relationship), # To show an example of embedding tf.feature_column.embedding_column(native_country, dimension=8), tf.feature_column.embedding_column(occupation, dimension=8), age, education_num, capital_gain, capital_loss, hours_per_week, ] def maybe_download(train_data, test_data): """Maybe downloads training data and returns train and test file names.""" if train_data: train_file_name = train_data else: train_file = tempfile.NamedTemporaryFile(delete=False) urllib.request.urlretrieve( "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", train_file.name) # pylint: disable=line-too-long train_file_name = train_file.name train_file.close() print("Training data is downloaded to %s" % train_file_name) if test_data: test_file_name = test_data else: test_file = tempfile.NamedTemporaryFile(delete=False) urllib.request.urlretrieve( "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test", test_file.name) # pylint: disable=line-too-long test_file_name = test_file.name test_file.close() print("Test data is downloaded to %s"% test_file_name) return train_file_name, test_file_name def build_estimator(model_dir, model_type): """Build an estimator.""" if model_type == "wide": m = tf.estimator.LinearClassifier( model_dir=model_dir, feature_columns=base_columns + crossed_columns) elif model_type == "deep": m = tf.estimator.DNNClassifier( model_dir=model_dir, feature_columns=deep_columns, hidden_units=[100, 50]) else: m = tf.estimator.DNNLinearCombinedClassifier( model_dir=model_dir, linear_feature_columns=crossed_columns, dnn_feature_columns=deep_columns, dnn_hidden_units=[100, 50]) return m def input_fn(data_file, num_epochs, shuffle): """Input builder function.""" df_data = pd.read_csv( tf.gfile.Open(data_file), names=CSV_COLUMNS, skipinitialspace=True, engine="python", skiprows=1) # remove NaN elements df_data = df_data.dropna(how="any", axis=0) labels = df_data["income_bracket"].apply(lambda x: ">50K" in x).astype(int) return tf.estimator.inputs.pandas_input_fn( x=df_data, y=labels, batch_size=100, num_epochs=num_epochs, shuffle=shuffle, num_threads=5) def train_and_eval(model_dir, model_type, train_steps, train_data, test_data): """Train and evaluate the model.""" train_file_name, test_file_name = maybe_download(train_data, test_data) # Specify file path below if want to find the output easily model_dir = tempfile.mkdtemp() if not model_dir else model_dir m = build_estimator(model_dir, model_type) # set num_epochs to None to get infinite stream of data. m.train( input_fn=input_fn(train_file_name, num_epochs=None, shuffle=True), steps=train_steps) # set steps to None to run evaluation until all data consumed. results = m.evaluate( input_fn=input_fn(test_file_name, num_epochs=1, shuffle=False), steps=None) print("model directory = %s" % model_dir) for key in sorted(results): print("%s: %s" % (key, results[key])) # Manual cleanup shutil.rmtree(model_dir) FLAGS = None def main(_): train_and_eval(FLAGS.model_dir, FLAGS.model_type, FLAGS.train_steps, FLAGS.train_data, FLAGS.test_data) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.register("type", "bool", lambda v: v.lower() == "true") parser.add_argument( "--model_dir", type=str, default="", help="Base directory for output models." ) parser.add_argument( "--model_type", type=str, default="wide_n_deep", help="Valid model types: {'wide', 'deep', 'wide_n_deep'}." ) parser.add_argument( "--train_steps", type=int, default=2000, help="Number of training steps." ) parser.add_argument( "--train_data", type=str, default="", help="Path to the training data." ) parser.add_argument( "--test_data", type=str, default="", help="Path to the test data." ) FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)	apache-2.0
mjgrav2001/scikit-learn	examples/model_selection/plot_confusion_matrix.py	244	2496	""" ================ Confusion matrix ================ Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. The diagonal elements represent the number of points for which the predicted label is equal to the true label, while off-diagonal elements are those that are mislabeled by the classifier. The higher the diagonal values of the confusion matrix the better, indicating many correct predictions. The figures show the confusion matrix with and without normalization by class support size (number of elements in each class). This kind of normalization can be interesting in case of class imbalance to have a more visual interpretation of which class is being misclassified. Here the results are not as good as they could be as our choice for the regularization parameter C was not the best. In real life applications this parameter is usually chosen using :ref:`grid_search`. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets from sklearn.cross_validation import train_test_split from sklearn.metrics import confusion_matrix # import some data to play with iris = datasets.load_iris() X = iris.data y = iris.target # Split the data into a training set and a test set X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) # Run classifier, using a model that is too regularized (C too low) to see # the impact on the results classifier = svm.SVC(kernel='linear', C=0.01) y_pred = classifier.fit(X_train, y_train).predict(X_test) def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues): plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(iris.target_names)) plt.xticks(tick_marks, iris.target_names, rotation=45) plt.yticks(tick_marks, iris.target_names) plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') # Compute confusion matrix cm = confusion_matrix(y_test, y_pred) np.set_printoptions(precision=2) print('Confusion matrix, without normalization') print(cm) plt.figure() plot_confusion_matrix(cm) # Normalize the confusion matrix by row (i.e by the number of samples # in each class) cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print('Normalized confusion matrix') print(cm_normalized) plt.figure() plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix') plt.show()	bsd-3-clause
valexandersaulys/prudential_insurance_kaggle	venv/lib/python2.7/site-packages/sklearn/mixture/tests/test_gmm.py	48	17414	import unittest import copy import sys from nose.tools import assert_true import numpy as np from numpy.testing import (assert_array_equal, assert_array_almost_equal, assert_raises) from scipy import stats from sklearn import mixture from sklearn.datasets.samples_generator import make_spd_matrix from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_raise_message from sklearn.metrics.cluster import adjusted_rand_score from sklearn.externals.six.moves import cStringIO as StringIO rng = np.random.RandomState(0) def test_sample_gaussian(): # Test sample generation from mixture.sample_gaussian where covariance # is diagonal, spherical and full n_features, n_samples = 2, 300 axis = 1 mu = rng.randint(10) * rng.rand(n_features) cv = (rng.rand(n_features) + 1.0) 2 samples = mixture.sample_gaussian( mu, cv, covariance_type='diag', n_samples=n_samples) assert_true(np.allclose(samples.mean(axis), mu, atol=1.3)) assert_true(np.allclose(samples.var(axis), cv, atol=1.5)) # the same for spherical covariances cv = (rng.rand() + 1.0) 2 samples = mixture.sample_gaussian( mu, cv, covariance_type='spherical', n_samples=n_samples) assert_true(np.allclose(samples.mean(axis), mu, atol=1.5)) assert_true(np.allclose( samples.var(axis), np.repeat(cv, n_features), atol=1.5)) # and for full covariances A = rng.randn(n_features, n_features) cv = np.dot(A.T, A) + np.eye(n_features) samples = mixture.sample_gaussian( mu, cv, covariance_type='full', n_samples=n_samples) assert_true(np.allclose(samples.mean(axis), mu, atol=1.3)) assert_true(np.allclose(np.cov(samples), cv, atol=2.5)) # Numerical stability check: in SciPy 0.12.0 at least, eigh may return # tiny negative values in its second return value. from sklearn.mixture import sample_gaussian x = sample_gaussian([0, 0], [[4, 3], [1, .1]], covariance_type='full', random_state=42) assert_true(np.isfinite(x).all()) def _naive_lmvnpdf_diag(X, mu, cv): # slow and naive implementation of lmvnpdf ref = np.empty((len(X), len(mu))) stds = np.sqrt(cv) for i, (m, std) in enumerate(zip(mu, stds)): ref[:, i] = np.log(stats.norm.pdf(X, m, std)).sum(axis=1) return ref def test_lmvnpdf_diag(): # test a slow and naive implementation of lmvnpdf and # compare it to the vectorized version (mixture.lmvnpdf) to test # for correctness n_features, n_components, n_samples = 2, 3, 10 mu = rng.randint(10) * rng.rand(n_components, n_features) cv = (rng.rand(n_components, n_features) + 1.0) ** 2 X = rng.randint(10) * rng.rand(n_samples, n_features) ref = _naive_lmvnpdf_diag(X, mu, cv) lpr = mixture.log_multivariate_normal_density(X, mu, cv, 'diag') assert_array_almost_equal(lpr, ref) def test_lmvnpdf_spherical(): n_features, n_components, n_samples = 2, 3, 10 mu = rng.randint(10) * rng.rand(n_components, n_features) spherecv = rng.rand(n_components, 1) ** 2 + 1 X = rng.randint(10) * rng.rand(n_samples, n_features) cv = np.tile(spherecv, (n_features, 1)) reference = _naive_lmvnpdf_diag(X, mu, cv) lpr = mixture.log_multivariate_normal_density(X, mu, spherecv, 'spherical') assert_array_almost_equal(lpr, reference) def test_lmvnpdf_full(): n_features, n_components, n_samples = 2, 3, 10 mu = rng.randint(10) * rng.rand(n_components, n_features) cv = (rng.rand(n_components, n_features) + 1.0) ** 2 X = rng.randint(10) * rng.rand(n_samples, n_features) fullcv = np.array([np.diag(x) for x in cv]) reference = _naive_lmvnpdf_diag(X, mu, cv) lpr = mixture.log_multivariate_normal_density(X, mu, fullcv, 'full') assert_array_almost_equal(lpr, reference) def test_lvmpdf_full_cv_non_positive_definite(): n_features, n_samples = 2, 10 rng = np.random.RandomState(0) X = rng.randint(10) * rng.rand(n_samples, n_features) mu = np.mean(X, 0) cv = np.array([[[-1, 0], [0, 1]]]) expected_message = "'covars' must be symmetric, positive-definite" assert_raise_message(ValueError, expected_message, mixture.log_multivariate_normal_density, X, mu, cv, 'full') def test_GMM_attributes(): n_components, n_features = 10, 4 covariance_type = 'diag' g = mixture.GMM(n_components, covariance_type, random_state=rng) weights = rng.rand(n_components) weights = weights / weights.sum() means = rng.randint(-20, 20, (n_components, n_features)) assert_true(g.n_components == n_components) assert_true(g.covariance_type == covariance_type) g.weights_ = weights assert_array_almost_equal(g.weights_, weights) g.means_ = means assert_array_almost_equal(g.means_, means) covars = (0.1 + 2 * rng.rand(n_components, n_features)) ** 2 g.covars_ = covars assert_array_almost_equal(g.covars_, covars) assert_raises(ValueError, g._set_covars, []) assert_raises(ValueError, g._set_covars, np.zeros((n_components - 2, n_features))) assert_raises(ValueError, mixture.GMM, n_components=20, covariance_type='badcovariance_type') class GMMTester(): do_test_eval = True def _setUp(self): self.n_components = 10 self.n_features = 4 self.weights = rng.rand(self.n_components) self.weights = self.weights / self.weights.sum() self.means = rng.randint(-20, 20, (self.n_components, self.n_features)) self.threshold = -0.5 self.I = np.eye(self.n_features) self.covars = { 'spherical': (0.1 + 2 * rng.rand(self.n_components, self.n_features)) ** 2, 'tied': (make_spd_matrix(self.n_features, random_state=0) + 5 * self.I), 'diag': (0.1 + 2 * rng.rand(self.n_components, self.n_features)) ** 2, 'full': np.array([make_spd_matrix(self.n_features, random_state=0) + 5 * self.I for x in range(self.n_components)])} def test_eval(self): if not self.do_test_eval: return # DPGMM does not support setting the means and # covariances before fitting There is no way of fixing this # due to the variational parameters being more expressive than # covariance matrices g = self.model(n_components=self.n_components, covariance_type=self.covariance_type, random_state=rng) # Make sure the means are far apart so responsibilities.argmax() # picks the actual component used to generate the observations. g.means_ = 20 * self.means g.covars_ = self.covars[self.covariance_type] g.weights_ = self.weights gaussidx = np.repeat(np.arange(self.n_components), 5) n_samples = len(gaussidx) X = rng.randn(n_samples, self.n_features) + g.means_[gaussidx] ll, responsibilities = g.score_samples(X) self.assertEqual(len(ll), n_samples) self.assertEqual(responsibilities.shape, (n_samples, self.n_components)) assert_array_almost_equal(responsibilities.sum(axis=1), np.ones(n_samples)) assert_array_equal(responsibilities.argmax(axis=1), gaussidx) def test_sample(self, n=100): g = self.model(n_components=self.n_components, covariance_type=self.covariance_type, random_state=rng) # Make sure the means are far apart so responsibilities.argmax() # picks the actual component used to generate the observations. g.means_ = 20 * self.means g.covars_ = np.maximum(self.covars[self.covariance_type], 0.1) g.weights_ = self.weights samples = g.sample(n) self.assertEqual(samples.shape, (n, self.n_features)) def test_train(self, params='wmc'): g = mixture.GMM(n_components=self.n_components, covariance_type=self.covariance_type) g.weights_ = self.weights g.means_ = self.means g.covars_ = 20 * self.covars[self.covariance_type] # Create a training set by sampling from the predefined distribution. X = g.sample(n_samples=100) g = self.model(n_components=self.n_components, covariance_type=self.covariance_type, random_state=rng, min_covar=1e-1, n_iter=1, init_params=params) g.fit(X) # Do one training iteration at a time so we can keep track of # the log likelihood to make sure that it increases after each # iteration. trainll = [] for _ in range(5): g.params = params g.init_params = '' g.fit(X) trainll.append(self.score(g, X)) g.n_iter = 10 g.init_params = '' g.params = params g.fit(X) # finish fitting # Note that the log likelihood will sometimes decrease by a # very small amount after it has more or less converged due to # the addition of min_covar to the covariance (to prevent # underflow). This is why the threshold is set to -0.5 # instead of 0. delta_min = np.diff(trainll).min() self.assertTrue( delta_min > self.threshold, "The min nll increase is %f which is lower than the admissible" " threshold of %f, for model %s. The likelihoods are %s." % (delta_min, self.threshold, self.covariance_type, trainll)) def test_train_degenerate(self, params='wmc'): # Train on degenerate data with 0 in some dimensions # Create a training set by sampling from the predefined distribution. X = rng.randn(100, self.n_features) X.T[1:] = 0 g = self.model(n_components=2, covariance_type=self.covariance_type, random_state=rng, min_covar=1e-3, n_iter=5, init_params=params) g.fit(X) trainll = g.score(X) self.assertTrue(np.sum(np.abs(trainll / 100 / X.shape[1])) < 5) def test_train_1d(self, params='wmc'): # Train on 1-D data # Create a training set by sampling from the predefined distribution. X = rng.randn(100, 1) # X.T[1:] = 0 g = self.model(n_components=2, covariance_type=self.covariance_type, random_state=rng, min_covar=1e-7, n_iter=5, init_params=params) g.fit(X) trainll = g.score(X) if isinstance(g, mixture.DPGMM): self.assertTrue(np.sum(np.abs(trainll / 100)) < 5) else: self.assertTrue(np.sum(np.abs(trainll / 100)) < 2) def score(self, g, X): return g.score(X).sum() class TestGMMWithSphericalCovars(unittest.TestCase, GMMTester): covariance_type = 'spherical' model = mixture.GMM setUp = GMMTester._setUp class TestGMMWithDiagonalCovars(unittest.TestCase, GMMTester): covariance_type = 'diag' model = mixture.GMM setUp = GMMTester._setUp class TestGMMWithTiedCovars(unittest.TestCase, GMMTester): covariance_type = 'tied' model = mixture.GMM setUp = GMMTester._setUp class TestGMMWithFullCovars(unittest.TestCase, GMMTester): covariance_type = 'full' model = mixture.GMM setUp = GMMTester._setUp def test_multiple_init(): # Test that multiple inits does not much worse than a single one X = rng.randn(30, 5) X[:10] += 2 g = mixture.GMM(n_components=2, covariance_type='spherical', random_state=rng, min_covar=1e-7, n_iter=5) train1 = g.fit(X).score(X).sum() g.n_init = 5 train2 = g.fit(X).score(X).sum() assert_true(train2 >= train1 - 1.e-2) def test_n_parameters(): # Test that the right number of parameters is estimated n_samples, n_dim, n_components = 7, 5, 2 X = rng.randn(n_samples, n_dim) n_params = {'spherical': 13, 'diag': 21, 'tied': 26, 'full': 41} for cv_type in ['full', 'tied', 'diag', 'spherical']: g = mixture.GMM(n_components=n_components, covariance_type=cv_type, random_state=rng, min_covar=1e-7, n_iter=1) g.fit(X) assert_true(g._n_parameters() == n_params[cv_type]) def test_1d_1component(): # Test all of the covariance_types return the same BIC score for # 1-dimensional, 1 component fits. n_samples, n_dim, n_components = 100, 1, 1 X = rng.randn(n_samples, n_dim) g_full = mixture.GMM(n_components=n_components, covariance_type='full', random_state=rng, min_covar=1e-7, n_iter=1) g_full.fit(X) g_full_bic = g_full.bic(X) for cv_type in ['tied', 'diag', 'spherical']: g = mixture.GMM(n_components=n_components, covariance_type=cv_type, random_state=rng, min_covar=1e-7, n_iter=1) g.fit(X) assert_array_almost_equal(g.bic(X), g_full_bic) def assert_fit_predict_correct(model, X): model2 = copy.deepcopy(model) predictions_1 = model.fit(X).predict(X) predictions_2 = model2.fit_predict(X) assert adjusted_rand_score(predictions_1, predictions_2) == 1.0 def test_fit_predict(): """ test that gmm.fit_predict is equivalent to gmm.fit + gmm.predict """ lrng = np.random.RandomState(101) n_samples, n_dim, n_comps = 100, 2, 2 mu = np.array([[8, 8]]) component_0 = lrng.randn(n_samples, n_dim) component_1 = lrng.randn(n_samples, n_dim) + mu X = np.vstack((component_0, component_1)) for m_constructor in (mixture.GMM, mixture.VBGMM, mixture.DPGMM): model = m_constructor(n_components=n_comps, covariance_type='full', min_covar=1e-7, n_iter=5, random_state=np.random.RandomState(0)) assert_fit_predict_correct(model, X) model = mixture.GMM(n_components=n_comps, n_iter=0) z = model.fit_predict(X) assert np.all(z == 0), "Quick Initialization Failed!" def test_aic(): # Test the aic and bic criteria n_samples, n_dim, n_components = 50, 3, 2 X = rng.randn(n_samples, n_dim) SGH = 0.5 * (X.var() + np.log(2 * np.pi)) # standard gaussian entropy for cv_type in ['full', 'tied', 'diag', 'spherical']: g = mixture.GMM(n_components=n_components, covariance_type=cv_type, random_state=rng, min_covar=1e-7) g.fit(X) aic = 2 * n_samples * SGH * n_dim + 2 * g._n_parameters() bic = (2 * n_samples * SGH * n_dim + np.log(n_samples) * g._n_parameters()) bound = n_dim * 3. / np.sqrt(n_samples) assert_true(np.abs(g.aic(X) - aic) / n_samples < bound) assert_true(np.abs(g.bic(X) - bic) / n_samples < bound) def check_positive_definite_covars(covariance_type): r"""Test that covariance matrices do not become non positive definite Due to the accumulation of round-off errors, the computation of the covariance matrices during the learning phase could lead to non-positive definite covariance matrices. Namely the use of the formula: .. math:: C = (\sum_i w_i x_i x_i^T) - \mu \mu^T instead of: .. math:: C = \sum_i w_i (x_i - \mu)(x_i - \mu)^T while mathematically equivalent, was observed a ``LinAlgError`` exception, when computing a ``GMM`` with full covariance matrices and fixed mean. This function ensures that some later optimization will not introduce the problem again. """ rng = np.random.RandomState(1) # we build a dataset with 2 2d component. The components are unbalanced # (respective weights 0.9 and 0.1) X = rng.randn(100, 2) X[-10:] += (3, 3) # Shift the 10 last points gmm = mixture.GMM(2, params="wc", covariance_type=covariance_type, min_covar=1e-3) # This is a non-regression test for issue #2640. The following call used # to trigger: # numpy.linalg.linalg.LinAlgError: 2-th leading minor not positive definite gmm.fit(X) if covariance_type == "diag" or covariance_type == "spherical": assert_greater(gmm.covars_.min(), 0) else: if covariance_type == "tied": covs = [gmm.covars_] else: covs = gmm.covars_ for c in covs: assert_greater(np.linalg.det(c), 0) def test_positive_definite_covars(): # Check positive definiteness for all covariance types for covariance_type in ["full", "tied", "diag", "spherical"]: yield check_positive_definite_covars, covariance_type def test_verbose_first_level(): # Create sample data X = rng.randn(30, 5) X[:10] += 2 g = mixture.GMM(n_components=2, n_init=2, verbose=1) old_stdout = sys.stdout sys.stdout = StringIO() try: g.fit(X) finally: sys.stdout = old_stdout def test_verbose_second_level(): # Create sample data X = rng.randn(30, 5) X[:10] += 2 g = mixture.GMM(n_components=2, n_init=2, verbose=2) old_stdout = sys.stdout sys.stdout = StringIO() try: g.fit(X) finally: sys.stdout = old_stdout	gpl-2.0
unnati-xyz/droidcon-twitter-analytics	geeksrus/utils/dbconn.py	1	2020	from pymongo import MongoClient import pandas as pd def connect_mongo(db, host='localhost', port=27017, username=None, password=None): """ A util for making a connection to mongo """ if username and password: mongo_uri = 'mongodb://%s:%s@%s:%s/%s' % (username, password, host, port, db) conn = MongoClient(mongo_uri) else: conn = MongoClient(host, port) return conn[db] def read_mongo(db_conn, collection, query={}, no_id=True): """ Read from Mongo and Store into DataFrame """ # Make a query to the specific DB and Collection print(query) cursor = db_conn[collection].find(query) # Expand the cursor and construct the DataFrame df = pd.DataFrame(list(cursor)) # Delete the _id if no_id: del df['_id'] return df def read_mongo_projection(db_conn, collection, query={}, no_id=True): """ Read from Mongo and Store into DataFrame """ # Make a query to the specific DB and Collection cursor = db_conn[collection].find(projection=query) # Expand the cursor and construct the DataFrame df = pd.DataFrame(list(cursor)) # Delete the _id if no_id: del df['_id'] return df def write_mongo(db_conn, collection, document): result = db_conn[collection].insert(document) def find_and_sort_desc(db_conn, collection, field, no_id = True): # Make a query to the specific DB and Collection cursor = db_conn[collection].find().sort(field, -1) # Expand the cursor and construct the DataFrame df = pd.DataFrame(list(cursor)) # Delete the _id if no_id: del df['_id'] return df def find_with_project_and_sort(db_conn, collection, field,query={}, no_id = True): # Make a query to the specific DB and Collection cursor = db_conn[collection].find(projection=query).sort(field, -1) # Expand the cursor and construct the DataFrame df = pd.DataFrame(list(cursor)) # Delete the _id if no_id: del df['_id'] return df	mit
IndraVikas/scikit-learn	examples/calibration/plot_calibration_curve.py	225	5903	""" ============================== Probability Calibration curves ============================== When performing classification one often wants to predict not only the class label, but also the associated probability. This probability gives some kind of confidence on the prediction. This example demonstrates how to display how well calibrated the predicted probabilities are and how to calibrate an uncalibrated classifier. The experiment is performed on an artificial dataset for binary classification with 100.000 samples (1.000 of them are used for model fitting) with 20 features. Of the 20 features, only 2 are informative and 10 are redundant. The first figure shows the estimated probabilities obtained with logistic regression, Gaussian naive Bayes, and Gaussian naive Bayes with both isotonic calibration and sigmoid calibration. The calibration performance is evaluated with Brier score, reported in the legend (the smaller the better). One can observe here that logistic regression is well calibrated while raw Gaussian naive Bayes performs very badly. This is because of the redundant features which violate the assumption of feature-independence and result in an overly confident classifier, which is indicated by the typical transposed-sigmoid curve. Calibration of the probabilities of Gaussian naive Bayes with isotonic regression can fix this issue as can be seen from the nearly diagonal calibration curve. Sigmoid calibration also improves the brier score slightly, albeit not as strongly as the non-parametric isotonic regression. This can be attributed to the fact that we have plenty of calibration data such that the greater flexibility of the non-parametric model can be exploited. The second figure shows the calibration curve of a linear support-vector classifier (LinearSVC). LinearSVC shows the opposite behavior as Gaussian naive Bayes: the calibration curve has a sigmoid curve, which is typical for an under-confident classifier. In the case of LinearSVC, this is caused by the margin property of the hinge loss, which lets the model focus on hard samples that are close to the decision boundary (the support vectors). Both kinds of calibration can fix this issue and yield nearly identical results. This shows that sigmoid calibration can deal with situations where the calibration curve of the base classifier is sigmoid (e.g., for LinearSVC) but not where it is transposed-sigmoid (e.g., Gaussian naive Bayes). """ print(__doc__) # Author: Alexandre Gramfort <[email protected]> # Jan Hendrik Metzen <[email protected]> # License: BSD Style. import matplotlib.pyplot as plt from sklearn import datasets from sklearn.naive_bayes import GaussianNB from sklearn.svm import LinearSVC from sklearn.linear_model import LogisticRegression from sklearn.metrics import (brier_score_loss, precision_score, recall_score, f1_score) from sklearn.calibration import CalibratedClassifierCV, calibration_curve from sklearn.cross_validation import train_test_split # Create dataset of classification task with many redundant and few # informative features X, y = datasets.make_classification(n_samples=100000, n_features=20, n_informative=2, n_redundant=10, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.99, random_state=42) def plot_calibration_curve(est, name, fig_index): """Plot calibration curve for est w/o and with calibration. """ # Calibrated with isotonic calibration isotonic = CalibratedClassifierCV(est, cv=2, method='isotonic') # Calibrated with sigmoid calibration sigmoid = CalibratedClassifierCV(est, cv=2, method='sigmoid') # Logistic regression with no calibration as baseline lr = LogisticRegression(C=1., solver='lbfgs') fig = plt.figure(fig_index, figsize=(10, 10)) ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2) ax2 = plt.subplot2grid((3, 1), (2, 0)) ax1.plot([0, 1], [0, 1], "k:", label="Perfectly calibrated") for clf, name in [(lr, 'Logistic'), (est, name), (isotonic, name + ' + Isotonic'), (sigmoid, name + ' + Sigmoid')]: clf.fit(X_train, y_train) y_pred = clf.predict(X_test) if hasattr(clf, "predict_proba"): prob_pos = clf.predict_proba(X_test)[:, 1] else: # use decision function prob_pos = clf.decision_function(X_test) prob_pos = \ (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min()) clf_score = brier_score_loss(y_test, prob_pos, pos_label=y.max()) print("%s:" % name) print("\tBrier: %1.3f" % (clf_score)) print("\tPrecision: %1.3f" % precision_score(y_test, y_pred)) print("\tRecall: %1.3f" % recall_score(y_test, y_pred)) print("\tF1: %1.3f\n" % f1_score(y_test, y_pred)) fraction_of_positives, mean_predicted_value = \ calibration_curve(y_test, prob_pos, n_bins=10) ax1.plot(mean_predicted_value, fraction_of_positives, "s-", label="%s (%1.3f)" % (name, clf_score)) ax2.hist(prob_pos, range=(0, 1), bins=10, label=name, histtype="step", lw=2) ax1.set_ylabel("Fraction of positives") ax1.set_ylim([-0.05, 1.05]) ax1.legend(loc="lower right") ax1.set_title('Calibration plots (reliability curve)') ax2.set_xlabel("Mean predicted value") ax2.set_ylabel("Count") ax2.legend(loc="upper center", ncol=2) plt.tight_layout() # Plot calibration cuve for Gaussian Naive Bayes plot_calibration_curve(GaussianNB(), "Naive Bayes", 1) # Plot calibration cuve for Linear SVC plot_calibration_curve(LinearSVC(), "SVC", 2) plt.show()	bsd-3-clause
CalebBell/thermo	tests/test_interaction_parameters.py	1	3433	# -- coding: utf-8 -- '''Chemical Engineering Design Library (ChEDL). Utilities for process modeling. Copyright (C) 2017 Caleb Bell <[email protected]> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.''' from math import exp, log import pytest import numpy as np import pandas as pd from fluids.constants import calorie, R from thermo.interaction_parameters import IPDB from fluids.numerics import assert_close, assert_close1d, assert_close2d def run_validate_db(): from thermo.interaction_parameters import ip_files for name in ip_files.keys(): IPDB.validate_table(name) def test_basic_chemsep_PR(): kij = IPDB.get_ip_specific('ChemSep PR', ['124-38-9', '67-56-1'], 'kij') assert_close(kij, 0.0583) kij_auto = IPDB.get_ip_automatic(['124-38-9', '67-56-1'], 'PR kij', 'kij') assert_close(kij, kij_auto) kij_missing = IPDB.get_ip_specific('ChemSep PR', ['1249-38-9', '67-56-1'], 'kij') assert kij_missing == 0 assert False == IPDB.has_ip_specific('ChemSep PR', ['1249-38-9', '67-56-1'], 'kij') assert True == IPDB.has_ip_specific('ChemSep PR', ['124-38-9', '67-56-1'], 'kij') assert IPDB.get_tables_with_type('PR kij') == ['ChemSep PR'] # interaction parameter matrix kij_C1C4 = IPDB.get_ip_symmetric_matrix('ChemSep PR', ['74-82-8', '74-84-0', '74-98-6', '106-97-8'], 'kij') kij_C1C4_known = [[0.0, -0.0059, 0.0119, 0.0185], [-0.0059, 0.0, 0.0011, 0.0089], [0.0119, 0.0011, 0.0, 0.0033], [0.0185, 0.0089, 0.0033, 0.0]] assert_close2d(kij_C1C4, kij_C1C4_known) # Test for asymetric works the same since the model is asymmetric kij_C1C4 = IPDB.get_ip_symmetric_matrix('ChemSep PR', ['74-82-8', '74-84-0', '74-98-6', '106-97-8'], 'kij') assert_close2d(kij_C1C4, kij_C1C4_known) def test_basic_chemsep_NRTL(): # ethanol water, converted to metric, simple T dependence bijs = IPDB.get_ip_asymmetric_matrix('ChemSep NRTL', ['64-17-5', '7732-18-5'], 'bij') alphas_known = [[0.0, 0.2937, 0.3009], [0.2937, 0.0, 0.2999], [0.3009, 0.2999, 0.0]] # Test is works both symmetric and asymmetric alphas = IPDB.get_ip_asymmetric_matrix('ChemSep NRTL', ['64-17-5', '7732-18-5', '67-56-1'], 'alphaij') assert_close2d(alphas, alphas_known) alphas = IPDB.get_ip_symmetric_matrix('ChemSep NRTL', ['64-17-5', '7732-18-5', '67-56-1'], 'alphaij') assert_close2d(alphas, alphas_known)	mit
antoinecarme/pyaf	tests/model_control/test_ozone_custom_models_enabled.py	1	1923	import pandas as pd import numpy as np import pyaf.ForecastEngine as autof import pyaf.Bench.TS_datasets as tsds #get_ipython().magic('matplotlib inline') def pickleModel(iModel): import pickle output = pickle.dumps(iModel) lReloadedObject = pickle.loads(output) output2 = pickle.dumps(lReloadedObject) assert(iModel.to_json() == lReloadedObject.to_json()) return lReloadedObject; def build_model(transformations, trends, periodics, autoregs): b1 = tsds.load_ozone_exogenous() df = b1.mPastData lEngine = autof.cForecastEngine() lEngine H = b1.mHorizon; # lEngine.mOptions.enable_slow_mode(); # lEngine.mOptions.mDebugPerformance = True; lEngine.mOptions.set_active_transformations(transformations); lEngine.mOptions.set_active_trends(trends); lEngine.mOptions.set_active_periodics(periodics); lEngine.mOptions.set_active_autoregressions(autoregs); lExogenousData = (b1.mExogenousDataFrame , b1.mExogenousVariables) lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H); lEngine.getModelInfo(); print(lEngine.mSignalDecomposition.mTrPerfDetails.head()); lEngine2 = pickleModel(lEngine) lEngine2.mSignalDecomposition.mBestModel.mTimeInfo.mResolution lEngine2.standardPlots("outputs/my_ozone"); dfapp_in = df.copy(); dfapp_in.tail() #H = 12 dfapp_out = lEngine2.forecast(dfapp_in, H); #dfapp_out.to_csv("outputs/ozone_apply_out.csv") dfapp_out.tail(2 * H) print("Forecast Columns " , dfapp_out.columns); Forecast_DF = dfapp_out[[b1.mTimeVar , b1.mSignalVar, b1.mSignalVar + '_Forecast']] print(Forecast_DF.info()) print("Forecasts\n" , Forecast_DF.tail(H)); print("\n\n<ModelInfo>") print(lEngine2.to_json()); print("</ModelInfo>\n\n") print("\n\n<Forecast>") print(Forecast_DF.tail(2*H).to_json(date_format='iso')) print("</Forecast>\n\n")	bsd-3-clause
hurricane42/data	pew-religions/Religion-Leah.py	37	3271	#!/usr/bin/env python import numpy as np import pandas as pd religions = ['Buddhist', 'Catholic', 'Evangel Prot', 'Hindu', 'Hist Black Prot', 'Jehovahs Witness', 'Jewish', 'Mainline Prot', 'Mormon', 'Muslim', 'Orthodox Christian', 'Unaffiliated'] csv = open("current.csv", 'w') csv.truncate() def write_row(matrix): arr = np.asarray(matrix[0])[0] row = ','.join([str(a) for a in arr]) + '\n' csv.write(row) # Intitial distribution of religions in US first = np.matrix([.007, .208, .254, .007, .065, .008, .019, .147, .016, .009, .005, .228]) # Normed to sum to 100% current = first / np.sum(first) t0 = current write_row(current) # Transition matrix trans = np.matrix(((0.390296314, 0.027141947, 0.06791021, 0.001857564, 0, 0, 0.011166082, 0.059762879, 0, 0, 0, 0.396569533), (0.005370791, 0.593173325, 0.103151608, 0.000649759, 0.010486747, 0.005563864, 0.002041424, 0.053825329, 0.004760476, 0.001130529, 0.000884429, 0.199488989), (0.00371836, 0.023900817, 0.650773331, 0.000250102, 0.016774503, 0.003098214, 0.001865491, 0.122807467, 0.004203107, 0.000186572, 0.002123778, 0.151866648), (0, 0, 0.0033732, 0.804072618, 0, 0.001511151, 0, 0.01234639, 0, 0.00209748, 0, 0.17659916), (0.002051357, 0.016851659, 0.09549708, 0, 0.699214315, 0.010620473, 0.000338804, 0.024372871, 0.000637016, 0.009406884, 0.000116843, 0.129892558), (0, 0.023278276, 0.109573979, 0, 0.077957568, 0.336280578, 0, 0.074844833, 0.007624035, 0, 0, 0.35110361), (0.006783201, 0.004082693, 0.014329604, 0, 0, 0.000610585, 0.745731278, 0.009587587, 0, 0, 0.002512334, 0.184058682), (0.005770357, 0.038017215, 0.187857555, 0.000467601, 0.008144075, 0.004763516, 0.003601208, 0.451798506, 0.005753587, 0.000965543, 0.00109818, 0.25750798), (0.007263135, 0.01684885, 0.06319935, 0.000248467, 0.0059394, 0, 0.001649896, 0.03464334, 0.642777489, 0.002606278, 0, 0.208904711), (0, 0.005890381, 0.023573308, 0, 0.011510643, 0, 0.005518343, 0.014032084, 0, 0.772783807, 0, 0.15424369), (0.004580353, 0.042045841, 0.089264134 , 0, 0.00527346, 0, 0, 0.061471387, 0.005979218, 0.009113978, 0.526728084, 0.243246723), (0.006438308, 0.044866331, 0.1928814, 0.002035375, 0.04295005, 0.010833621, 0.011541439, 0.09457963, 0.01365141, 0.005884336, 0.002892072, 0.525359211))) # Fertility array fert = np.matrix(((2.1, 2.3, 2.3, 2.1, 2.5, 2.1, 2, 1.9, 3.4, 2.8, 2.1, 1.7))) # Create data frame for printing later religionDataFrame = pd.DataFrame() for x in range(0,100): ### beginning of conversion step # apply transition matrix to current distribution current = current * trans ### beginning of fertility step # divide by two for couple number current = current/2 # adjust by fertility current = np.multiply(fert, current) # normalize to 100% current = current / np.sum(current) write_row(current) # add to data frame religionDataFrame = religionDataFrame.append(pd.DataFrame(current), ignore_index=True) csv.close() religionDataFrame.columns = religions religionDataFrame.to_csv("current_pandas_save.csv")	mit
oesteban/seaborn	seaborn/rcmod.py	4	16004	"""Functions that alter the matplotlib rc dictionary on the fly.""" from distutils.version import LooseVersion import functools import numpy as np import matplotlib as mpl from . import palettes mpl_ge_150 = LooseVersion(mpl.__version__) >= '1.5.0' _style_keys = ( "axes.facecolor", "axes.edgecolor", "axes.grid", "axes.axisbelow", "axes.linewidth", "axes.labelcolor", "figure.facecolor", "grid.color", "grid.linestyle", "text.color", "xtick.color", "ytick.color", "xtick.direction", "ytick.direction", "xtick.major.size", "ytick.major.size", "xtick.minor.size", "ytick.minor.size", "legend.frameon", "legend.numpoints", "legend.scatterpoints", "lines.solid_capstyle", "image.cmap", "font.family", "font.sans-serif", ) _context_keys = ( "figure.figsize", "font.size", "axes.labelsize", "axes.titlesize", "xtick.labelsize", "ytick.labelsize", "legend.fontsize", "grid.linewidth", "lines.linewidth", "patch.linewidth", "lines.markersize", "lines.markeredgewidth", "xtick.major.width", "ytick.major.width", "xtick.minor.width", "ytick.minor.width", "xtick.major.pad", "ytick.major.pad" ) def set(context="notebook", style="darkgrid", palette="deep", font="sans-serif", font_scale=1, color_codes=False, rc=None): """Set aesthetic parameters in one step. Each set of parameters can be set directly or temporarily, see the referenced functions below for more information. Parameters ---------- context : string or dict Plotting context parameters, see :func:`plotting_context` style : string or dict Axes style parameters, see :func:`axes_style` palette : string or sequence Color palette, see :func:`color_palette` font : string Font family, see matplotlib font manager. font_scale : float, optional Separate scaling factor to independently scale the size of the font elements. color_codes : bool If ``True`` and ``palette`` is a seaborn palette, remap the shorthand color codes (e.g. "b", "g", "r", etc.) to the colors from this palette. rc : dict or None Dictionary of rc parameter mappings to override the above. """ set_context(context, font_scale) set_style(style, rc={"font.family": font}) set_palette(palette, color_codes=color_codes) if rc is not None: mpl.rcParams.update(rc) def reset_defaults(): """Restore all RC params to default settings.""" mpl.rcParams.update(mpl.rcParamsDefault) def reset_orig(): """Restore all RC params to original settings (respects custom rc).""" mpl.rcParams.update(mpl.rcParamsOrig) def axes_style(style=None, rc=None): """Return a parameter dict for the aesthetic style of the plots. This affects things like the color of the axes, whether a grid is enabled by default, and other aesthetic elements. This function returns an object that can be used in a ``with`` statement to temporarily change the style parameters. Parameters ---------- style : dict, None, or one of {darkgrid, whitegrid, dark, white, ticks} A dictionary of parameters or the name of a preconfigured set. rc : dict, optional Parameter mappings to override the values in the preset seaborn style dictionaries. This only updates parameters that are considered part of the style definition. Examples -------- >>> st = axes_style("whitegrid") >>> set_style("ticks", {"xtick.major.size": 8, "ytick.major.size": 8}) >>> import matplotlib.pyplot as plt >>> with axes_style("white"): ... f, ax = plt.subplots() ... ax.plot(x, y) # doctest: +SKIP See Also -------- set_style : set the matplotlib parameters for a seaborn theme plotting_context : return a parameter dict to to scale plot elements color_palette : define the color palette for a plot """ if style is None: style_dict = {k: mpl.rcParams[k] for k in _style_keys} elif isinstance(style, dict): style_dict = style else: styles = ["white", "dark", "whitegrid", "darkgrid", "ticks"] if style not in styles: raise ValueError("style must be one of %s" % ", ".join(styles)) # Define colors here dark_gray = ".15" light_gray = ".8" # Common parameters style_dict = { "figure.facecolor": "white", "text.color": dark_gray, "axes.labelcolor": dark_gray, "legend.frameon": False, "legend.numpoints": 1, "legend.scatterpoints": 1, "xtick.direction": "out", "ytick.direction": "out", "xtick.color": dark_gray, "ytick.color": dark_gray, "axes.axisbelow": True, "image.cmap": "Greys", "font.family": ["sans-serif"], "font.sans-serif": ["Arial", "Liberation Sans", "Bitstream Vera Sans", "sans-serif"], "grid.linestyle": "-", "lines.solid_capstyle": "round", } # Set grid on or off if "grid" in style: style_dict.update({ "axes.grid": True, }) else: style_dict.update({ "axes.grid": False, }) # Set the color of the background, spines, and grids if style.startswith("dark"): style_dict.update({ "axes.facecolor": "#EAEAF2", "axes.edgecolor": "white", "axes.linewidth": 0, "grid.color": "white", }) elif style == "whitegrid": style_dict.update({ "axes.facecolor": "white", "axes.edgecolor": light_gray, "axes.linewidth": 1, "grid.color": light_gray, }) elif style in ["white", "ticks"]: style_dict.update({ "axes.facecolor": "white", "axes.edgecolor": dark_gray, "axes.linewidth": 1.25, "grid.color": light_gray, }) # Show or hide the axes ticks if style == "ticks": style_dict.update({ "xtick.major.size": 6, "ytick.major.size": 6, "xtick.minor.size": 3, "ytick.minor.size": 3, }) else: style_dict.update({ "xtick.major.size": 0, "ytick.major.size": 0, "xtick.minor.size": 0, "ytick.minor.size": 0, }) # Override these settings with the provided rc dictionary if rc is not None: rc = {k: v for k, v in rc.items() if k in _style_keys} style_dict.update(rc) # Wrap in an _AxesStyle object so this can be used in a with statement style_object = _AxesStyle(style_dict) return style_object def set_style(style=None, rc=None): """Set the aesthetic style of the plots. This affects things like the color of the axes, whether a grid is enabled by default, and other aesthetic elements. Parameters ---------- style : dict, None, or one of {darkgrid, whitegrid, dark, white, ticks} A dictionary of parameters or the name of a preconfigured set. rc : dict, optional Parameter mappings to override the values in the preset seaborn style dictionaries. This only updates parameters that are considered part of the style definition. Examples -------- >>> set_style("whitegrid") >>> set_style("ticks", {"xtick.major.size": 8, "ytick.major.size": 8}) See Also -------- axes_style : return a dict of parameters or use in a ``with`` statement to temporarily set the style. set_context : set parameters to scale plot elements set_palette : set the default color palette for figures """ style_object = axes_style(style, rc) mpl.rcParams.update(style_object) def plotting_context(context=None, font_scale=1, rc=None): """Return a parameter dict to scale elements of the figure. This affects things like the size of the labels, lines, and other elements of the plot, but not the overall style. The base context is "notebook", and the other contexts are "paper", "talk", and "poster", which are version of the notebook parameters scaled by .8, 1.3, and 1.6, respectively. This function returns an object that can be used in a ``with`` statement to temporarily change the context parameters. Parameters ---------- context : dict, None, or one of {paper, notebook, talk, poster} A dictionary of parameters or the name of a preconfigured set. font_scale : float, optional Separate scaling factor to independently scale the size of the font elements. rc : dict, optional Parameter mappings to override the values in the preset seaborn context dictionaries. This only updates parameters that are considered part of the context definition. Examples -------- >>> c = plotting_context("poster") >>> c = plotting_context("notebook", font_scale=1.5) >>> c = plotting_context("talk", rc={"lines.linewidth": 2}) >>> import matplotlib.pyplot as plt >>> with plotting_context("paper"): ... f, ax = plt.subplots() ... ax.plot(x, y) # doctest: +SKIP See Also -------- set_context : set the matplotlib parameters to scale plot elements axes_style : return a dict of parameters defining a figure style color_palette : define the color palette for a plot """ if context is None: context_dict = {k: mpl.rcParams[k] for k in _context_keys} elif isinstance(context, dict): context_dict = context else: contexts = ["paper", "notebook", "talk", "poster"] if context not in contexts: raise ValueError("context must be in %s" % ", ".join(contexts)) # Set up dictionary of default parameters base_context = { "figure.figsize": np.array([8, 5.5]), "font.size": 12, "axes.labelsize": 11, "axes.titlesize": 12, "xtick.labelsize": 10, "ytick.labelsize": 10, "legend.fontsize": 10, "grid.linewidth": 1, "lines.linewidth": 1.75, "patch.linewidth": .3, "lines.markersize": 7, "lines.markeredgewidth": 0, "xtick.major.width": 1, "ytick.major.width": 1, "xtick.minor.width": .5, "ytick.minor.width": .5, "xtick.major.pad": 7, "ytick.major.pad": 7, } # Scale all the parameters by the same factor depending on the context scaling = dict(paper=.8, notebook=1, talk=1.3, poster=1.6)[context] context_dict = {k: v * scaling for k, v in base_context.items()} # Now independently scale the fonts font_keys = ["axes.labelsize", "axes.titlesize", "legend.fontsize", "xtick.labelsize", "ytick.labelsize", "font.size"] font_dict = {k: context_dict[k] * font_scale for k in font_keys} context_dict.update(font_dict) # Implement hack workaround for matplotlib bug # See https://github.com/mwaskom/seaborn/issues/344 # There is a bug in matplotlib 1.4.2 that makes points invisible when # they don't have an edgewidth. It will supposedly be fixed in 1.4.3. if mpl.__version__ == "1.4.2": context_dict["lines.markeredgewidth"] = 0.01 # Override these settings with the provided rc dictionary if rc is not None: rc = {k: v for k, v in rc.items() if k in _context_keys} context_dict.update(rc) # Wrap in a _PlottingContext object so this can be used in a with statement context_object = _PlottingContext(context_dict) return context_object def set_context(context=None, font_scale=1, rc=None): """Set the plotting context parameters. This affects things like the size of the labels, lines, and other elements of the plot, but not the overall style. The base context is "notebook", and the other contexts are "paper", "talk", and "poster", which are version of the notebook parameters scaled by .8, 1.3, and 1.6, respectively. Parameters ---------- context : dict, None, or one of {paper, notebook, talk, poster} A dictionary of parameters or the name of a preconfigured set. font_scale : float, optional Separate scaling factor to independently scale the size of the font elements. rc : dict, optional Parameter mappings to override the values in the preset seaborn context dictionaries. This only updates parameters that are considered part of the context definition. Examples -------- >>> set_context("paper") >>> set_context("talk", font_scale=1.4) >>> set_context("talk", rc={"lines.linewidth": 2}) See Also -------- plotting_context : return a dictionary of rc parameters, or use in a ``with`` statement to temporarily set the context. set_style : set the default parameters for figure style set_palette : set the default color palette for figures """ context_object = plotting_context(context, font_scale, rc) mpl.rcParams.update(context_object) class _RCAesthetics(dict): def __enter__(self): rc = mpl.rcParams self._orig = {k: rc[k] for k in self._keys} self._set(self) def __exit__(self, exc_type, exc_value, exc_tb): self._set(self._orig) def __call__(self, func): @functools.wraps(func) def wrapper(args, kwargs): with self: return func(args, **kwargs) return wrapper class _AxesStyle(_RCAesthetics): """Light wrapper on a dict to set style temporarily.""" _keys = _style_keys _set = staticmethod(set_style) class _PlottingContext(_RCAesthetics): """Light wrapper on a dict to set context temporarily.""" _keys = _context_keys _set = staticmethod(set_context) def set_palette(palette, n_colors=None, desat=None, color_codes=False): """Set the matplotlib color cycle using a seaborn palette. Parameters ---------- palette : hls \| husl \| matplotlib colormap \| seaborn color palette Palette definition. Should be something that :func:`color_palette` can process. n_colors : int Number of colors in the cycle. The default number of colors will depend on the format of ``palette``, see the :func:`color_palette` documentation for more information. desat : float Proportion to desaturate each color by. color_codes : bool If ``True`` and ``palette`` is a seaborn palette, remap the shorthand color codes (e.g. "b", "g", "r", etc.) to the colors from this palette. Examples -------- >>> set_palette("Reds") >>> set_palette("Set1", 8, .75) See Also -------- color_palette : build a color palette or set the color cycle temporarily in a ``with`` statement. set_context : set parameters to scale plot elements set_style : set the default parameters for figure style """ colors = palettes.color_palette(palette, n_colors, desat) if mpl_ge_150: from cycler import cycler cyl = cycler('color', colors) mpl.rcParams['axes.prop_cycle'] = cyl else: mpl.rcParams["axes.color_cycle"] = list(colors) mpl.rcParams["patch.facecolor"] = colors[0] if color_codes: palettes.set_color_codes(palette)	bsd-3-clause
tody411/NPR-SFS	npr_sfs/results/compare.py	1	1859	# -- coding: utf-8 -- ## @package npr_sfs.results.compare # # npr_sfs.results.compare utility package. # @author tody # @date 2015/09/01 import os import matplotlib.pyplot as plt from npr_sfs.results.results import batchResults, resultFile, resultDir from npr_sfs.datasets.loader import loadData from npr_sfs.io_util.image import loadRGBA from npr_sfs.plot.window import showMaximize batch_name="Compare" _root_dir = os.path.dirname(__file__) def methodNames(): dirs = os.listdir(_root_dir) method_dirs = [metohd_dir for metohd_dir in dirs if os.path.isdir(metohd_dir) and metohd_dir != batch_name] return method_dirs def methodDir(method_name): return os.path.join(_root_dir, method_name) def methodFile(method_name, data_name): return os.path.join(methodDir(method_name), data_name + ".png") def batch_func(data_name): method_names = methodNames() NO_32F = loadData(data_name, loader_func=loadRGBA) fig = plt.figure(figsize=(10, 4)) fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.9, wspace=0.05, hspace=0.1) font_size = 15 fig.suptitle("NPR-SFS", fontsize=font_size) num_cols = len(method_names) + 1 fig.add_subplot(1, num_cols, 1) plt.title("Ground truth", fontsize=font_size) plt.imshow(NO_32F) plt.axis('off') col_id = 2 for method_name in method_names: method_file = methodFile(method_name, data_name) N_32F = loadRGBA(method_file) fig.add_subplot(1, num_cols, col_id) plt.title(method_name, fontsize=font_size) plt.imshow(N_32F) plt.axis('off') col_id += 1 result_dir = resultDir(batch_name) result_file = resultFile(result_dir, data_name) plt.savefig(result_file) if __name__ == '__main__': print methodNames() batchResults(batch_func, batch_name)	mit
dialounke/pylayers	pylayers/mobility/agent.py	1	11549	""" .. automodule:: :members: Agent Class ================== .. autoclass:: Agent :members: """ from SimPy.SimulationRT import Simulation #from simpy.simulation import * from pylayers.mobility.transit.Person import Person from pylayers.mobility.transit.World import world from pylayers.mobility.transit.SteeringBehavior import Seek, Separation, Containment, InterpenetrationConstraint, queue_steering_mind import numpy as np import networkx as nx import time import ConfigParser import pandas as pd import pylayers.util.pyutil as pyu from pylayers.network.network import Node, Network from pylayers.network.communication import Gcom, TX, RX from pylayers.location.localization import Localization, PLocalization from pylayers.gis.layout import Layout from pylayers.util.utilnet import * #from pylayers.util.pymysqldb import Database import pdb """" .. currentmodule:: pylayers.mobility.agent .. autosummary:: :toctree: generated/ """ class Agent(object): """ Class Agent Members ------- args ID name typ net epwr gcom sim wstd sens dcond meca : transit.Person net : pylayers.network.Network sim : PN : rxt rxr """ def __init__(self, args): """ Mobile Agent Init Parameters ---------- 'ID': string agent ID 'name': string Agent name 'typ': string agent typ . 'ag' for moving agent, 'ap' for static acces point 'pos' : np.array([]) numpy array containing the initial position of the agent 'roomId': int Room number where the agent is initialized (Layout.Gr) 'meca_updt': float update time interval for the mechanical process 'loc': bool enable/disable localization process of the agent 'loc_updt': float update time interval for localization process 'L': pylayers.gis.Layout() 'net':pylayers.network.Network(), 'wstd': list of string list of used radio access techology of the agent 'world': transit.world() Soon deprecated 'save': list of string list of save method ( soon deprecated) 'sim':Simpy.SimulationRT.Simulation(), 'epwr': dictionnary dictionnary of emmited power of transsmitter{'wstd#':epwr value} 'sens': dictionnary dictionnary of sensitivity of reveicer {'wstd#':sens value} 'dcond': dictionnary Not used yet 'gcom':pylayers.communication.Gcom() Communication graph 'comm_mod': string Communication between nodes mode: 'autonomous': all TOAs are refreshed regulary 'synchro' : only visilbe TOAs are refreshed """ defaults = {'ID': '0', 'name': 'johndoe', 'typ': 'ag', 'color': 'k', 'pdshow': False, 'pos': np.array([]), 'roomId': -1, 'froom': [], 'wait': [], 'seed': 0, 'cdest': 'random', 'meca_updt': 0.1, 'loc': False, 'loc_updt': 0.5, 'loc_method': ['geo'], 'L': Layout(), 'network': True, 'net': Network(), 'wstd': ['rat1'], 'world': world(), 'save': [], 'sim': Simulation(), 'epwr': {}, 'sens': {}, 'dcond': {}, 'gcom': Gcom(), 'comm_mode': 'autonomous'} for key, value in defaults.items(): if key not in args: args[key] = value self.args = args self.ID = args['ID'] self.name = args['name'] self.typ = args['typ'] # Create Network self.net = args['net'] self.epwr = args['epwr'] self.gcom = args['gcom'] self.sim = args['sim'] self.wstd = args['wstd'] if args['epwr'] == {}: self.epwr = {x: 0 for x in self.wstd} else: self.epwr = args['epwr'] if args['sens'] == {}: self.sens = {x: -180 for x in self.wstd} else: self.sens = args['sens'] try: self.dcond = args['dcond'] except: pass # check if node id already given if self.ID in self.net.nodes(): raise NameError( 'another agent has the ID: ' + self.ID + ' .Please use an other ID') if self.typ == 'ag': # mechanical init self.meca = Person(ID=self.ID, color=args['color'], pdshow=args['pdshow'], roomId=args['roomId'], L=args['L'], net=self.net, interval=args['meca_updt'], wld=args['world'], sim=args['sim'], seed=args['seed'], moving=True, froom=args['froom'], wait=args['wait'], cdest=args['cdest'], save=args['save'] ) self.meca.behaviors = [Seek(), Containment(), Separation(), InterpenetrationConstraint()] self.meca.steering_mind = queue_steering_mind # Network init self.node = Node(ID=self.ID,name=self.name, p=conv_vecarr(self.meca.position), t=self.sim.now(), wstd=args['wstd'], epwr=self.epwr, sens=self.sens, typ=self.typ) self.net.add_nodes_from(self.node.nodes(data=True)) self.sim.activate(self.meca, self.meca.move(), 0.0) self.PN = self.net.node[self.ID]['PN'] # Communication init if args['comm_mode'] == 'synchro' and args['network']: # The TOA requests are made every refreshTOA time ( can be modified in agent.ini) # This Mode will be deprecated in future version self.rxr = RX(net=self.net, ID=self.ID, dcond=self.dcond, gcom=self.gcom, sim=self.sim) self.rxt = RX(net=self.net, ID=self.ID, dcond=self.dcond, gcom=self.gcom, sim=self.sim) self.sim.activate(self.rxr, self.rxr.refresh_RSS(), 0.0) self.sim.activate(self.rxt, self.rxt.refresh_TOA(), 0.0) elif args['comm_mode'] == 'autonomous' and args['network']: # The requests are made by node only when they are in # visibility of pairs. # self.rxr only manage a refresh RSS process self.rxr = RX(net=self.net, ID=self.ID, gcom=self.gcom, sim=self.sim) # self.tx manage all requests to other nodes self.tx = TX(net=self.net, ID=self.ID, gcom=self.gcom, sim=self.sim) # self.tx replies to requests from self.tx self.rx = RX(net=self.net, ID=self.ID, gcom=self.gcom, sim=self.sim) self.sim.activate(self.rxr, self.rxr.refresh_RSS(), 0.0) self.sim.activate(self.tx, self.tx.request(), 0.0) self.sim.activate(self.rx, self.rx.wait_request(), 0.0) elif self.typ == 'ap': if args['roomId'] == -1: self.node = Node(ID=self.ID, p=self.args['pos'], t=self.sim.now(), wstd=args['wstd'], epwr=self.epwr, sens=self.sens, typ=self.typ) else: pp = np.array(args['L'].Gr.pos[self.args['roomId']]) self.node = Node( ID=self.ID, p=pp, t=self.sim.now(), wstd=args['wstd'], epwr=self.epwr, sens=self.sens, typ=self.typ) self.net.add_nodes_from(self.node.nodes(data=True)) self.sim = args['sim'] self.PN = self.net.node[self.ID]['PN'] self.PN.node[self.ID]['pe'] = self.net.node[self.ID]['p'] if args['comm_mode'] == 'autonomous' and args['network']: self.rx = RX(net=self.net, ID=self.ID, gcom=self.gcom, sim=self.sim) self.sim.activate(self.rx, self.rx.wait_request(), 0.0) p = self.args['pos'] self.posdf = pd.DataFrame( {'t': pd.Timestamp(0), 'x': p[0], 'y': p[1], 'z': p[2], 'vx': np.array([0.0]), 'vy': np.array([0.0]), 'ax': np.array([0.0]), 'ay': np.array([0.0]), }, columns=['t', 'x', 'y', 'z', 'vx', 'vy', 'ax', 'ay'], index=np.array([0])) else: raise NameError( 'wrong agent typ, it must be either agent (ag) or acces point (ap) ') if self.typ == 'ap': self.MoA = 1 else: self.MoA = 0 if 'mysql' in args['save']: config = ConfigParser.ConfigParser() config.read(pyu.getlong('simulnet.ini', 'ini')) sql_opt = dict(config.items('Mysql')) db = Database(sql_opt['host'], sql_opt['user'], sql_opt['passwd'], sql_opt['dbname']) db.writenode(self.ID, self.name, self.MoA) if 'txt' in args['save']: pyu.writenode(self) if self.typ != 'ap' and args['loc']: self.loc = Localization(net=self.net, ID=self.ID, method=args['loc_method']) self.Ploc = PLocalization(loc=self.loc, loc_updt_time=args['loc_updt'], tx=self.tx, sim=args['sim']) self.sim.activate(self.Ploc, self.Ploc.run(), 1.5) def __repr__(self): s = 'General Agent info \n******************\n' s = s + 'name : ' + self.name + '\n' s = s + 'ID: ' + self.ID + '\n' s = s + 'typ: ' + self.typ s = s + '\n\n More Agent information about:' s = s + '\n+ Mecanichal => self.meca' s = s + '\n+ Network => self.net' s = s + '\n+ Personnal Network => self.PN' s = s + '\n+ Localization => self.loc\n\n' try: s = s + self.PN.__repr__() + '\n\n' except: s = s + 'No network simulated' if self.typ != 'ap': s = s + self.meca.__repr__() + '\n\n' try: s = s + self.loc.__repr__() + '\n\n' except: s = s + 'no localization simulated' return s	mit
cleverhans-lab/cleverhans	cleverhans_v3.1.0/scripts/plot_success_fail_curve.py	1	1459	#!/usr/bin/env python3 """ Plots a success-fail curve ( https://openreview.net/forum?id=H1g0piA9tQ ) Usage: plot_success_fail_curve.py model.joblib plot_success_fail_curve.py model1.joblib model2.joblib This script is mostly intended to rapidly visualize success-fail curves during model development and testing. To make nicely labeled plots formatted to fit the page / column of a publication, you should probably write your own script that calls some of the same plotting commands. """ from matplotlib import pyplot import tensorflow as tf from cleverhans.utils_tf import silence silence() # silence call must precede this imports. pylint doesn't like that # pylint: disable=C0413 from cleverhans.compat import flags from cleverhans.plot.success_fail import DEFAULT_FAIL_NAMES from cleverhans.plot.success_fail import plot_report_from_path FLAGS = flags.FLAGS def main(argv=None): """Takes the path to a directory with reports and renders success fail plots.""" report_paths = argv[1:] fail_names = FLAGS.fail_names.split(",") for report_path in report_paths: plot_report_from_path(report_path, label=report_path, fail_names=fail_names) pyplot.legend() pyplot.xlim(-0.01, 1.0) pyplot.ylim(0.0, 1.0) pyplot.show() if __name__ == "__main__": flags.DEFINE_string( "fail_names", ",".join(DEFAULT_FAIL_NAMES), "Names of adversarial datasets for failure rate", ) tf.app.run()	mit
d-meiser/lindblad	examples/scanEIT.py	1	3959	#!/usr/bin/python import sys import subprocess import numpy as np try: from matplotlib import rc rc('font',*{'family':'sans-serif','sans-serif':['Helvetica']}) rc('text', usetex=True) import matplotlib.pyplot as plt have_matplotlib = True except ImportError: print "matplotlib not available" have_matplotlib = False try: import joblib from multiprocessing import cpu_count cpuNum = cpu_count() # use maximum number of cores available if cpuNum > 1: n_jobs = cpuNum print "Using joblib with", n_jobs, "processes." else: n_jobs = 1 print "Using joblib with 1 process." have_joblib = True except ImportError: print "joblib not available." have_joblib = False if (len(sys.argv) == 2): # one argument has been provided for output plot name outputFilename = str(sys.argv[1]) elif (len(sys.argv) == 1): # no filename provided, use default outputFilename = 'test.png' else: # improper usage, use default and inform print("Usage: python scanEIT-SmallRange.py filename") outputFilename = 'test.png' def get_steady_state(arguments = None): if arguments: output = subprocess.check_output(["./SteadyStateEIT"] + [str(a) for a in arguments]) else: output = subprocess.check_output(["./SteadyStateEIT"]) density_matrix = np.matrix(output) density_matrix = density_matrix.reshape(4, 8) density_matrix = density_matrix[:, 0:8:2] + 1.0j density_matrix[:,1:8:2] return density_matrix def get_polarization(arguments = None): density_matrix = get_steady_state(arguments) return [density_matrix[0, 3] , density_matrix[2, 3]] def absorption(polarizationArray): # post-process density matrix elements # to get absorption from atomic polarization return polarizationArray[:,0].imag - polarizationArray[:,1].imag def rotation(polarizationArray): # post-process density matrix elements # to get light polarization rotation from atomic polarization return polarizationArray[:,0].real + polarizationArray[:,1].real def compute_polarization(OmegaR, OmegaBs, Delta, gamma, Gamma, deltaB): if have_joblib: polarizations = joblib.Parallel(n_jobs = n_jobs)( joblib.delayed(get_polarization)( [OmegaR, ob, Delta, gamma, Gamma, deltaB]) for ob in OmegaBs) else: polarizations = [get_polarization([OmegaR, ob, Delta, gamma, Gamma, deltaB]) for ob in OmegaBs] polarizations = np.array(polarizations) return polarizations def main(argv): Bunits = 700e3 * 2.0 * np.pi OmegaR = 1.25e6 * 2.0 * np.pi gamma = 1.0e3 * 2.0 * np.pi Gamma = 6.0e6 * 2.0 * np.pi Delta = 0.0 * Gamma; OmegaB = np.arange(-0.1, 0.1, 0.0005) * Bunits deltaB = Bunits * 0.01 NonZeroPolarization = compute_polarization(OmegaR, OmegaB, Delta, gamma, Gamma, deltaB) absorptionNonZeroField = absorption(NonZeroPolarization) rotationNonZeroField = rotation(NonZeroPolarization) deltaB = Bunits * 0.0 ZeroPolarization = compute_polarization(OmegaR, OmegaB, Delta, gamma, Gamma, deltaB) absorptionZeroField = absorption(ZeroPolarization) rotationZeroField = rotation(ZeroPolarization) if not have_matplotlib: return plt.subplot(2,1,1) plt.plot(OmegaB / (Bunits), 1.0e3 * absorptionZeroField) plt.plot(OmegaB / (Bunits), 1.0e3 * absorptionNonZeroField,'--r') plt.ylabel(r'${\rm Absorption\; [arb. units]}$') plt.subplot(2,1,2) plt.plot(OmegaB / (Bunits), 1.0e3 * rotationZeroField) plt.plot(OmegaB / (Bunits), 1.0e3 * rotationNonZeroField,'--r') plt.xlabel(r'$B_z({\rm G})$') plt.ylabel(r'${\rm Faraday Rotation\; [arb. units]}$') plt.gcf().set_size_inches(4, 6) plt.gcf().subplots_adjust(bottom = 0.1, left = 0.2, top = 0.97, right = 0.95) plt.savefig(outputFilename,format='png') if __name__== "__main__": main(sys.argv)	gpl-3.0
NunoEdgarGub1/scikit-learn	examples/svm/plot_svm_anova.py	250	2000	""" ================================================= SVM-Anova: SVM with univariate feature selection ================================================= This example shows how to perform univariate feature before running a SVC (support vector classifier) to improve the classification scores. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets, feature_selection, cross_validation from sklearn.pipeline import Pipeline ############################################################################### # Import some data to play with digits = datasets.load_digits() y = digits.target # Throw away data, to be in the curse of dimension settings y = y[:200] X = digits.data[:200] n_samples = len(y) X = X.reshape((n_samples, -1)) # add 200 non-informative features X = np.hstack((X, 2 * np.random.random((n_samples, 200)))) ############################################################################### # Create a feature-selection transform and an instance of SVM that we # combine together to have an full-blown estimator transform = feature_selection.SelectPercentile(feature_selection.f_classif) clf = Pipeline([('anova', transform), ('svc', svm.SVC(C=1.0))]) ############################################################################### # Plot the cross-validation score as a function of percentile of features score_means = list() score_stds = list() percentiles = (1, 3, 6, 10, 15, 20, 30, 40, 60, 80, 100) for percentile in percentiles: clf.set_params(anova__percentile=percentile) # Compute cross-validation score using all CPUs this_scores = cross_validation.cross_val_score(clf, X, y, n_jobs=1) score_means.append(this_scores.mean()) score_stds.append(this_scores.std()) plt.errorbar(percentiles, score_means, np.array(score_stds)) plt.title( 'Performance of the SVM-Anova varying the percentile of features selected') plt.xlabel('Percentile') plt.ylabel('Prediction rate') plt.axis('tight') plt.show()	bsd-3-clause
OshynSong/scikit-learn	examples/svm/plot_custom_kernel.py	171	1546	""" ====================== SVM with custom kernel ====================== Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface and the support vectors. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets # import some data to play with iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. We could # avoid this ugly slicing by using a two-dim dataset Y = iris.target def my_kernel(X, Y): """ We create a custom kernel: (2 0) k(X, Y) = X ( ) Y.T (0 1) """ M = np.array([[2, 0], [0, 1.0]]) return np.dot(np.dot(X, M), Y.T) h = .02 # step size in the mesh # we create an instance of SVM and fit out data. clf = svm.SVC(kernel=my_kernel) clf.fit(X, Y) # Plot the decision boundary. For that, we will assign a color to each # point in the mesh [x_min, m_max]x[y_min, y_max]. x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired) # Plot also the training points plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired) plt.title('3-Class classification using Support Vector Machine with custom' ' kernel') plt.axis('tight') plt.show()	bsd-3-clause
anna-effeindzourou/trunk	examples/anna_scripts/triax/triaxial_uniformrm.py	1	11635	# -- coding: utf-8 from yade import ymport, utils,pack,export import gts,os from yade import geom #import matplotlib from yade import plot #from pylab import #import os.path, locale #### set False when running in batch mode #defaultTable = True defaultTable = False ####------------------------------------- ####------------------------------------- utils.readParamsFromTable( rm = 0.33, noTableOk = True ) from yade.params.table import * print 'rm=',rm O.tags['description']='triaxial_rm_'+str(rm) ################################# ##### FUNCTIONS #### ################################# def hMax(n): idHMax=0 hMax=-1000000.0 for i in O.bodies: h=i.state.pos[n] if (h>hMax): hMax=h idHMax=i.id hMax=hMax+O.bodies[idHMax].shape.radius return (hMax) def hMin(n): idHMin=0 hMin=100000.0 for i in O.bodies: h=i.state.pos[n] if (h<hMin): hMin=h idHMin=i.id hMin=hMin-O.bodies[idHMin].shape.radius return (hMin) #Function in order to calculate rmin (minimum radius) and rmax (maximum radius) def MinMax(): rmax=0 rmin=10 r=0 for i in O.bodies: if(type(i.shape)==Sphere): r=i.shape.radius if(r>rmax): rmax=r if(r<rmin): rmin=r l=[rmin,rmax] return (l) def sup(): for i in O.bodies: if (type(i.shape)==Sphere) and (i.state.pos[2]>0.098): O.bodies.erase(i.id) def scalar(u,v): ps=u[0]v[0]+u[1]v[1]+u[2]v[2] return ps def cross(u,v): ps=Vector3(u[1]v[2]-u[2]v[1], u[2]v[0]-u[0]v[2] ,u[0]v[1]-u[1]v[0]) return ps def limitfinder(): for b in O.bodies: if(b.state.pos[2]>=L-2radius): if isinstance(b.shape,GridNode): top_boundary.append(b.id) b.shape.color=(1,0,0) b.state.blockedDOFs='z' if(b.state.pos[2]<0.1radius ): if isinstance(b.shape,GridNode): bottom_boundary.append(b.id) b.state.blockedDOFs='z' b.shape.color=(1,0,0) ############################## ##### SCRIPT #### ############################## try: os.mkdir('data') except: pass try: os.mkdir('paraview') except: pass isBatch = runningInBatch() #################### ### ENGINES ### #################### O.engines=[ ForceResetter(), InsertionSortCollider([ Bo1_Sphere_Aabb(), Bo1_Wall_Aabb(), Bo1_PFacet_Aabb(), Bo1_Facet_Aabb(), ]), InteractionLoop([ Ig2_GridNode_GridNode_GridNodeGeom6D(), Ig2_GridConnection_GridConnection_GridCoGridCoGeom(), Ig2_Sphere_PFacet_ScGridCoGeom(), Ig2_Sphere_Sphere_ScGeom(), Ig2_Facet_Sphere_ScGeom(), Ig2_Wall_Sphere_ScGeom() ], [Ip2_CohFrictMat_CohFrictMat_CohFrictPhys(setCohesionNow=True,setCohesionOnNewContacts=True), Ip2_FrictMat_FrictMat_FrictPhys()], [Law2_ScGeom6D_CohFrictPhys_CohesionMoment(), Law2_ScGeom_FrictPhys_CundallStrack(), Law2_ScGridCoGeom_FrictPhys_CundallStrack(), Law2_GridCoGridCoGeom_FrictPhys_CundallStrack() ] ), ] ###################### ### PROPERTIES ### ###################### radius=0.0025rm sigma=-3e6 #### Parameters of a rectangular grid ### L=0.205 #length [m] l=0.101/2. #half width (radius) [m] nbL=36#number of nodes for the length [#] doit etre paire nbl=44 #number of nodes for the perimeter [#] ABSOLUMENT MULTIPLE de 4 !!! #nbL=1 #number of nodes for the length [#] doit etre paire #nbl=4 #number of nodes for the perimeter [#] ABSOLUMENT MULTIPLE de 4 !!! r=radius color=[155./255.,155./255.,100./255.] oriBody = Quaternion(Vector3(0,0,1),(pi/2)) nodesIds=[] nodesIds1=[] cylIds=[] pfIds=[] top_boundary=[] bottom_boundary=[] #################### ### MATERIAL ### #################### poisson=0.28 E=27.9e10(1+poisson) ##1e11 density=7.8e10 Et=0 Emem=E/1e3 frictionAngle=0.096 frictionAngleW=0.228 O.materials.append(CohFrictMat(young=Emem,poisson=poisson,density=density,frictionAngle=0,normalCohesion=1e19,shearCohesion=1e19,momentRotationLaw=False,alphaKr=0,label='NodeMat')) O.materials.append(FrictMat(young=Emem,poisson=poisson,density=density,frictionAngle=0,label='memMat')) O.materials.append(FrictMat(young=E,poisson=poisson,density=density,frictionAngle=frictionAngleW,label='Wallmat')) O.materials.append(FrictMat(young=E,poisson=poisson,density=density,frictionAngle=frictionAngle,label='Smat')) ############################## ### SAMPLE GENERATION ### ############################## kw={'color':[0.6,0.6,0.6],'wire':False,'dynamic':True,'material':3} #pile=ymport.text('spheres.txt',*kw) #pile2=O.bodies.append(pile) #sup() #print hMin(2), hMax(2) #zmin=hMin(2) #zmax=hMax(2) ##L=hMax(2) ################################## ##### MEMBRANE GENERATION ### ################################## #mesh=2 #Create all nodes first : for i in range(0,nbL+1): for j in range(0,nbl): z=iL/float(nbL) y=lsin(2pij/float(nbl)) x=lcos(2pij/float(nbl)) nodesIds.append( O.bodies.append(gridNode([x,y,z],r,wire=False,fixed=False,material='NodeMat',color=color)) ) ##Create connection between the nodes for i in range(0,nbL+1): for j in range(0,nbl-1): O.bodies.append( gridConnection(nodesIds[inbl+j],nodesIds[inbl+j+1],r,color=color,mask=5,material='memMat') ) for i in range(0,nbL,1): for j in range(0,nbl): O.bodies.append( gridConnection(nodesIds[inbl+j],nodesIds[(i+1)nbl+j],r,color=color,mask=5,material='memMat') ) for i in range(-1,nbL): j=nbl O.bodies.append( gridConnection(nodesIds[inbl+j],nodesIds[(i+1)nbl+j-1],r,color=color,mask=5,material='memMat') ) for i in range(0,nbL): for j in range(0,nbl-1): if (j%2==0): O.bodies.append( gridConnection(nodesIds[inbl+j],nodesIds[(i+1)nbl+j+1],r,color=color,mask=5,material='memMat') ) else: O.bodies.append( gridConnection(nodesIds[(i+1)nbl+j],nodesIds[inbl+j+1],r,color=color,mask=5,material='memMat') ) for i in range(0,nbL): j=nbl #O.bodies[nodesIds[(i-1)nbl+j]].shape.color=Vector3(155./255.,155./255.,1.) #O.bodies[nodesIds[(i)nbl+j-1]].shape.color=Vector3(1,0,0) O.bodies.append( gridConnection(nodesIds[(i-1)nbl+j],nodesIds[(i+1)nbl+j-1],r,color=color,mask=5,material='memMat') ) ###Create PFacets ##wire=True for i in range(0,nbL): for j in range(0,nbl-1): if (j%2==0): pfIds.append(O.bodies.append(pfacet(nodesIds[inbl+j],nodesIds[(i+1)nbl+j],nodesIds[(i+1)nbl+j+1],color=color,mask=5,material='memMat'))) pfIds.append(O.bodies.append(pfacet(nodesIds[inbl+j],nodesIds[(i+1)nbl+j+1],nodesIds[(i)nbl+j+1],color=color,mask=5,material='memMat'))) else: pfIds.append(O.bodies.append(pfacet(nodesIds[inbl+j],nodesIds[(i+1)nbl+j],nodesIds[(i)nbl+j+1],color=color,mask=5,material='memMat'))) pfIds.append(O.bodies.append(pfacet(nodesIds[inbl+j+1],nodesIds[(i+1)nbl+j],nodesIds[(i+1)nbl+j+1],color=color,mask=5,material='memMat'))) for i in range(0,nbL,1): j=nbl pfIds.append(O.bodies.append(pfacet( nodesIds[inbl+j],nodesIds[(i-1)nbl+j],nodesIds[(i+1)nbl+j-1],color=color,material='memMat' ))) pfIds.append(O.bodies.append(pfacet( nodesIds[(i)nbl+j-1],nodesIds[(i+1)nbl+j-1],nodesIds[(i-1)nbl+j],color=color,material='memMat' ))) limitfinder() ######################### ##### WALL GENERATION ## ######################### O.materials.append(FrictMat(young=E,poisson=poisson,density=density,frictionAngle=frictionAngleW,label='Wallmat')) topPlate=utils.wall(position=hMax(2)+radius,sense=0, axis=2,color=Vector3(1,0,0),material='Wallmat') O.bodies.append(topPlate) bottomPlate=utils.wall(position=-hMin(2)-radius,sense=0, axis=2,color=Vector3(1,0,0),material='Wallmat') O.bodies.append(bottomPlate) ################### #### APPLY LOAD ## ################### normalVEL=0 loading=True S0=pil2 normalSTRESS=sigma shearing=False sigmaN=0 #### APPLY CONFINING PRESSURE def Apply_load(): global sigmaN, Fn, top, load,shearing,loading,u Fn=abs(O.forces.f(topPlate.id)[2]) sigmaN=Fn/S0 if abs((normalSTRESS-sigmaN)/normalSTRESS)<0.001: topPlate.state.vel[2]=0 def Apply_confiningpressure(): #print 'Apply_confiningpressure' for i in pfIds: e0 =O.bodies[i].shape.node3.state.pos - O.bodies[i].shape.node1.state.pos e1 =O.bodies[i].shape.node2.state.pos - O.bodies[i].shape.node1.state.pos e2 =O.bodies[i].shape.node2.state.pos - O.bodies[i].shape.node3.state.pos P=(O.bodies[i].shape.node1.state.pos+O.bodies[i].shape.node2.state.pos+O.bodies[i].shape.node3.state.pos)/3 #print e0,e1,e2 #nodesIds.append( O.bodies.append(gridNode([P[0],P[1],P[2]],r,wire=False,fixed=True,material='NodeMat',color=color)) ) #print 'P=',P v0 = e0 v1 = e1 v2 = P - O.bodies[i].shape.node1.state.pos ##// Compute dot products dot00 = scalar(v0,v0) dot01 = scalar(v0,v1) dot02 = scalar(v0,v2) dot11 = scalar(v1,v1) dot12 = scalar(v1,v2) ##// Compute the barycentric coordinates of the projection P invDenom = 1 / (dot00 dot11 - dot01 * dot01) p1 = (dot11 * dot02 - dot01 * dot12) * invDenom p2 = (dot00 * dot12 - dot01 * dot02) * invDenom p3 = 1-p1-p2 a = sqrt(scalar(e0,e0)) b = sqrt(scalar(e1,e1)) c = sqrt(scalar(e2,e2)) s=0.5(a+b+c) area= sqrt(s(s-a)(s-b)(s-c)) Fapplied=areasigma normal = cross(e0,e1) normal=normal/normal.norm() F=Fapplied p1normal=Fp1normal p2normal=Fp2normal p3normal=Fp3normal O.forces.addF(O.bodies[i].shape.node1.id,p1normal,permanent=False) O.forces.addF(O.bodies[i].shape.node2.id,p2normal,permanent=False) O.forces.addF(O.bodies[i].shape.node3.id,p3normal,permanent=False) #Apply_confiningpressure() sigma3=0 def check_confiningpressure(): global sigma3 sigma3=0 for i in pfIds: e0 =O.bodies[i].shape.node3.state.pos - O.bodies[i].shape.node1.state.pos e1 =O.bodies[i].shape.node2.state.pos - O.bodies[i].shape.node1.state.pos e2 =O.bodies[i].shape.node2.state.pos - O.bodies[i].shape.node3.state.pos a = sqrt(scalar(e0,e0)) b = sqrt(scalar(e1,e1)) c = sqrt(scalar(e2,e2)) s=0.5(a+b+c) area= sqrt(s(s-a)(s-b)(s-c)) F=(O.forces.f(O.bodies[i].shape.node1.id) + O.forces.f(O.bodies[i].shape.node2.id)+O.forces.f(O.bodies[i].shape.node3.id)).norm() sigma3=sigma3+F/area #print sigma3 return sigma3 pos=topPlate.state.pos[2] def dataCollector(): global pos,p,q,sigma1 #if(pos<0.16): #O.wait() #saveData() #O.exitNoBacktrace() S=pil*2 Fnt=O.forces.f(topPlate.id)[2] Fnb=O.forces.f(bottomPlate.id)[2] sigma1=Fnt/S sigma3=check_confiningpressure() pos=topPlate.state.pos[2] q=(sigma1-3e6) p=(sigma1+23e6)/2 plot.addData(t=O.time,pos=pos,Fnt=Fnt,Fnb=Fnb,sigma1=sigma1,sigma3=sigma3,unbF=unbalancedForce(),p=p,q=q) def saveData(): plot.saveDataTxt('data/'+O.tags['description']+'.dat',vars=('t','pos','Fnt','Fnb','sigma1','sigma3','unbF')) plot.plots={'p':('q')} #### MOVE TOP AND BOTTOM WALL #v=1.7e-03 v=1.7e-05 def moveWall(v): topPlate.state.vel=(0,0,-v) #bottomPlate.state.vel=(0,0,v) #g=-9.81 g=0 #moveWall(v) #limitfinder() ########################### ##### ENGINE DEFINITION ## ########################### O.dt=0.5*PWaveTimeStep() O.engines=O.engines+[ #PyRunner(iterPeriod=1,initRun=True,command='Apply_load()'), PyRunner(iterPeriod=1,dead=False,command='Apply_confiningpressure()'), #PyRunner(iterPeriod=1,initRun=True,command='Apply_load()'), NewtonIntegrator(damping=0.7,gravity=(0,0,g),label='Newton'), #PyRunner(initRun=True,iterPeriod=1,command='dataCollector()'), #VTKRecorder(iterPeriod=500,initRun=True,fileName='paraview/'+O.tags['description']+'_',recorders=['spheres','velocity']), ] if not isBatch: # VISUALIZATION from yade import qt qt.Controller() #qtv = qt.View() #qtr = qt.Renderer() plot.plot(noShow=False, subPlots=True) #O.run(5000) #moveWall(v) else: O.run(1,True) moveWall(v) O.wait() saveData()	gpl-2.0
gotomypc/scikit-learn	sklearn/linear_model/tests/test_omp.py	272	7752	# Author: Vlad Niculae # Licence: BSD 3 clause import numpy as np from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_warns from sklearn.utils.testing import ignore_warnings from sklearn.linear_model import (orthogonal_mp, orthogonal_mp_gram, OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV, LinearRegression) from sklearn.utils import check_random_state from sklearn.datasets import make_sparse_coded_signal n_samples, n_features, n_nonzero_coefs, n_targets = 20, 30, 5, 3 y, X, gamma = make_sparse_coded_signal(n_targets, n_features, n_samples, n_nonzero_coefs, random_state=0) G, Xy = np.dot(X.T, X), np.dot(X.T, y) # this makes X (n_samples, n_features) # and y (n_samples, 3) def test_correct_shapes(): assert_equal(orthogonal_mp(X, y[:, 0], n_nonzero_coefs=5).shape, (n_features,)) assert_equal(orthogonal_mp(X, y, n_nonzero_coefs=5).shape, (n_features, 3)) def test_correct_shapes_gram(): assert_equal(orthogonal_mp_gram(G, Xy[:, 0], n_nonzero_coefs=5).shape, (n_features,)) assert_equal(orthogonal_mp_gram(G, Xy, n_nonzero_coefs=5).shape, (n_features, 3)) def test_n_nonzero_coefs(): assert_true(np.count_nonzero(orthogonal_mp(X, y[:, 0], n_nonzero_coefs=5)) <= 5) assert_true(np.count_nonzero(orthogonal_mp(X, y[:, 0], n_nonzero_coefs=5, precompute=True)) <= 5) def test_tol(): tol = 0.5 gamma = orthogonal_mp(X, y[:, 0], tol=tol) gamma_gram = orthogonal_mp(X, y[:, 0], tol=tol, precompute=True) assert_true(np.sum((y[:, 0] - np.dot(X, gamma)) 2) <= tol) assert_true(np.sum((y[:, 0] - np.dot(X, gamma_gram)) 2) <= tol) def test_with_without_gram(): assert_array_almost_equal( orthogonal_mp(X, y, n_nonzero_coefs=5), orthogonal_mp(X, y, n_nonzero_coefs=5, precompute=True)) def test_with_without_gram_tol(): assert_array_almost_equal( orthogonal_mp(X, y, tol=1.), orthogonal_mp(X, y, tol=1., precompute=True)) def test_unreachable_accuracy(): assert_array_almost_equal( orthogonal_mp(X, y, tol=0), orthogonal_mp(X, y, n_nonzero_coefs=n_features)) assert_array_almost_equal( assert_warns(RuntimeWarning, orthogonal_mp, X, y, tol=0, precompute=True), orthogonal_mp(X, y, precompute=True, n_nonzero_coefs=n_features)) def test_bad_input(): assert_raises(ValueError, orthogonal_mp, X, y, tol=-1) assert_raises(ValueError, orthogonal_mp, X, y, n_nonzero_coefs=-1) assert_raises(ValueError, orthogonal_mp, X, y, n_nonzero_coefs=n_features + 1) assert_raises(ValueError, orthogonal_mp_gram, G, Xy, tol=-1) assert_raises(ValueError, orthogonal_mp_gram, G, Xy, n_nonzero_coefs=-1) assert_raises(ValueError, orthogonal_mp_gram, G, Xy, n_nonzero_coefs=n_features + 1) def test_perfect_signal_recovery(): idx, = gamma[:, 0].nonzero() gamma_rec = orthogonal_mp(X, y[:, 0], 5) gamma_gram = orthogonal_mp_gram(G, Xy[:, 0], 5) assert_array_equal(idx, np.flatnonzero(gamma_rec)) assert_array_equal(idx, np.flatnonzero(gamma_gram)) assert_array_almost_equal(gamma[:, 0], gamma_rec, decimal=2) assert_array_almost_equal(gamma[:, 0], gamma_gram, decimal=2) def test_estimator(): omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs) omp.fit(X, y[:, 0]) assert_equal(omp.coef_.shape, (n_features,)) assert_equal(omp.intercept_.shape, ()) assert_true(np.count_nonzero(omp.coef_) <= n_nonzero_coefs) omp.fit(X, y) assert_equal(omp.coef_.shape, (n_targets, n_features)) assert_equal(omp.intercept_.shape, (n_targets,)) assert_true(np.count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs) omp.set_params(fit_intercept=False, normalize=False) omp.fit(X, y[:, 0]) assert_equal(omp.coef_.shape, (n_features,)) assert_equal(omp.intercept_, 0) assert_true(np.count_nonzero(omp.coef_) <= n_nonzero_coefs) omp.fit(X, y) assert_equal(omp.coef_.shape, (n_targets, n_features)) assert_equal(omp.intercept_, 0) assert_true(np.count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs) def test_identical_regressors(): newX = X.copy() newX[:, 1] = newX[:, 0] gamma = np.zeros(n_features) gamma[0] = gamma[1] = 1. newy = np.dot(newX, gamma) assert_warns(RuntimeWarning, orthogonal_mp, newX, newy, 2) def test_swapped_regressors(): gamma = np.zeros(n_features) # X[:, 21] should be selected first, then X[:, 0] selected second, # which will take X[:, 21]'s place in case the algorithm does # column swapping for optimization (which is the case at the moment) gamma[21] = 1.0 gamma[0] = 0.5 new_y = np.dot(X, gamma) new_Xy = np.dot(X.T, new_y) gamma_hat = orthogonal_mp(X, new_y, 2) gamma_hat_gram = orthogonal_mp_gram(G, new_Xy, 2) assert_array_equal(np.flatnonzero(gamma_hat), [0, 21]) assert_array_equal(np.flatnonzero(gamma_hat_gram), [0, 21]) def test_no_atoms(): y_empty = np.zeros_like(y) Xy_empty = np.dot(X.T, y_empty) gamma_empty = ignore_warnings(orthogonal_mp)(X, y_empty, 1) gamma_empty_gram = ignore_warnings(orthogonal_mp)(G, Xy_empty, 1) assert_equal(np.all(gamma_empty == 0), True) assert_equal(np.all(gamma_empty_gram == 0), True) def test_omp_path(): path = orthogonal_mp(X, y, n_nonzero_coefs=5, return_path=True) last = orthogonal_mp(X, y, n_nonzero_coefs=5, return_path=False) assert_equal(path.shape, (n_features, n_targets, 5)) assert_array_almost_equal(path[:, :, -1], last) path = orthogonal_mp_gram(G, Xy, n_nonzero_coefs=5, return_path=True) last = orthogonal_mp_gram(G, Xy, n_nonzero_coefs=5, return_path=False) assert_equal(path.shape, (n_features, n_targets, 5)) assert_array_almost_equal(path[:, :, -1], last) def test_omp_return_path_prop_with_gram(): path = orthogonal_mp(X, y, n_nonzero_coefs=5, return_path=True, precompute=True) last = orthogonal_mp(X, y, n_nonzero_coefs=5, return_path=False, precompute=True) assert_equal(path.shape, (n_features, n_targets, 5)) assert_array_almost_equal(path[:, :, -1], last) def test_omp_cv(): y_ = y[:, 0] gamma_ = gamma[:, 0] ompcv = OrthogonalMatchingPursuitCV(normalize=True, fit_intercept=False, max_iter=10, cv=5) ompcv.fit(X, y_) assert_equal(ompcv.n_nonzero_coefs_, n_nonzero_coefs) assert_array_almost_equal(ompcv.coef_, gamma_) omp = OrthogonalMatchingPursuit(normalize=True, fit_intercept=False, n_nonzero_coefs=ompcv.n_nonzero_coefs_) omp.fit(X, y_) assert_array_almost_equal(ompcv.coef_, omp.coef_) def test_omp_reaches_least_squares(): # Use small simple data; it's a sanity check but OMP can stop early rng = check_random_state(0) n_samples, n_features = (10, 8) n_targets = 3 X = rng.randn(n_samples, n_features) Y = rng.randn(n_samples, n_targets) omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_features) lstsq = LinearRegression() omp.fit(X, Y) lstsq.fit(X, Y) assert_array_almost_equal(omp.coef_, lstsq.coef_)	bsd-3-clause
dkriegner/xrayutilities	lib/xrayutilities/materials/material.py	1	66506	# This file is part of xrayutilities. # # xrayutilities is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, see <http://www.gnu.org/licenses/>. # # Copyright (C) 2009 Eugen Wintersberger <[email protected]> # Copyright (C) 2009-2020 Dominik Kriegner <[email protected]> # Copyright (C) 2012 Tanja Etzelstorfer <[email protected]> """ Classes decribing materials. Materials are devided with respect to their crystalline state in either Amorphous or Crystal types. While for most materials their crystalline state is defined few materials are also included as amorphous which can be useful for calculation of their optical properties. """ import abc import copy import numbers import operator import re import warnings from math import ceil, copysign, isclose import numpy import scipy.optimize from .. import config, math, utilities from ..exception import InputError from ..math import VecCross, VecDot, VecNorm from . import cif, elements from .atom import Atom from .spacegrouplattice import WyckoffBase numpy.seterr(divide='ignore', invalid='ignore') map_ijkl2ij = {"00": 0, "11": 1, "22": 2, "12": 3, "20": 4, "01": 5, "21": 6, "02": 7, "10": 8} map_ij2ijkl = {"0": [0, 0], "1": [1, 1], "2": [2, 2], "3": [1, 2], "4": [2, 0], "5": [0, 1], "6": [2, 1], "7": [0, 2], "8": [1, 0]} def index_map_ijkl2ij(i, j): return map_ijkl2ij["%i%i" % (i, j)] def index_map_ij2ijkl(ij): return map_ij2ijkl["%i" % ij] def Cij2Cijkl(cij): """ Converts the elastic constants matrix (tensor of rank 2) to the full rank 4 cijkl tensor. Parameters ---------- cij : array-like (6, 6) cij matrix Returns ------- cijkl ndarray (3, 3, 3, 3) cijkl tensor as numpy array """ # first have to build a 9x9 matrix from the 6x6 one m = numpy.zeros((9, 9), dtype=numpy.double) m[0:6, 0:6] = cij[:, :] m[6:9, 0:6] = cij[3:6, :] m[0:6, 6:9] = cij[:, 3:6] m[6:9, 6:9] = cij[3:6, 3:6] # now create the full tensor cijkl = numpy.empty((3, 3, 3, 3), dtype=numpy.double) for i in range(0, 3): for j in range(0, 3): for k in range(0, 3): for n in range(0, 3): mi = index_map_ijkl2ij(i, j) mj = index_map_ijkl2ij(k, n) cijkl[i, j, k, n] = m[mi, mj] return cijkl def Cijkl2Cij(cijkl): """ Converts the full rank 4 tensor of the elastic constants to the (6, 6) matrix of elastic constants. Parameters ---------- cijkl ndarray (3, 3, 3, 3) cijkl tensor as numpy array Returns ------- cij : array-like (6, 6) cij matrix """ cij = numpy.empty((6, 6), dtype=numpy.double) for i in range(6): for j in range(6): ij = index_map_ij2ijkl(i) kl = index_map_ij2ijkl(j) cij[i, j] = cijkl[ij[0], ij[1], kl[0], kl[1]] return cij class Material(utilities.ABC): """ base class for all Materials. common properties of amorphous and crystalline materials are described by this class from which Amorphous and Crystal are derived from. """ def __init__(self, name, cij=None): if cij is None: self.cij = numpy.zeros((6, 6), dtype=numpy.double) self.cijkl = numpy.zeros((3, 3, 3, 3), dtype=numpy.double) elif isinstance(cij, (tuple, list, numpy.ndarray)): self.cij = numpy.asarray(cij, dtype=numpy.double) self.cijkl = Cij2Cijkl(self.cij) else: raise TypeError("Elastic constants must be a list or numpy array!") self.name = name self.transform = None self._density = None def __getattr__(self, name): if name.startswith("c"): index = name[1:] if len(index) > 2: raise AttributeError("Cij indices must be between 1 and 6") i = int(index[0]) j = int(index[1]) if i > 6 or i < 1 or j > 6 or j < 1: raise AttributeError("Cij indices must be between 1 and 6") if callable(self.transform): cij = Cijkl2Cij(self.transform(Cij2Cijkl(self.cij))) else: cij = self.cij return cij[i - 1, j - 1] else: object.__getattribute__(self, name) def _getmu(self): return self.cij[3, 3] def _getlam(self): return self.cij[0, 1] def _getnu(self): return self.lam / 2. / (self.mu + self.lam) def _getdensity(self): return self._density density = property(_getdensity) mu = property(_getmu) lam = property(_getlam) nu = property(_getnu) @abc.abstractmethod def delta(self, en='config'): """ abstract method which every implementation of a Material has to override """ pass @abc.abstractmethod def ibeta(self, en='config'): """ abstract method which every implementation of a Material has to override """ pass def chi0(self, en='config'): """ calculates the complex chi_0 values often needed in simulations. They are closely related to delta and beta (n = 1 + chi_r0/2 + ichi_i0/2 vs. n = 1 - delta + ibeta) """ return (-2 * self.delta(en) + 2j * self.ibeta(en)) def idx_refraction(self, en="config"): """ function to calculate the complex index of refraction of a material in the x-ray range Parameters ---------- en : energy of the x-rays, if omitted the value from the xrayutilities configuration is used Returns ------- n (complex) """ n = 1. - self.delta(en) + 1.j * self.ibeta(en) return n def critical_angle(self, en='config', deg=True): """ calculate critical angle for total external reflection Parameters ---------- en : float or str, optional energy of the x-rays in eV, if omitted the value from the xrayutilities configuration is used deg : bool, optional return angle in degree if True otherwise radians (default:True) Returns ------- float Angle of total external reflection """ rn = 1. - self.delta(en) alphac = numpy.arccos(rn) if deg: alphac = numpy.degrees(alphac) return alphac def absorption_length(self, en='config'): """ wavelength dependent x-ray absorption length defined as mu = lambda/(2pi2beta) with lambda and beta as the x-ray wavelength and complex part of the refractive index respectively. Parameters ---------- en : float or str, optional energy of the x-rays in eV Returns ------- float the absorption length in um """ if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) return utilities.en2lam(en) / (2 numpy.pi * self.ibeta(en) * 2) / 1e4 def __str__(self): ostr = "%s: %s\n" % (self.__class__.__name__, self.name) if numpy.any(self.cij): ostr += "Elastic tensor (6x6):\n" d = numpy.get_printoptions() numpy.set_printoptions(precision=2, linewidth=78, suppress=False) ostr += str(self.cij) + '\n' numpy.set_printoptions(*d) return ostr class Amorphous(Material): """ amorphous materials are described by this class """ def __init__(self, name, density, atoms=None, cij=None): """ constructor of an amorphous material. The amorphous material is described by its density and atom composition. Parameters ---------- name : str name of the material. To allow automatic parsing of the chemical elements use the abbreviation of the chemical element from the periodic table. To specify alloys, use e.g. 'Ir0.2Mn0.8' or 'H2O'. density : float mass density in kg/m^3 atoms : list, optional list of atoms together with their fractional content. When the name is a simply chemical formula then this can be None. To specify more complicated materials use [('Ir', 0.2), ('Mn', 0.8), ...]. Instead of the elements as string you can also use an Atom object. If the contents to not add up to 1 they will be normalized without notice. cij : array-like, optional elasticity matrix """ super().__init__(name, cij) self._density = density self.base = list() if atoms is None: comp = Amorphous.parseChemForm(name) if config.VERBOSITY >= config.DEBUG: print("XU.materials.Amorphous: using '%s' as chemical formula" % ''.join(['%s%.2f ' % (e.name, c) for e, c in comp])) for (e, c) in comp: self.base.append((e, c)) else: frsum = numpy.sum([at[1] for at in atoms]) for at, fr in atoms: if not isinstance(at, Atom): a = getattr(elements, at) else: a = at self.base.append((a, fr/frsum)) @staticmethod def parseChemForm(cstring): """ Parse a string containing a simple chemical formula and transform it to a list of elements together with their relative atomic fraction. e.g. 'H2O' -> [(H, 2/3), (O, 1/3)], where H and O are the Element objects of Hydrogen and Oxygen. Note that every chemical element needs to start with a capital letter! Complicated formulas containing bracket are not supported! Parameters ---------- cstring : str string containing the chemical fomula Returns ------- list of tuples chemical element and atomic fraction """ if re.findall(r'[\(\)]', cstring): raise ValueError('unsupported chemical formula (%s) given.' % cstring) elems = re.findall('[A-Z][^A-Z]', cstring) r = re.compile(r"([a-zA-Z]+)([0-9\.]+)") ret = [] csum = 0 for e in elems: if r.match(e): elstr, cont = r.match(e).groups() cont = float(cont) else: elstr, cont = (e, 1.0) ret.append((elstr, cont)) csum += cont for i, r in enumerate(ret): ret[i] = (getattr(elements, r[0]), r[1]/csum) return ret def _get_f(self, q, en): """ optimized method to calculate the atomic scattering factor for all atoms in the unit cell by calling the database only as much as needed. Parameters ---------- q : float or array-like momentum transfer for which the atomic scattering factor should be calculated en : float or str x-ray energy (eV) Returns ------- list atomic scattering factors for every atom in the unit cell """ f = {} for at, occ in self.base: if at.num not in f: f[at.num] = at.f(q, en) return [f[a.num] for a, o in self.base] def delta(self, en='config'): """ function to calculate the real part of the deviation of the refractive index from 1 (n=1-delta+ibeta) Parameters ---------- en : float, array-like or str, optional energy of the x-rays in eV Returns ------- float or array-like """ re = scipy.constants.physical_constants['classical electron radius'][0] re = 1e10 if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) lam = utilities.en2lam(en) delta = 0. m = 0. f = self._get_f(0., en) for (at, occ), fa in zip(self.base, f): delta += numpy.real(fa) * occ m += at.weight * occ delta = re / (2 numpy.pi) * lam ** 2 / (m / self.density) * 1e-30 return delta def ibeta(self, en='config'): """ function to calculate the imaginary part of the deviation of the refractive index from 1 (n=1-delta+ibeta) Parameters ---------- en : float, array-like or str, optional energy of the x-rays in eV Returns ------- float or array-like """ re = scipy.constants.physical_constants['classical electron radius'][0] re = 1e10 if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) lam = utilities.en2lam(en) beta = 0. m = 0. f = self._get_f(0., en) for (at, occ), fa in zip(self.base, f): beta += numpy.imag(fa) * occ m += at.weight * occ beta = re / (2 numpy.pi) * lam ** 2 / (m / self.density) * 1e-30 return beta def chi0(self, en='config'): """ calculates the complex chi_0 values often needed in simulations. They are closely related to delta and beta (n = 1 + chi_r0/2 + ichi_i0/2 vs. n = 1 - delta + ibeta) """ re = scipy.constants.physical_constants['classical electron radius'][0] re = 1e10 if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) lam = utilities.en2lam(en) beta = 0. delta = 0. m = 0. f = self._get_f(0., en) for (at, occ), f0 in zip(self.base, f): beta += numpy.imag(f0) occ delta += numpy.real(f0) * occ m += at.weight * occ beta = re / (2 numpy.pi) * lam ** 2 / (m / self.density) * 1e-30 delta = re / (2 numpy.pi) * lam ** 2 / (m / self.density) * 1e-30 return (-2 * delta + 2j * beta) def __str__(self): ostr = super().__str__() ostr += "density: %.2f\n" % self.density if self.base: ostr += "atoms: " for at, o in self.base: ostr += "(%s, %.3f) " % (at.name, o) ostr += "\n" return ostr class Crystal(Material): """ Crystalline materials are described by this class """ def __init__(self, name, lat, cij=None, thetaDebye=None): super().__init__(name, cij) self.lattice = lat if isinstance(thetaDebye, numbers.Number): self.thetaDebye = float(thetaDebye) else: self.thetaDebye = thetaDebye @classmethod def fromCIF(cls, ciffilestr, kwargs): """ Create a Crystal from a CIF file. The default data-set from the cif file will be used to create the Crystal. Parameters ---------- ciffilestr : str, bytes filename of the CIF file or string representation of the CIF file kwargs : dict keyword arguments are passed to the init-method of CIFFile Returns ------- Crystal """ cf = cif.CIFFile(ciffilestr, kwargs) lat = cf.SGLattice() return cls(cf.data[cf._default_dataset].name, lat) def loadLatticefromCIF(self, ciffilestr): """ load the unit cell data (lattice) from the CIF file. Other material properties stay unchanged. Parameters ---------- ciffilestr : str, bytes filename of the CIF file or string representation of the CIF file """ cf = cif.CIFFile(ciffilestr) self.lattice = cf.SGLattice() def toCIF(self, ciffilename): """ Export the Crystal to a CIF file. Parameters ---------- ciffilename : str filename of the CIF file """ cif.cifexport(ciffilename, self) @property def a(self): return self.lattice.a @property def b(self): return self.lattice.b @property def c(self): return self.lattice.c @property def alpha(self): return self.lattice.alpha @property def beta(self): return self.lattice.beta @property def gamma(self): return self.lattice.gamma @property def a1(self): return self.lattice._ai[0, :] @property def a2(self): return self.lattice._ai[1, :] @property def a3(self): return self.lattice._ai[2, :] @property def B(self): return self.lattice._qtransform.matrix def __eq__(self, other): """ compare if another Crystal instance is equal to the current one. Currently this considers only the lattice to be equal. Additional parameters like thetaDebye and the eleastic parameters are ignored. Parameters ---------- other: Crystal another instance of Crystal to compare """ return self.lattice == other.lattice def Q(self, hkl): """ Return the Q-space position for a certain material. Parameters ---------- hkl : list or array-like Miller indices (or Q(h, k, l) is also possible) """ return self.lattice.GetQ(hkl) def HKL(self, q): """ Return the HKL-coordinates for a certain Q-space position. Parameters ---------- q : list or array-like Q-position. its also possible to use HKL(qx, qy, qz). """ return self.lattice.GetHKL(q) def chemical_composition(self, natoms=None, with_spaces=False, ndigits=2): """ determine chemical composition from occupancy of atomic positions. Parameters ---------- mat : Crystal instance of Crystal natoms : int, optional number of atoms to normalize the formula, if None some automatic normalization is attempted using the greatest common divisor of the number of atoms per unit cell. If the number of atoms of any element is fractional natoms=1 is used. with_spaces : bool, optional add spaces between the different entries in the output string for CIF combatibility ndigits : int, optional number of digits to which floating point numbers are rounded to Returns ------- str representation of the chemical composition """ elem = {} for a in self.lattice.base(): e = a[0].name occ = a[2] if e in elem: elem[e] += occ else: elem[e] = occ natom = sum([elem[e] for e in elem]) isint = True for e in elem: if not float(elem[e]).is_integer(): isint = False # determine number of atoms if not natoms: if isint: gcd = math.gcd([int(elem[e]) for e in elem]) natoms = natom/gcd else: natoms = 1 # generate output strig cstr = '' fmtstr = '%d' if isint else '%%.%df' % ndigits for e in elem: n = elem[e] / float(natom) * natoms cstr += e if n != 1: cstr += fmtstr % n cstr += ' ' if with_spaces else '' return cstr.strip() def environment(self, pos, kwargs): """ Returns a list of neighboring atoms for a given position within the unit cell. If the material does not contain any atoms a dummy atom will be placed on the unit cell corners. Parameters ---------- pos : list or array-like fractional coordinate in the unit cell maxdist : float maximum distance wanted in the list of neighbors (default: 7) Returns ------- list of tuples (distance, atomType, multiplicity) giving distance sorted list of atoms """ valid_kwargs = {'maxdist': 'maximum distance needed in the output'} utilities.check_kwargs(kwargs, valid_kwargs, 'Crystal.environment') maxdist = kwargs.get('maxdist', 7) if len(pos) < 3: pos = pos[0] if len(pos) < 3: raise InputError("need 3 coordinates of the " "reference position") refpos = self.lattice._ai.T @ pos lst = [] # determine lattice base if self.lattice.nsites > 0: base = list(self.lattice.base()) else: base = [(elements.Dummy, (0, 0, 0), 1, 0)] # find maximally needed super cell na = int(ceil(maxdist / math.VecNorm(self.a1))) nb = int(ceil(maxdist / math.VecNorm(self.a2))) nc = int(ceil(maxdist / math.VecNorm(self.a3))) nab = int(ceil(maxdist / math.VecNorm(self.a1 + self.a2))) nac = int(ceil(maxdist / math.VecNorm(self.a1 + self.a3))) nbc = int(ceil(maxdist / math.VecNorm(self.a2 + self.a3))) nabc = int(ceil(maxdist / math.VecNorm(self.a1 + self.a2 + self.a3))) Na = max(na, nab, nac, nabc) Nb = max(nb, nab, nbc, nabc) Nc = max(nc, nac, nbc, nabc) # determine distance of all atoms w.r.t. the refpos ucidx = numpy.mgrid[-Na:Na+1, -Nb:Nb+1, -Nc:Nc+1].reshape(3, -1) for a, p, o, b in base: ucpos = self.lattice._ai.T @ p pos = ucpos + numpy.einsum('ji, ...i', self.lattice._ai.T, ucidx.T) distance = math.VecNorm(pos - refpos) lst += [(d, a, o) for d in distance] # sort and merge return list lst.sort(key=operator.itemgetter(0, 1)) rl = [] if len(lst) < 1 or lst[0][0] > maxdist: return rl mult = lst[0][2] for i in range(1, len(lst)): if (isclose(lst[i - 1][0] - lst[i][0], 0, abs_tol=1e-8) and lst[i - 1][1] == lst[i][1]): mult += lst[i - 1][2] # add occupancy else: rl.append((lst[i - 1][0], lst[i - 1][1], mult)) mult = lst[i][2] if lst[i][0] > maxdist: break return rl def planeDistance(self, hkl): """ determines the lattice plane spacing for the planes specified by (hkl) Parameters ---------- h, k, l : list, tuple or floats Miller indices of the lattice planes given either as list, tuple or seperate arguments Returns ------- float the lattice plane spacing Examples -------- >>> xu.materials.Si.planeDistance(0, 0, 4) 1.3577600000000001 or >>> xu.materials.Si.planeDistance((1, 1, 1)) 3.1356124059796255 """ if len(hkl) < 3: hkl = hkl[0] if len(hkl) < 3: raise InputError("need 3 indices for the lattice point") return 2 * numpy.pi / math.VecNorm(self.Q(hkl)) def _getdensity(self): """ calculates the mass density of an material from the mass of the atoms in the unit cell. Returns ------- float mass density in kg/m^3 """ m = 0. for at, pos, occ, b in self.lattice.base(): m += at.weight * occ return m / self.lattice.UnitCellVolume() * 1e30 density = property(_getdensity) def _get_f(self, q, en): """ optimized method to calculate the atomic scattering factor for all atoms in the unit cell by calling the database only as much as needed. Parameters ---------- q : float or array-like momentum transfer for which the atomic scattering factor should be calculated en : float or str x-ray energy (eV) Returns ------- list atomic scattering factors for every atom in the unit cell """ f = {} if self.lattice.nsites > 0: for at, pos, occ, b in self.lattice.base(): if at.num not in f: f[at.num] = at.f(q, en) return [f[a.num] for a, p, o, b in self.lattice.base()] else: return None def _get_lamen(self, en): if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) lam = utilities.en2lam(en) return lam, en def delta(self, en='config'): """ function to calculate the real part of the deviation of the refractive index from 1 (n=1-delta+ibeta) Parameters ---------- en : float or str, optional x-ray energy eV, if omitted the value from the xrayutilities configuration is used Returns ------- float """ re = scipy.constants.physical_constants['classical electron radius'][0] re = 1e10 lam, en = self._get_lamen(en) delta = 0. f = self._get_f(0, en) for (at, pos, occ, b), fa in zip(self.lattice.base(), f): delta += numpy.real(fa) * occ delta = re / (2 numpy.pi) * lam ** 2 / \ self.lattice.UnitCellVolume() return delta def ibeta(self, en='config'): """ function to calculate the imaginary part of the deviation of the refractive index from 1 (n=1-delta+ibeta) Parameters ---------- en : float or str, optional x-ray energy eV, if omitted the value from the xrayutilities configuration is used Returns ------- float """ re = scipy.constants.physical_constants['classical electron radius'][0] re = 1e10 lam, en = self._get_lamen(en) beta = 0. f = self._get_f(0, en) for (at, pos, occ, b), fa in zip(self.lattice.base(), f): beta += numpy.imag(fa) * occ beta = re / (2 numpy.pi) * lam ** 2 / self.lattice.UnitCellVolume() return beta def chi0(self, en='config'): """ calculates the complex chi_0 values often needed in simulations. They are closely related to delta and beta (n = 1 + chi_r0/2 + ichi_i0/2 vs. n = 1 - delta + ibeta) """ re = scipy.constants.physical_constants['classical electron radius'][0] re = 1e10 lam, en = self._get_lamen(en) beta = 0. delta = 0. if self.lattice.nsites > 0: f = self._get_f(0, en) for (at, pos, occ, b), f0 in zip(self.lattice.base(), f): beta += numpy.imag(f0) occ delta += numpy.real(f0) * occ v = self.lattice.UnitCellVolume() beta = re / (2 numpy.pi) * lam ** 2 / v delta = re / (2 numpy.pi) * lam ** 2 / v return (-2 * delta + 2j * beta) def _debyewallerfactor(self, temp, qnorm): """ Calculate the Debye Waller temperature factor according to the Debye temperature Parameters ---------- temp : float actual temperature (K) qnorm : float or array-like norm of the q-vector(s) for which the factor should be calculated Returns ------- float or array-like the Debye Waller factor(s) with the same shape as qnorm """ if temp != 0 and self.thetaDebye: # W(q) = 3/2* hbar^2q^2/(mkBtD) (D1(tD/T)/(tD/T) + 1/4) # DWF = exp(-W(q)) consistent with Vaclav H. and several books hbar = scipy.constants.hbar kb = scipy.constants.Boltzmann x = self.thetaDebye / float(temp) m = 0. im = 0 for a, p, o, b in self.lattice.base(): m += a.weight im += 1 m = m / float(im) exponentf = 3 / 2. * hbar ** 2 * 1.0e20 / \ (m * kb * self.thetaDebye) * (math.Debye1(x) / x + 0.25) if config.VERBOSITY >= config.DEBUG: print("XU.materials.Crystal: DWF = exp(-Wq2) W= %g" % exponentf) dwf = numpy.exp(-exponentf qnorm ** 2) else: dwf = 1.0 return dwf def chih(self, q, en='config', temp=0, polarization='S'): """ calculates the complex polarizability of a material for a certain momentum transfer and energy Parameters ---------- q : list, tuple or array-like momentum transfer vector in (1/A) en : float or str, optional x-ray energy eV, if omitted the value from the xrayutilities configuration is used temp : float, optional temperature used for Debye-Waller-factor calculation polarization : {'S', 'P'}, optional sigma or pi polarization Returns ------- tuple (abs(chih_real), abs(chih_imag)) complex polarizability """ if isinstance(q, (list, tuple)): q = numpy.array(q, dtype=numpy.double) elif isinstance(q, numpy.ndarray): pass else: raise TypeError("q must be a list or numpy array!") qnorm = math.VecNorm(q) if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) if polarization not in ('S', 'P'): raise ValueError("polarization must be 'S':sigma or 'P': pi!") if self.lattice.nsites == 0: return (0, 0) dwf = self._debyewallerfactor(temp, qnorm) sr = 0. + 0.j si = 0. + 0.j # a: atom, p: position, o: occupancy, b: temperature-factor f = self._get_f(qnorm, en) for (a, p, o, b), F in zip(self.lattice.base(), f): r = self.lattice.GetPoint(p) if temp == 0: dwf = numpy.exp(-b * qnorm ** 2 / (4 * numpy.pi) ** 2) fr = numpy.real(F) * o fi = numpy.imag(F) * o sr += fr * numpy.exp(-1.j * math.VecDot(q, r)) * dwf si += fi * numpy.exp(-1.j * math.VecDot(q, r)) * dwf # classical electron radius c = scipy.constants r_e = 1 / (4 * numpy.pi * c.epsilon_0) * c.e ** 2 / \ (c.electron_mass * c.speed_of_light ** 2) * 1e10 lam = utilities.en2lam(en) fact = -lam ** 2 * r_e / (numpy.pi * self.lattice.UnitCellVolume()) rchi = numpy.abs(fact * sr) ichi = numpy.abs(fact * si) if polarization == 'P': theta = numpy.arcsin(qnorm * utilities.en2lam(en) / (4numpy.pi)) rchi = numpy.cos(2 * theta) ichi = numpy.cos(2 theta) return rchi, ichi def dTheta(self, Q, en='config'): """ function to calculate the refractive peak shift Parameters ---------- Q : list, tuple or array-like momentum transfer vector (1/A) en : float or str, optional x-ray energy eV, if omitted the value from the xrayutilities configuration is used Returns ------- float peak shift in degree """ if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) lam = utilities.en2lam(en) dth = numpy.degrees( 2 * self.delta(en) / numpy.sin(2 * numpy.arcsin( lam * VecNorm(Q) / (4 * numpy.pi)))) return dth def __str__(self): ostr = super().__str__() ostr += "Lattice:\n" ostr += str(self.lattice) return ostr def StructureFactor(self, q, en='config', temp=0): """ calculates the structure factor of a material for a certain momentum transfer and energy at a certain temperature of the material Parameters ---------- q : list, tuple or array-like vectorial momentum transfer en : float or str, optional x-ray energy eV, if omitted the value from the xrayutilities configuration is used temp : float temperature used for Debye-Waller-factor calculation Returns ------- complex the complex structure factor """ if isinstance(q, (list, tuple)): q = numpy.array(q, dtype=numpy.double) elif isinstance(q, numpy.ndarray): pass else: raise TypeError("q must be a list or numpy array!") if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) if self.lattice.nsites == 0: return 1. qnorm = math.VecNorm(q) dwf = self._debyewallerfactor(temp, qnorm) s = 0. + 0.j f = self._get_f(qnorm, en) # a: atom, p: position, o: occupancy, b: temperature-factor for (a, p, o, b), fq in zip(self.lattice.base(), f): r = self.lattice.GetPoint(p) if temp == 0: dwf = numpy.exp(-b * qnorm ** 2 / (4 * numpy.pi) ** 2) s += fq * o * numpy.exp(-1.j * math.VecDot(q, r)) * dwf return s def StructureFactorForEnergy(self, q0, en, temp=0): """ calculates the structure factor of a material for a certain momentum transfer and a bunch of energies Parameters ---------- q0 : list, tuple or array-like vectorial momentum transfer en : list, tuple or array-like energy values in eV temp : float temperature used for Debye-Waller-factor calculation Returns ------- array-like complex valued structure factor array """ if isinstance(q0, (list, tuple)): q = numpy.array(q0, dtype=numpy.double) elif isinstance(q0, numpy.ndarray): q = q0 else: raise TypeError("q must be a list or numpy array!") qnorm = math.VecNorm(q) if isinstance(en, (list, tuple)): en = numpy.array(en, dtype=numpy.double) elif isinstance(en, numpy.ndarray): pass else: raise TypeError("Energy data must be provided as a list " "or numpy array!") if self.lattice.nsites == 0: return numpy.ones(len(en)) dwf = self._debyewallerfactor(temp, qnorm) s = 0. + 0.j f = self._get_f(qnorm, en) # a: atom, p: position, o: occupancy, b: temperature-factor for (a, p, o, b), fq in zip(self.lattice.base(), f): if temp == 0: dwf = numpy.exp(-b * qnorm ** 2 / (4 * numpy.pi) ** 2) r = self.lattice.GetPoint(p) s += fq * o * dwf * numpy.exp(-1.j * math.VecDot(q, r)) return s def StructureFactorForQ(self, q, en0='config', temp=0): """ calculates the structure factor of a material for a bunch of momentum transfers and a certain energy Parameters ---------- q : list of vectors or array-like vectorial momentum transfers; list of vectores (list, tuple or array) of length 3 e.g.: (Si.Q(0, 0, 4), Si.Q(0, 0, 4.1),...) or numpy.array([Si.Q(0, 0, 4), Si.Q(0, 0, 4.1)]) en0 : float or str, optional x-ray energy eV, if omitted the value from the xrayutilities configuration is used temp : float temperature used for Debye-Waller-factor calculation Returns ------- array-like complex valued structure factor array """ if isinstance(q, (list, tuple, numpy.ndarray)): q = numpy.asarray(q, dtype=numpy.double) else: raise TypeError("q must be a list or numpy array!") if len(q.shape) != 2: raise ValueError("q does not have the correct shape (shape = %s)" % str(q.shape)) qnorm = numpy.linalg.norm(q, axis=1) if isinstance(en0, str) and en0 == 'config': en0 = utilities.energy(config.ENERGY) if self.lattice.nsites == 0: return numpy.ones(len(q)) dwf = self._debyewallerfactor(temp, qnorm) s = 0. + 0.j f = self._get_f(qnorm, en0) # a: atom, p: position, o: occupancy, b: temperature-factor for (a, p, o, b), fq in zip(self.lattice.base(), f): if temp == 0: dwf = numpy.exp(-b * qnorm ** 2 / (4 * numpy.pi) ** 2) r = self.lattice.GetPoint(p) s += fq * o * numpy.exp(-1.j * numpy.dot(q, r)) * dwf return s def ApplyStrain(self, strain): """ Applies a certain strain on the lattice of the material. The result is a change in the base vectors of the real space as well as reciprocal space lattice. The full strain matrix (3x3) needs to be given. Note: NO elastic response of the material will be considered! """ # let strain act on the unit cell vectors self.lattice.ApplyStrain(strain) def GetMismatch(self, mat): """ Calculate the mismatch strain between the material and a second material """ raise NotImplementedError("XU.material.GetMismatch: " "not implemented yet") def distances(self): """ function to obtain distances of atoms in the crystal up to the unit cell size (largest value of a, b, c is the cut-off) returns a list of tuples with distance d and number of occurence n [(d1, n1), (d2, n2),...] Note: if the base of the material is empty the list will be empty """ if self.lattice.nsites == 0: return [] cutoff = numpy.max((self.lattice.a, self.lattice.b, self.lattice.c)) tmp_data = [] for at1 in self.lattice.base(): for at2 in self.lattice.base(): dis = math.VecNorm(self.lattice.GetPoint(at1[1] - at2[1])) dis2 = math.VecNorm(self.lattice.GetPoint( at1[1] - at2[1] + numpy.array((1, 0, 0)))) dis3 = math.VecNorm(self.lattice.GetPoint( at1[1] - at2[1] + numpy.array((0, 1, 0)))) dis4 = math.VecNorm(self.lattice.GetPoint( at1[1] - at2[1] + numpy.array((0, 0, 1)))) dis5 = math.VecNorm(self.lattice.GetPoint( at1[1] - at2[1] + numpy.array((-1, 0, 0)))) dis6 = math.VecNorm(self.lattice.GetPoint( at1[1] - at2[1] + numpy.array((0, -1, 0)))) dis7 = math.VecNorm(self.lattice.GetPoint( at1[1] - at2[1] + numpy.array((0, 0, -1)))) distances = sorted([dis, dis2, dis3, dis4, dis5, dis6, dis7]) for dis in distances: if dis < cutoff: tmp_data.append(dis) # sort the list and compress equal entries tmp_data.sort() self._distances = [0] self._dis_hist = [0] for dis in tmp_data: if numpy.round(dis - self._distances[-1], config.DIGITS) == 0: self._dis_hist[-1] += 1 else: self._distances.append(dis) self._dis_hist.append(1) # create return value ret = [] for i in range(len(self._distances)): ret.append((self._distances[i], self._dis_hist[i])) return ret def show_unitcell(self, fig=None, subplot=111, scale=0.6, complexity=11, linewidth=1.5, mode='matplotlib'): """ visualization of the unit cell using either matplotlibs basic 3D functionality (expect rendering inaccuracies!) or the mayavi mlab package (accurate rendering -> recommended!) Note: For more flexible visualization consider using the CIF-export feature and use a proper crystal structure viewer. Parameters ---------- fig : matplotlib Figure, Mayavi Scene, or None, optional subplot : int or list, optional subplot to use for the visualization when using matplotlib. This argument of fowarded to the first argument of matplotlibs `add_subplot` function scale : float, optional scale the size of the atoms by this additional factor. By default the size of the atoms corresponds to 60% of their atomic radius. complexity : int, optional number of steps to approximate the atoms as spheres. Higher values make spheres more accurate, but cause slower plotting. linewidth : float, optional line thickness of the unit cell outline mode : str, optional defines the plot backend used, can be 'matplotlib' (default) or 'mayavi'. Returns ------- figure object of either matplotlib or Mayavi """ if mode == 'matplotlib': plot, plt = utilities.import_matplotlib_pyplot('XU.materials') try: import mpl_toolkits.mplot3d except ImportError: plot = False else: plot, mlab = utilities.import_mayavi_mlab('XU.materials') try: import mayavi from matplotlib.colors import to_rgb except ImportError: plot = False if not plot: print('matplotlib and/or mayavi.mlab needed for show_unitcell()') return def plot_sphere(fig, vecpos, r, alpha, complexity, color): """ Visualize a sphere using either matplotlib or Mayavi """ if mode == 'matplotlib': ax = fig.gca() phi, theta = numpy.mgrid[0:numpy.pi:1jcomplexity, 0:2numpy.pi:1jcomplexity] x = rnumpy.sin(phi)numpy.cos(theta) + vecpos[0] y = rnumpy.sin(phi)numpy.sin(theta) + vecpos[1] z = rnumpy.cos(phi) + vecpos[2] ax.plot_surface(x, y, z, rstride=1, cstride=1, color=color, alpha=alpha, linewidth=0) else: mlab.points3d(vecpos[0], vecpos[1], vecpos[2], r, opacity=alpha, transparent=False, color=to_rgb(color), resolution=complexity, scale_factor=2, figure=fig) def plot_line(fig, start, end, color, linewidth): """ Draw a line between two 3D points, either using matplotlib or Mayavi. """ if mode == 'matplotlib': ax = fig.gca() ax.plot((start[0], end[0]), (start[1], end[1]), (start[2], end[2]), color=color, lw=linewidth) else: mlab.plot3d((start[0], end[0]), (start[1], end[1]), (start[2], end[2]), color=to_rgb(color), tube_radius=linewidth/20, figure=fig) if mode == 'matplotlib': if fig is None: fig = plt.figure() elif not isinstance(fig, plt.Figure): raise TypeError("'fig' argument must be a matplotlib figure!") ax = fig.add_subplot(subplot, projection='3d') else: if fig is None: fig = mlab.figure(bgcolor=(1, 1, 1)) elif not isinstance(fig, mayavi.core.scene.Scene): raise TypeError("'fig' argument must be a Mayavi Scene!") for a, pos, occ, b in self.lattice.base(): r = a.radius * scale for i in range(-1, 2): for j in range(-1, 2): for k in range(-1, 2): atpos = (pos + [i, j, k]) if all(a > -config.EPSILON and a < 1+config.EPSILON for a in atpos): vecpos = atpos[0]self.a1 + atpos[1]self.a2 +\ atpos[2]self.a3 plot_sphere(fig, vecpos, r, occ, complexity, a.color) # plot unit cell outlines plot_line(fig, (0, 0, 0), self.a1, 'k', linewidth) plot_line(fig, (0, 0, 0), self.a2, 'k', linewidth) plot_line(fig, (0, 0, 0), self.a3, 'k', linewidth) plot_line(fig, self.a1, self.a1+self.a2, 'k', linewidth) plot_line(fig, self.a1, self.a1+self.a3, 'k', linewidth) plot_line(fig, self.a2, self.a1+self.a2, 'k', linewidth) plot_line(fig, self.a2, self.a2+self.a3, 'k', linewidth) plot_line(fig, self.a3, self.a1+self.a3, 'k', linewidth) plot_line(fig, self.a3, self.a2+self.a3, 'k', linewidth) plot_line(fig, self.a1+self.a2, self.a1+self.a2+self.a3, 'k', linewidth) plot_line(fig, self.a1+self.a3, self.a1+self.a2+self.a3, 'k', linewidth) plot_line(fig, self.a2+self.a3, self.a1+self.a2+self.a3, 'k', linewidth) if mode == 'matplotib': if config.VERBOSITY >= config.INFO_LOW: warnings.warn("show_unitcell: 3D projection might appear " "distorted (limited 3D capabilities of " "matplotlib!). Use mayavi mode or CIF " "export and other viewers for better " "visualization.") plt.tight_layout() return fig def CubicElasticTensor(c11, c12, c44): """ Assemble the 6x6 matrix of elastic constants for a cubic material from the three independent components of a cubic crystal Parameters ---------- c11, c12, c44 : float independent components of the elastic tensor of cubic materials Returns ------- cij : ndarray 6x6 matrix with elastic constants """ m = numpy.zeros((6, 6), dtype=numpy.double) m[0, 0] = c11 m[1, 1] = c11 m[2, 2] = c11 m[3, 3] = c44 m[4, 4] = c44 m[5, 5] = c44 m[0, 1] = m[0, 2] = c12 m[1, 0] = m[1, 2] = c12 m[2, 0] = m[2, 1] = c12 return m def HexagonalElasticTensor(c11, c12, c13, c33, c44): """ Assemble the 6x6 matrix of elastic constants for a hexagonal material from the five independent components of a hexagonal crystal Parameters ---------- c11, c12, c13, c33, c44 : float independent components of the elastic tensor of a hexagonal material Returns ------- cij : ndarray 6x6 matrix with elastic constants """ m = numpy.zeros((6, 6), dtype=numpy.double) m[0, 0] = m[1, 1] = c11 m[2, 2] = c33 m[3, 3] = m[4, 4] = c44 m[5, 5] = 0.5 (c11 - c12) m[0, 1] = m[1, 0] = c12 m[0, 2] = m[1, 2] = m[2, 0] = m[2, 1] = c13 return m def WZTensorFromCub(c11ZB, c12ZB, c44ZB): """ Determines the hexagonal elastic tensor from the values of the cubic elastic tensor under the assumptions presented in Phys. Rev. B 6, 4546 (1972), which are valid for the WZ <-> ZB polymorphs. Parameters ---------- c11, c12, c44 : float independent components of the elastic tensor of cubic materials Returns ------- cij : ndarray 6x6 matrix with elastic constants Implementation according to a patch submitted by Julian Stangl """ # matrix conversions: cubic (111) to hexagonal (001) direction P = (1 / 6.) * numpy.array([[3, 3, 6], [2, 4, 8], [1, 5, -2], [2, 4, -4], [2, -2, 2], [1, -1, 4]]) Q = (1 / (3 * numpy.sqrt(2))) * numpy.array([1, -1, -2]) cZBvec = numpy.array([c11ZB, c12ZB, c44ZB]) cWZvec_BAR = numpy.dot(P, cZBvec) delta = numpy.dot(Q, cZBvec) D = numpy.array([delta2 / cWZvec_BAR[2], 0, -delta2 / cWZvec_BAR[2], 0, delta2 / cWZvec_BAR[0], delta2 / cWZvec_BAR[2]]) cWZvec = cWZvec_BAR - D.T return HexagonalElasticTensor(cWZvec[0], cWZvec[2], cWZvec[3], cWZvec[1], cWZvec[4]) class Alloy(Crystal): """ alloys two materials from the same crystal system. If the materials have the same space group the Wyckoff positions within the unit cell will also reflect the alloying. """ def __init__(self, matA, matB, x): self.check_compatibility(matA, matB) lat = copy.deepcopy(matA.lattice) super().__init__("None", lat, matA.cij) self.matA = matA self.matB = matB self._setxb(x) @staticmethod def check_compatibility(matA, matB): csA = matA.lattice.crystal_system.split(':')[0] csB = matB.lattice.crystal_system.split(':')[0] if csA != csB: raise InputError("Crystal systems of the two materials are " "incompatible!") @staticmethod def lattice_const_AB(latA, latB, x, name=''): """ method to calculated the interpolation of lattice parameters and unit cell angles of the Alloy. By default linear interpolation between the value of material A and B is performed. Parameters ---------- latA, latB : float or vector property (lattice parameter/angle) of material A and B. A property can be a scalar or vector. x : float fraction of material B in the alloy. name : str, optional label of the property which is interpolated. Can be 'a', 'b', 'c', 'alpha', 'beta', or 'gamma'. """ return (latB - latA) * x + latA def _getxb(self): return self._xb def _setxb(self, x): self._xb = x self.name = ("%s(%2.2f)%s(%2.2f)" % (self.matA.name, 1-x, self.matB.name, x)) # modify the free parameters of the lattice for k in self.lattice.free_parameters: setattr(self.lattice, k, self.lattice_const_AB(getattr(self.matA, k), getattr(self.matB, k), x, name=k)) # set elastic constants self.cij = (self.matB.cij - self.matA.cij) * x + self.matA.cij self.cijkl = (self.matB.cijkl - self.matA.cijkl) * x + self.matA.cijkl # alloying in unit cell if self.matA.lattice.space_group == self.matB.lattice.space_group: self.lattice._wbase = WyckoffBase() for a, wp, o, b in self.matA.lattice._wbase: self.lattice._wbase.append(a, wp, occ=o(1-x), b=b) for a, wp, o, b in self.matB.lattice._wbase: if (a, wp, o, b) in self.lattice._wbase: idx = self.lattice._wbase.index((a, wp, o, b)) occ = self.lattice._wbase[idx][2] self.lattice._wbase[idx] = (a, wp, occ+ox, b) else: self.lattice._wbase.append(a, wp, occ=ox, b=b) x = property(_getxb, _setxb) def _checkfinitenumber(self, arg, name=""): if isinstance(arg, numbers.Number) and numpy.isfinite(arg): return float(arg) else: raise TypeError("argument (%s) must be a scalar!" % name) def _checkarray(self, arg, name=""): if isinstance(arg, (list, tuple, numpy.ndarray)): return numpy.asarray(arg, dtype=numpy.double) else: raise TypeError("argument (%s) must be of type " "list, tuple or numpy.ndarray" % name) def _definehelpers(self, hkl, cijA, cijB): """ define helper functions for solving the content from reciprocal space positions """ def a1(x): return self.lattice_const_AB(self.matA.a1, self.matB.a1, x, name='a') def a2(x): return self.lattice_const_AB(self.matA.a2, self.matB.a2, x, name='b') def a3(x): return self.lattice_const_AB(self.matA.a3, self.matB.a3, x, name='c') def V(x): return numpy.dot(a3(x), numpy.cross(a1(x), a2(x))) def b1(x): return 2 numpy.pi / V(x) * numpy.cross(a2(x), a3(x)) def b2(x): return 2 * numpy.pi / V(x) * numpy.cross(a3(x), a1(x)) def b3(x): return 2 * numpy.pi / V(x) * numpy.cross(a1(x), a2(x)) def qhklx(x): return hkl[0] * b1(x) + hkl[1] * b2(x) + hkl[2] * b3(x) def frac(x): return ((cijB[0, 2] + cijB[1, 2] - (cijA[0, 2] + cijA[1, 2])) * x + (cijA[0, 2] + cijA[1, 2])) / \ ((cijB[2, 2] - cijA[2, 2]) * x + cijA[2, 2]) return a1, a2, a3, V, b1, b2, b3, qhklx, frac def RelaxationTriangle(self, hkl, sub, exp): """ function which returns the relaxation triangle for a Alloy of given composition. Reciprocal space coordinates are calculated using the user-supplied experimental class Parameters ---------- hkl : list or array-like Miller Indices sub : Crystal, or float substrate material or lattice constant exp : Experiment object from which the Transformation object and ndir are needed Returns ------- qy, qz : float reciprocal space coordinates of the corners of the relaxation triangle """ hkl = self._checkarray(hkl, "hkl") trans = exp._transform ndir = exp.ndir / VecNorm(exp.ndir) if isinstance(sub, Crystal): asub = sub.lattice.a elif isinstance(sub, float): asub = sub else: raise TypeError("Second argument (sub) must be of type float or " "an instance of xrayutilities.materials.Crystal") # test if inplane direction of hkl is the same as the one for the # experiment otherwise warn the user hklinplane = VecCross(VecCross(exp.ndir, hkl), exp.ndir) if not numpy.isclose(VecNorm(VecCross(hklinplane, exp.idir)), 0): warnings.warn("Alloy: given hkl differs from the geometry of the " "Experiment instance in the azimuthal direction") # transform elastic constants to correct coordinate frame cijA = Cijkl2Cij(trans(self.matA.cijkl, rank=4)) cijB = Cijkl2Cij(trans(self.matB.cijkl, rank=4)) a1, a2, a3, V, b1, b2, b3, qhklx, frac = self._definehelpers(hkl, cijA, cijB) qr_i = trans(qhklx(self.x))[1] qr_p = trans(qhklx(self.x))[2] qs_i = copysign(2numpy.pi/asub VecNorm(VecCross(ndir, hkl)), qr_i) qs_p = 2numpy.pi/asub abs(VecDot(ndir, hkl)) # calculate pseudomorphic points for A and B def abulk(x): return math.VecNorm(a1(x)) def aperp(x): return abulk(self.x) * (1 + frac(x) * (1 - asub / abulk(self.x))) qp_i = copysign(2numpy.pi/asub VecNorm(VecCross(ndir, hkl)), qr_i) qp_p = 2numpy.pi/aperp(self.x) abs(VecDot(ndir, hkl)) # assembly return values qy = numpy.array([qr_i, qp_i, qs_i, qr_i], dtype=numpy.double) qz = numpy.array([qr_p, qp_p, qs_p, qr_p], dtype=numpy.double) return qy, qz class CubicAlloy(Alloy): def __init__(self, matA, matB, x): # here one could check if material is really cubic!! Alloy.__init__(self, matA, matB, x) def ContentBsym(self, q_perp, hkl, inpr, asub, relax): """ function that determines the content of B in the alloy from the reciprocal space position of a symetric peak. As an additional input the substrates lattice parameter and the degree of relaxation must be given Parameters ---------- q_perp : float perpendicular peak position of the reflection hkl of the alloy in reciprocal space hkl : list Miller indices of the measured symmetric reflection (also defines the surface normal inpr : list Miller indices of a Bragg peak defining the inplane reference direction asub : float substrate lattice parameter relax : float degree of relaxation (needed to obtain the content from symmetric reciprocal space position) Returns ------- content : float the content of B in the alloy determined from the input variables """ # check input parameters q_perp = self._checkfinitenumber(q_perp, "q_perp") hkl = self._checkarray(hkl, "hkl") inpr = self._checkarray(inpr, "inpr") asub = self._checkfinitenumber(asub, "asub") relax = self._checkfinitenumber(relax, "relax") # calculate lattice constants from reciprocal space positions n = self.Q(hkl) / VecNorm(self.Q(hkl)) # the following line is not generally true! only cubic materials aperp = 2 * numpy.pi / q_perp * abs(VecDot(n, hkl)) # transform the elastic tensors to a coordinate frame attached to the # surface normal inp1 = VecCross(n, inpr) / VecNorm(VecCross(n, inpr)) inp2 = VecCross(n, inp1) trans = math.CoordinateTransform(inp1, inp2, n) if config.VERBOSITY >= config.DEBUG: print("XU.materials.Alloy.ContentB: inp1/inp2: ", inp1, inp2) cijA = Cijkl2Cij(trans(self.matA.cijkl, rank=4)) cijB = Cijkl2Cij(trans(self.matB.cijkl, rank=4)) a1, a2, a3, V, b1, b2, b3, qhklx, frac = self._definehelpers(hkl, cijA, cijB) # the following line is not generally true! only cubic materials def abulk_perp(x): return abs(2 * numpy.pi / numpy.inner(qhklx(x), n) * numpy.inner(n, hkl)) # can we use abulk_perp here? for cubic materials this should work?! def ainp(x): return asub + relax * (abulk_perp(x) - asub) if config.VERBOSITY >= config.DEBUG: print("XU.materials.Alloy.ContentB: abulk_perp: %8.5g" % (abulk_perp(0.))) def equation(x): return ((aperp - abulk_perp(x)) + (ainp(x) - abulk_perp(x)) * frac(x)) x = scipy.optimize.brentq(equation, -0.1, 1.1) return x def ContentBasym(self, q_inp, q_perp, hkl, sur): """ function that determines the content of B in the alloy from the reciprocal space position of an asymmetric peak. Parameters ---------- q_inp : float inplane peak position of reflection hkl of the alloy in reciprocal space q_perp : float perpendicular peak position of the reflection hkl of the alloy in reciprocal space hkl : list Miller indices of the measured asymmetric reflection sur : list Miller indices of the surface (determines the perpendicular direction) Returns ------- content : float content of B in the alloy determined from the input variables list [a_inplane a_perp, a_bulk_perp(x), eps_inplane, eps_perp]; lattice parameters calculated from the reciprocal space positions as well as the strain (eps) of the layer """ # check input parameters q_inp = self._checkfinitenumber(q_inp, "q_inp") q_perp = self._checkfinitenumber(q_perp, "q_perp") hkl = self._checkarray(hkl, "hkl") sur = self._checkarray(sur, "sur") # check if reflection is asymmetric if math.VecNorm(math.VecCross(self.Q(hkl), self.Q(sur))) < 1.e-8: raise InputError("Miller indices of a symmetric reflection were" "given where an asymmetric reflection is needed") # calculate lattice constants from reciprocal space positions n = self.Q(sur) / VecNorm(self.Q(sur)) q_hkl = self.Q(hkl) # the following two lines are not generally true! only cubic materials ainp = 2 * numpy.pi / abs(q_inp) * VecNorm(VecCross(n, hkl)) aperp = 2 * numpy.pi / abs(q_perp) * abs(VecDot(n, hkl)) # transform the elastic tensors to a coordinate frame attached to the # surface normal inp1 = VecCross(n, q_hkl) / VecNorm(VecCross(n, q_hkl)) inp2 = VecCross(n, inp1) trans = math.CoordinateTransform(inp1, inp2, n) cijA = Cijkl2Cij(trans(self.matA.cijkl, rank=4)) cijB = Cijkl2Cij(trans(self.matB.cijkl, rank=4)) a1, a2, a3, V, b1, b2, b3, qhklx, frac = self._definehelpers(hkl, cijA, cijB) # the following two lines are not generally true! only cubic materials def abulk_inp(x): return abs(2 * numpy.pi / numpy.inner(qhklx(x), inp2) * VecNorm(VecCross(n, hkl))) def abulk_perp(x): return abs(2 * numpy.pi / numpy.inner(qhklx(x), n) * numpy.inner(n, hkl)) if config.VERBOSITY >= config.DEBUG: print("XU.materials.Alloy.ContentB: abulk_inp/perp: %8.5g %8.5g" % (abulk_inp(0.), abulk_perp(0.))) def equation(x): return ((aperp - abulk_perp(x)) + (ainp - abulk_inp(x)) * frac(x)) x = scipy.optimize.brentq(equation, -0.1, 1.1) eps_inplane = (ainp - abulk_perp(x)) / abulk_perp(x) eps_perp = (aperp - abulk_perp(x)) / abulk_perp(x) return x, [ainp, aperp, abulk_perp(x), eps_inplane, eps_perp] def PseudomorphicMaterial(sub, layer, relaxation=0, trans=None): """ This function returns a material whos lattice is pseudomorphic on a particular substrate material. The two materials must have similar unit cell definitions for the algorithm to work correctly, i.e. it does not work for combiniations of materials with different lattice symmetry. It is also crucial that the layer object includes values for the elastic tensor. Parameters ---------- sub : Crystal substrate material layer : Crystal bulk material of the layer, including its elasticity tensor relaxation : float, optional degree of relaxation 0: pseudomorphic, 1: relaxed (default: 0) trans : Tranform Transformation which transforms lattice directions into a surface orientated coordinate frame (x, y inplane, z out of plane). If None a (001) surface geometry of a cubic material is assumed. Returns ------- An instance of Crystal holding the new pseudomorphically strained material. Raises ------ InputError If the layer material has no elastic parameters """ def get_inplane(lat): """determine inplane lattice parameter""" return (math.VecNorm(lat.GetPoint(trans.inverse((1, 0, 0)))) + math.VecNorm(lat.GetPoint(trans.inverse((0, 1, 0))))) / 2. if not trans: trans = math.Transform(numpy.identity(3)) if numpy.all(layer.cijkl == 0): raise InputError("'layer' argument needs elastic parameters") # calculate the strain asub = get_inplane(sub.lattice) abulk = get_inplane(layer.lattice) apar = asub + (abulk - asub) * relaxation epar = (apar - abulk) / abulk cT = trans(layer.cijkl, rank=4) eperp = -epar * (cT[1, 1, 2, 2] + cT[2, 2, 0, 0]) / (cT[2, 2, 2, 2]) eps = trans.inverse(numpy.diag((epar, epar, eperp)), rank=2) if config.VERBOSITY >= config.INFO_ALL: print("XU.materials.PseudomorphicMaterial: applying strain (inplane, " "perpendicular): %.4g %.4g" % (epar, eperp)) # create the pseudomorphic material pmlatt = copy.deepcopy(layer.lattice) pmat = Crystal(layer.name, pmlatt, layer.cij) pmat.ApplyStrain(eps) return pmat	gpl-2.0
hsiaoyi0504/scikit-learn	sklearn/linear_model/logistic.py	105	56686	""" Logistic Regression """ # Author: Gael Varoquaux <[email protected]> # Fabian Pedregosa <[email protected]> # Alexandre Gramfort <[email protected]> # Manoj Kumar <[email protected]> # Lars Buitinck # Simon Wu <[email protected]> import numbers import warnings import numpy as np from scipy import optimize, sparse from .base import LinearClassifierMixin, SparseCoefMixin, BaseEstimator from ..feature_selection.from_model import _LearntSelectorMixin from ..preprocessing import LabelEncoder, LabelBinarizer from ..svm.base import _fit_liblinear from ..utils import check_array, check_consistent_length, compute_class_weight from ..utils import check_random_state from ..utils.extmath import (logsumexp, log_logistic, safe_sparse_dot, squared_norm) from ..utils.optimize import newton_cg from ..utils.validation import (as_float_array, DataConversionWarning, check_X_y) from ..utils.fixes import expit from ..externals.joblib import Parallel, delayed from ..cross_validation import check_cv from ..externals import six from ..metrics import SCORERS # .. some helper functions for logistic_regression_path .. def _intercept_dot(w, X, y): """Computes y * np.dot(X, w). It takes into consideration if the intercept should be fit or not. Parameters ---------- w : ndarray, shape (n_features,) or (n_features + 1,) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. y : ndarray, shape (n_samples,) Array of labels. """ c = 0. if w.size == X.shape[1] + 1: c = w[-1] w = w[:-1] z = safe_sparse_dot(X, w) + c return w, c, y * z def _logistic_loss_and_grad(w, X, y, alpha, sample_weight=None): """Computes the logistic loss and gradient. Parameters ---------- w : ndarray, shape (n_features,) or (n_features + 1,) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. y : ndarray, shape (n_samples,) Array of labels. alpha : float Regularization parameter. alpha is equal to 1 / C. sample_weight : ndarray, shape (n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. Returns ------- out : float Logistic loss. grad : ndarray, shape (n_features,) or (n_features + 1,) Logistic gradient. """ _, n_features = X.shape grad = np.empty_like(w) w, c, yz = _intercept_dot(w, X, y) if sample_weight is None: sample_weight = np.ones(y.shape[0]) # Logistic loss is the negative of the log of the logistic function. out = -np.sum(sample_weight * log_logistic(yz)) + .5 * alpha * np.dot(w, w) z = expit(yz) z0 = sample_weight * (z - 1) * y grad[:n_features] = safe_sparse_dot(X.T, z0) + alpha * w # Case where we fit the intercept. if grad.shape[0] > n_features: grad[-1] = z0.sum() return out, grad def _logistic_loss(w, X, y, alpha, sample_weight=None): """Computes the logistic loss. Parameters ---------- w : ndarray, shape (n_features,) or (n_features + 1,) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. y : ndarray, shape (n_samples,) Array of labels. alpha : float Regularization parameter. alpha is equal to 1 / C. sample_weight : ndarray, shape (n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. Returns ------- out : float Logistic loss. """ w, c, yz = _intercept_dot(w, X, y) if sample_weight is None: sample_weight = np.ones(y.shape[0]) # Logistic loss is the negative of the log of the logistic function. out = -np.sum(sample_weight * log_logistic(yz)) + .5 * alpha * np.dot(w, w) return out def _logistic_grad_hess(w, X, y, alpha, sample_weight=None): """Computes the gradient and the Hessian, in the case of a logistic loss. Parameters ---------- w : ndarray, shape (n_features,) or (n_features + 1,) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. y : ndarray, shape (n_samples,) Array of labels. alpha : float Regularization parameter. alpha is equal to 1 / C. sample_weight : ndarray, shape (n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. Returns ------- grad : ndarray, shape (n_features,) or (n_features + 1,) Logistic gradient. Hs : callable Function that takes the gradient as a parameter and returns the matrix product of the Hessian and gradient. """ n_samples, n_features = X.shape grad = np.empty_like(w) fit_intercept = grad.shape[0] > n_features w, c, yz = _intercept_dot(w, X, y) if sample_weight is None: sample_weight = np.ones(y.shape[0]) z = expit(yz) z0 = sample_weight * (z - 1) * y grad[:n_features] = safe_sparse_dot(X.T, z0) + alpha * w # Case where we fit the intercept. if fit_intercept: grad[-1] = z0.sum() # The mat-vec product of the Hessian d = sample_weight * z * (1 - z) if sparse.issparse(X): dX = safe_sparse_dot(sparse.dia_matrix((d, 0), shape=(n_samples, n_samples)), X) else: # Precompute as much as possible dX = d[:, np.newaxis] * X if fit_intercept: # Calculate the double derivative with respect to intercept # In the case of sparse matrices this returns a matrix object. dd_intercept = np.squeeze(np.array(dX.sum(axis=0))) def Hs(s): ret = np.empty_like(s) ret[:n_features] = X.T.dot(dX.dot(s[:n_features])) ret[:n_features] += alpha * s[:n_features] # For the fit intercept case. if fit_intercept: ret[:n_features] += s[-1] * dd_intercept ret[-1] = dd_intercept.dot(s[:n_features]) ret[-1] += d.sum() * s[-1] return ret return grad, Hs def _multinomial_loss(w, X, Y, alpha, sample_weight): """Computes multinomial loss and class probabilities. Parameters ---------- w : ndarray, shape (n_classes * n_features,) or (n_classes * (n_features + 1),) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. Y : ndarray, shape (n_samples, n_classes) Transformed labels according to the output of LabelBinarizer. alpha : float Regularization parameter. alpha is equal to 1 / C. sample_weight : ndarray, shape (n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. Returns ------- loss : float Multinomial loss. p : ndarray, shape (n_samples, n_classes) Estimated class probabilities. w : ndarray, shape (n_classes, n_features) Reshaped param vector excluding intercept terms. """ n_classes = Y.shape[1] n_features = X.shape[1] fit_intercept = w.size == (n_classes * (n_features + 1)) w = w.reshape(n_classes, -1) sample_weight = sample_weight[:, np.newaxis] if fit_intercept: intercept = w[:, -1] w = w[:, :-1] else: intercept = 0 p = safe_sparse_dot(X, w.T) p += intercept p -= logsumexp(p, axis=1)[:, np.newaxis] loss = -(sample_weight * Y * p).sum() loss += 0.5 * alpha * squared_norm(w) p = np.exp(p, p) return loss, p, w def _multinomial_loss_grad(w, X, Y, alpha, sample_weight): """Computes the multinomial loss, gradient and class probabilities. Parameters ---------- w : ndarray, shape (n_classes * n_features,) or (n_classes * (n_features + 1),) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. Y : ndarray, shape (n_samples, n_classes) Transformed labels according to the output of LabelBinarizer. alpha : float Regularization parameter. alpha is equal to 1 / C. sample_weight : ndarray, shape (n_samples,) optional Array of weights that are assigned to individual samples. Returns ------- loss : float Multinomial loss. grad : ndarray, shape (n_classes * n_features,) or (n_classes * (n_features + 1),) Ravelled gradient of the multinomial loss. p : ndarray, shape (n_samples, n_classes) Estimated class probabilities """ n_classes = Y.shape[1] n_features = X.shape[1] fit_intercept = (w.size == n_classes * (n_features + 1)) grad = np.zeros((n_classes, n_features + bool(fit_intercept))) loss, p, w = _multinomial_loss(w, X, Y, alpha, sample_weight) sample_weight = sample_weight[:, np.newaxis] diff = sample_weight * (p - Y) grad[:, :n_features] = safe_sparse_dot(diff.T, X) grad[:, :n_features] += alpha * w if fit_intercept: grad[:, -1] = diff.sum(axis=0) return loss, grad.ravel(), p def _multinomial_grad_hess(w, X, Y, alpha, sample_weight): """ Computes the gradient and the Hessian, in the case of a multinomial loss. Parameters ---------- w : ndarray, shape (n_classes * n_features,) or (n_classes * (n_features + 1),) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. Y : ndarray, shape (n_samples, n_classes) Transformed labels according to the output of LabelBinarizer. alpha : float Regularization parameter. alpha is equal to 1 / C. sample_weight : ndarray, shape (n_samples,) optional Array of weights that are assigned to individual samples. Returns ------- grad : array, shape (n_classes * n_features,) or (n_classes * (n_features + 1),) Ravelled gradient of the multinomial loss. hessp : callable Function that takes in a vector input of shape (n_classes * n_features) or (n_classes * (n_features + 1)) and returns matrix-vector product with hessian. References ---------- Barak A. Pearlmutter (1993). Fast Exact Multiplication by the Hessian. http://www.bcl.hamilton.ie/~barak/papers/nc-hessian.pdf """ n_features = X.shape[1] n_classes = Y.shape[1] fit_intercept = w.size == (n_classes * (n_features + 1)) # `loss` is unused. Refactoring to avoid computing it does not # significantly speed up the computation and decreases readability loss, grad, p = _multinomial_loss_grad(w, X, Y, alpha, sample_weight) sample_weight = sample_weight[:, np.newaxis] # Hessian-vector product derived by applying the R-operator on the gradient # of the multinomial loss function. def hessp(v): v = v.reshape(n_classes, -1) if fit_intercept: inter_terms = v[:, -1] v = v[:, :-1] else: inter_terms = 0 # r_yhat holds the result of applying the R-operator on the multinomial # estimator. r_yhat = safe_sparse_dot(X, v.T) r_yhat += inter_terms r_yhat += (-p * r_yhat).sum(axis=1)[:, np.newaxis] r_yhat = p r_yhat = sample_weight hessProd = np.zeros((n_classes, n_features + bool(fit_intercept))) hessProd[:, :n_features] = safe_sparse_dot(r_yhat.T, X) hessProd[:, :n_features] += v * alpha if fit_intercept: hessProd[:, -1] = r_yhat.sum(axis=0) return hessProd.ravel() return grad, hessp def _check_solver_option(solver, multi_class, penalty, dual): if solver not in ['liblinear', 'newton-cg', 'lbfgs']: raise ValueError("Logistic Regression supports only liblinear," " newton-cg and lbfgs solvers, got %s" % solver) if multi_class not in ['multinomial', 'ovr']: raise ValueError("multi_class should be either multinomial or " "ovr, got %s" % multi_class) if multi_class == 'multinomial' and solver == 'liblinear': raise ValueError("Solver %s does not support " "a multinomial backend." % solver) if solver != 'liblinear': if penalty != 'l2': raise ValueError("Solver %s supports only l2 penalties, " "got %s penalty." % (solver, penalty)) if dual: raise ValueError("Solver %s supports only " "dual=False, got dual=%s" % (solver, dual)) def logistic_regression_path(X, y, pos_class=None, Cs=10, fit_intercept=True, max_iter=100, tol=1e-4, verbose=0, solver='lbfgs', coef=None, copy=True, class_weight=None, dual=False, penalty='l2', intercept_scaling=1., multi_class='ovr', random_state=None): """Compute a Logistic Regression model for a list of regularization parameters. This is an implementation that uses the result of the previous model to speed up computations along the set of solutions, making it faster than sequentially calling LogisticRegression for the different parameters. Read more in the :ref:`User Guide <logistic_regression>`. Parameters ---------- X : array-like or sparse matrix, shape (n_samples, n_features) Input data. y : array-like, shape (n_samples,) Input data, target values. Cs : int \| array-like, shape (n_cs,) List of values for the regularization parameter or integer specifying the number of regularization parameters that should be used. In this case, the parameters will be chosen in a logarithmic scale between 1e-4 and 1e4. pos_class : int, None The class with respect to which we perform a one-vs-all fit. If None, then it is assumed that the given problem is binary. fit_intercept : bool Whether to fit an intercept for the model. In this case the shape of the returned array is (n_cs, n_features + 1). max_iter : int Maximum number of iterations for the solver. tol : float Stopping criterion. For the newton-cg and lbfgs solvers, the iteration will stop when ``max{\|g_i \| i = 1, ..., n} <= tol`` where ``g_i`` is the i-th component of the gradient. verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. solver : {'lbfgs', 'newton-cg', 'liblinear'} Numerical solver to use. coef : array-like, shape (n_features,), default None Initialization value for coefficients of logistic regression. copy : bool, default True Whether or not to produce a copy of the data. Setting this to True will be useful in cases, when logistic_regression_path is called repeatedly with the same data, as y is modified along the path. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` dual : bool Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features. penalty : str, 'l1' or 'l2' Used to specify the norm used in the penalization. The newton-cg and lbfgs solvers support only l2 penalties. intercept_scaling : float, default 1. This parameter is useful only when the solver 'liblinear' is used and self.fit_intercept is set to True. In this case, x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. multi_class : str, {'ovr', 'multinomial'} Multiclass option can be either 'ovr' or 'multinomial'. If the option chosen is 'ovr', then a binary problem is fit for each label. Else the loss minimised is the multinomial loss fit across the entire probability distribution. Works only for the 'lbfgs' and 'newton-cg' solvers. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use when shuffling the data. Returns ------- coefs : ndarray, shape (n_cs, n_features) or (n_cs, n_features + 1) List of coefficients for the Logistic Regression model. If fit_intercept is set to True then the second dimension will be n_features + 1, where the last item represents the intercept. Cs : ndarray Grid of Cs used for cross-validation. Notes ----- You might get slighly different results with the solver liblinear than with the others since this uses LIBLINEAR which penalizes the intercept. """ if isinstance(Cs, numbers.Integral): Cs = np.logspace(-4, 4, Cs) _check_solver_option(solver, multi_class, penalty, dual) # Preprocessing. X = check_array(X, accept_sparse='csr', dtype=np.float64) y = check_array(y, ensure_2d=False, copy=copy, dtype=None) _, n_features = X.shape check_consistent_length(X, y) classes = np.unique(y) random_state = check_random_state(random_state) if pos_class is None and multi_class != 'multinomial': if (classes.size > 2): raise ValueError('To fit OvR, use the pos_class argument') # np.unique(y) gives labels in sorted order. pos_class = classes[1] # If class_weights is a dict (provided by the user), the weights # are assigned to the original labels. If it is "auto", then # the class_weights are assigned after masking the labels with a OvR. sample_weight = np.ones(X.shape[0]) le = LabelEncoder() if isinstance(class_weight, dict): if solver == "liblinear": if classes.size == 2: # Reconstruct the weights with keys 1 and -1 temp = {1: class_weight[pos_class], -1: class_weight[classes[0]]} class_weight = temp.copy() else: raise ValueError("In LogisticRegressionCV the liblinear " "solver cannot handle multiclass with " "class_weight of type dict. Use the lbfgs, " "newton-cg solvers or set " "class_weight='auto'") else: class_weight_ = compute_class_weight(class_weight, classes, y) sample_weight = class_weight_[le.fit_transform(y)] # For doing a ovr, we need to mask the labels first. for the # multinomial case this is not necessary. if multi_class == 'ovr': w0 = np.zeros(n_features + int(fit_intercept)) mask_classes = [-1, 1] mask = (y == pos_class) y[mask] = 1 y[~mask] = -1 # To take care of object dtypes, i.e 1 and -1 are in the form of # strings. y = as_float_array(y, copy=False) else: lbin = LabelBinarizer() Y_bin = lbin.fit_transform(y) if Y_bin.shape[1] == 1: Y_bin = np.hstack([1 - Y_bin, Y_bin]) w0 = np.zeros((Y_bin.shape[1], n_features + int(fit_intercept)), order='F') mask_classes = classes if class_weight == "auto": class_weight_ = compute_class_weight(class_weight, mask_classes, y) sample_weight = class_weight_[le.fit_transform(y)] if coef is not None: # it must work both giving the bias term and not if multi_class == 'ovr': if coef.size not in (n_features, w0.size): raise ValueError( 'Initialization coef is of shape %d, expected shape ' '%d or %d' % (coef.size, n_features, w0.size)) w0[:coef.size] = coef else: # For binary problems coef.shape[0] should be 1, otherwise it # should be classes.size. n_vectors = classes.size if n_vectors == 2: n_vectors = 1 if (coef.shape[0] != n_vectors or coef.shape[1] not in (n_features, n_features + 1)): raise ValueError( 'Initialization coef is of shape (%d, %d), expected ' 'shape (%d, %d) or (%d, %d)' % ( coef.shape[0], coef.shape[1], classes.size, n_features, classes.size, n_features + 1)) w0[:, :coef.shape[1]] = coef if multi_class == 'multinomial': # fmin_l_bfgs_b and newton-cg accepts only ravelled parameters. w0 = w0.ravel() target = Y_bin if solver == 'lbfgs': func = lambda x, args: _multinomial_loss_grad(x, args)[0:2] elif solver == 'newton-cg': func = lambda x, args: _multinomial_loss(x, args)[0] grad = lambda x, args: _multinomial_loss_grad(x, args)[1] hess = _multinomial_grad_hess else: target = y if solver == 'lbfgs': func = _logistic_loss_and_grad elif solver == 'newton-cg': func = _logistic_loss grad = lambda x, args: _logistic_loss_and_grad(x, args)[1] hess = _logistic_grad_hess coefs = list() for C in Cs: if solver == 'lbfgs': try: w0, loss, info = optimize.fmin_l_bfgs_b( func, w0, fprime=None, args=(X, target, 1. / C, sample_weight), iprint=(verbose > 0) - 1, pgtol=tol, maxiter=max_iter) except TypeError: # old scipy doesn't have maxiter w0, loss, info = optimize.fmin_l_bfgs_b( func, w0, fprime=None, args=(X, target, 1. / C, sample_weight), iprint=(verbose > 0) - 1, pgtol=tol) if info["warnflag"] == 1 and verbose > 0: warnings.warn("lbfgs failed to converge. Increase the number " "of iterations.") elif solver == 'newton-cg': args = (X, target, 1. / C, sample_weight) w0 = newton_cg(hess, func, grad, w0, args=args, maxiter=max_iter, tol=tol) elif solver == 'liblinear': coef_, intercept_, _, = _fit_liblinear( X, y, C, fit_intercept, intercept_scaling, class_weight, penalty, dual, verbose, max_iter, tol, random_state) if fit_intercept: w0 = np.concatenate([coef_.ravel(), intercept_]) else: w0 = coef_.ravel() else: raise ValueError("solver must be one of {'liblinear', 'lbfgs', " "'newton-cg'}, got '%s' instead" % solver) if multi_class == 'multinomial': multi_w0 = np.reshape(w0, (classes.size, -1)) if classes.size == 2: multi_w0 = multi_w0[1][np.newaxis, :] coefs.append(multi_w0) else: coefs.append(w0) return coefs, np.array(Cs) # helper function for LogisticCV def _log_reg_scoring_path(X, y, train, test, pos_class=None, Cs=10, scoring=None, fit_intercept=False, max_iter=100, tol=1e-4, class_weight=None, verbose=0, solver='lbfgs', penalty='l2', dual=False, copy=True, intercept_scaling=1., multi_class='ovr'): """Computes scores across logistic_regression_path Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. y : array-like, shape (n_samples,) or (n_samples, n_targets) Target labels. train : list of indices The indices of the train set. test : list of indices The indices of the test set. pos_class : int, None The class with respect to which we perform a one-vs-all fit. If None, then it is assumed that the given problem is binary. Cs : list of floats \| int Each of the values in Cs describes the inverse of regularization strength. If Cs is as an int, then a grid of Cs values are chosen in a logarithmic scale between 1e-4 and 1e4. If not provided, then a fixed set of values for Cs are used. scoring : callable For a list of scoring functions that can be used, look at :mod:`sklearn.metrics`. The default scoring option used is accuracy_score. fit_intercept : bool If False, then the bias term is set to zero. Else the last term of each coef_ gives us the intercept. max_iter : int Maximum number of iterations for the solver. tol : float Tolerance for stopping criteria. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. solver : {'lbfgs', 'newton-cg', 'liblinear'} Decides which solver to use. penalty : str, 'l1' or 'l2' Used to specify the norm used in the penalization. The newton-cg and lbfgs solvers support only l2 penalties. dual : bool Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features. intercept_scaling : float, default 1. This parameter is useful only when the solver 'liblinear' is used and self.fit_intercept is set to True. In this case, x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. multi_class : str, {'ovr', 'multinomial'} Multiclass option can be either 'ovr' or 'multinomial'. If the option chosen is 'ovr', then a binary problem is fit for each label. Else the loss minimised is the multinomial loss fit across the entire probability distribution. Works only for the 'lbfgs' solver. copy : bool, default True Whether or not to produce a copy of the data. Setting this to True will be useful in cases, when ``_log_reg_scoring_path`` is called repeatedly with the same data, as y is modified along the path. Returns ------- coefs : ndarray, shape (n_cs, n_features) or (n_cs, n_features + 1) List of coefficients for the Logistic Regression model. If fit_intercept is set to True then the second dimension will be n_features + 1, where the last item represents the intercept. Cs : ndarray Grid of Cs used for cross-validation. scores : ndarray, shape (n_cs,) Scores obtained for each Cs. """ _check_solver_option(solver, multi_class, penalty, dual) log_reg = LogisticRegression(fit_intercept=fit_intercept) X_train = X[train] X_test = X[test] y_train = y[train] y_test = y[test] # The score method of Logistic Regression has a classes_ attribute. if multi_class == 'ovr': log_reg.classes_ = np.array([-1, 1]) elif multi_class == 'multinomial': log_reg.classes_ = np.unique(y_train) else: raise ValueError("multi_class should be either multinomial or ovr, " "got %d" % multi_class) if pos_class is not None: mask = (y_test == pos_class) y_test[mask] = 1 y_test[~mask] = -1 # To deal with object dtypes, we need to convert into an array of floats. y_test = as_float_array(y_test, copy=False) coefs, Cs = logistic_regression_path(X_train, y_train, Cs=Cs, fit_intercept=fit_intercept, solver=solver, max_iter=max_iter, class_weight=class_weight, copy=copy, pos_class=pos_class, multi_class=multi_class, tol=tol, verbose=verbose, dual=dual, penalty=penalty, intercept_scaling=intercept_scaling) scores = list() if isinstance(scoring, six.string_types): scoring = SCORERS[scoring] for w in coefs: if multi_class == 'ovr': w = w[np.newaxis, :] if fit_intercept: log_reg.coef_ = w[:, :-1] log_reg.intercept_ = w[:, -1] else: log_reg.coef_ = w log_reg.intercept_ = 0. if scoring is None: scores.append(log_reg.score(X_test, y_test)) else: scores.append(scoring(log_reg, X_test, y_test)) return coefs, Cs, np.array(scores) class LogisticRegression(BaseEstimator, LinearClassifierMixin, _LearntSelectorMixin, SparseCoefMixin): """Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr' and uses the cross-entropy loss, if the 'multi_class' option is set to 'multinomial'. (Currently the 'multinomial' option is supported only by the 'lbfgs' and 'newton-cg' solvers.) This class implements regularized logistic regression using the `liblinear` library, newton-cg and lbfgs solvers. It can handle both dense and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit floats for optimal performance; any other input format will be converted (and copied). The newton-cg and lbfgs solvers support only L2 regularization with primal formulation. The liblinear solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. Read more in the :ref:`User Guide <logistic_regression>`. Parameters ---------- penalty : str, 'l1' or 'l2' Used to specify the norm used in the penalization. The newton-cg and lbfgs solvers support only l2 penalties. dual : bool Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features. C : float, optional (default=1.0) Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization. fit_intercept : bool, default: True Specifies if a constant (a.k.a. bias or intercept) should be added the decision function. intercept_scaling : float, default: 1 Useful only if solver is liblinear. when self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` max_iter : int Useful only for the newton-cg and lbfgs solvers. Maximum number of iterations taken for the solvers to converge. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use when shuffling the data. solver : {'newton-cg', 'lbfgs', 'liblinear'} Algorithm to use in the optimization problem. tol : float, optional Tolerance for stopping criteria. multi_class : str, {'ovr', 'multinomial'} Multiclass option can be either 'ovr' or 'multinomial'. If the option chosen is 'ovr', then a binary problem is fit for each label. Else the loss minimised is the multinomial loss fit across the entire probability distribution. Works only for the 'lbfgs' solver. verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. Attributes ---------- coef_ : array, shape (n_classes, n_features) Coefficient of the features in the decision function. intercept_ : array, shape (n_classes,) Intercept (a.k.a. bias) added to the decision function. If `fit_intercept` is set to False, the intercept is set to zero. n_iter_ : int Maximum of the actual number of iterations across all classes. Valid only for the liblinear solver. See also -------- SGDClassifier : incrementally trained logistic regression (when given the parameter ``loss="log"``). sklearn.svm.LinearSVC : learns SVM models using the same algorithm. Notes ----- The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon, to have slightly different results for the same input data. If that happens, try with a smaller tol parameter. Predict output may not match that of standalone liblinear in certain cases. See :ref:`differences from liblinear <liblinear_differences>` in the narrative documentation. References ---------- LIBLINEAR -- A Library for Large Linear Classification http://www.csie.ntu.edu.tw/~cjlin/liblinear/ Hsiang-Fu Yu, Fang-Lan Huang, Chih-Jen Lin (2011). Dual coordinate descent methods for logistic regression and maximum entropy models. Machine Learning 85(1-2):41-75. http://www.csie.ntu.edu.tw/~cjlin/papers/maxent_dual.pdf See also -------- sklearn.linear_model.SGDClassifier """ def __init__(self, penalty='l2', dual=False, tol=1e-4, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='liblinear', max_iter=100, multi_class='ovr', verbose=0): self.penalty = penalty self.dual = dual self.tol = tol self.C = C self.fit_intercept = fit_intercept self.intercept_scaling = intercept_scaling self.class_weight = class_weight self.random_state = random_state self.solver = solver self.max_iter = max_iter self.multi_class = multi_class self.verbose = verbose def fit(self, X, y): """Fit the model according to the given training data. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Training vector, where n_samples in the number of samples and n_features is the number of features. y : array-like, shape (n_samples,) Target vector relative to X. Returns ------- self : object Returns self. """ if not isinstance(self.C, numbers.Number) or self.C < 0: raise ValueError("Penalty term must be positive; got (C=%r)" % self.C) if not isinstance(self.max_iter, numbers.Number) or self.max_iter < 0: raise ValueError("Maximum number of iteration must be positive;" " got (max_iter=%r)" % self.max_iter) if not isinstance(self.tol, numbers.Number) or self.tol < 0: raise ValueError("Tolerance for stopping criteria must be " "positive; got (tol=%r)" % self.tol) X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64, order="C") self.classes_ = np.unique(y) _check_solver_option(self.solver, self.multi_class, self.penalty, self.dual) if self.solver == 'liblinear': self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear( X, y, self.C, self.fit_intercept, self.intercept_scaling, self.class_weight, self.penalty, self.dual, self.verbose, self.max_iter, self.tol, self.random_state) return self n_classes = len(self.classes_) classes_ = self.classes_ if n_classes < 2: raise ValueError("This solver needs samples of at least 2 classes" " in the data, but the data contains only one" " class: %r" % classes_[0]) if len(self.classes_) == 2: n_classes = 1 classes_ = classes_[1:] self.coef_ = list() self.intercept_ = np.zeros(n_classes) # Hack so that we iterate only once for the multinomial case. if self.multi_class == 'multinomial': classes_ = [None] for ind, class_ in enumerate(classes_): coef_, _ = logistic_regression_path( X, y, pos_class=class_, Cs=[self.C], fit_intercept=self.fit_intercept, tol=self.tol, verbose=self.verbose, solver=self.solver, multi_class=self.multi_class, max_iter=self.max_iter, class_weight=self.class_weight) self.coef_.append(coef_[0]) self.coef_ = np.squeeze(self.coef_) # For the binary case, this get squeezed to a 1-D array. if self.coef_.ndim == 1: self.coef_ = self.coef_[np.newaxis, :] self.coef_ = np.asarray(self.coef_) if self.fit_intercept: self.intercept_ = self.coef_[:, -1] self.coef_ = self.coef_[:, :-1] return self def predict_proba(self, X): """Probability estimates. The returned estimates for all classes are ordered by the label of classes. Parameters ---------- X : array-like, shape = [n_samples, n_features] Returns ------- T : array-like, shape = [n_samples, n_classes] Returns the probability of the sample for each class in the model, where classes are ordered as they are in ``self.classes_``. """ return self._predict_proba_lr(X) def predict_log_proba(self, X): """Log of probability estimates. The returned estimates for all classes are ordered by the label of classes. Parameters ---------- X : array-like, shape = [n_samples, n_features] Returns ------- T : array-like, shape = [n_samples, n_classes] Returns the log-probability of the sample for each class in the model, where classes are ordered as they are in ``self.classes_``. """ return np.log(self.predict_proba(X)) class LogisticRegressionCV(LogisticRegression, BaseEstimator, LinearClassifierMixin, _LearntSelectorMixin): """Logistic Regression CV (aka logit, MaxEnt) classifier. This class implements logistic regression using liblinear, newton-cg or LBFGS optimizer. The newton-cg and lbfgs solvers support only L2 regularization with primal formulation. The liblinear solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. For the grid of Cs values (that are set by default to be ten values in a logarithmic scale between 1e-4 and 1e4), the best hyperparameter is selected by the cross-validator StratifiedKFold, but it can be changed using the cv parameter. In the case of newton-cg and lbfgs solvers, we warm start along the path i.e guess the initial coefficients of the present fit to be the coefficients got after convergence in the previous fit, so it is supposed to be faster for high-dimensional dense data. For a multiclass problem, the hyperparameters for each class are computed using the best scores got by doing a one-vs-rest in parallel across all folds and classes. Hence this is not the true multinomial loss. Read more in the :ref:`User Guide <logistic_regression>`. Parameters ---------- Cs : list of floats \| int Each of the values in Cs describes the inverse of regularization strength. If Cs is as an int, then a grid of Cs values are chosen in a logarithmic scale between 1e-4 and 1e4. Like in support vector machines, smaller values specify stronger regularization. fit_intercept : bool, default: True Specifies if a constant (a.k.a. bias or intercept) should be added the decision function. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` cv : integer or cross-validation generator The default cross-validation generator used is Stratified K-Folds. If an integer is provided, then it is the number of folds used. See the module :mod:`sklearn.cross_validation` module for the list of possible cross-validation objects. penalty : str, 'l1' or 'l2' Used to specify the norm used in the penalization. The newton-cg and lbfgs solvers support only l2 penalties. dual : bool Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features. scoring : callabale Scoring function to use as cross-validation criteria. For a list of scoring functions that can be used, look at :mod:`sklearn.metrics`. The default scoring option used is accuracy_score. solver : {'newton-cg', 'lbfgs', 'liblinear'} Algorithm to use in the optimization problem. tol : float, optional Tolerance for stopping criteria. max_iter : int, optional Maximum number of iterations of the optimization algorithm. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` n_jobs : int, optional Number of CPU cores used during the cross-validation loop. If given a value of -1, all cores are used. verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. refit : bool If set to True, the scores are averaged across all folds, and the coefs and the C that corresponds to the best score is taken, and a final refit is done using these parameters. Otherwise the coefs, intercepts and C that correspond to the best scores across folds are averaged. multi_class : str, {'ovr', 'multinomial'} Multiclass option can be either 'ovr' or 'multinomial'. If the option chosen is 'ovr', then a binary problem is fit for each label. Else the loss minimised is the multinomial loss fit across the entire probability distribution. Works only for the 'lbfgs' solver. intercept_scaling : float, default 1. Useful only if solver is liblinear. This parameter is useful only when the solver 'liblinear' is used and self.fit_intercept is set to True. In this case, x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. Attributes ---------- coef_ : array, shape (1, n_features) or (n_classes, n_features) Coefficient of the features in the decision function. `coef_` is of shape (1, n_features) when the given problem is binary. `coef_` is readonly property derived from `raw_coef_` that follows the internal memory layout of liblinear. intercept_ : array, shape (1,) or (n_classes,) Intercept (a.k.a. bias) added to the decision function. It is available only when parameter intercept is set to True and is of shape(1,) when the problem is binary. Cs_ : array Array of C i.e. inverse of regularization parameter values used for cross-validation. coefs_paths_ : array, shape ``(n_folds, len(Cs_), n_features)`` or \ ``(n_folds, len(Cs_), n_features + 1)`` dict with classes as the keys, and the path of coefficients obtained during cross-validating across each fold and then across each Cs after doing an OvR for the corresponding class as values. If the 'multi_class' option is set to 'multinomial', then the coefs_paths are the coefficients corresponding to each class. Each dict value has shape ``(n_folds, len(Cs_), n_features)`` or ``(n_folds, len(Cs_), n_features + 1)`` depending on whether the intercept is fit or not. scores_ : dict dict with classes as the keys, and the values as the grid of scores obtained during cross-validating each fold, after doing an OvR for the corresponding class. If the 'multi_class' option given is 'multinomial' then the same scores are repeated across all classes, since this is the multinomial class. Each dict value has shape (n_folds, len(Cs)) C_ : array, shape (n_classes,) or (n_classes - 1,) Array of C that maps to the best scores across every class. If refit is set to False, then for each class, the best C is the average of the C's that correspond to the best scores for each fold. See also -------- LogisticRegression """ def __init__(self, Cs=10, fit_intercept=True, cv=None, dual=False, penalty='l2', scoring=None, solver='lbfgs', tol=1e-4, max_iter=100, class_weight=None, n_jobs=1, verbose=0, refit=True, intercept_scaling=1., multi_class='ovr'): self.Cs = Cs self.fit_intercept = fit_intercept self.cv = cv self.dual = dual self.penalty = penalty self.scoring = scoring self.tol = tol self.max_iter = max_iter self.class_weight = class_weight self.n_jobs = n_jobs self.verbose = verbose self.solver = solver self.refit = refit self.intercept_scaling = intercept_scaling self.multi_class = multi_class def fit(self, X, y): """Fit the model according to the given training data. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Training vector, where n_samples in the number of samples and n_features is the number of features. y : array-like, shape (n_samples,) Target vector relative to X. Returns ------- self : object Returns self. """ _check_solver_option(self.solver, self.multi_class, self.penalty, self.dual) if not isinstance(self.max_iter, numbers.Number) or self.max_iter < 0: raise ValueError("Maximum number of iteration must be positive;" " got (max_iter=%r)" % self.max_iter) if not isinstance(self.tol, numbers.Number) or self.tol < 0: raise ValueError("Tolerance for stopping criteria must be " "positive; got (tol=%r)" % self.tol) X = check_array(X, accept_sparse='csr', dtype=np.float64) y = check_array(y, ensure_2d=False, dtype=None) if y.ndim == 2 and y.shape[1] == 1: warnings.warn( "A column-vector y was passed when a 1d array was" " expected. Please change the shape of y to " "(n_samples, ), for example using ravel().", DataConversionWarning) y = np.ravel(y) check_consistent_length(X, y) # init cross-validation generator cv = check_cv(self.cv, X, y, classifier=True) folds = list(cv) self._enc = LabelEncoder() self._enc.fit(y) labels = self.classes_ = np.unique(y) n_classes = len(labels) if n_classes < 2: raise ValueError("This solver needs samples of at least 2 classes" " in the data, but the data contains only one" " class: %r" % self.classes_[0]) if n_classes == 2: # OvR in case of binary problems is as good as fitting # the higher label n_classes = 1 labels = labels[1:] # We need this hack to iterate only once over labels, in the case of # multi_class = multinomial, without changing the value of the labels. iter_labels = labels if self.multi_class == 'multinomial': iter_labels = [None] if self.class_weight and not(isinstance(self.class_weight, dict) or self.class_weight in ['balanced', 'auto']): raise ValueError("class_weight provided should be a " "dict or 'balanced'") path_func = delayed(_log_reg_scoring_path) fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)( path_func(X, y, train, test, pos_class=label, Cs=self.Cs, fit_intercept=self.fit_intercept, penalty=self.penalty, dual=self.dual, solver=self.solver, tol=self.tol, max_iter=self.max_iter, verbose=self.verbose, class_weight=self.class_weight, scoring=self.scoring, multi_class=self.multi_class, intercept_scaling=self.intercept_scaling ) for label in iter_labels for train, test in folds) if self.multi_class == 'multinomial': multi_coefs_paths, Cs, multi_scores = zip(fold_coefs_) multi_coefs_paths = np.asarray(multi_coefs_paths) multi_scores = np.asarray(multi_scores) # This is just to maintain API similarity between the ovr and # multinomial option. # Coefs_paths in now n_folds X len(Cs) X n_classes X n_features # we need it to be n_classes X len(Cs) X n_folds X n_features # to be similar to "ovr". coefs_paths = np.rollaxis(multi_coefs_paths, 2, 0) # Multinomial has a true score across all labels. Hence the # shape is n_folds X len(Cs). We need to repeat this score # across all labels for API similarity. scores = np.tile(multi_scores, (n_classes, 1, 1)) self.Cs_ = Cs[0] else: coefs_paths, Cs, scores = zip(fold_coefs_) self.Cs_ = Cs[0] coefs_paths = np.reshape(coefs_paths, (n_classes, len(folds), len(self.Cs_), -1)) self.coefs_paths_ = dict(zip(labels, coefs_paths)) scores = np.reshape(scores, (n_classes, len(folds), -1)) self.scores_ = dict(zip(labels, scores)) self.C_ = list() self.coef_ = np.empty((n_classes, X.shape[1])) self.intercept_ = np.zeros(n_classes) # hack to iterate only once for multinomial case. if self.multi_class == 'multinomial': scores = multi_scores coefs_paths = multi_coefs_paths for index, label in enumerate(iter_labels): if self.multi_class == 'ovr': scores = self.scores_[label] coefs_paths = self.coefs_paths_[label] if self.refit: best_index = scores.sum(axis=0).argmax() C_ = self.Cs_[best_index] self.C_.append(C_) if self.multi_class == 'multinomial': coef_init = np.mean(coefs_paths[:, best_index, :, :], axis=0) else: coef_init = np.mean(coefs_paths[:, best_index, :], axis=0) w, _ = logistic_regression_path( X, y, pos_class=label, Cs=[C_], solver=self.solver, fit_intercept=self.fit_intercept, coef=coef_init, max_iter=self.max_iter, tol=self.tol, penalty=self.penalty, class_weight=self.class_weight, multi_class=self.multi_class, verbose=max(0, self.verbose - 1)) w = w[0] else: # Take the best scores across every fold and the average of all # coefficients corresponding to the best scores. best_indices = np.argmax(scores, axis=1) w = np.mean([coefs_paths[i][best_indices[i]] for i in range(len(folds))], axis=0) self.C_.append(np.mean(self.Cs_[best_indices])) if self.multi_class == 'multinomial': self.C_ = np.tile(self.C_, n_classes) self.coef_ = w[:, :X.shape[1]] if self.fit_intercept: self.intercept_ = w[:, -1] else: self.coef_[index] = w[: X.shape[1]] if self.fit_intercept: self.intercept_[index] = w[-1] self.C_ = np.asarray(self.C_) return self	bsd-3-clause
fredhusser/scikit-learn	benchmarks/bench_sparsify.py	323	3372	""" Benchmark SGD prediction time with dense/sparse coefficients. Invoke with ----------- $ kernprof.py -l sparsity_benchmark.py $ python -m line_profiler sparsity_benchmark.py.lprof Typical output -------------- input data sparsity: 0.050000 true coef sparsity: 0.000100 test data sparsity: 0.027400 model sparsity: 0.000024 r^2 on test data (dense model) : 0.233651 r^2 on test data (sparse model) : 0.233651 Wrote profile results to sparsity_benchmark.py.lprof Timer unit: 1e-06 s File: sparsity_benchmark.py Function: benchmark_dense_predict at line 51 Total time: 0.532979 s Line # Hits Time Per Hit % Time Line Contents ============================================================== 51 @profile 52 def benchmark_dense_predict(): 53 301 640 2.1 0.1 for _ in range(300): 54 300 532339 1774.5 99.9 clf.predict(X_test) File: sparsity_benchmark.py Function: benchmark_sparse_predict at line 56 Total time: 0.39274 s Line # Hits Time Per Hit % Time Line Contents ============================================================== 56 @profile 57 def benchmark_sparse_predict(): 58 1 10854 10854.0 2.8 X_test_sparse = csr_matrix(X_test) 59 301 477 1.6 0.1 for _ in range(300): 60 300 381409 1271.4 97.1 clf.predict(X_test_sparse) """ from scipy.sparse.csr import csr_matrix import numpy as np from sklearn.linear_model.stochastic_gradient import SGDRegressor from sklearn.metrics import r2_score np.random.seed(42) def sparsity_ratio(X): return np.count_nonzero(X) / float(n_samples * n_features) n_samples, n_features = 5000, 300 X = np.random.randn(n_samples, n_features) inds = np.arange(n_samples) np.random.shuffle(inds) X[inds[int(n_features / 1.2):]] = 0 # sparsify input print("input data sparsity: %f" % sparsity_ratio(X)) coef = 3 * np.random.randn(n_features) inds = np.arange(n_features) np.random.shuffle(inds) coef[inds[n_features/2:]] = 0 # sparsify coef print("true coef sparsity: %f" % sparsity_ratio(coef)) y = np.dot(X, coef) # add noise y += 0.01 * np.random.normal((n_samples,)) # Split data in train set and test set n_samples = X.shape[0] X_train, y_train = X[:n_samples / 2], y[:n_samples / 2] X_test, y_test = X[n_samples / 2:], y[n_samples / 2:] print("test data sparsity: %f" % sparsity_ratio(X_test)) ############################################################################### clf = SGDRegressor(penalty='l1', alpha=.2, fit_intercept=True, n_iter=2000) clf.fit(X_train, y_train) print("model sparsity: %f" % sparsity_ratio(clf.coef_)) def benchmark_dense_predict(): for _ in range(300): clf.predict(X_test) def benchmark_sparse_predict(): X_test_sparse = csr_matrix(X_test) for _ in range(300): clf.predict(X_test_sparse) def score(y_test, y_pred, case): r2 = r2_score(y_test, y_pred) print("r^2 on test data (%s) : %f" % (case, r2)) score(y_test, clf.predict(X_test), 'dense model') benchmark_dense_predict() clf.sparsify() score(y_test, clf.predict(X_test), 'sparse model') benchmark_sparse_predict()	bsd-3-clause
nbarba/py3DRec	image_sequence.py	1	2644	import numpy as np; import pandas as pd from PIL import Image, ImageDraw, ImageFont class ImageSequence: ''' Class to hold the necessary information about the image sequence ''' def __init__(self, filename): self.load_features(filename) def feat_2d(self): return self._feat_2d def length(self): return self._length def number_of_features(self): return self._number_of_features def width(self): return self._width def height(self): return self._height def load_features(self, filename): ''' Method that loads a txt file containing 2D coordinates of image features. The format of each line should be: [x y feature_number image_number image_filename] ''' features_df = pd.read_csv(filename, delimiter=r"\s+", index_col=False) # get length of sequence and number of features self._length = int(features_df['image_id'].max()) self._number_of_features = int(features_df['feature_id'].max()) # get the 2d features self.feat_2d = np.zeros(shape=[self._length, 4, self._number_of_features]) for i in range(1, self._length + 1): self.feat_2d[i - 1, :, :] = np.transpose(features_df.loc[features_df['image_id'] == i].values)[0:4] # keep the image filenames self._image_filenames = features_df.image_filename.unique() # get the image sequence width and height image = Image.open(self._image_filenames[0]) self._width = 1024 # image.width self._height = 768 # image.height def get_normalized_coordinates(self): ''' Method to normalize the coordinates to the range [-1,1] ''' mm = (self._width + self._height) / 2; rows = (self.feat_2d[:, 0] - np.ones(self.number_of_features) * self._width) / mm cols = (self.feat_2d[:, 1] - np.ones(self.number_of_features) * self._height) / mm return np.dstack((rows, cols)).swapaxes(1, 2) def show(self): ''' Method to display the sequence, with the 2D features superimposed ''' font = ImageFont.truetype('/Library/fonts/arial.ttf', 30) for i in range(0, self.length): filename = self._image_filenames[i] image = Image.open(filename) draw = ImageDraw.Draw(image) for j in range(0, self.number_of_features): x = self.feat_2d[i, :, j][0]; y = image.height - self.feat_2d[i, :, j][1] draw.text((x, y), "+" + str(j), font=font, fill=(0, 255, 0)) image.show()	mit
Titan-C/scikit-learn	sklearn/manifold/setup.py	43	1283	import os from os.path import join import numpy from numpy.distutils.misc_util import Configuration from sklearn._build_utils import get_blas_info def configuration(parent_package="", top_path=None): config = Configuration("manifold", parent_package, top_path) libraries = [] if os.name == 'posix': libraries.append('m') config.add_extension("_utils", sources=["_utils.pyx"], include_dirs=[numpy.get_include()], libraries=libraries, extra_compile_args=["-O3"]) cblas_libs, blas_info = get_blas_info() eca = blas_info.pop('extra_compile_args', []) eca.append("-O4") config.add_extension("_barnes_hut_tsne", libraries=cblas_libs, sources=["_barnes_hut_tsne.pyx"], include_dirs=[join('..', 'src', 'cblas'), numpy.get_include(), blas_info.pop('include_dirs', [])], extra_compile_args=eca, blas_info) config.add_subpackage('tests') return config if __name__ == "__main__": from numpy.distutils.core import setup setup(configuration().todict())	bsd-3-clause
sanketloke/scikit-learn	examples/neighbors/plot_approximate_nearest_neighbors_hyperparameters.py	102	5177	""" ================================================= Hyper-parameters of Approximate Nearest Neighbors ================================================= This example demonstrates the behaviour of the accuracy of the nearest neighbor queries of Locality Sensitive Hashing Forest as the number of candidates and the number of estimators (trees) vary. In the first plot, accuracy is measured with the number of candidates. Here, the term "number of candidates" refers to maximum bound for the number of distinct points retrieved from each tree to calculate the distances. Nearest neighbors are selected from this pool of candidates. Number of estimators is maintained at three fixed levels (1, 5, 10). In the second plot, the number of candidates is fixed at 50. Number of trees is varied and the accuracy is plotted against those values. To measure the accuracy, the true nearest neighbors are required, therefore :class:`sklearn.neighbors.NearestNeighbors` is used to compute the exact neighbors. """ from __future__ import division print(__doc__) # Author: Maheshakya Wijewardena <[email protected]> # # License: BSD 3 clause ############################################################################### import numpy as np from sklearn.datasets.samples_generator import make_blobs from sklearn.neighbors import LSHForest from sklearn.neighbors import NearestNeighbors import matplotlib.pyplot as plt # Initialize size of the database, iterations and required neighbors. n_samples = 10000 n_features = 100 n_queries = 30 rng = np.random.RandomState(42) # Generate sample data X, _ = make_blobs(n_samples=n_samples + n_queries, n_features=n_features, centers=10, random_state=0) X_index = X[:n_samples] X_query = X[n_samples:] # Get exact neighbors nbrs = NearestNeighbors(n_neighbors=1, algorithm='brute', metric='cosine').fit(X_index) neighbors_exact = nbrs.kneighbors(X_query, return_distance=False) # Set `n_candidate` values n_candidates_values = np.linspace(10, 500, 5).astype(np.int) n_estimators_for_candidate_value = [1, 5, 10] n_iter = 10 stds_accuracies = np.zeros((len(n_estimators_for_candidate_value), n_candidates_values.shape[0]), dtype=float) accuracies_c = np.zeros((len(n_estimators_for_candidate_value), n_candidates_values.shape[0]), dtype=float) # LSH Forest is a stochastic index: perform several iteration to estimate # expected accuracy and standard deviation displayed as error bars in # the plots for j, value in enumerate(n_estimators_for_candidate_value): for i, n_candidates in enumerate(n_candidates_values): accuracy_c = [] for seed in range(n_iter): lshf = LSHForest(n_estimators=value, n_candidates=n_candidates, n_neighbors=1, random_state=seed) # Build the LSH Forest index lshf.fit(X_index) # Get neighbors neighbors_approx = lshf.kneighbors(X_query, return_distance=False) accuracy_c.append(np.sum(np.equal(neighbors_approx, neighbors_exact)) / n_queries) stds_accuracies[j, i] = np.std(accuracy_c) accuracies_c[j, i] = np.mean(accuracy_c) # Set `n_estimators` values n_estimators_values = [1, 5, 10, 20, 30, 40, 50] accuracies_trees = np.zeros(len(n_estimators_values), dtype=float) # Calculate average accuracy for each value of `n_estimators` for i, n_estimators in enumerate(n_estimators_values): lshf = LSHForest(n_estimators=n_estimators, n_neighbors=1) # Build the LSH Forest index lshf.fit(X_index) # Get neighbors neighbors_approx = lshf.kneighbors(X_query, return_distance=False) accuracies_trees[i] = np.sum(np.equal(neighbors_approx, neighbors_exact))/n_queries ############################################################################### # Plot the accuracy variation with `n_candidates` plt.figure() colors = ['c', 'm', 'y'] for i, n_estimators in enumerate(n_estimators_for_candidate_value): label = 'n_estimators = %d ' % n_estimators plt.plot(n_candidates_values, accuracies_c[i, :], 'o-', c=colors[i], label=label) plt.errorbar(n_candidates_values, accuracies_c[i, :], stds_accuracies[i, :], c=colors[i]) plt.legend(loc='upper left', prop=dict(size='small')) plt.ylim([0, 1.2]) plt.xlim(min(n_candidates_values), max(n_candidates_values)) plt.ylabel("Accuracy") plt.xlabel("n_candidates") plt.grid(which='both') plt.title("Accuracy variation with n_candidates") # Plot the accuracy variation with `n_estimators` plt.figure() plt.scatter(n_estimators_values, accuracies_trees, c='k') plt.plot(n_estimators_values, accuracies_trees, c='g') plt.ylim([0, 1.2]) plt.xlim(min(n_estimators_values), max(n_estimators_values)) plt.ylabel("Accuracy") plt.xlabel("n_estimators") plt.grid(which='both') plt.title("Accuracy variation with n_estimators") plt.show()	bsd-3-clause
kernc/scikit-learn	examples/calibration/plot_calibration.py	33	4794	""" ====================================== Probability calibration of classifiers ====================================== When performing classification you often want to predict not only the class label, but also the associated probability. This probability gives you some kind of confidence on the prediction. However, not all classifiers provide well-calibrated probabilities, some being over-confident while others being under-confident. Thus, a separate calibration of predicted probabilities is often desirable as a postprocessing. This example illustrates two different methods for this calibration and evaluates the quality of the returned probabilities using Brier's score (see http://en.wikipedia.org/wiki/Brier_score). Compared are the estimated probability using a Gaussian naive Bayes classifier without calibration, with a sigmoid calibration, and with a non-parametric isotonic calibration. One can observe that only the non-parametric model is able to provide a probability calibration that returns probabilities close to the expected 0.5 for most of the samples belonging to the middle cluster with heterogeneous labels. This results in a significantly improved Brier score. """ print(__doc__) # Author: Mathieu Blondel <[email protected]> # Alexandre Gramfort <[email protected]> # Balazs Kegl <[email protected]> # Jan Hendrik Metzen <[email protected]> # License: BSD Style. import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from sklearn.datasets import make_blobs from sklearn.naive_bayes import GaussianNB from sklearn.metrics import brier_score_loss from sklearn.calibration import CalibratedClassifierCV from sklearn.model_selection import train_test_split n_samples = 50000 n_bins = 3 # use 3 bins for calibration_curve as we have 3 clusters here # Generate 3 blobs with 2 classes where the second blob contains # half positive samples and half negative samples. Probability in this # blob is therefore 0.5. centers = [(-5, -5), (0, 0), (5, 5)] X, y = make_blobs(n_samples=n_samples, n_features=2, cluster_std=1.0, centers=centers, shuffle=False, random_state=42) y[:n_samples // 2] = 0 y[n_samples // 2:] = 1 sample_weight = np.random.RandomState(42).rand(y.shape[0]) # split train, test for calibration X_train, X_test, y_train, y_test, sw_train, sw_test = \ train_test_split(X, y, sample_weight, test_size=0.9, random_state=42) # Gaussian Naive-Bayes with no calibration clf = GaussianNB() clf.fit(X_train, y_train) # GaussianNB itself does not support sample-weights prob_pos_clf = clf.predict_proba(X_test)[:, 1] # Gaussian Naive-Bayes with isotonic calibration clf_isotonic = CalibratedClassifierCV(clf, cv=2, method='isotonic') clf_isotonic.fit(X_train, y_train, sw_train) prob_pos_isotonic = clf_isotonic.predict_proba(X_test)[:, 1] # Gaussian Naive-Bayes with sigmoid calibration clf_sigmoid = CalibratedClassifierCV(clf, cv=2, method='sigmoid') clf_sigmoid.fit(X_train, y_train, sw_train) prob_pos_sigmoid = clf_sigmoid.predict_proba(X_test)[:, 1] print("Brier scores: (the smaller the better)") clf_score = brier_score_loss(y_test, prob_pos_clf, sw_test) print("No calibration: %1.3f" % clf_score) clf_isotonic_score = brier_score_loss(y_test, prob_pos_isotonic, sw_test) print("With isotonic calibration: %1.3f" % clf_isotonic_score) clf_sigmoid_score = brier_score_loss(y_test, prob_pos_sigmoid, sw_test) print("With sigmoid calibration: %1.3f" % clf_sigmoid_score) ############################################################################### # Plot the data and the predicted probabilities plt.figure() y_unique = np.unique(y) colors = cm.rainbow(np.linspace(0.0, 1.0, y_unique.size)) for this_y, color in zip(y_unique, colors): this_X = X_train[y_train == this_y] this_sw = sw_train[y_train == this_y] plt.scatter(this_X[:, 0], this_X[:, 1], s=this_sw * 50, c=color, alpha=0.5, label="Class %s" % this_y) plt.legend(loc="best") plt.title("Data") plt.figure() order = np.lexsort((prob_pos_clf, )) plt.plot(prob_pos_clf[order], 'r', label='No calibration (%1.3f)' % clf_score) plt.plot(prob_pos_isotonic[order], 'g', linewidth=3, label='Isotonic calibration (%1.3f)' % clf_isotonic_score) plt.plot(prob_pos_sigmoid[order], 'b', linewidth=3, label='Sigmoid calibration (%1.3f)' % clf_sigmoid_score) plt.plot(np.linspace(0, y_test.size, 51)[1::2], y_test[order].reshape(25, -1).mean(1), 'k', linewidth=3, label=r'Empirical') plt.ylim([-0.05, 1.05]) plt.xlabel("Instances sorted according to predicted probability " "(uncalibrated GNB)") plt.ylabel("P(y=1)") plt.legend(loc="upper left") plt.title("Gaussian naive Bayes probabilities") plt.show()	bsd-3-clause
ratschlab/oqtans_tools	EasySVM/0.3.3/build/lib.linux-x86_64-2.7/esvm/plots.py	4	8318	""" This module contains code for commonly used plots """ ############################################################################################# # # # This program is free software; you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation; either version 3 of the License, or # # (at your option) any later version. # # # # This program is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with this program; if not, see http://www.gnu.org/licenses # # or write to the Free Software Foundation, Inc., 51 Franklin Street, # # Fifth Floor, Boston, MA 02110-1301 USA # # # ############################################################################################# import sys import random import numpy import warnings import shutil from shogun.Features import BinaryLabels from shogun.Evaluation import * def plotroc(output, LTE, draw_random=False, figure_fname="", roc_label='ROC'): """Plot the receiver operating characteristic curve""" import pylab import matplotlib pylab.figure(1,dpi=300,figsize=(8,8)) fontdict=dict(family="serif", weight="bold",size=7,y=1.05) ; # family="cursive" pm=ROCEvaluation() pm.evaluate(BinaryLabels(numpy.array(output)), BinaryLabels(numpy.array(LTE))) points=pm.get_ROC() points=numpy.array(points).T # for pylab.plot pylab.plot(points[0], points[1], 'b-', label=roc_label) if draw_random: pylab.plot([0, 1], [0, 1], 'r-', label='random guessing') pylab.axis([0, 1, 0, 1]) ticks=numpy.arange(0., 1., .1, dtype=numpy.float64) pylab.xticks(ticks,size=10) pylab.yticks(ticks,size=10) pylab.xlabel('1 - specificity (false positive rate)',size=10) pylab.ylabel('sensitivity (true positive rate)',size=10) pylab.legend(loc='lower right') #, prop = matplotlib.font_manager.FontProperties('small')) if figure_fname!=None: warnings.filterwarnings('ignore','Could not match') tempfname = figure_fname + '.png' pylab.savefig(tempfname) shutil.move(tempfname,figure_fname) auROC=pm.get_auROC() return auROC ; def plotprc(output, LTE, figure_fname="", prc_label='PRC'): """Plot the precision recall curve""" import pylab import matplotlib pylab.figure(2,dpi=300,figsize=(8,8)) pm=PRCEvaluation() pm.evaluate(BinaryLabels(numpy.array(output)), BinaryLabels(numpy.array(LTE))) points=pm.get_PRC() points=numpy.array(points).T # for pylab.plot pylab.plot(points[0], points[1], 'b-', label=prc_label) pylab.axis([0, 1, 0, 1]) ticks=numpy.arange(0., 1., .1, dtype=numpy.float64) pylab.xticks(ticks,size=10) pylab.yticks(ticks,size=10) pylab.xlabel('sensitivity (true positive rate)',size=10) pylab.ylabel('precision (1 - false discovery rate)',size=10) pylab.legend(loc='lower right') if figure_fname!=None: warnings.filterwarnings('ignore','Could not match') tempfname = figure_fname + '.png' pylab.savefig(tempfname) shutil.move(tempfname,figure_fname) auPRC=pm.get_auPRC() return auPRC ; def plotcloud(cloud, figure_fname="", label='cloud'): """Plot the cloud of points (the first two dimensions only)""" import pylab import matplotlib pylab.figure(1,dpi=300,figsize=(8,8)) pos = [] neg = [] for i in xrange(len(cloud)): if cloud[i][0]==1: pos.append(cloud[i][1:]) elif cloud[i][0]==-1: neg.append(cloud[i][1:]) fontdict=dict(family="serif", weight="bold",size=10,y=1.05) ; # family="cursive" pylab.title(label, fontdict) points=numpy.array(pos).T # for pylab.plot pylab.plot(points[0], points[1], 'b+', label='positive') points=numpy.array(neg).T # for pylab.plot pylab.plot(points[0], points[1], 'rx', label='negative') #pylab.axis([0, 1, 0, 1]) #ticks=numpy.arange(0., 1., .1, dtype=numpy.float64) #pylab.xticks(ticks,size=10) #pylab.yticks(ticks,size=10) pylab.xlabel('dimension 1',size=10) pylab.ylabel('dimension 2',size=10) pylab.legend(loc='lower right') if figure_fname!=None: warnings.filterwarnings('ignore','Could not match') tempfname = figure_fname + '.png' pylab.savefig(tempfname) shutil.move(tempfname,figure_fname) def plot_poims(poimfilename, poim, max_poim, diff_poim, poim_totalmass, poimdegree, max_len): """Plot a summary of the information in poims""" import pylab import matplotlib pylab.figure(3, dpi=300, figsize=(8,8)) # summary figures fontdict=dict(family="serif", weight="bold",size=7,y=1.05) ; # family="cursive" pylab.subplot(3,2,1) pylab.title('Total POIM Mass', fontdict) pylab.plot(poim_totalmass) ; pylab.ylabel('weight mass', size=5) pylab.subplot(3,2,3) pylab.title('POIMs', fontdict) pylab.pcolor(max_poim, shading='flat') ; pylab.subplot(3,2,5) pylab.title('Differential POIMs', fontdict) pylab.pcolor(diff_poim, shading='flat') ; for plot in [3, 5]: pylab.subplot(3,2,plot) ticks=numpy.arange(1., poimdegree+1, 1, dtype=numpy.float64) ticks_str = [] for i in xrange(0, poimdegree): ticks_str.append("%i" % (i+1)) ticks[i] = i + 0.5 pylab.yticks(ticks, ticks_str) pylab.ylabel('degree', size=5) # per k-mer figures fontdict=dict(family="serif", weight="bold",size=7,y=1.04) ; # family="cursive" # 1-mers pylab.subplot(3,2,2) pylab.title('1-mer Positional Importance', fontdict) pylab.pcolor(poim[0], shading='flat') ; ticks_str = ['A', 'C', 'G', 'T'] ticks = [0.5, 1.5, 2.5, 3.5] pylab.yticks(ticks, ticks_str, size=5) pylab.axis([0, max_len, 0, 4]) # 2-mers pylab.subplot(3,2,4) pylab.title('2-mer Positional Importance', fontdict) pylab.pcolor(poim[1], shading='flat') ; i=0 ; ticks=[] ; ticks_str=[] ; for l1 in ['A', 'C', 'G', 'T']: for l2 in ['A', 'C', 'G', 'T']: ticks_str.append(l1+l2) ticks.append(0.5+i) ; i+=1 ; pylab.yticks(ticks, ticks_str, fontsize=5) pylab.axis([0, max_len, 0, 16]) # 3-mers pylab.subplot(3,2,6) pylab.title('3-mer Positional Importance', fontdict) pylab.pcolor(poim[2], shading='flat') ; i=0 ; ticks=[] ; ticks_str=[] ; for l1 in ['A', 'C', 'G', 'T']: for l2 in ['A', 'C', 'G', 'T']: for l3 in ['A', 'C', 'G', 'T']: if numpy.mod(i,4)==0: ticks_str.append(l1+l2+l3) ticks.append(0.5+i) ; i+=1 ; pylab.yticks(ticks, ticks_str, fontsize=5) pylab.axis([0, max_len, 0, 64]) # x-axis on last two figures for plot in [5, 6]: pylab.subplot(3,2,plot) pylab.xlabel('sequence position', size=5) # finishing up for plot in xrange(0,6): pylab.subplot(3,2,plot+1) pylab.xticks(fontsize=5) for plot in [1,3,5]: pylab.subplot(3,2,plot) pylab.yticks(fontsize=5) pylab.subplots_adjust(hspace=0.35) ; # write to file warnings.filterwarnings('ignore','Could not match') pylab.savefig('/tmp/temppylabfig.png') shutil.move('/tmp/temppylabfig.png',poimfilename)	mit
HoliestCow/ece692_deeplearning	project5/data/make_shuffling_integrations.py	1	7172	import numpy as np import matplotlib.pyplot as plt import os.path from rebin import rebin import glob from random import shuffle from joblib import Parallel, delayed # import time import h5py def label_datasets(): targetfile = '/home/holiestcow/Documents/zephyr/datasets/muse/trainingData/answers.csv' head, tail = os.path.split(targetfile) # filename = [] source_labels = {} id2string = {0: 'Background', 1: 'HEU', 2: 'WGPu', 3: 'I131', 4: 'Co60', 5: 'Tc99', 6: 'HEUandTc99'} f = open(targetfile, 'r') a = f.readlines() for i in range(len(a)): line = a[i].strip() if line[0] == 'R': continue parsed = line.split(',') filename = parsed[0] source = parsed[1] source_time = parsed[2] source_labels[filename] = {'source': id2string[int(source)], 'time': float(source_time)} f.close() return source_labels def parse_datafiles(targetfile, binno, outdir): item = targetfile # for item in filelist: f = open(item, 'r') a = f.readlines() binnumber = 1024 counter = 0 spectra = np.zeros((0, binnumber)) timetracker = 0 energy_deposited = [] for i in range(len(a)): b = a[i].strip() b_parsed = b.split(',') event_time = int(b_parsed[0]) energy_deposited += [float(b_parsed[1])] timetracker += event_time # print(timetracker) if timetracker >= 1E6: timetracker = 0 source_id = 0 counts, energy_edges = np.histogram(energy_deposited, bins=binnumber, range=(0.0, 3000.0)) spectra = np.vstack((spectra, counts)) counter += 1 # print(max(energy_deposited)) energy_deposited = [] # if counter >= 100: # break # print(np.sum(spectra[0, :])) time = np.linspace(0, counter, counter) time = time.reshape((time.shape[0], 1)) # print(time.shape, spectra.shape) tosave = np.hstack((time, spectra)) f.close() head, tail = os.path.split(item) print(tail, spectra.shape) # f = open(os.path.join('./integrations', tail), 'w') # np.savetxt(f, tosave, delimiter=',') # f.close() np.save(os.path.join(outdir, tail[:-4] + '.npy'), tosave) return def main(): # only need to do this once. binnumber = 1024 ncores = 4 nsamples = 50000 # nsamples = 0 filename = 'naive_dataset' id2string = {0: 'Background', 1: 'HEU', 2: 'WGPu', 3: 'I131', 4: 'Co60', 5: 'Tc99', 6: 'HEUandTc99'} string2id = {'Background': 0, 'HEU': 1, 'WGPu': 2, 'I131': 3, 'Co60': 4, 'Tc99': 5, 'HEUandTc99': 6} # sequence_length = 30 # 30 seconds used to guess the next one filelist = glob.glob('/home/holiestcow/Documents/zephyr/datasets/muse/trainingData/1.csv') # shuffle(filelist) # Parallel(n_jobs=ncores)(delayed(parse_datafiles)(item, binnumber, './integrations') for item in filelist) # test_filelist = glob.glob('/home/holiestcow/Documents/zephyr/datasets/muse/testData/2.csv') # HACK: RIGHT HERE test_filelist = glob.glob('./test_integrations/2.npy') # Parallel(n_jobs=ncores)(delayed(parse_datafiles)(item, binnumber, './test_integrations') for item in test_filelist) labels = label_datasets() # NOTE: Slice for background segments f = h5py.File(filename + '.h5', 'w') train = f.create_group('training') test = f.create_group('testing') validation = f.create_group('validation') number_of_testing_files = 4800 number_of_training_files = len(labels.keys()) - number_of_testing_files # The last 10000 are for testing test2train_ratio = number_of_testing_files / number_of_training_files tostore_spectra = np.zeros((nsamples, 1024)) tostore_labels = np.zeros((nsamples, 1)) filelist = list(labels.keys()) for i in range(nsamples): # create training dataset random_file = filelist[np.random.randint(number_of_training_files)] if i % 100 == 0: print('training sample: {}'.format(i)) x = np.load('./integrations/' + random_file + '.npy') # time = x[:, 0] start = np.random.randint(x.shape[0]) source = 'Background' if labels[random_file]['source'] != 'Background' and start >= 30: start = int(labels[random_file]['time']) source = labels[random_file]['source'] spectra = x[start, 1:] tostore_spectra[i, :] = spectra tostore_labels[i] = int(string2id[source]) # g = train.create_group('sample_' + str(i)) # g.create_dataset('spectra', data=spectra, compression='gzip') # g.create_dataset('spectra', data=spectra) # g.create_dataset('label', data=int(string2id[source])) train.create_dataset('spectra', data=tostore_spectra, compression='gzip') train.create_dataset('labels', data=tostore_labels, compression='gzip') tostore_spectra = np.zeros((int(nsamples test2train_ratio), 1024)) tostore_labels = np.zeros((int(nsamples * test2train_ratio), 1)) for i in range(int(nsamples * test2train_ratio)): # create training dataset random_file = filelist[number_of_training_files + np.random.randint(number_of_testing_files)] if i % 100 == 0: print('testing sample: {}'.format(i)) x = np.load('./integrations/' + random_file + '.npy') # time = x[:, 0] start = np.random.randint(x.shape[0]) source = 'Background' if labels[random_file]['source'] != 'Background' and start >= 30: start = int(labels[random_file]['time']) source = labels[random_file]['source'] spectra = x[start, 1:] tostore_spectra[i, :] = spectra tostore_labels[i] = int(string2id[source]) # g = test.create_group('sample_' + str(i)) # g.create_dataset('spectra', data=spectra, compression='gzip') # g.create_dataset('label', data=int(string2id[source])) test.create_dataset('spectra', data=tostore_spectra, compression='gzip') test.create_dataset('labels', data=tostore_labels, compression='gzip') # this is for the validation set, where i have to analyze # each file individual for i in range(len(test_filelist)): if i % 100 == 0: print('validation sample {}'.format(i)) filename = test_filelist[i] head, tail = os.path.split(filename) dataname = tail[:-4] x = np.load(os.path.join('./test_integrations', dataname + '.npy')) t = x[:, 0] spectra = x[:, 1:] file_sample = validation.create_group(dataname) file_sample.create_dataset('time', data=t, compression='gzip') file_sample.create_dataset('spectra', data=spectra, compression='gzip') f.close() return main()	mit
slinderman/pyhawkes	data/chalearn/make_figure.py	1	1779	import pickle import os import gzip import numpy as np import matplotlib.pyplot as plt from hips.plotting.layout import create_figure from hips.plotting.colormaps import harvard_colors def make_figure_a(S, F, C): """ Plot fluorescence traces, filtered fluorescence, and spike times for three neurons """ col = harvard_colors() dt = 0.02 T_start = 0 T_stop = 1 * 50 * 60 t = dt * np.arange(T_start, T_stop) ks = [0,1] nk = len(ks) fig = create_figure((3,3)) for ind,k in enumerate(ks): ax = fig.add_subplot(nk,1,ind+1) ax.plot(t, F[T_start:T_stop, k], color=col[1], label="$F$") # Plot the raw flourescence in blue ax.plot(t, C[T_start:T_stop, k], color=col[0], lw=1.5, label="$\widehat{F}$") # Plot the filtered flourescence in red spks = np.where(S[T_start:T_stop, k])[0] ax.plot(t[spks], C[spks,k], 'ko', label="S") # Plot the spike times in black # Make a legend if ind == 0: # Put a legend above plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=3, mode="expand", borderaxespad=0., prop={'size':9}) # Add labels ax.set_ylabel("$F_%d(t)$" % (k+1)) if ind == nk-1: ax.set_xlabel("Time $t$ [sec]") # Format the ticks ax.set_ylim([-0.1,1.0]) plt.locator_params(nbins=5, axis="y") plt.subplots_adjust(left=0.2, bottom=0.2) fig.savefig("figure3a.pdf") plt.show() data_path = os.path.join("data", "chalearn", "small", "network1_oopsi.pkl.gz") with gzip.open(data_path, 'r') as f: P, F, Cf, network, pos = pickle.load(f) S_full = (P > 0.1).astype(np.int) make_figure_a(S_full, F, Cf)	mit
zhonghualiu/FaST-LMM	fastlmm/pyplink/setup.py	1	2856	""" file to set up python package, see http://docs.python.org/2/distutils/setupscript.html for details. """ import platform import os import sys import shutil from distutils.core import setup from distutils.extension import Extension from distutils.command.clean import clean as Clean try: from Cython.Distutils import build_ext except Exception: print "cython needed for installation, please install cython first" sys.exit() try: import numpy except Exception: print "numpy needed for installation, please install numpy first" sys.exit() def readme(): with open('README.txt') as f: return f.read() class CleanCommand(Clean): description = "Remove build directories, and compiled files (including .pyc)" def run(self): Clean.run(self) if os.path.exists('build'): shutil.rmtree('build') for dirpath, dirnames, filenames in os.walk('fastlmm'): for filename in filenames: if (filename.endswith('.so') or filename.endswith('.pyd') #or filename.endswith('.dll') #or filename.endswith('.pyc') ): os.unlink(os.path.join(dirpath, filename)) # set up macro if platform.system() == "Darwin": macros = [("__APPLE__", "1")] elif "win" in platform.system().lower(): macros = [("_WIN32", "1")] ext = [Extension("fastlmm.util.stats.quadform.qfc_src.wrap_qfc", ["fastlmm/util/stats/quadform/qfc_src/wrap_qfc.pyx", "fastlmm/util/stats/quadform/qfc_src/QFC.cpp"], language="c++", define_macros=macros)] setup( name='fastlmm', version='0.1', description='Fast GWAS', long_description=readme(), keywords='gwas bioinformatics LMMs MLMs', url='', author='MSR', author_email='...', license='non-commercial (MSR-LA)', packages=[ "fastlmm/association/tests", "fastlmm/association", "fastlmm/external/sklearn/externals", "fastlmm/external/sklearn/metrics", "fastlmm/external/sklearn", "fastlmm/external/util", "fastlmm/external", "fastlmm/feature_selection", "fastlmm/inference/bingpc", "fastlmm/inference", "fastlmm/pyplink/altset_list", "fastlmm/pyplink/snpreader", "fastlmm/pyplink/snpset", "fastlmm/pyplink", "fastlmm/util/runner", "fastlmm/util/stats/quadform", "fastlmm/util/stats", "fastlmm/util", "fastlmm" ], install_requires=['cython', 'numpy', 'scipy', 'pandas', 'scikit-learn', 'matplotlib'], #zip_safe=False, # extensions cmdclass = {'build_ext': build_ext, 'clean': CleanCommand}, ext_modules = ext, include_dirs = [numpy.get_include()], )	apache-2.0
equialgo/scikit-learn	examples/svm/plot_svm_regression.py	120	1520	""" =================================================================== Support Vector Regression (SVR) using linear and non-linear kernels =================================================================== Toy example of 1D regression using linear, polynomial and RBF kernels. """ print(__doc__) import numpy as np from sklearn.svm import SVR import matplotlib.pyplot as plt ############################################################################### # Generate sample data X = np.sort(5 * np.random.rand(40, 1), axis=0) y = np.sin(X).ravel() ############################################################################### # Add noise to targets y[::5] += 3 * (0.5 - np.random.rand(8)) ############################################################################### # Fit regression model svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1) svr_lin = SVR(kernel='linear', C=1e3) svr_poly = SVR(kernel='poly', C=1e3, degree=2) y_rbf = svr_rbf.fit(X, y).predict(X) y_lin = svr_lin.fit(X, y).predict(X) y_poly = svr_poly.fit(X, y).predict(X) ############################################################################### # look at the results lw = 2 plt.scatter(X, y, color='darkorange', label='data') plt.hold('on') plt.plot(X, y_rbf, color='navy', lw=lw, label='RBF model') plt.plot(X, y_lin, color='c', lw=lw, label='Linear model') plt.plot(X, y_poly, color='cornflowerblue', lw=lw, label='Polynomial model') plt.xlabel('data') plt.ylabel('target') plt.title('Support Vector Regression') plt.legend() plt.show()	bsd-3-clause
ryklith/pyltesim	plotting/sinr_analysis_plot_ICC2013.py	1	1189	#!/usr/bin/env python ''' Plot a cdf from a csv file File: plot_CDF_from_file.py ''' __author__ = "Hauke Holtkamp" __credits__ = "Hauke Holtkamp" __license__ = "unknown" __version__ = "unknown" __maintainer__ = "Hauke Holtkamp" __email__ = "[email protected]" __status__ = "Development" def plot_cdf_from_file(filename): """Open file, store cdf to .pdf and .png""" import numpy as np import matplotlib.pyplot as plt data = np.genfromtxt(filename, delimiter=',') # convert zeros to nans and clear empty rows data[np.where(data==0)] = np.nan data = data[~np.isnan(data).all(1)] if not data.size: print 'No data in ' + str(filename) # SINR data is best presented in dB from utils import utils data = utils.WTodB(data) import cdf_plot label = [ "Iteration %d" %i for i in np.arange(data.shape[0])+1] cdf_plot.cdf_plot(data, '-', label=label) # plt.xlabel(xlabel) # plt.ylabel(ylabel) # plt.title(title) plt.savefig(filename+'.pdf', format='pdf') plt.savefig(filename+'.png', format='png') if __name__ == '__main__': import sys filename = sys.argv[1] plot_cdf_from_file(filename)	gpl-2.0
stuart-knock/bokeh	bokeh/charts/builder/donut_builder.py	31	8206	"""This is the Bokeh charts interface. It gives you a high level API to build complex plot is a simple way. This is the Donut class which lets you build your Donut charts just passing the arguments to the Chart class and calling the proper functions. It also add a new chained stacked method. """ #----------------------------------------------------------------------------- # Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved. # # Powered by the Bokeh Development Team. # # The full license is in the file LICENSE.txt, distributed with this software. #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- from __future__ import absolute_import, division from math import pi import pandas as pd from ..utils import cycle_colors, polar_to_cartesian from .._builder import Builder, create_and_build from ...models import ColumnDataSource, GlyphRenderer, Range1d from ...models.glyphs import AnnularWedge, Text, Wedge from ...properties import Any, Bool, Either, List #----------------------------------------------------------------------------- # Classes and functions #----------------------------------------------------------------------------- def Donut(values, cat=None, width=800, height=800, xgrid=False, ygrid=False, kws): """ Creates a Donut chart using :class:`DonutBuilder <bokeh.charts.builder.donut_builder.DonutBuilder>` to render the geometry from values and cat. Args: values (iterable): iterable 2d representing the data series values matrix. cat (list or bool, optional): list of string representing the categories. Defaults to None. In addition the the parameters specific to this chart, :ref:`userguide_charts_generic_arguments` are also accepted as keyword parameters. Returns: a new :class:`Chart <bokeh.charts.Chart>` Examples: .. bokeh-plot:: :source-position: above from bokeh.charts import Donut, output_file, show # dict, OrderedDict, lists, arrays and DataFrames are valid inputs xyvalues = [[2., 5., 3.], [4., 1., 4.], [6., 4., 3.]] donut = Donut(xyvalues, ['cpu1', 'cpu2', 'cpu3']) output_file('donut.html') show(donut) """ return create_and_build( DonutBuilder, values, cat=cat, width=width, height=height, xgrid=xgrid, ygrid=ygrid, kws ) class DonutBuilder(Builder): """This is the Donut class and it is in charge of plotting Donut chart in an easy and intuitive way. Essentially, it provides a way to ingest the data, make the proper calculations and push the references into a source object. We additionally make calculations for the donut slices and angles. And finally add the needed glyphs (Wedges and AnnularWedges) taking the references from the source. """ cat = Either(Bool, List(Any), help=""" List of string representing the categories. (Defaults to None.) """) def _process_data(self): """Take the chart data from self._values. It calculates the chart properties accordingly (start/end angles). Then build a dict containing references to all the calculated points to be used by the Wedge glyph inside the ``_yield_renderers`` method. """ dd = dict(zip(self._values.keys(), self._values.values())) self._df = df = pd.DataFrame(dd) self._groups = df.index = self.cat df.columns = self._values.keys() # Get the sum per category aggregated = df.T.sum() # Get the total (sum of all categories) self._total_units = total = aggregated.sum() radians = lambda x: 2pi(x/total) angles = aggregated.map(radians).cumsum() end_angles = angles.tolist() start_angles = [0] + end_angles[:-1] colors = cycle_colors(self.cat, self.palette) self.set_and_get("", "colors", colors) self.set_and_get("", "end", end_angles) self.set_and_get("", "start", start_angles) def _set_sources(self): """Push the Donut data into the ColumnDataSource and calculate the proper ranges. """ self._source = ColumnDataSource(self._data) self.x_range = Range1d(start=-2, end=2) self.y_range = Range1d(start=-2, end=2) def draw_central_wedge(self): """Draw the central part of the donut wedge from donut.source and its calculated start and end angles. """ glyph = Wedge( x=0, y=0, radius=1, start_angle="start", end_angle="end", line_color="white", line_width=2, fill_color="colors" ) yield GlyphRenderer(data_source=self._source, glyph=glyph) def draw_central_descriptions(self): """Draw the descriptions to be placed on the central part of the donut wedge """ text = ["%s" % cat for cat in self.cat] x, y = polar_to_cartesian(0.7, self._data["start"], self._data["end"]) text_source = ColumnDataSource(dict(text=text, x=x, y=y)) glyph = Text( x="x", y="y", text="text", text_align="center", text_baseline="middle" ) yield GlyphRenderer(data_source=text_source, glyph=glyph) def draw_external_ring(self, colors=None): """Draw the external part of the donut wedge from donut.source and its related descriptions """ if colors is None: colors = cycle_colors(self.cat, self.palette) first = True for i, (cat, start_angle, end_angle) in enumerate(zip( self.cat, self._data['start'], self._data['end'])): details = self._df.ix[i] radians = lambda x: 2pi(x/self._total_units) angles = details.map(radians).cumsum() + start_angle end = angles.tolist() + [end_angle] start = [start_angle] + end[:-1] base_color = colors[i] #fill = [ base_color.lighten(i0.05) for i in range(len(details) + 1) ] fill = [base_color for i in range(len(details) + 1)] text = [rowlabel for rowlabel in details.index] x, y = polar_to_cartesian(1.25, start, end) source = ColumnDataSource(dict(start=start, end=end, fill=fill)) glyph = AnnularWedge( x=0, y=0, inner_radius=1, outer_radius=1.5, start_angle="start", end_angle="end", line_color="white", line_width=2, fill_color="fill" ) yield GlyphRenderer(data_source=source, glyph=glyph) text_angle = [(start[i]+end[i])/2 for i in range(len(start))] text_angle = [angle + pi if pi/2 < angle < 3pi/2 else angle for angle in text_angle] if first and text: text.insert(0, '') offset = pi / 48 text_angle.insert(0, text_angle[0] - offset) start.insert(0, start[0] - offset) end.insert(0, end[0] - offset) x, y = polar_to_cartesian(1.25, start, end) first = False data = dict(text=text, x=x, y=y, angle=text_angle) text_source = ColumnDataSource(data) glyph = Text( x="x", y="y", text="text", angle="angle", text_align="center", text_baseline="middle" ) yield GlyphRenderer(data_source=text_source, glyph=glyph) def _yield_renderers(self): """Use the AnnularWedge and Wedge glyphs to display the wedges. Takes reference points from data loaded at the ColumnDataSurce. """ # build the central round area of the donut renderers = [] renderers += self.draw_central_wedge() # write central descriptions renderers += self.draw_central_descriptions() # build external donut ring renderers += self.draw_external_ring() return renderers	bsd-3-clause
CosmicFish/CosmicFish	camb/eftcamb/tests_EFT/python/CAMB_plots_lib/CAMB_comp_plots.py	1	31632	import numpy as np import matplotlib.pyplot as plt import math from CMB_plots import CMB_plots class CAMB_results_compare_plot: """ class that contains the necessary tools to plot the comparison of two CAMB run """ # general plot settings: color1 = 'red' # default color of the line of the first model color2 = 'blue' # default color of the line of the second model color_compa = 'green' # default color of the line of the difference x_size_inc = 8.30 # x size of the final plots, in inches y_size_inc = 11.7 # y size of the final plots, in inches def __init__(self, root1, root2, outpath, tensor=False, lensing=False, transfer=False, name1='', name2=''): """ Class constructor: root1 = name of the first CAMB run root2 = name of the second CAMB run outpath = path to the output folder tensor = (optional) specifies wether the results contains the tensor Cls lensing = (optional) specifies wether the results contains lensing transfer = (optional) specifies wether the results contains transfer functions name1 = (optional) specifies the name of the first model. Used for the legend. name2 = (optional) specifies the name of the second model. Used for the legend. """ # store the constructor options: self.root1 = root1 self.root2 = root2 self.outpath = outpath self.tensor = tensor self.lensing = lensing self.transfer = transfer # extract the name of the models from the roots: self.name1 = root1.split("/")[len(root1.split("/"))-1] self.name2 = root2.split("/")[len(root2.split("/"))-1] # set the human readable name if name1=='': self.name_h1 = ''.join(i for i in self.name1.replace('_',' ') if not i.isdigit()) else: self.name_h1 = name1 if name2=='': self.name_h2 = ''.join(i for i in self.name2.replace('_',' ') if not i.isdigit()) else: self.name_h2 = name2 # load the data: self.scalCls_1 = np.loadtxt(root1+'_scalCls.dat') self.scalCovCls_1 = np.loadtxt(root1+'_scalCovCls.dat') self.scalCls_2 = np.loadtxt(root2+'_scalCls.dat') self.scalCovCls_2 = np.loadtxt(root2+'_scalCovCls.dat') if self.lensing: self.lensedCls_1 = np.loadtxt(root1+'_lensedCls.dat') self.lenspotentialCls_1 = np.loadtxt(root1+'_lenspotentialCls.dat') self.lensedCls_2 = np.loadtxt(root2+'_lensedCls.dat') self.lenspotentialCls_2 = np.loadtxt(root2+'_lenspotentialCls.dat') if self.transfer: self.matterpower_1 = np.loadtxt(root1+'_matterpower.dat') self.transfer_func_1 = np.loadtxt(root1+'_transfer_out.dat') self.matterpower_2 = np.loadtxt(root2+'_matterpower.dat') self.transfer_func_2 = np.loadtxt(root2+'_transfer_out.dat') if self.tensor: self.tensCls_1 = np.loadtxt(root1+'_tensCls.dat') self.totCls_1 = np.loadtxt(root1+'_totCls.dat') self.tensCls_2 = np.loadtxt(root2+'_tensCls.dat') self.totCls_2 = np.loadtxt(root2+'_totCls.dat') if self.lensing and self.tensor: self.lensedtotCls_1 = np.loadtxt(root1+'_lensedtotCls.dat') self.lensedtotCls_2 = np.loadtxt(root2+'_lensedtotCls.dat') def plot_compare_scalCls(self): """ Plots and saves the comparison of all the scalar Cls in a unique image """ # number of Cls in the two files: num1 = self.scalCls_1.shape[1]-1 num2 = self.scalCls_2.shape[1]-1 # protection against different runs if num1!=num2: print 'wrong number of Cls' return if len(self.scalCls_1[:,0])!=len(self.scalCls_2[:,0]): print 'different lmax' return if len(self.matterpower_1[:,0])!=len(self.matterpower_2[:,0]): self.transfer = False # add the matter power spectrum if required: if self.transfer: num1 += 1 # values of l xvalues = self.scalCls_1[:,0] # set up the plots: plots_1 = CMB_plots() plots_2 = CMB_plots() plots_compa = CMB_plots() plots_1.color = self.color1 plots_2.color = self.color2 plots_compa.color = self.color_compa plots_compa.comparison = True plots_compa.axes_label_position = 'right' fig = plt.gcf() # do the plots: for ind in xrange(1,num1+1): # distribute the plots in the figure: temp = plt.subplot2grid((num1,2), (ind-1, 0)) temp_comp = plt.subplot2grid((num1,2), (ind-1, 1)) if ind == 1: # TT power spectrum: yvalues_1 = self.scalCls_1[:,ind] yvalues_2 = self.scalCls_2[:,ind] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0(-16), [10.0(-16)]) # make the plots: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) temp_comp.set_yscale('Log') temp.set_title('TT power spectrum') elif ind == 2: # EE power spectrum: yvalues_1 = self.scalCls_1[:,ind] yvalues_2 = self.scalCls_2[:,ind] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0(-16), [10.0(-16)]) # make the plots: plots_1.EE_plot(temp, xvalues, yvalues_1) plots_2.EE_plot(temp, xvalues, yvalues_2) plots_compa.EE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('EE power spectrum') elif ind == 3: # TE power spectrum: yvalues_1 = self.scalCls_1[:,ind] yvalues_2 = self.scalCls_2[:,ind] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0(-16), [10.0(-16)]) # make the plots: plots_1.TE_plot(temp, xvalues, yvalues_1) plots_2.TE_plot(temp, xvalues, yvalues_2) plots_compa.TE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('TE power spectrum') elif ind == 4 and self.lensing: # CMB lensing power spectrum: yvalues_1 = self.scalCls_1[:,ind] yvalues_2 = self.scalCls_2[:,ind] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0(-16), [10.0(-16)]) # make the plots: plots_1.Phi_plot(temp, xvalues, yvalues_1) plots_2.Phi_plot(temp, xvalues, yvalues_2) plots_compa.Phi_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('$\phi$ power spectrum') elif ind == 5 and self.lensing: # CMB lensing - Temperature power spectrum: yvalues_1 = self.scalCls_1[:,ind] yvalues_2 = self.scalCls_2[:,ind] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0(-16), [10.0(-16)]) # make the plots: plots_1.PhiT_plot(temp, xvalues, yvalues_1) plots_2.PhiT_plot(temp, xvalues, yvalues_2) plots_compa.PhiT_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('$\phi$T power spectrum') elif ind == num1 and self.transfer: # matter power spectrum: xvalues = self.matterpower_2[:,0] yvalues_1 = self.matterpower_1[:,1] yvalues_2 = self.matterpower_2[:,1] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0(-16), [10.0(-16)]) # make the plots: plots_1.Matter_plot(temp, xvalues, yvalues_1) plots_2.Matter_plot(temp, xvalues, yvalues_2) plots_compa.Matter_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('Matter power spectrum') else: # generic Cl comparison: yvalues_1 = self.scalCls_1[:,ind] yvalues_2 = self.scalCls_2[:,ind] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0(-16), [10.0(-16)]) # make the plots: plots_1.Generic_Cl(temp, xvalues, yvalues_1) plots_2.Generic_Cl(temp, xvalues, yvalues_2) plots_compa.Generic_Cl(temp_comp, xvalues, yvalues_comp) # set the size of the image fig.set_size_inches( self.x_size_inc, self.y_size_inc) # set a tight layout fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3) # set the global title plt.suptitle(self.name_h1+' VS '+self.name_h2+ ' comparison of scalar Cls', fontsize=16) # set the global legend fig.legend( handles = [plots_1.TT_p, plots_2.TT_p, plots_compa.CV_p], labels = [self.name_h1, self.name_h2, 'Cosmic variance'], loc='lower center', ncol=3 ,fancybox=True) # adjust the size of the plot fig.subplots_adjust(top=0.92, bottom=0.08) # save the result and close plt.savefig(self.outpath+self.name1+'_'+self.name2+'_scalCls_comp.pdf') plt.clf() plt.close("all") def plot_compare_scalCovCls(self): """ Plots and saves the comparison of all the scalar Cov Cls in a unique image """ # number of Cls: num1 = self.scalCovCls_1.shape[1]-1 num2 = self.scalCovCls_2.shape[1]-1 # protection against different runs if num1!=num2: print 'wrong number of Cls' return if len(self.scalCovCls_1[:,0])!=len(self.scalCovCls_2[:,0]): print 'different lmax' return # size of the Cl Cov matrix: num1 = int(math.sqrt(num1)) num_tot = sum(xrange(1,num1+1)) # set up the plots: plots_1 = CMB_plots() plots_2 = CMB_plots() plots_compa = CMB_plots() plots_1.color = self.color1 plots_2.color = self.color2 plots_compa.color = self.color_compa plots_compa.comparison = True plots_compa.axes_label_position = 'right' fig = plt.gcf() # setup a dictionary with the names of the Cls dict = { 1: 'T', 2: 'E', 3: '$\phi$'} for i in xrange(4, num1+1): dict[i] = 'W'+str(i) # values of the multipoles: xvalues = self.scalCovCls_1[:,0] # other stuff: ind_tot = 0 # do the plots: for ind in xrange(1,num1+1): for ind2 in xrange(1, ind+1): ind_tot += 1 # place the plots: temp = plt.subplot2grid((num_tot,2), (ind_tot-1, 0)) temp_comp = plt.subplot2grid((num_tot,2), (ind_tot-1, 1)) # values of the Cls: col = ind + num1(ind2-1) yvalues_1 = self.scalCovCls_1[:,col] yvalues_2 = self.scalCovCls_2[:,col] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0(-16), [10.0(-16)]) # make the plots: plots_1.Generic_Cl(temp, xvalues, yvalues_1) plots_2.Generic_Cl(temp, xvalues, yvalues_2) plots_compa.Generic_Cl(temp_comp, xvalues, yvalues_comp) temp.set_title(dict[ind2]+dict[ind]+' power spectrum') # set the size of the image fig.set_size_inches( self.x_size_inc, self.y_size_inc/5.0num_tot) # set a tight layout fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3) # set the global legend fig.legend( handles = [plots_1.Generic_Cl_plot, plots_2.Generic_Cl_plot, plots_compa.CV_p], labels = [self.name_h1, self.name_h2, 'Cosmic variance'], loc='lower center', ncol=3 ,fancybox=True) # set the global title plt.suptitle(self.name_h1+' VS '+self.name_h2+ ' comparison of scalar Cov Cls', fontsize=16) # adjust the size of the plot fig.subplots_adjust(top=0.92, bottom=0.08) # fig.subplots_adjust(top=0.96, bottom=0.01) # save the result and close plt.savefig(self.outpath+self.name1+'_'+self.name2+'_scalCovCls_comp.pdf') plt.clf() plt.close("all") def plot_compare_lensedCls(self): """ Plots and saves the comparison of all the lensed Cls in a unique image """ # protection from direct calls if lensing is not included if not self.lensing: return # number of Cls: num1 = self.lensedCls_1.shape[1]-1 num2 = self.lensedCls_2.shape[1]-1 # protection against different runs if num1!=num2: print 'wrong number of Cls' return if len(self.lensedCls_1[:,0])!=len(self.lensedCls_2[:,0]): print 'different lmax' return xvalues = self.lensedCls_1[:,0] # set up the plots: plots_1 = CMB_plots() plots_2 = CMB_plots() plots_compa = CMB_plots() plots_1.color = self.color1 plots_2.color = self.color2 plots_compa.color = self.color_compa plots_compa.comparison = True plots_compa.axes_label_position = 'right' fig = plt.gcf() # do the plots: for ind in xrange(1,num1+1): temp = plt.subplot2grid((num1,2), (ind-1, 0)) temp_comp = plt.subplot2grid((num1,2), (ind-1, 1)) yvalues_1 = self.lensedCls_1[:,ind] yvalues_2 = self.lensedCls_2[:,ind] min2val = np.min(yvalues_1[np.nonzero(yvalues_1)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)100 # Protection against all zero: put to machine precision the values that are zero np.place(yvalues_comp, abs(yvalues_comp)<10.0(-16), [10.0(-16)]) if ind == 1: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) temp_comp.set_yscale('Log') temp.set_title('TT power spectrum') elif ind == 2: plots_1.EE_plot(temp, xvalues, yvalues_1) plots_2.EE_plot(temp, xvalues, yvalues_2) plots_compa.EE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('EE power spectrum') elif ind == 3: plots_1.BB_plot(temp, xvalues, yvalues_1) plots_2.BB_plot(temp, xvalues, yvalues_2) plots_compa.BB_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('BB power spectrum') elif ind == 4: plots_1.TE_plot(temp, xvalues, yvalues_1) plots_2.TE_plot(temp, xvalues, yvalues_2) plots_compa.TE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('TE power spectrum') else: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) # set the size of the image fig.set_size_inches( self.x_size_inc, self.y_size_inc) # set a tight layout fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3) # set the global title plt.suptitle(self.name_h1+' VS '+self.name_h2+ ' comparison of lensed Cls', fontsize=16) # set the global legend fig.legend( handles = [plots_1.TT_p, plots_2.TT_p, plots_compa.CV_p], labels = [self.name_h1, self.name_h2, 'Cosmic variance'], loc='lower center', ncol=3 ,fancybox=True) # adjust the size of the plot fig.subplots_adjust(top=0.92, bottom=0.08) # save the result and close plt.savefig(self.outpath+self.name1+'_'+self.name2+'_lensedCls_comp.pdf') plt.clf() plt.close("all") def plot_compare_tensCls(self): """ Plots and saves the comparison of all the tensor Cls in a unique image """ # protection from direct calls if tensors are not included if not self.tensor: return # number of Cls: num1 = self.tensCls_1.shape[1]-1 num2 = self.tensCls_2.shape[1]-1 # protection against different runs if num1!=num2: print 'wrong number of Cls' return if len(self.tensCls_1[:,0])!=len(self.tensCls_2[:,0]): print 'different lmax' return xvalues = self.tensCls_1[:,0] # set up the plots: plots_1 = CMB_plots() plots_2 = CMB_plots() plots_compa = CMB_plots() plots_1.color = self.color1 plots_2.color = self.color2 plots_compa.color = self.color_compa plots_compa.comparison = True plots_compa.axes_label_position = 'right' fig = plt.gcf() # do the plots: for ind in xrange(1,num1+1): temp = plt.subplot2grid((num1,2), (ind-1, 0)) temp_comp = plt.subplot2grid((num1,2), (ind-1, 1)) yvalues_1 = self.tensCls_1[:,ind] yvalues_2 = self.tensCls_2[:,ind] min2val = np.min(yvalues_1[np.nonzero(yvalues_1)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)100 # Protection against all zero: put to machine precision the values that are zero np.place(yvalues_comp, abs(yvalues_comp)<10.0(-16), [10.0(-16)]) if ind == 1: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) temp.set_yscale('Log') temp.set_title('TT power spectrum') elif ind == 2: plots_1.EE_plot(temp, xvalues, yvalues_1) plots_2.EE_plot(temp, xvalues, yvalues_2) plots_compa.EE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('EE power spectrum') elif ind == 3: plots_1.BB_plot(temp, xvalues, yvalues_1) plots_2.BB_plot(temp, xvalues, yvalues_2) plots_compa.BB_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('BB power spectrum') elif ind == 4: plots_1.TE_plot(temp, xvalues, yvalues_1) plots_2.TE_plot(temp, xvalues, yvalues_2) plots_compa.TE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('TE power spectrum') else: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) # set the size of the image fig.set_size_inches( self.x_size_inc, self.y_size_inc) # set a tight layout fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3) # set the global title plt.suptitle(self.name_h1+' VS '+self.name_h2+ ' comparison of tensor Cls', fontsize=16) # set the global legend fig.legend( handles = [plots_1.TT_p, plots_2.TT_p, plots_compa.CV_p], labels = [self.name_h1, self.name_h2, 'Cosmic variance'], loc='lower center', ncol=3 ,fancybox=True) # adjust the size of the plot fig.subplots_adjust(top=0.92, bottom=0.08) # save the result and close plt.savefig(self.outpath+self.name1+'_'+self.name2+'_tensCls_comp.pdf') plt.clf() plt.close("all") def plot_compare_totalCls(self): """ Plots and saves the comparison of all the total (scalar + tensor) Cls in a unique image If lensing is included lensed Cls are used. """ # protection from direct calls if tensors are not included if not self.tensor: return # decide what data to use: if self.lensing: data1 = self.lensedtotCls_1 data2 = self.lensedtotCls_2 else: data1 = self.totCls_1 data2 = self.totCls_2 # number of Cls: num1 = data1.shape[1]-1 num2 = data2.shape[1]-1 # protection against different runs if num1!=num2: print 'wrong number of Cls' return if len(data1[:,0])!=len(data2[:,0]): print 'different lmax' return xvalues = data1[:,0] # set up the plots: plots_1 = CMB_plots() plots_2 = CMB_plots() plots_compa = CMB_plots() plots_1.color = self.color1 plots_2.color = self.color2 plots_compa.color = self.color_compa plots_compa.comparison = True plots_compa.axes_label_position = 'right' fig = plt.gcf() # do the plots: for ind in xrange(1,num1+1): temp = plt.subplot2grid((num1,2), (ind-1, 0)) temp_comp = plt.subplot2grid((num1,2), (ind-1, 1)) yvalues_1 = data1[:,ind] yvalues_2 = data2[:,ind] min2val = np.min(yvalues_1[np.nonzero(yvalues_1)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)100 # Protection against all zero: put to machine precision the values that are zero np.place(yvalues_comp, abs(yvalues_comp)<10.0(-16), [10.0(-16)]) if ind == 1: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) temp.set_yscale('Log') temp.set_title('TT power spectrum') elif ind == 2: plots_1.EE_plot(temp, xvalues, yvalues_1) plots_2.EE_plot(temp, xvalues, yvalues_2) plots_compa.EE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('EE power spectrum') elif ind == 3: plots_1.BB_plot(temp, xvalues, yvalues_1) plots_2.BB_plot(temp, xvalues, yvalues_2) plots_compa.BB_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('BB power spectrum') elif ind == 4: plots_1.TE_plot(temp, xvalues, yvalues_1) plots_2.TE_plot(temp, xvalues, yvalues_2) plots_compa.TE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('TE power spectrum') else: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) # set the size of the image fig.set_size_inches( self.x_size_inc, self.y_size_inc) # set a tight layout fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3) # set the global title if self.lensing: plt.suptitle(self.name_h1+' VS '+self.name_h2+' comparison of total lensed Cls', fontsize=16) else: plt.suptitle(self.name_h1+' VS '+self.name_h2+' comparison of total Cls', fontsize=16) # set the global legend fig.legend( handles = [plots_1.TT_p, plots_2.TT_p, plots_compa.CV_p], labels = [self.name_h1, self.name_h2, 'Cosmic variance'], loc='lower center', ncol=3 ,fancybox=True) # adjust the size of the plot fig.subplots_adjust(top=0.92, bottom=0.08) # save the result and close plt.savefig(self.outpath+self.name1+'_'+self.name2+'_totCls_comp.pdf') plt.clf() plt.close("all") def plot_compare_Transfer(self): """ Plots and saves the comparison of all the transfer functions in a unique image """ # protection from direct calls if transfer functions are not included if not self.transfer: return data1 = self.transfer_func_1 data2 = self.transfer_func_2 # number of transfer functions: num1 = data1.shape[1]-1 num2 = data2.shape[1]-1 # protection against different runs if num1!=num2: print 'wrong number of transfer functions' return if len(data1[:,0])!=len(data2[:,0]): print 'Different values of k' return xvalues = data1[:,0] # set up the plots: plots_1 = CMB_plots() plots_2 = CMB_plots() plots_compa = CMB_plots() plots_1.color = self.color1 plots_2.color = self.color2 plots_compa.color = self.color_compa plots_compa.comparison = True plots_compa.axes_label_position = 'right' fig = plt.gcf() labels = [ 'CDM', 'baryons', 'photons', 'massless neutrinos', 'massive neutrinos', 'CDM+baryons+massive neutrinos', 'CDM+baryons', 'CDM+baryons+massive neutrinos+ de', 'The Weyl potential', 'vel_Newt_cdm', 'vel_Newt_b', 'relative baryon-CDM velocity' ] if not len(labels) == num1: print 'Not enough transfer functions' return # do the plots: for ind in xrange(1,num1+1): temp = plt.subplot2grid((num1,2), (ind-1, 0)) temp_comp = plt.subplot2grid((num1,2), (ind-1, 1)) yvalues_1 = data1[:,ind] yvalues_2 = data2[:,ind] min2val = np.min(yvalues_1[np.nonzero(yvalues_1)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)100 # Protection against all zero: put to machine precision the values that are zero np.place(yvalues_comp, abs(yvalues_comp)<10.0(-16), [10.0(-16)]) plots_1.Transfer_plot(temp, xvalues, yvalues_1) plots_2.Transfer_plot(temp, xvalues, yvalues_2) plots_compa.Transfer_plot(temp_comp, xvalues, yvalues_comp) temp.set_title(labels[ind-1]) # set the size of the image fig.set_size_inches( self.x_size_inc, 1.61803398875self.x_size_inc/6.num1 ) # set a tight layout fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3) # set the global title plt.suptitle(self.name_h1+' VS '+self.name_h2+' comparison of transfer functions', fontsize=16) # set the global legend fig.legend( handles = [plots_1.Transfer_p, plots_2.Transfer_p], labels = [self.name_h1, self.name_h2], loc='lower center', ncol=3 ,fancybox=True) # adjust the size of the plot fig.subplots_adjust(top=0.95, bottom=0.05) # save the result and close plt.savefig(self.outpath+self.name1+'_'+self.name2+'_transfer_comp.pdf') plt.clf() plt.close("all")	gpl-3.0
DonBeo/scikit-learn	sklearn/cluster/tests/test_dbscan.py	7	10974	""" Tests for DBSCAN clustering algorithm """ import pickle import numpy as np from scipy.spatial import distance from scipy import sparse from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_in from sklearn.utils.testing import assert_not_in from sklearn.cluster.dbscan_ import DBSCAN from sklearn.cluster.dbscan_ import dbscan from sklearn.cluster.tests.common import generate_clustered_data from sklearn.metrics.pairwise import pairwise_distances n_clusters = 3 X = generate_clustered_data(n_clusters=n_clusters) def test_dbscan_similarity(): # Tests the DBSCAN algorithm with a similarity array. # Parameters chosen specifically for this task. eps = 0.15 min_samples = 10 # Compute similarities D = distance.squareform(distance.pdist(X)) D /= np.max(D) # Compute DBSCAN core_samples, labels = dbscan(D, metric="precomputed", eps=eps, min_samples=min_samples) # number of clusters, ignoring noise if present n_clusters_1 = len(set(labels)) - (1 if -1 in labels else 0) assert_equal(n_clusters_1, n_clusters) db = DBSCAN(metric="precomputed", eps=eps, min_samples=min_samples) labels = db.fit(D).labels_ n_clusters_2 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_2, n_clusters) def test_dbscan_feature(): # Tests the DBSCAN algorithm with a feature vector array. # Parameters chosen specifically for this task. # Different eps to other test, because distance is not normalised. eps = 0.8 min_samples = 10 metric = 'euclidean' # Compute DBSCAN # parameters chosen for task core_samples, labels = dbscan(X, metric=metric, eps=eps, min_samples=min_samples) # number of clusters, ignoring noise if present n_clusters_1 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_1, n_clusters) db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples) labels = db.fit(X).labels_ n_clusters_2 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_2, n_clusters) def test_dbscan_sparse(): core_sparse, labels_sparse = dbscan(sparse.lil_matrix(X), eps=.8, min_samples=10) core_dense, labels_dense = dbscan(X, eps=.8, min_samples=10) assert_array_equal(core_dense, core_sparse) assert_array_equal(labels_dense, labels_sparse) def test_dbscan_no_core_samples(): rng = np.random.RandomState(0) X = rng.rand(40, 10) X[X < .8] = 0 for X_ in [X, sparse.csr_matrix(X)]: db = DBSCAN(min_samples=6).fit(X_) assert_array_equal(db.components_, np.empty((0, X_.shape[1]))) assert_array_equal(db.labels_, -1) assert_equal(db.core_sample_indices_.shape, (0,)) def test_dbscan_callable(): # Tests the DBSCAN algorithm with a callable metric. # Parameters chosen specifically for this task. # Different eps to other test, because distance is not normalised. eps = 0.8 min_samples = 10 # metric is the function reference, not the string key. metric = distance.euclidean # Compute DBSCAN # parameters chosen for task core_samples, labels = dbscan(X, metric=metric, eps=eps, min_samples=min_samples, algorithm='ball_tree') # number of clusters, ignoring noise if present n_clusters_1 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_1, n_clusters) db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples, algorithm='ball_tree') labels = db.fit(X).labels_ n_clusters_2 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_2, n_clusters) def test_dbscan_balltree(): # Tests the DBSCAN algorithm with balltree for neighbor calculation. eps = 0.8 min_samples = 10 D = pairwise_distances(X) core_samples, labels = dbscan(D, metric="precomputed", eps=eps, min_samples=min_samples) # number of clusters, ignoring noise if present n_clusters_1 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_1, n_clusters) db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm='ball_tree') labels = db.fit(X).labels_ n_clusters_2 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_2, n_clusters) db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm='kd_tree') labels = db.fit(X).labels_ n_clusters_3 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_3, n_clusters) db = DBSCAN(p=1.0, eps=eps, min_samples=min_samples, algorithm='ball_tree') labels = db.fit(X).labels_ n_clusters_4 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_4, n_clusters) db = DBSCAN(leaf_size=20, eps=eps, min_samples=min_samples, algorithm='ball_tree') labels = db.fit(X).labels_ n_clusters_5 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_5, n_clusters) def test_input_validation(): # DBSCAN.fit should accept a list of lists. X = [[1., 2.], [3., 4.]] DBSCAN().fit(X) # must not raise exception def test_dbscan_badargs(): # Test bad argument values: these should all raise ValueErrors assert_raises(ValueError, dbscan, X, eps=-1.0) assert_raises(ValueError, dbscan, X, algorithm='blah') assert_raises(ValueError, dbscan, X, metric='blah') assert_raises(ValueError, dbscan, X, leaf_size=-1) assert_raises(ValueError, dbscan, X, p=-1) def test_pickle(): obj = DBSCAN() s = pickle.dumps(obj) assert_equal(type(pickle.loads(s)), obj.__class__) def test_boundaries(): # ensure min_samples is inclusive of core point core, _ = dbscan([[0], [1]], eps=2, min_samples=2) assert_in(0, core) # ensure eps is inclusive of circumference core, _ = dbscan([[0], [1], [1]], eps=1, min_samples=2) assert_in(0, core) core, _ = dbscan([[0], [1], [1]], eps=.99, min_samples=2) assert_not_in(0, core) def test_weighted_dbscan(): # ensure sample_weight is validated assert_raises(ValueError, dbscan, [[0], [1]], sample_weight=[2]) assert_raises(ValueError, dbscan, [[0], [1]], sample_weight=[2, 3, 4]) # ensure sample_weight has an effect assert_array_equal([], dbscan([[0], [1]], sample_weight=None, min_samples=6)[0]) assert_array_equal([], dbscan([[0], [1]], sample_weight=[5, 5], min_samples=6)[0]) assert_array_equal([0], dbscan([[0], [1]], sample_weight=[6, 5], min_samples=6)[0]) assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[6, 6], min_samples=6)[0]) # points within eps of each other: assert_array_equal([0, 1], dbscan([[0], [1]], eps=1.5, sample_weight=[5, 1], min_samples=6)[0]) # and effect of non-positive and non-integer sample_weight: assert_array_equal([], dbscan([[0], [1]], sample_weight=[5, 0], eps=1.5, min_samples=6)[0]) assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[5.9, 0.1], eps=1.5, min_samples=6)[0]) assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[6, 0], eps=1.5, min_samples=6)[0]) assert_array_equal([], dbscan([[0], [1]], sample_weight=[6, -1], eps=1.5, min_samples=6)[0]) # for non-negative sample_weight, cores should be identical to repetition rng = np.random.RandomState(42) sample_weight = rng.randint(0, 5, X.shape[0]) core1, label1 = dbscan(X, sample_weight=sample_weight) assert_equal(len(label1), len(X)) X_repeated = np.repeat(X, sample_weight, axis=0) core_repeated, label_repeated = dbscan(X_repeated) core_repeated_mask = np.zeros(X_repeated.shape[0], dtype=bool) core_repeated_mask[core_repeated] = True core_mask = np.zeros(X.shape[0], dtype=bool) core_mask[core1] = True assert_array_equal(np.repeat(core_mask, sample_weight), core_repeated_mask) # sample_weight should work with precomputed distance matrix D = pairwise_distances(X) core3, label3 = dbscan(D, sample_weight=sample_weight, metric='precomputed') assert_array_equal(core1, core3) assert_array_equal(label1, label3) # sample_weight should work with estimator est = DBSCAN().fit(X, sample_weight=sample_weight) core4 = est.core_sample_indices_ label4 = est.labels_ assert_array_equal(core1, core4) assert_array_equal(label1, label4) est = DBSCAN() label5 = est.fit_predict(X, sample_weight=sample_weight) core5 = est.core_sample_indices_ assert_array_equal(core1, core5) assert_array_equal(label1, label5) assert_array_equal(label1, est.labels_) def test_dbscan_core_samples_toy(): X = [[0], [2], [3], [4], [6], [8], [10]] n_samples = len(X) for algorithm in ['brute', 'kd_tree', 'ball_tree']: # Degenerate case: every sample is a core sample, either with its own # cluster or including other close core samples. core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=1) assert_array_equal(core_samples, np.arange(n_samples)) assert_array_equal(labels, [0, 1, 1, 1, 2, 3, 4]) # With eps=1 and min_samples=2 only the 3 samples from the denser area # are core samples. All other points are isolated and considered noise. core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=2) assert_array_equal(core_samples, [1, 2, 3]) assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1]) # Only the sample in the middle of the dense area is core. Its two # neighbors are edge samples. Remaining samples are noise. core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=3) assert_array_equal(core_samples, [2]) assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1]) # It's no longer possible to extract core samples with eps=1: # everything is noise. core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=4) assert_array_equal(core_samples, []) assert_array_equal(labels, -np.ones(n_samples))	bsd-3-clause
sssllliang/BuildingMachineLearningSystemsWithPython	ch02/chapter.py	17	4700	# This code is supporting material for the book # Building Machine Learning Systems with Python # by Willi Richert and Luis Pedro Coelho # published by PACKT Publishing # # It is made available under the MIT License from matplotlib import pyplot as plt import numpy as np # We load the data with load_iris from sklearn from sklearn.datasets import load_iris data = load_iris() # load_iris returns an object with several fields features = data.data feature_names = data.feature_names target = data.target target_names = data.target_names for t in range(3): if t == 0: c = 'r' marker = '>' elif t == 1: c = 'g' marker = 'o' elif t == 2: c = 'b' marker = 'x' plt.scatter(features[target == t, 0], features[target == t, 1], marker=marker, c=c) # We use NumPy fancy indexing to get an array of strings: labels = target_names[target] # The petal length is the feature at position 2 plength = features[:, 2] # Build an array of booleans: is_setosa = (labels == 'setosa') # This is the important step: max_setosa =plength[is_setosa].max() min_non_setosa = plength[~is_setosa].min() print('Maximum of setosa: {0}.'.format(max_setosa)) print('Minimum of others: {0}.'.format(min_non_setosa)) # ~ is the boolean negation operator features = features[~is_setosa] labels = labels[~is_setosa] # Build a new target variable, is_virigina is_virginica = (labels == 'virginica') # Initialize best_acc to impossibly low value best_acc = -1.0 for fi in range(features.shape[1]): # We are going to test all possible thresholds thresh = features[:,fi] for t in thresh: # Get the vector for feature `fi` feature_i = features[:, fi] # apply threshold `t` pred = (feature_i > t) acc = (pred == is_virginica).mean() rev_acc = (pred == ~is_virginica).mean() if rev_acc > acc: reverse = True acc = rev_acc else: reverse = False if acc > best_acc: best_acc = acc best_fi = fi best_t = t best_reverse = reverse print(best_fi, best_t, best_reverse, best_acc) def is_virginica_test(fi, t, reverse, example): 'Apply threshold model to a new example' test = example[fi] > t if reverse: test = not test return test from threshold import fit_model, predict # ning accuracy was 96.0%. # ing accuracy was 90.0% (N = 50). correct = 0.0 for ei in range(len(features)): # select all but the one at position `ei`: training = np.ones(len(features), bool) training[ei] = False testing = ~training model = fit_model(features[training], is_virginica[training]) predict(model, features[testing]) predictions = predict(model, features[testing]) correct += np.sum(predictions == is_virginica[testing]) acc = correct/float(len(features)) print('Accuracy: {0:.1%}'.format(acc)) ########################################### ############## SEEDS DATASET ############## ########################################### from load import load_dataset feature_names = [ 'area', 'perimeter', 'compactness', 'length of kernel', 'width of kernel', 'asymmetry coefficien', 'length of kernel groove', ] features, labels = load_dataset('seeds') from sklearn.neighbors import KNeighborsClassifier classifier = KNeighborsClassifier(n_neighbors=1) from sklearn.cross_validation import KFold kf = KFold(len(features), n_folds=5, shuffle=True) means = [] for training,testing in kf: # We learn a model for this fold with `fit` and then apply it to the # testing data with `predict`: classifier.fit(features[training], labels[training]) prediction = classifier.predict(features[testing]) # np.mean on an array of booleans returns fraction # of correct decisions for this fold: curmean = np.mean(prediction == labels[testing]) means.append(curmean) print('Mean accuracy: {:.1%}'.format(np.mean(means))) from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler classifier = KNeighborsClassifier(n_neighbors=1) classifier = Pipeline([('norm', StandardScaler()), ('knn', classifier)]) means = [] for training,testing in kf: # We learn a model for this fold with `fit` and then apply it to the # testing data with `predict`: classifier.fit(features[training], labels[training]) prediction = classifier.predict(features[testing]) # np.mean on an array of booleans returns fraction # of correct decisions for this fold: curmean = np.mean(prediction == labels[testing]) means.append(curmean) print('Mean accuracy: {:.1%}'.format(np.mean(means)))	mit
nikitasingh981/scikit-learn	sklearn/metrics/tests/test_classification.py	8	57459	from __future__ import division, print_function import numpy as np from scipy import linalg from functools import partial from itertools import product import warnings from sklearn import datasets from sklearn import svm from sklearn.datasets import make_multilabel_classification from sklearn.preprocessing import label_binarize from sklearn.utils.fixes import np_version from sklearn.utils.validation import check_random_state from sklearn.utils.testing import assert_raises, clean_warning_registry from sklearn.utils.testing import assert_raise_message from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_warns from sklearn.utils.testing import assert_no_warnings from sklearn.utils.testing import assert_warns_message from sklearn.utils.testing import assert_not_equal from sklearn.utils.testing import ignore_warnings from sklearn.utils.mocking import MockDataFrame from sklearn.metrics import accuracy_score from sklearn.metrics import average_precision_score from sklearn.metrics import classification_report from sklearn.metrics import cohen_kappa_score from sklearn.metrics import confusion_matrix from sklearn.metrics import f1_score from sklearn.metrics import fbeta_score from sklearn.metrics import hamming_loss from sklearn.metrics import hinge_loss from sklearn.metrics import jaccard_similarity_score from sklearn.metrics import log_loss from sklearn.metrics import matthews_corrcoef from sklearn.metrics import precision_recall_fscore_support from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import zero_one_loss from sklearn.metrics import brier_score_loss from sklearn.metrics.classification import _check_targets from sklearn.exceptions import UndefinedMetricWarning from scipy.spatial.distance import hamming as sp_hamming ############################################################################### # Utilities for testing def make_prediction(dataset=None, binary=False): """Make some classification predictions on a toy dataset using a SVC If binary is True restrict to a binary classification problem instead of a multiclass classification problem """ if dataset is None: # import some data to play with dataset = datasets.load_iris() X = dataset.data y = dataset.target if binary: # restrict to a binary classification task X, y = X[y < 2], y[y < 2] n_samples, n_features = X.shape p = np.arange(n_samples) rng = check_random_state(37) rng.shuffle(p) X, y = X[p], y[p] half = int(n_samples / 2) # add noisy features to make the problem harder and avoid perfect results rng = np.random.RandomState(0) X = np.c_[X, rng.randn(n_samples, 200 * n_features)] # run classifier, get class probabilities and label predictions clf = svm.SVC(kernel='linear', probability=True, random_state=0) probas_pred = clf.fit(X[:half], y[:half]).predict_proba(X[half:]) if binary: # only interested in probabilities of the positive case # XXX: do we really want a special API for the binary case? probas_pred = probas_pred[:, 1] y_pred = clf.predict(X[half:]) y_true = y[half:] return y_true, y_pred, probas_pred ############################################################################### # Tests def test_multilabel_accuracy_score_subset_accuracy(): # Dense label indicator matrix format y1 = np.array([[0, 1, 1], [1, 0, 1]]) y2 = np.array([[0, 0, 1], [1, 0, 1]]) assert_equal(accuracy_score(y1, y2), 0.5) assert_equal(accuracy_score(y1, y1), 1) assert_equal(accuracy_score(y2, y2), 1) assert_equal(accuracy_score(y2, np.logical_not(y2)), 0) assert_equal(accuracy_score(y1, np.logical_not(y1)), 0) assert_equal(accuracy_score(y1, np.zeros(y1.shape)), 0) assert_equal(accuracy_score(y2, np.zeros(y1.shape)), 0) def test_precision_recall_f1_score_binary(): # Test Precision Recall and F1 Score for binary classification task y_true, y_pred, _ = make_prediction(binary=True) # detailed measures for each class p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None) assert_array_almost_equal(p, [0.73, 0.85], 2) assert_array_almost_equal(r, [0.88, 0.68], 2) assert_array_almost_equal(f, [0.80, 0.76], 2) assert_array_equal(s, [25, 25]) # individual scoring function that can be used for grid search: in the # binary class case the score is the value of the measure for the positive # class (e.g. label == 1). This is deprecated for average != 'binary'. for kwargs, my_assert in [({}, assert_no_warnings), ({'average': 'binary'}, assert_no_warnings)]: ps = my_assert(precision_score, y_true, y_pred, kwargs) assert_array_almost_equal(ps, 0.85, 2) rs = my_assert(recall_score, y_true, y_pred, kwargs) assert_array_almost_equal(rs, 0.68, 2) fs = my_assert(f1_score, y_true, y_pred, kwargs) assert_array_almost_equal(fs, 0.76, 2) assert_almost_equal(my_assert(fbeta_score, y_true, y_pred, beta=2, kwargs), (1 + 2 ** 2) * ps * rs / (2 ** 2 * ps + rs), 2) def test_precision_recall_f_binary_single_class(): # Test precision, recall and F1 score behave with a single positive or # negative class # Such a case may occur with non-stratified cross-validation assert_equal(1., precision_score([1, 1], [1, 1])) assert_equal(1., recall_score([1, 1], [1, 1])) assert_equal(1., f1_score([1, 1], [1, 1])) assert_equal(0., precision_score([-1, -1], [-1, -1])) assert_equal(0., recall_score([-1, -1], [-1, -1])) assert_equal(0., f1_score([-1, -1], [-1, -1])) @ignore_warnings def test_precision_recall_f_extra_labels(): # Test handling of explicit additional (not in input) labels to PRF y_true = [1, 3, 3, 2] y_pred = [1, 1, 3, 2] y_true_bin = label_binarize(y_true, classes=np.arange(5)) y_pred_bin = label_binarize(y_pred, classes=np.arange(5)) data = [(y_true, y_pred), (y_true_bin, y_pred_bin)] for i, (y_true, y_pred) in enumerate(data): # No average: zeros in array actual = recall_score(y_true, y_pred, labels=[0, 1, 2, 3, 4], average=None) assert_array_almost_equal([0., 1., 1., .5, 0.], actual) # Macro average is changed actual = recall_score(y_true, y_pred, labels=[0, 1, 2, 3, 4], average='macro') assert_array_almost_equal(np.mean([0., 1., 1., .5, 0.]), actual) # No effect otheriwse for average in ['micro', 'weighted', 'samples']: if average == 'samples' and i == 0: continue assert_almost_equal(recall_score(y_true, y_pred, labels=[0, 1, 2, 3, 4], average=average), recall_score(y_true, y_pred, labels=None, average=average)) # Error when introducing invalid label in multilabel case # (although it would only affect performance if average='macro'/None) for average in [None, 'macro', 'micro', 'samples']: assert_raises(ValueError, recall_score, y_true_bin, y_pred_bin, labels=np.arange(6), average=average) assert_raises(ValueError, recall_score, y_true_bin, y_pred_bin, labels=np.arange(-1, 4), average=average) @ignore_warnings def test_precision_recall_f_ignored_labels(): # Test a subset of labels may be requested for PRF y_true = [1, 1, 2, 3] y_pred = [1, 3, 3, 3] y_true_bin = label_binarize(y_true, classes=np.arange(5)) y_pred_bin = label_binarize(y_pred, classes=np.arange(5)) data = [(y_true, y_pred), (y_true_bin, y_pred_bin)] for i, (y_true, y_pred) in enumerate(data): recall_13 = partial(recall_score, y_true, y_pred, labels=[1, 3]) recall_all = partial(recall_score, y_true, y_pred, labels=None) assert_array_almost_equal([.5, 1.], recall_13(average=None)) assert_almost_equal((.5 + 1.) / 2, recall_13(average='macro')) assert_almost_equal((.5 * 2 + 1. * 1) / 3, recall_13(average='weighted')) assert_almost_equal(2. / 3, recall_13(average='micro')) # ensure the above were meaningful tests: for average in ['macro', 'weighted', 'micro']: assert_not_equal(recall_13(average=average), recall_all(average=average)) def test_average_precision_score_score_non_binary_class(): # Test that average_precision_score function returns an error when trying # to compute average_precision_score for multiclass task. rng = check_random_state(404) y_pred = rng.rand(10) # y_true contains three different class values y_true = rng.randint(0, 3, size=10) assert_raise_message(ValueError, "multiclass format is not supported", average_precision_score, y_true, y_pred) def test_average_precision_score_duplicate_values(): # Duplicate values with precision-recall require a different # processing than when computing the AUC of a ROC, because the # precision-recall curve is a decreasing curve # The following situation corresponds to a perfect # test statistic, the average_precision_score should be 1 y_true = [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1] y_score = [0, .1, .1, .4, .5, .6, .6, .9, .9, 1, 1] assert_equal(average_precision_score(y_true, y_score), 1) def test_average_precision_score_tied_values(): # Here if we go from left to right in y_true, the 0 values are # are separated from the 1 values, so it appears that we've # Correctly sorted our classifications. But in fact the first two # values have the same score (0.5) and so the first two values # could be swapped around, creating an imperfect sorting. This # imperfection should come through in the end score, making it less # than one. y_true = [0, 1, 1] y_score = [.5, .5, .6] assert_not_equal(average_precision_score(y_true, y_score), 1.) @ignore_warnings def test_precision_recall_fscore_support_errors(): y_true, y_pred, _ = make_prediction(binary=True) # Bad beta assert_raises(ValueError, precision_recall_fscore_support, y_true, y_pred, beta=0.0) # Bad pos_label assert_raises(ValueError, precision_recall_fscore_support, y_true, y_pred, pos_label=2, average='binary') # Bad average option assert_raises(ValueError, precision_recall_fscore_support, [0, 1, 2], [1, 2, 0], average='mega') def test_precision_recall_f_unused_pos_label(): # Check warning that pos_label unused when set to non-default value # but average != 'binary'; even if data is binary. assert_warns_message(UserWarning, "Note that pos_label (set to 2) is " "ignored when average != 'binary' (got 'macro'). You " "may use labels=[pos_label] to specify a single " "positive class.", precision_recall_fscore_support, [1, 2, 1], [1, 2, 2], pos_label=2, average='macro') def test_confusion_matrix_binary(): # Test confusion matrix - binary classification case y_true, y_pred, _ = make_prediction(binary=True) def test(y_true, y_pred): cm = confusion_matrix(y_true, y_pred) assert_array_equal(cm, [[22, 3], [8, 17]]) tp, fp, fn, tn = cm.flatten() num = (tp * tn - fp * fn) den = np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)) true_mcc = 0 if den == 0 else num / den mcc = matthews_corrcoef(y_true, y_pred) assert_array_almost_equal(mcc, true_mcc, decimal=2) assert_array_almost_equal(mcc, 0.57, decimal=2) test(y_true, y_pred) test([str(y) for y in y_true], [str(y) for y in y_pred]) def test_cohen_kappa(): # These label vectors reproduce the contingency matrix from Artstein and # Poesio (2008), Table 1: np.array([[20, 20], [10, 50]]). y1 = np.array([0] * 40 + [1] * 60) y2 = np.array([0] * 20 + [1] * 20 + [0] * 10 + [1] * 50) kappa = cohen_kappa_score(y1, y2) assert_almost_equal(kappa, .348, decimal=3) assert_equal(kappa, cohen_kappa_score(y2, y1)) # Add spurious labels and ignore them. y1 = np.append(y1, [2] * 4) y2 = np.append(y2, [2] * 4) assert_equal(cohen_kappa_score(y1, y2, labels=[0, 1]), kappa) assert_almost_equal(cohen_kappa_score(y1, y1), 1.) # Multiclass example: Artstein and Poesio, Table 4. y1 = np.array([0] * 46 + [1] * 44 + [2] * 10) y2 = np.array([0] * 52 + [1] * 32 + [2] * 16) assert_almost_equal(cohen_kappa_score(y1, y2), .8013, decimal=4) # Weighting example: none, linear, quadratic. y1 = np.array([0] * 46 + [1] * 44 + [2] * 10) y2 = np.array([0] * 50 + [1] * 40 + [2] * 10) assert_almost_equal(cohen_kappa_score(y1, y2), .9315, decimal=4) assert_almost_equal(cohen_kappa_score(y1, y2, weights="linear"), .9412, decimal=4) assert_almost_equal(cohen_kappa_score(y1, y2, weights="quadratic"), .9541, decimal=4) @ignore_warnings def test_matthews_corrcoef_nan(): assert_equal(matthews_corrcoef([0], [1]), 0.0) assert_equal(matthews_corrcoef([0, 0], [0, 1]), 0.0) def test_matthews_corrcoef_against_numpy_corrcoef(): rng = np.random.RandomState(0) y_true = rng.randint(0, 2, size=20) y_pred = rng.randint(0, 2, size=20) assert_almost_equal(matthews_corrcoef(y_true, y_pred), np.corrcoef(y_true, y_pred)[0, 1], 10) def test_matthews_corrcoef(): rng = np.random.RandomState(0) y_true = ["a" if i == 0 else "b" for i in rng.randint(0, 2, size=20)] # corrcoef of same vectors must be 1 assert_almost_equal(matthews_corrcoef(y_true, y_true), 1.0) # corrcoef, when the two vectors are opposites of each other, should be -1 y_true_inv = ["b" if i == "a" else "a" for i in y_true] assert_almost_equal(matthews_corrcoef(y_true, y_true_inv), -1) y_true_inv2 = label_binarize(y_true, ["a", "b"]) y_true_inv2 = np.where(y_true_inv2, 'a', 'b') assert_almost_equal(matthews_corrcoef(y_true, y_true_inv2), -1) # For the zero vector case, the corrcoef cannot be calculated and should # result in a RuntimeWarning mcc = assert_warns_message(RuntimeWarning, 'invalid value encountered', matthews_corrcoef, [0, 0, 0, 0], [0, 0, 0, 0]) # But will output 0 assert_almost_equal(mcc, 0.) # And also for any other vector with 0 variance mcc = assert_warns_message(RuntimeWarning, 'invalid value encountered', matthews_corrcoef, y_true, ['a'] * len(y_true)) # But will output 0 assert_almost_equal(mcc, 0.) # These two vectors have 0 correlation and hence mcc should be 0 y_1 = [1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1] y_2 = [1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1] assert_almost_equal(matthews_corrcoef(y_1, y_2), 0.) # Check that sample weight is able to selectively exclude mask = [1] * 10 + [0] * 10 # Now the first half of the vector elements are alone given a weight of 1 # and hence the mcc will not be a perfect 0 as in the previous case assert_raises(AssertionError, assert_almost_equal, matthews_corrcoef(y_1, y_2, sample_weight=mask), 0.) def test_precision_recall_f1_score_multiclass(): # Test Precision Recall and F1 Score for multiclass classification task y_true, y_pred, _ = make_prediction(binary=False) # compute scores with default labels introspection p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None) assert_array_almost_equal(p, [0.83, 0.33, 0.42], 2) assert_array_almost_equal(r, [0.79, 0.09, 0.90], 2) assert_array_almost_equal(f, [0.81, 0.15, 0.57], 2) assert_array_equal(s, [24, 31, 20]) # averaging tests ps = precision_score(y_true, y_pred, pos_label=1, average='micro') assert_array_almost_equal(ps, 0.53, 2) rs = recall_score(y_true, y_pred, average='micro') assert_array_almost_equal(rs, 0.53, 2) fs = f1_score(y_true, y_pred, average='micro') assert_array_almost_equal(fs, 0.53, 2) ps = precision_score(y_true, y_pred, average='macro') assert_array_almost_equal(ps, 0.53, 2) rs = recall_score(y_true, y_pred, average='macro') assert_array_almost_equal(rs, 0.60, 2) fs = f1_score(y_true, y_pred, average='macro') assert_array_almost_equal(fs, 0.51, 2) ps = precision_score(y_true, y_pred, average='weighted') assert_array_almost_equal(ps, 0.51, 2) rs = recall_score(y_true, y_pred, average='weighted') assert_array_almost_equal(rs, 0.53, 2) fs = f1_score(y_true, y_pred, average='weighted') assert_array_almost_equal(fs, 0.47, 2) assert_raises(ValueError, precision_score, y_true, y_pred, average="samples") assert_raises(ValueError, recall_score, y_true, y_pred, average="samples") assert_raises(ValueError, f1_score, y_true, y_pred, average="samples") assert_raises(ValueError, fbeta_score, y_true, y_pred, average="samples", beta=0.5) # same prediction but with and explicit label ordering p, r, f, s = precision_recall_fscore_support( y_true, y_pred, labels=[0, 2, 1], average=None) assert_array_almost_equal(p, [0.83, 0.41, 0.33], 2) assert_array_almost_equal(r, [0.79, 0.90, 0.10], 2) assert_array_almost_equal(f, [0.81, 0.57, 0.15], 2) assert_array_equal(s, [24, 20, 31]) def test_precision_refcall_f1_score_multilabel_unordered_labels(): # test that labels need not be sorted in the multilabel case y_true = np.array([[1, 1, 0, 0]]) y_pred = np.array([[0, 0, 1, 1]]) for average in ['samples', 'micro', 'macro', 'weighted', None]: p, r, f, s = precision_recall_fscore_support( y_true, y_pred, labels=[3, 0, 1, 2], warn_for=[], average=average) assert_array_equal(p, 0) assert_array_equal(r, 0) assert_array_equal(f, 0) if average is None: assert_array_equal(s, [0, 1, 1, 0]) def test_precision_recall_f1_score_binary_averaged(): y_true = np.array([0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1]) y_pred = np.array([1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1]) # compute scores with default labels introspection ps, rs, fs, _ = precision_recall_fscore_support(y_true, y_pred, average=None) p, r, f, _ = precision_recall_fscore_support(y_true, y_pred, average='macro') assert_equal(p, np.mean(ps)) assert_equal(r, np.mean(rs)) assert_equal(f, np.mean(fs)) p, r, f, _ = precision_recall_fscore_support(y_true, y_pred, average='weighted') support = np.bincount(y_true) assert_equal(p, np.average(ps, weights=support)) assert_equal(r, np.average(rs, weights=support)) assert_equal(f, np.average(fs, weights=support)) def test_zero_precision_recall(): # Check that pathological cases do not bring NaNs old_error_settings = np.seterr(all='raise') try: y_true = np.array([0, 1, 2, 0, 1, 2]) y_pred = np.array([2, 0, 1, 1, 2, 0]) assert_almost_equal(precision_score(y_true, y_pred, average='macro'), 0.0, 2) assert_almost_equal(recall_score(y_true, y_pred, average='macro'), 0.0, 2) assert_almost_equal(f1_score(y_true, y_pred, average='macro'), 0.0, 2) finally: np.seterr(*old_error_settings) def test_confusion_matrix_multiclass(): # Test confusion matrix - multi-class case y_true, y_pred, _ = make_prediction(binary=False) def test(y_true, y_pred, string_type=False): # compute confusion matrix with default labels introspection cm = confusion_matrix(y_true, y_pred) assert_array_equal(cm, [[19, 4, 1], [4, 3, 24], [0, 2, 18]]) # compute confusion matrix with explicit label ordering labels = ['0', '2', '1'] if string_type else [0, 2, 1] cm = confusion_matrix(y_true, y_pred, labels=labels) assert_array_equal(cm, [[19, 1, 4], [0, 18, 2], [4, 24, 3]]) test(y_true, y_pred) test(list(str(y) for y in y_true), list(str(y) for y in y_pred), string_type=True) def test_confusion_matrix_sample_weight(): """Test confusion matrix - case with sample_weight""" y_true, y_pred, _ = make_prediction(binary=False) weights = [.1] 25 + [.2] * 25 + [.3] * 25 cm = confusion_matrix(y_true, y_pred, sample_weight=weights) true_cm = (.1 * confusion_matrix(y_true[:25], y_pred[:25]) + .2 * confusion_matrix(y_true[25:50], y_pred[25:50]) + .3 * confusion_matrix(y_true[50:], y_pred[50:])) assert_array_almost_equal(cm, true_cm) assert_raises( ValueError, confusion_matrix, y_true, y_pred, sample_weight=weights[:-1]) def test_confusion_matrix_multiclass_subset_labels(): # Test confusion matrix - multi-class case with subset of labels y_true, y_pred, _ = make_prediction(binary=False) # compute confusion matrix with only first two labels considered cm = confusion_matrix(y_true, y_pred, labels=[0, 1]) assert_array_equal(cm, [[19, 4], [4, 3]]) # compute confusion matrix with explicit label ordering for only subset # of labels cm = confusion_matrix(y_true, y_pred, labels=[2, 1]) assert_array_equal(cm, [[18, 2], [24, 3]]) # a label not in y_true should result in zeros for that row/column extra_label = np.max(y_true) + 1 cm = confusion_matrix(y_true, y_pred, labels=[2, extra_label]) assert_array_equal(cm, [[18, 0], [0, 0]]) # check for exception when none of the specified labels are in y_true assert_raises(ValueError, confusion_matrix, y_true, y_pred, labels=[extra_label, extra_label + 1]) def test_classification_report_multiclass(): # Test performance report iris = datasets.load_iris() y_true, y_pred, _ = make_prediction(dataset=iris, binary=False) # print classification report with class names expected_report = """\ precision recall f1-score support setosa 0.83 0.79 0.81 24 versicolor 0.33 0.10 0.15 31 virginica 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ report = classification_report( y_true, y_pred, labels=np.arange(len(iris.target_names)), target_names=iris.target_names) assert_equal(report, expected_report) # print classification report with label detection expected_report = """\ precision recall f1-score support 0 0.83 0.79 0.81 24 1 0.33 0.10 0.15 31 2 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ report = classification_report(y_true, y_pred) assert_equal(report, expected_report) def test_classification_report_multiclass_with_digits(): # Test performance report with added digits in floating point values iris = datasets.load_iris() y_true, y_pred, _ = make_prediction(dataset=iris, binary=False) # print classification report with class names expected_report = """\ precision recall f1-score support setosa 0.82609 0.79167 0.80851 24 versicolor 0.33333 0.09677 0.15000 31 virginica 0.41860 0.90000 0.57143 20 avg / total 0.51375 0.53333 0.47310 75 """ report = classification_report( y_true, y_pred, labels=np.arange(len(iris.target_names)), target_names=iris.target_names, digits=5) assert_equal(report, expected_report) # print classification report with label detection expected_report = """\ precision recall f1-score support 0 0.83 0.79 0.81 24 1 0.33 0.10 0.15 31 2 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ report = classification_report(y_true, y_pred) assert_equal(report, expected_report) def test_classification_report_multiclass_with_string_label(): y_true, y_pred, _ = make_prediction(binary=False) y_true = np.array(["blue", "green", "red"])[y_true] y_pred = np.array(["blue", "green", "red"])[y_pred] expected_report = """\ precision recall f1-score support blue 0.83 0.79 0.81 24 green 0.33 0.10 0.15 31 red 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ report = classification_report(y_true, y_pred) assert_equal(report, expected_report) expected_report = """\ precision recall f1-score support a 0.83 0.79 0.81 24 b 0.33 0.10 0.15 31 c 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ report = classification_report(y_true, y_pred, target_names=["a", "b", "c"]) assert_equal(report, expected_report) def test_classification_report_multiclass_with_unicode_label(): y_true, y_pred, _ = make_prediction(binary=False) labels = np.array([u"blue\xa2", u"green\xa2", u"red\xa2"]) y_true = labels[y_true] y_pred = labels[y_pred] expected_report = u"""\ precision recall f1-score support blue\xa2 0.83 0.79 0.81 24 green\xa2 0.33 0.10 0.15 31 red\xa2 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ if np_version[:3] < (1, 7, 0): expected_message = ("NumPy < 1.7.0 does not implement" " searchsorted on unicode data correctly.") assert_raise_message(RuntimeError, expected_message, classification_report, y_true, y_pred) else: report = classification_report(y_true, y_pred) assert_equal(report, expected_report) def test_classification_report_multiclass_with_long_string_label(): y_true, y_pred, _ = make_prediction(binary=False) labels = np.array(["blue", "green"5, "red"]) y_true = labels[y_true] y_pred = labels[y_pred] expected_report = """\ precision recall f1-score support blue 0.83 0.79 0.81 24 greengreengreengreengreen 0.33 0.10 0.15 31 red 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ report = classification_report(y_true, y_pred) assert_equal(report, expected_report) def test_classification_report_labels_target_names_unequal_length(): y_true = [0, 0, 2, 0, 0] y_pred = [0, 2, 2, 0, 0] target_names = ['class 0', 'class 1', 'class 2'] assert_warns_message(UserWarning, "labels size, 2, does not " "match size of target_names, 3", classification_report, y_true, y_pred, target_names=target_names) def test_multilabel_classification_report(): n_classes = 4 n_samples = 50 _, y_true = make_multilabel_classification(n_features=1, n_samples=n_samples, n_classes=n_classes, random_state=0) _, y_pred = make_multilabel_classification(n_features=1, n_samples=n_samples, n_classes=n_classes, random_state=1) expected_report = """\ precision recall f1-score support 0 0.50 0.67 0.57 24 1 0.51 0.74 0.61 27 2 0.29 0.08 0.12 26 3 0.52 0.56 0.54 27 avg / total 0.45 0.51 0.46 104 """ report = classification_report(y_true, y_pred) assert_equal(report, expected_report) def test_multilabel_zero_one_loss_subset(): # Dense label indicator matrix format y1 = np.array([[0, 1, 1], [1, 0, 1]]) y2 = np.array([[0, 0, 1], [1, 0, 1]]) assert_equal(zero_one_loss(y1, y2), 0.5) assert_equal(zero_one_loss(y1, y1), 0) assert_equal(zero_one_loss(y2, y2), 0) assert_equal(zero_one_loss(y2, np.logical_not(y2)), 1) assert_equal(zero_one_loss(y1, np.logical_not(y1)), 1) assert_equal(zero_one_loss(y1, np.zeros(y1.shape)), 1) assert_equal(zero_one_loss(y2, np.zeros(y1.shape)), 1) def test_multilabel_hamming_loss(): # Dense label indicator matrix format y1 = np.array([[0, 1, 1], [1, 0, 1]]) y2 = np.array([[0, 0, 1], [1, 0, 1]]) w = np.array([1, 3]) assert_equal(hamming_loss(y1, y2), 1 / 6) assert_equal(hamming_loss(y1, y1), 0) assert_equal(hamming_loss(y2, y2), 0) assert_equal(hamming_loss(y2, 1 - y2), 1) assert_equal(hamming_loss(y1, 1 - y1), 1) assert_equal(hamming_loss(y1, np.zeros(y1.shape)), 4 / 6) assert_equal(hamming_loss(y2, np.zeros(y1.shape)), 0.5) assert_equal(hamming_loss(y1, y2, sample_weight=w), 1. / 12) assert_equal(hamming_loss(y1, 1-y2, sample_weight=w), 11. / 12) assert_equal(hamming_loss(y1, np.zeros_like(y1), sample_weight=w), 2. / 3) # sp_hamming only works with 1-D arrays assert_equal(hamming_loss(y1[0], y2[0]), sp_hamming(y1[0], y2[0])) assert_warns(DeprecationWarning, hamming_loss, y1, y2, classes=[0, 1]) def test_multilabel_jaccard_similarity_score(): # Dense label indicator matrix format y1 = np.array([[0, 1, 1], [1, 0, 1]]) y2 = np.array([[0, 0, 1], [1, 0, 1]]) # size(y1 \inter y2) = [1, 2] # size(y1 \union y2) = [2, 2] assert_equal(jaccard_similarity_score(y1, y2), 0.75) assert_equal(jaccard_similarity_score(y1, y1), 1) assert_equal(jaccard_similarity_score(y2, y2), 1) assert_equal(jaccard_similarity_score(y2, np.logical_not(y2)), 0) assert_equal(jaccard_similarity_score(y1, np.logical_not(y1)), 0) assert_equal(jaccard_similarity_score(y1, np.zeros(y1.shape)), 0) assert_equal(jaccard_similarity_score(y2, np.zeros(y1.shape)), 0) @ignore_warnings def test_precision_recall_f1_score_multilabel_1(): # Test precision_recall_f1_score on a crafted multilabel example # First crafted example y_true = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 1]]) y_pred = np.array([[0, 1, 0, 0], [0, 1, 0, 0], [1, 0, 1, 0]]) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None) # tp = [0, 1, 1, 0] # fn = [1, 0, 0, 1] # fp = [1, 1, 0, 0] # Check per class assert_array_almost_equal(p, [0.0, 0.5, 1.0, 0.0], 2) assert_array_almost_equal(r, [0.0, 1.0, 1.0, 0.0], 2) assert_array_almost_equal(f, [0.0, 1 / 1.5, 1, 0.0], 2) assert_array_almost_equal(s, [1, 1, 1, 1], 2) f2 = fbeta_score(y_true, y_pred, beta=2, average=None) support = s assert_array_almost_equal(f2, [0, 0.83, 1, 0], 2) # Check macro p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="macro") assert_almost_equal(p, 1.5 / 4) assert_almost_equal(r, 0.5) assert_almost_equal(f, 2.5 / 1.5 0.25) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="macro"), np.mean(f2)) # Check micro p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="micro") assert_almost_equal(p, 0.5) assert_almost_equal(r, 0.5) assert_almost_equal(f, 0.5) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="micro"), (1 + 4) * p * r / (4 * p + r)) # Check weighted p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="weighted") assert_almost_equal(p, 1.5 / 4) assert_almost_equal(r, 0.5) assert_almost_equal(f, 2.5 / 1.5 * 0.25) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="weighted"), np.average(f2, weights=support)) # Check samples # \|h(x_i) inter y_i \| = [0, 1, 1] # \|y_i\| = [1, 1, 2] # \|h(x_i)\| = [1, 1, 2] p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="samples") assert_almost_equal(p, 0.5) assert_almost_equal(r, 0.5) assert_almost_equal(f, 0.5) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="samples"), 0.5) @ignore_warnings def test_precision_recall_f1_score_multilabel_2(): # Test precision_recall_f1_score on a crafted multilabel example 2 # Second crafted example y_true = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 0]]) y_pred = np.array([[0, 0, 0, 1], [0, 0, 0, 1], [1, 1, 0, 0]]) # tp = [ 0. 1. 0. 0.] # fp = [ 1. 0. 0. 2.] # fn = [ 1. 1. 1. 0.] p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None) assert_array_almost_equal(p, [0.0, 1.0, 0.0, 0.0], 2) assert_array_almost_equal(r, [0.0, 0.5, 0.0, 0.0], 2) assert_array_almost_equal(f, [0.0, 0.66, 0.0, 0.0], 2) assert_array_almost_equal(s, [1, 2, 1, 0], 2) f2 = fbeta_score(y_true, y_pred, beta=2, average=None) support = s assert_array_almost_equal(f2, [0, 0.55, 0, 0], 2) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="micro") assert_almost_equal(p, 0.25) assert_almost_equal(r, 0.25) assert_almost_equal(f, 2 * 0.25 * 0.25 / 0.5) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="micro"), (1 + 4) * p * r / (4 * p + r)) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="macro") assert_almost_equal(p, 0.25) assert_almost_equal(r, 0.125) assert_almost_equal(f, 2 / 12) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="macro"), np.mean(f2)) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="weighted") assert_almost_equal(p, 2 / 4) assert_almost_equal(r, 1 / 4) assert_almost_equal(f, 2 / 3 * 2 / 4) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="weighted"), np.average(f2, weights=support)) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="samples") # Check samples # \|h(x_i) inter y_i \| = [0, 0, 1] # \|y_i\| = [1, 1, 2] # \|h(x_i)\| = [1, 1, 2] assert_almost_equal(p, 1 / 6) assert_almost_equal(r, 1 / 6) assert_almost_equal(f, 2 / 4 * 1 / 3) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="samples"), 0.1666, 2) @ignore_warnings def test_precision_recall_f1_score_with_an_empty_prediction(): y_true = np.array([[0, 1, 0, 0], [1, 0, 0, 0], [0, 1, 1, 0]]) y_pred = np.array([[0, 0, 0, 0], [0, 0, 0, 1], [0, 1, 1, 0]]) # true_pos = [ 0. 1. 1. 0.] # false_pos = [ 0. 0. 0. 1.] # false_neg = [ 1. 1. 0. 0.] p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None) assert_array_almost_equal(p, [0.0, 1.0, 1.0, 0.0], 2) assert_array_almost_equal(r, [0.0, 0.5, 1.0, 0.0], 2) assert_array_almost_equal(f, [0.0, 1 / 1.5, 1, 0.0], 2) assert_array_almost_equal(s, [1, 2, 1, 0], 2) f2 = fbeta_score(y_true, y_pred, beta=2, average=None) support = s assert_array_almost_equal(f2, [0, 0.55, 1, 0], 2) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="macro") assert_almost_equal(p, 0.5) assert_almost_equal(r, 1.5 / 4) assert_almost_equal(f, 2.5 / (4 * 1.5)) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="macro"), np.mean(f2)) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="micro") assert_almost_equal(p, 2 / 3) assert_almost_equal(r, 0.5) assert_almost_equal(f, 2 / 3 / (2 / 3 + 0.5)) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="micro"), (1 + 4) * p * r / (4 * p + r)) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="weighted") assert_almost_equal(p, 3 / 4) assert_almost_equal(r, 0.5) assert_almost_equal(f, (2 / 1.5 + 1) / 4) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="weighted"), np.average(f2, weights=support)) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="samples") # \|h(x_i) inter y_i \| = [0, 0, 2] # \|y_i\| = [1, 1, 2] # \|h(x_i)\| = [0, 1, 2] assert_almost_equal(p, 1 / 3) assert_almost_equal(r, 1 / 3) assert_almost_equal(f, 1 / 3) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="samples"), 0.333, 2) def test_precision_recall_f1_no_labels(): y_true = np.zeros((20, 3)) y_pred = np.zeros_like(y_true) # tp = [0, 0, 0] # fn = [0, 0, 0] # fp = [0, 0, 0] # support = [0, 0, 0] # \|y_hat_i inter y_i \| = [0, 0, 0] # \|y_i\| = [0, 0, 0] # \|y_hat_i\| = [0, 0, 0] for beta in [1]: p, r, f, s = assert_warns(UndefinedMetricWarning, precision_recall_fscore_support, y_true, y_pred, average=None, beta=beta) assert_array_almost_equal(p, [0, 0, 0], 2) assert_array_almost_equal(r, [0, 0, 0], 2) assert_array_almost_equal(f, [0, 0, 0], 2) assert_array_almost_equal(s, [0, 0, 0], 2) fbeta = assert_warns(UndefinedMetricWarning, fbeta_score, y_true, y_pred, beta=beta, average=None) assert_array_almost_equal(fbeta, [0, 0, 0], 2) for average in ["macro", "micro", "weighted", "samples"]: p, r, f, s = assert_warns(UndefinedMetricWarning, precision_recall_fscore_support, y_true, y_pred, average=average, beta=beta) assert_almost_equal(p, 0) assert_almost_equal(r, 0) assert_almost_equal(f, 0) assert_equal(s, None) fbeta = assert_warns(UndefinedMetricWarning, fbeta_score, y_true, y_pred, beta=beta, average=average) assert_almost_equal(fbeta, 0) def test_prf_warnings(): # average of per-label scores f, w = precision_recall_fscore_support, UndefinedMetricWarning my_assert = assert_warns_message for average in [None, 'weighted', 'macro']: msg = ('Precision and F-score are ill-defined and ' 'being set to 0.0 in labels with no predicted samples.') my_assert(w, msg, f, [0, 1, 2], [1, 1, 2], average=average) msg = ('Recall and F-score are ill-defined and ' 'being set to 0.0 in labels with no true samples.') my_assert(w, msg, f, [1, 1, 2], [0, 1, 2], average=average) # average of per-sample scores msg = ('Precision and F-score are ill-defined and ' 'being set to 0.0 in samples with no predicted labels.') my_assert(w, msg, f, np.array([[1, 0], [1, 0]]), np.array([[1, 0], [0, 0]]), average='samples') msg = ('Recall and F-score are ill-defined and ' 'being set to 0.0 in samples with no true labels.') my_assert(w, msg, f, np.array([[1, 0], [0, 0]]), np.array([[1, 0], [1, 0]]), average='samples') # single score: micro-average msg = ('Precision and F-score are ill-defined and ' 'being set to 0.0 due to no predicted samples.') my_assert(w, msg, f, np.array([[1, 1], [1, 1]]), np.array([[0, 0], [0, 0]]), average='micro') msg = ('Recall and F-score are ill-defined and ' 'being set to 0.0 due to no true samples.') my_assert(w, msg, f, np.array([[0, 0], [0, 0]]), np.array([[1, 1], [1, 1]]), average='micro') # single postive label msg = ('Precision and F-score are ill-defined and ' 'being set to 0.0 due to no predicted samples.') my_assert(w, msg, f, [1, 1], [-1, -1], average='binary') msg = ('Recall and F-score are ill-defined and ' 'being set to 0.0 due to no true samples.') my_assert(w, msg, f, [-1, -1], [1, 1], average='binary') def test_recall_warnings(): assert_no_warnings(recall_score, np.array([[1, 1], [1, 1]]), np.array([[0, 0], [0, 0]]), average='micro') clean_warning_registry() with warnings.catch_warnings(record=True) as record: warnings.simplefilter('always') recall_score(np.array([[0, 0], [0, 0]]), np.array([[1, 1], [1, 1]]), average='micro') assert_equal(str(record.pop().message), 'Recall is ill-defined and ' 'being set to 0.0 due to no true samples.') def test_precision_warnings(): clean_warning_registry() with warnings.catch_warnings(record=True) as record: warnings.simplefilter('always') precision_score(np.array([[1, 1], [1, 1]]), np.array([[0, 0], [0, 0]]), average='micro') assert_equal(str(record.pop().message), 'Precision is ill-defined and ' 'being set to 0.0 due to no predicted samples.') assert_no_warnings(precision_score, np.array([[0, 0], [0, 0]]), np.array([[1, 1], [1, 1]]), average='micro') def test_fscore_warnings(): clean_warning_registry() with warnings.catch_warnings(record=True) as record: warnings.simplefilter('always') for score in [f1_score, partial(fbeta_score, beta=2)]: score(np.array([[1, 1], [1, 1]]), np.array([[0, 0], [0, 0]]), average='micro') assert_equal(str(record.pop().message), 'F-score is ill-defined and ' 'being set to 0.0 due to no predicted samples.') score(np.array([[0, 0], [0, 0]]), np.array([[1, 1], [1, 1]]), average='micro') assert_equal(str(record.pop().message), 'F-score is ill-defined and ' 'being set to 0.0 due to no true samples.') def test_prf_average_binary_data_non_binary(): # Error if user does not explicitly set non-binary average mode y_true_mc = [1, 2, 3, 3] y_pred_mc = [1, 2, 3, 1] y_true_ind = np.array([[0, 1, 1], [1, 0, 0], [0, 0, 1]]) y_pred_ind = np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]]) for y_true, y_pred, y_type in [ (y_true_mc, y_pred_mc, 'multiclass'), (y_true_ind, y_pred_ind, 'multilabel-indicator'), ]: for metric in [precision_score, recall_score, f1_score, partial(fbeta_score, beta=2)]: assert_raise_message(ValueError, "Target is %s but average='binary'. Please " "choose another average setting." % y_type, metric, y_true, y_pred) def test__check_targets(): # Check that _check_targets correctly merges target types, squeezes # output and fails if input lengths differ. IND = 'multilabel-indicator' MC = 'multiclass' BIN = 'binary' CNT = 'continuous' MMC = 'multiclass-multioutput' MCN = 'continuous-multioutput' # all of length 3 EXAMPLES = [ (IND, np.array([[0, 1, 1], [1, 0, 0], [0, 0, 1]])), # must not be considered binary (IND, np.array([[0, 1], [1, 0], [1, 1]])), (MC, [2, 3, 1]), (BIN, [0, 1, 1]), (CNT, [0., 1.5, 1.]), (MC, np.array([[2], [3], [1]])), (BIN, np.array([[0], [1], [1]])), (CNT, np.array([[0.], [1.5], [1.]])), (MMC, np.array([[0, 2], [1, 3], [2, 3]])), (MCN, np.array([[0.5, 2.], [1.1, 3.], [2., 3.]])), ] # expected type given input types, or None for error # (types will be tried in either order) EXPECTED = { (IND, IND): IND, (MC, MC): MC, (BIN, BIN): BIN, (MC, IND): None, (BIN, IND): None, (BIN, MC): MC, # Disallowed types (CNT, CNT): None, (MMC, MMC): None, (MCN, MCN): None, (IND, CNT): None, (MC, CNT): None, (BIN, CNT): None, (MMC, CNT): None, (MCN, CNT): None, (IND, MMC): None, (MC, MMC): None, (BIN, MMC): None, (MCN, MMC): None, (IND, MCN): None, (MC, MCN): None, (BIN, MCN): None, } for (type1, y1), (type2, y2) in product(EXAMPLES, repeat=2): try: expected = EXPECTED[type1, type2] except KeyError: expected = EXPECTED[type2, type1] if expected is None: assert_raises(ValueError, _check_targets, y1, y2) if type1 != type2: assert_raise_message( ValueError, "Can't handle mix of {0} and {1}".format(type1, type2), _check_targets, y1, y2) else: if type1 not in (BIN, MC, IND): assert_raise_message(ValueError, "{0} is not supported".format(type1), _check_targets, y1, y2) else: merged_type, y1out, y2out = _check_targets(y1, y2) assert_equal(merged_type, expected) if merged_type.startswith('multilabel'): assert_equal(y1out.format, 'csr') assert_equal(y2out.format, 'csr') else: assert_array_equal(y1out, np.squeeze(y1)) assert_array_equal(y2out, np.squeeze(y2)) assert_raises(ValueError, _check_targets, y1[:-1], y2) # Make sure seq of seq is not supported y1 = [(1, 2,), (0, 2, 3)] y2 = [(2,), (0, 2,)] msg = ('You appear to be using a legacy multi-label data representation. ' 'Sequence of sequences are no longer supported; use a binary array' ' or sparse matrix instead.') assert_raise_message(ValueError, msg, _check_targets, y1, y2) def test__check_targets_multiclass_with_both_y_true_and_y_pred_binary(): # https://github.com/scikit-learn/scikit-learn/issues/8098 y_true = [0, 1] y_pred = [0, -1] assert_equal(_check_targets(y_true, y_pred)[0], 'multiclass') def test_hinge_loss_binary(): y_true = np.array([-1, 1, 1, -1]) pred_decision = np.array([-8.5, 0.5, 1.5, -0.3]) assert_equal(hinge_loss(y_true, pred_decision), 1.2 / 4) y_true = np.array([0, 2, 2, 0]) pred_decision = np.array([-8.5, 0.5, 1.5, -0.3]) assert_equal(hinge_loss(y_true, pred_decision), 1.2 / 4) def test_hinge_loss_multiclass(): pred_decision = np.array([ [+0.36, -0.17, -0.58, -0.99], [-0.54, -0.37, -0.48, -0.58], [-1.45, -0.58, -0.38, -0.17], [-0.54, -0.38, -0.48, -0.58], [-2.36, -0.79, -0.27, +0.24], [-1.45, -0.58, -0.38, -0.17] ]) y_true = np.array([0, 1, 2, 1, 3, 2]) dummy_losses = np.array([ 1 - pred_decision[0][0] + pred_decision[0][1], 1 - pred_decision[1][1] + pred_decision[1][2], 1 - pred_decision[2][2] + pred_decision[2][3], 1 - pred_decision[3][1] + pred_decision[3][2], 1 - pred_decision[4][3] + pred_decision[4][2], 1 - pred_decision[5][2] + pred_decision[5][3] ]) dummy_losses[dummy_losses <= 0] = 0 dummy_hinge_loss = np.mean(dummy_losses) assert_equal(hinge_loss(y_true, pred_decision), dummy_hinge_loss) def test_hinge_loss_multiclass_missing_labels_with_labels_none(): y_true = np.array([0, 1, 2, 2]) pred_decision = np.array([ [+1.27, 0.034, -0.68, -1.40], [-1.45, -0.58, -0.38, -0.17], [-2.36, -0.79, -0.27, +0.24], [-2.36, -0.79, -0.27, +0.24] ]) error_message = ("Please include all labels in y_true " "or pass labels as third argument") assert_raise_message(ValueError, error_message, hinge_loss, y_true, pred_decision) def test_hinge_loss_multiclass_with_missing_labels(): pred_decision = np.array([ [+0.36, -0.17, -0.58, -0.99], [-0.55, -0.38, -0.48, -0.58], [-1.45, -0.58, -0.38, -0.17], [-0.55, -0.38, -0.48, -0.58], [-1.45, -0.58, -0.38, -0.17] ]) y_true = np.array([0, 1, 2, 1, 2]) labels = np.array([0, 1, 2, 3]) dummy_losses = np.array([ 1 - pred_decision[0][0] + pred_decision[0][1], 1 - pred_decision[1][1] + pred_decision[1][2], 1 - pred_decision[2][2] + pred_decision[2][3], 1 - pred_decision[3][1] + pred_decision[3][2], 1 - pred_decision[4][2] + pred_decision[4][3] ]) dummy_losses[dummy_losses <= 0] = 0 dummy_hinge_loss = np.mean(dummy_losses) assert_equal(hinge_loss(y_true, pred_decision, labels=labels), dummy_hinge_loss) def test_hinge_loss_multiclass_invariance_lists(): # Currently, invariance of string and integer labels cannot be tested # in common invariance tests because invariance tests for multiclass # decision functions is not implemented yet. y_true = ['blue', 'green', 'red', 'green', 'white', 'red'] pred_decision = [ [+0.36, -0.17, -0.58, -0.99], [-0.55, -0.38, -0.48, -0.58], [-1.45, -0.58, -0.38, -0.17], [-0.55, -0.38, -0.48, -0.58], [-2.36, -0.79, -0.27, +0.24], [-1.45, -0.58, -0.38, -0.17]] dummy_losses = np.array([ 1 - pred_decision[0][0] + pred_decision[0][1], 1 - pred_decision[1][1] + pred_decision[1][2], 1 - pred_decision[2][2] + pred_decision[2][3], 1 - pred_decision[3][1] + pred_decision[3][2], 1 - pred_decision[4][3] + pred_decision[4][2], 1 - pred_decision[5][2] + pred_decision[5][3] ]) dummy_losses[dummy_losses <= 0] = 0 dummy_hinge_loss = np.mean(dummy_losses) assert_equal(hinge_loss(y_true, pred_decision), dummy_hinge_loss) def test_log_loss(): # binary case with symbolic labels ("no" < "yes") y_true = ["no", "no", "no", "yes", "yes", "yes"] y_pred = np.array([[0.5, 0.5], [0.1, 0.9], [0.01, 0.99], [0.9, 0.1], [0.75, 0.25], [0.001, 0.999]]) loss = log_loss(y_true, y_pred) assert_almost_equal(loss, 1.8817971) # multiclass case; adapted from http://bit.ly/RJJHWA y_true = [1, 0, 2] y_pred = [[0.2, 0.7, 0.1], [0.6, 0.2, 0.2], [0.6, 0.1, 0.3]] loss = log_loss(y_true, y_pred, normalize=True) assert_almost_equal(loss, 0.6904911) # check that we got all the shapes and axes right # by doubling the length of y_true and y_pred y_true = 2 y_pred = 2 loss = log_loss(y_true, y_pred, normalize=False) assert_almost_equal(loss, 0.6904911 * 6, decimal=6) # check eps and handling of absolute zero and one probabilities y_pred = np.asarray(y_pred) > .5 loss = log_loss(y_true, y_pred, normalize=True, eps=.1) assert_almost_equal(loss, log_loss(y_true, np.clip(y_pred, .1, .9))) # raise error if number of classes are not equal. y_true = [1, 0, 2] y_pred = [[0.2, 0.7], [0.6, 0.5], [0.4, 0.1]] assert_raises(ValueError, log_loss, y_true, y_pred) # case when y_true is a string array object y_true = ["ham", "spam", "spam", "ham"] y_pred = [[0.2, 0.7], [0.6, 0.5], [0.4, 0.1], [0.7, 0.2]] loss = log_loss(y_true, y_pred) assert_almost_equal(loss, 1.0383217, decimal=6) # test labels option y_true = [2, 2] y_pred = [[0.2, 0.7], [0.6, 0.5]] y_score = np.array([[0.1, 0.9], [0.1, 0.9]]) error_str = ('y_true contains only one label (2). Please provide ' 'the true labels explicitly through the labels argument.') assert_raise_message(ValueError, error_str, log_loss, y_true, y_pred) y_pred = [[0.2, 0.7], [0.6, 0.5], [0.2, 0.3]] error_str = ('Found input variables with inconsistent numbers of samples: ' '[3, 2]') assert_raise_message(ValueError, error_str, log_loss, y_true, y_pred) # works when the labels argument is used true_log_loss = -np.mean(np.log(y_score[:, 1])) calculated_log_loss = log_loss(y_true, y_score, labels=[1, 2]) assert_almost_equal(calculated_log_loss, true_log_loss) # ensure labels work when len(np.unique(y_true)) != y_pred.shape[1] y_true = [1, 2, 2] y_score2 = [[0.2, 0.7, 0.3], [0.6, 0.5, 0.3], [0.3, 0.9, 0.1]] loss = log_loss(y_true, y_score2, labels=[1, 2, 3]) assert_almost_equal(loss, 1.0630345, decimal=6) def test_log_loss_pandas_input(): # case when input is a pandas series and dataframe gh-5715 y_tr = np.array(["ham", "spam", "spam", "ham"]) y_pr = np.array([[0.2, 0.7], [0.6, 0.5], [0.4, 0.1], [0.7, 0.2]]) types = [(MockDataFrame, MockDataFrame)] try: from pandas import Series, DataFrame types.append((Series, DataFrame)) except ImportError: pass for TrueInputType, PredInputType in types: # y_pred dataframe, y_true series y_true, y_pred = TrueInputType(y_tr), PredInputType(y_pr) loss = log_loss(y_true, y_pred) assert_almost_equal(loss, 1.0383217, decimal=6) def test_brier_score_loss(): # Check brier_score_loss function y_true = np.array([0, 1, 1, 0, 1, 1]) y_pred = np.array([0.1, 0.8, 0.9, 0.3, 1., 0.95]) true_score = linalg.norm(y_true - y_pred) ** 2 / len(y_true) assert_almost_equal(brier_score_loss(y_true, y_true), 0.0) assert_almost_equal(brier_score_loss(y_true, y_pred), true_score) assert_almost_equal(brier_score_loss(1. + y_true, y_pred), true_score) assert_almost_equal(brier_score_loss(2 * y_true - 1, y_pred), true_score) assert_raises(ValueError, brier_score_loss, y_true, y_pred[1:]) assert_raises(ValueError, brier_score_loss, y_true, y_pred + 1.) assert_raises(ValueError, brier_score_loss, y_true, y_pred - 1.) # calculate even if only single class in y_true (#6980) assert_almost_equal(brier_score_loss([0], [0.5]), 0.25) assert_almost_equal(brier_score_loss([1], [0.5]), 0.25)	bsd-3-clause
fumitoh/modelx	modelx/tests/io/test_pandas.py	1	5359	import modelx as mx import pandas as pd import numpy as np import pytest _pd_ver = tuple(int(i) for i in pd.__version__.split("."))[:-1] @pytest.fixture def testspace(): model = mx.new_model() space = model.new_space() def f0(): return 3 def f1(x): return 2 * x def f2(x, y=1): return x + y f0, f1, f2 = mx.defcells(f0, f1, f2) return space @pytest.fixture def space_with_string_index(): model = mx.new_model() space = model.new_space() def f0(strind): return strind def f1(): return 3 mx.defcells(f0, f1) return space # ------------------------------------------------------------------------- # Test Conversion from Cells to DataFrame and Series def test_cells_empty(testspace): for c in ["f0", "f1", "f2"]: assert testspace.cells[c].to_series().empty assert testspace.cells[c].series.empty assert testspace.cells[c].to_frame().empty assert testspace.cells[c].frame.empty @pytest.mark.parametrize( "cells, args, length", [ ["f0", ((),), 1], ["f1", (1, 2, 3), 3], ["f1", ((1, 2, 3),), 3], ["f2", ((1, 2), (3, 4), (5, 6)), 3], ["f2", (((1, 2), (3, 4), (5, 6))), 3], ], ) def test_cells_to_frame_with_args(testspace, cells, args, length): assert len(testspace.cells[cells].to_frame(args).index) == length assert len(testspace.cells[cells].to_frame()) == length assert len(testspace.cells[cells].frame) == length @pytest.mark.parametrize( "cells, args, length", [ ["f0", ((),), 1], ["f1", (1, 2, 3), 3], ["f1", ((1, 2, 3),), 3], ["f2", ((1, 2), (3, 4), (5, 6)), 3], ["f2", (((1, 2), (3, 4), (5, 6)),), 3], ], ) def test_cells_to_series_with_args(testspace, cells, args, length): assert len(testspace.cells[cells].to_series(args).index) == length assert len(testspace.cells[cells].to_series()) == length assert len(testspace.cells[cells].series) == length # ------------------------------------------------------------------------- # Test Conversion from Space to DataFrame def test_space_to_frame_empty(testspace): assert testspace.to_frame().empty assert testspace.frame.empty if _pd_ver >= (0, 20): @pytest.mark.parametrize( "args, idxlen, cols", [ [((1, 2), (3, 4), (5, 6)), 7, {"f0", "f1", "f2"}], [(((1, 2), (3, 4), (5, 6)),), 7, {"f0", "f1", "f2"}], ], ) def test_space_to_frame_args(testspace, args, idxlen, cols): assert testspace.to_frame().empty df = testspace.to_frame(args) assert set(df.columns) == cols assert len(df.index) == idxlen if len(args) == 1: args = args[0] for arg in args: dfx = df.xs(arg[0], level="x") assert int(dfx.loc[dfx.index.isnull(), "f1"]) == testspace.f1( arg[0] ) assert df.loc[arg, "f2"] == testspace.f2(arg) @pytest.mark.parametrize( "args, idxlen, cols", [ [(1, 2, 3), 7, {"f0", "f1", "f2"}], [((1, 2, 3),), 7, {"f0", "f1", "f2"}], ], ) def test_space_to_frame_args_defaults(testspace, args, idxlen, cols): assert testspace.to_frame().empty df = testspace.to_frame(args) assert set(df.columns) == cols assert len(df.index) == idxlen if isinstance(args[0], tuple): args = args[0] for arg in args: assert df.loc[(arg, 1), "f2"] == testspace.f2(arg, 1) def test_space_with_string_index_to_frame(space_with_string_index): """When index contains string and NaN""" s = space_with_string_index s.f0("foo") s.f1() df = pd.DataFrame( data={"f0": ["foo", np.NaN], "f1": [np.NaN, 3.0]}, index=pd.Index(["foo", np.NaN], name="strind"), ) assert s.frame.equals(df) # ------------------------------------------------------------------------- # Test Conversion from CellsView to DataFrame if _pd_ver >= (0, 20): @pytest.mark.parametrize( "args, idxlen, cols", [ [((1, 2), (3, 4), (5, 6)), 7, ["f0", "f1", "f2"]], [(((1, 2), (3, 4), (5, 6)),), 7, ["f0", "f1", "f2"]], ], ) def test_cellsview_to_frame_args(testspace, args, idxlen, cols): assert testspace.cells[cols].to_frame().empty df = testspace.cells[cols].to_frame(args) assert set(df.columns) == set(cols) assert len(df.index) == idxlen if len(args) == 1: args = args[0] for arg in args: dfx = df.xs(arg[0], level="x") assert int(dfx.loc[dfx.index.isnull(), "f1"]) == testspace.f1( arg[0] ) assert df.loc[arg, "f2"] == testspace.f2(arg) @pytest.mark.parametrize( "args, idxlen, cols", [ [(1, 2, 3), 7, ["f0", "f1", "f2"]], [((1, 2, 3),), 7, ["f0", "f1", "f2"]], ], ) def test_cellsview_to_frame_args_defaults(testspace, args, idxlen, cols): assert testspace.cells[cols].to_frame().empty df = testspace.cells[cols].to_frame(args) assert set(df.columns) == set(cols) assert len(df.index) == idxlen if isinstance(args[0], tuple): args = args[0] for arg in args: assert df.loc[(arg, 1), "f2"] == testspace.f2(arg, 1)	gpl-3.0
davemccormick/pyAnimalTrack	src/pyAnimalTrack/ui/Model/TableModel.py	1	4032	from PyQt5.QtCore import QAbstractTableModel, QVariant from PyQt5.Qt import Qt from pyAnimalTrack.backend.filehandlers.input_data import InputData class TableModel(QAbstractTableModel): def __init__(self, input_data): """ Constructor :returns: void """ super(TableModel, self).__init__() self.__dataFile = input_data self.__dataSet = self.__dataFile.getData() def rowCount(self, QModelIndex_parent=None, args, kwargs): """ Gets the number of data rows. Used by PyQt. :param QModelIndex_parent: - :param args: - :param kwargs: - :returns: The number of data rows """ return len(self.__dataSet.index) def columnCount(self, QModelIndex_parent=None, args, *kwargs): """ Gets the number of columns used in the dataset Used by PyQt. :param QModelIndex_parent: - :param args: - :param kwargs: - :returns: The number of columns for the dataset """ return len(self.__dataSet.columns) def headerData(self, index, Qt_Orientation, role=None): """ Gets a header for a row/column of data. Used by PyQt. :param index: The column/row index :param Qt_Orientation: The alignment of the header, Qt.Horizontal or Qt.Vertical :param role: ? :returns: A string containing the text to show as the header """ if role == Qt.DisplayRole: if Qt_Orientation == Qt.Horizontal: return self.__dataFile.getReadableColumns()[index] else: return index + 1 else: return QVariant() def data(self, QModelIndex, role=None): """ Gets an individual cell's value. Used by PyQt. :param QModelIndex: An object with a row() and column() function, used to determine the correct cell :param role: :returns: A string representation of the cell's value """ if role == Qt.DisplayRole: return str(self.__dataSet.iloc[QModelIndex.row()][QModelIndex.column()]) else: return QVariant() def get_dataset(self): """ Retrieve the entire dataset :returns: A pandas dataframe of the entire dataset """ return self.__dataSet def get_epoch_dataset(self, start=0, end=0, step=1, isMilliseconds=False, sampleRatePerSecond=10): """ Retrieve a subset of the dataset, by rows or milliseconds :param start: The first row (or millisecond) to get :param end: The last row (or millisecond) to get :param step: How far between each row to return :param isMilliseconds: To go by row, or by time :param sampleRatePerSecond: If working in milliseconds, how many samples per second were taken :returns: A pandas dataframe, sliced to the requested rows """ # Make sure we are working with integer values for the numerical parameters try: start = int(start) except: start = 0 try: end = int(end) except: end = 0 try: step = int(step) except: step = 1 try: sampleRatePerSecond = int(sampleRatePerSecond) except: sampleRatePerSecond = 10 # If working time based, we need a conversion if isMilliseconds: start = int((start / 1000.0) sampleRatePerSecond) end = int((end / 1000.0) * sampleRatePerSecond) # Sanity checks if end > len(self.__dataSet): end = len(self.__dataSet) elif end < 0: end = 0 # Correct the end first, so the start doesn't get left incorrect if modified if start > end: start = end elif start < 0: start = 0 # If the given end is 0, we actually want everything if end == 0: end = -1 return self.__dataSet[start:end:step]	gpl-3.0
baseband-geek/singlepulse-visualizer	singlepulse_tools.py	1	16869	#!/usr/bin/python # DM Sigma Time (s) Sample Downfact import numpy as np import matplotlib import matplotlib.patches as patches import matplotlib.pyplot as plt from pulsar_tools import disp_delay import math import sys class SinglePulse: """ A class to contain all the relevant information for each single pulse detection (i.e. S/N, box-car window size, DM, etc.). This is for ease of access during plotting/other interactive stuff. """ def __init__(self, DM, sig, t, samp, dfact, inf): self.dm = DM self.sigma = sig self.time = t self.sample = samp self.downfact = dfact self.inf_file = inf def print_params(self): print "DM:",self.dm print "Sigma:",self.sigma print "Time:",self.time print "Sample:",self.sample print "Downfactor:",self.downfact print "inf_file:",self.inf_file class SPList: """ A class to contain a number of SinglePulse objects in a numpy.array and grant easy acces to paramter lists of those objects. Contains the original list of SinglePulse objects, and a list of each object's: DM, sigma, time, sample, downfactor and inf_file name. """ def __init__(self, sp_list): self.list = np.array(sp_list) self.dm_list = np.array([sp.dm for sp in sp_list]) self.sigma_list = np.array([sp.sigma for sp in sp_list]) self.time_list = np.array([sp.time for sp in sp_list]) self.sample_list = np.array([sp.sample for sp in sp_list]) self.downfact_list = np.array([sp.downfact for sp in sp_list]) self.inf_list = np.array([sp.inf_file for sp in sp_list]) def print_lists(self): print "DM list:",self.dm_list print "Sigma list:",self.sigma_list print "Time list:",self.time_list print "Sample list:",self.sample_list print "Downfactor list:",self.downfact_list print "Inf_file list:",self.inf_list def load_file(filename): if filename==None: print "No filename supplied to read..." elif filename.endswith('.singlepulse'): DM = np.genfromtxt(filename, comments="#", autostrip=True, usecols=0, skip_header=1) Sigma = np.genfromtxt(filename, comments="#", autostrip=True, usecols=1, skip_header=1) Time = np.genfromtxt(filename, comments="#", autostrip=True, usecols=2, skip_header=1) Sample = np.genfromtxt(filename, comments="#", autostrip=True, usecols=3, skip_header=1) Downfact = np.genfromtxt(filename, comments="#", autostrip=True, usecols=4, skip_header=1) inf_file = np.genfromtxt(filename, comments="#", autostrip=True, usecols=5, dtype=str, skip_header=1) sp = [SinglePulse(dm, sig, time, samp, dfact, inf) for dm, sig, time, samp, dfact, inf \ in zip(DM, Sigma, Time, Sample, Downfact, inf_file)] return SPList(sp) #return sp elif filename.endswith('.flag'): flags = np.genfromtxt(filename ,comments="#", autostrip=True) if len(flags) == 0: print "No flags/bad times provided. Not times in final output will be masked." return flags else: print "File name suplied is not recognised. Must be either .singlepulse, .bad or .flag" #def load_flags(filename): # if filename==None: # print "No filename supplied to read into flags..." # # flags = np.genfromtxt(filename ,comments="#", autostrip=True) # if len(flags)==0: # print "No flags provided. Not times in final output will be hidden." # # return flags def obs_stats(time, flags): # Not doing total time correctly, depends on last single pulse detection instead of observation time flag_time = 0 # BWM: if there is only 1 masked region, flags is a list, # if there are 2+ masked regions, flags is a list of lists. if any(isinstance(l, np.ndarray) for l in flags): for flag in flags: flag_time += (float(flag[1]) - float(flag[0])) else: flag_time = float(flags[1]) - float(flags[0]) print "%.2f seconds flagged from %.2f seconds of data (%.2f percent)" % ( flag_time, time[-1], flag_time/time[-1]100) def flagfile(basename, max_DM=2097.2, freq_l=0.169615, freq_h=0.200335, padding=3): """ This function takes in a text file of bad 0 DM times and writes out one flagged over the correct de-dispersive smearing times, looking for overlaps along the way. There must be a text file named basename.bad with rows indicating bad times for this to work. """ from subprocess import check_call # BWM: originally planned to move this to the load_file function, # but left it incase we JUST want to call flagfile bads = np.genfromtxt(basename+'.bad', comments='#', autostrip=True) # BWM: again because how np.genfromtxt works, if there is only 1 bad line, we get a list, # if there are 2+ bad lines we get a list of lists. So have to check for np.ndarray # instances and change method accordingly. i = 0 # initialize counter for new list flags = [] if any(isinstance(b, np.ndarray) for b in bads): for bad in bads: start = bad[0] - (padding + disp_delay(freq1=freq_l, freq2=freq_h, DM=max_DM)/1000.0) if start < 0: start = 0 stop = bad[1] + padding if len(flags) > 0: if start <= flags[-1][1]: flags[-1][1] = stop else: flags.append([start, stop]) else: flags.append([start, stop]) else: start = bads[0] - (padding + disp_delay(freq1=freq_l, freq2=freq_h, DM=max_DM)/1000.0) if start < 0: start = 0 stop = bads[1] + padding if len(flags) > 0: if start <= flags[-1][1]: flags[-1][1] = stop else: flags.append([start, stop]) else: flags.append([start, stop]) # save new file as basename.flag np.savetxt(basename+'.flag', flags, fmt='%d') # call flag.sh script to creat masked singlepulse file check_call(['flag.sh', basename]) #Popen(['flag.sh', basename]).communicate()[0] def singlepulse_plot(basename=None, DMvTime=1, StatPlots=False, raw = False, threshold=5.0, movie=False): """ Plots up the flagged data, should switch to using genfromtxt when I have the time. BWM: switched to using load_file to load singlepulse and flags. Uses genfromtxt. """ print "Make sure you have run sort_singlepulse.py to gather the single pulse events into the one file {0}.singelpulse".format(basename) if raw: data = load_file(basename + '.singlepulse') #flag_times = False else: #flag_times = load_file(basename+'.bad') try: flagfile(basename) # BWM: should we be providing appropriate freqs and DM for this? except: print "No {}.bad file given. Creating one with entry [0 0]".format(basename) f=open('{}.bad'.format(basename),'w') f.write('0 0') f.close() print "Saved {}.bad".format(basename) print "Retrying..." flagfile(basename) data = load_file(basename + '_flagged.singlepulse') flags = load_file(basename + '.flag') #for a in vars(data).items(): # print a[0] #print [v[0] for v in vars(data).items()] #sys.exit(0) data = SPList(data.list[np.where(data.sigma_list >= threshold)]) #DM = [float(row.split()[0]) for row in data if float(row.split()[1]) >= threshold] #Sigma = [float(row.split()[1]) for row in data if float(row.split()[1]) >= threshold] #Time = [float(row.split()[2]) for row in data if float(row.split()[1]) >= threshold] #Sample = [int(row.split()[3]) for row in data if float(row.split()[1]) >= threshold] #Downfact = [int(row.split()[4]) for row in data if float(row.split()[1]) >= threshold] #DM = data.dm_list #Sigma = data.sigma_list #Time = data.time_list #Downfact = data.downfact_list Downfact_float = data.downfact_list.astype(float) fig = plt.figure() cm = plt.cm.get_cmap('gist_rainbow') if StatPlots: ax0 = fig.add_subplot(231) plt.hist(data.sigma_list, histtype='step', bins=int(0.2 len(set(data.sigma_list)))) ax0.set_xlabel('Signal-to-Noise', fontsize=18) ax0.set_ylabel('Number of Pulses', fontsize=18) ax0.set_xlim([data.sigma_list.min(), data.sigma_list.max()]) ax1 = fig.add_subplot(232) plt.hist(data.dm_list, histtype='step', bins=int(0.5 * len(set(data.dm_list)))) ax1.set_xlabel('DM ($\mathrm{pc\, cm^{-3}}$)', fontsize=18) ax1.set_ylabel('Number of Pulses', fontsize=18) ax1.set_xlim([data.dm_list.min(), data.dm_list.max()]) ax2 = fig.add_subplot(233, sharex=ax1) # BWM: now shares x-axis with ax1, so changing DM on one will change range on the other plt.scatter(data.dm_list, data.sigma_list, c=Downfact_float, cmap=cm, alpha=0.9) ax2.set_ylabel('Signal-to-Noise', fontsize=18) ax2.set_xlabel('DM ($\mathrm{p\, cm^{-3}}$)', fontsize=18) ax2.set_xlim([data.dm_list.min(), data.dm_list.max()]) ax2.set_ylim([data.sigma_list.min(), data.sigma_list.max()]) ax3 = fig.add_subplot(212) else: ax3 = fig.add_subplot(111) # TODO: need to figure out how (if at all) we can make the axis sharing work # for x-axis to y-axis share # ax3.set_title("Single Pulse Sigma") ax3.set_xlabel('Time (s)', fontsize=18) ax3.set_ylabel('DM ($\mathrm{pc\, cm^{-3}}$)', fontsize=18) ax3.set_ylim([data.dm_list.min(), data.dm_list.max()]) ax3.set_xlim([data.time_list.min(), data.time_list.max()]) #ax3.set_ylabel('Time (s)', fontsize=18) #ax3.set_xlabel('DM ($\mathrm{pc\, cm^{-3}}$)', fontsize=18) #ax3.set_ylim([data.time_list.min(), data.time_list.max()]) #ax3.set_xlim([data.dm_list.min(), data.dm_list.max()]) #cm = plt.cm.get_cmap('gist_rainbow') # grab axis3 size to allocate marker sizes bbox_pix = ax3.get_window_extent().transformed(fig.dpi_scale_trans.inverted()) width, height = bbox_pix.width, bbox_pix.height area = width * height # axes area in inches^2 (apparently) #TODO: need to try and use something like percentiles to make sure that just one # big pulse doesn't swamp the sizes or colorbars. print data.sigma_list.min() print data.sigma_list.max() print np.percentile(data.sigma_list, 99.5) Size = (3. * area / 2.) * (data.sigma_list*2 / np.percentile(data.sigma_list, 99.5)) Size[np.where(Size > np.percentile(data.sigma_list, 99.5))] = (3. area / 2.) * np.percentile(data.sigma_list, 99.5) #print len(Size[np.where(data.sigma_list>np.percentile(data.sigma_list, 99.5))]) print Size.min() print Size.max() obs_stats(data.time_list, flags) # sc=ax3.scatter(Time,DM, s=Size, c=Sigma, vmin=min(Sigma), vmax=max(Sigma),\ # cmap=cm, picker=1) sc = ax3.scatter(data.time_list, data.dm_list, s=Size, c=Downfact_float, cmap=cm, \ vmin=Downfact_float.min(), vmax=Downfact_float.max(), facecolors='none') # sc = ax3.scatter(data.dm_list, data.time_list, s=Size, c=Downfact_float, cmap=cm, \ # vmin=Downfact_float.min(), vmax=Downfact_float.max(), picker=1, facecolor='none') # leg = ax1.legend() #plt.colorbar(sc, label="Sigma", pad=0.01) #plt.colorbar(sc, label="Downfact", pad=0.01) # BWM: can't seem to get the bottom plot to extend the entire width when the color bar is active. fig.subplots_adjust(hspace=0.2, wspace=0.5) if not raw: if any(isinstance(l, np.ndarray) for l in flags): for flag in flags: flag_area = patches.Rectangle((float(flag[0]), data.dm_list.min()), \ (float(flag[1]) - float(flag[0])), \ (data.dm_list.max() - data.dm_list.min()), \ edgecolor='0', facecolor='0.66') ax3.add_patch(flag_area) else: flag_area = patches.Rectangle((float(flags[0]), data.dm_list.min()), \ (float(flags[1]) - float(flags[0])), \ (data.dm_list.max() - data.dm_list.min()), \ edgecolor='0', facecolor='0.66') ax3.add_patch(flag_area) def onpick(event): points = event.artist ind = event.ind mouseevent = event.mouseevent print '\n' print "Information for data points around click event %.4f, %.4f:" % (mouseevent.xdata, mouseevent.ydata) for i in ind: # These are fudge factors to turn samples into ms. if ( data.dm_list[i] < 150): boxcar = data.downfact_list[i] elif ( 150<= data.dm_list[i] < 823.2 ): boxcar = data.downfact_list[i] * 2 elif ( 823.2 <= data.dm_list[i] < 1486.2): boxcar = data.downfact_list[i] * 2 elif ( 1426.2 <= data.dm_list[i] < 2100): boxcar = data.downfact_list[i] * 2 print "%.2f seconds, %.2f Sigma event detected at a DM of %.2f with a boxcar of: %d ms" % (data.time_list[i], data.sigma_list[i], data.dm_list[i], boxcar) fig.canvas.mpl_connect('pick_event', onpick) ''' ax2 = fig.add_subplot(122) ax2.set_title("Single Pulse Boxcar") ax2.set_xlabel('Time (s)') ax2.set_ylabel('DM (pc cm^-3)') cm = plt.cm.get_cmap('RdYlBu') sc2=ax2.scatter(Time,DM, c=Downfact_float, vmin=min(Downfact_float), vmax=max(Downfact_float), cmap=cm) # leg = ax1.legend() plt.colorbar(sc2) if not raw: for flag in flags: flag_area = matplotlib.patches.Rectangle((float(flag.split()[0]), min(DM)), float(flag.split()[1])-float(flag.split()[0]), max(DM)-min(DM), edgecolor='0', facecolor='0.66') ax2.add_patch(flag_area) ''' fig.suptitle('Single Pulse Search results for ' + basename) #plt.tight_layout(w_pad=0.1, h_pad=0.1) #plt.savefig('test.png') #plt.close(fig) plt.show() #obs_stats(Time, flags) #def slice(infile, dm=None, timerange=None, sigma=None, downfact=None): # # Not properly implemented yet # # data = read_singlepulse(infile) # # slices = [None]*5 # # slice_map = {'dm':0, 'sigma':1, 'timerange':2, 'sample':3, 'downfact':4} # # # # # DM = [row.split()[0] for row in data] # Sigma = [row.split()[1] for row in data] # Time = [row.split()[2] for row in data] # Sample = [row.split()[3] for row in data] # Downfact = [row.split()[4] for row in data] # # if dm: # if type(dm) == type(0) or type(0.0): # data = [row for row in data if dm <= row.split()[0]] # elif type(dm) == type([]): # data = [row for row in data if dm[0] <= row.split()[0] <= dm[1]] # if sigma: # if type(sigma) == type(0) or type(0.0): # data = [row for row in data if sigma <= row.split()[1] ] # elif type(sigma) == type([]): # data = [row for row in data if sigma[0] <= row.split()[1] <= sigma[1]] if __name__ == '__main__': modes = ['interactive','movie'] from optparse import OptionParser, OptionGroup parser = OptionParser(description="A python tool to plot, flag, and do otherwise with singlepulse search data from PRESTO") parser.add_option("-m", "--mode", type="choice", choices=['interactive','movie'], help="Mode you want to run. {0}".format(modes)) parser.add_option("--dm_range", action="store", type="string", nargs=2, default=(0,2000), help="(Not yet implemented) The lowest and highest DM to plot. [default=%default]") parser.add_option("--obsid", action="store", type="string", help="Observation ID or other basename for files. [No default]") parser.add_option("--threshold", action="store", type="float", default=5.0, help="S/N threshold. [default=%default]") (opts, args) = parser.parse_args() if opts.mode == 'movie': singlepulse_plot(basename=opts.obsid, DMvTime=1, StatPlots=True, raw = False, threshold=opts.threshold, movie=True) elif opts.mode == 'interactive': singlepulse_plot(basename=opts.obsid, DMvTime=1, StatPlots=True, raw=False, threshold=opts.threshold, movie=False) else: print "Somehow your non-standard mode snuck through. Try again with one of {0}".format(modes) quit()	mit
ctogle/modular	src/dstoolm4/src/dstoolm4/writer.py	2	3313	import modular4.mpi as mmpi import numpy import PyDSTool as dst import matplotlib.pyplot as plt import pdb def convert_reactions(ss,rs,vs,fs,es): vns,vvs = zip(vs) if vs else ([],[]) fns,fvs = zip(fs) if fs else ([],[]) ens,evs = zip(es) if es else ([],[]) def rxr(r): if r in vns:return r elif r in fns: r = fvs[fns.index(r)] return '('+str(r)+')' else: print('reaction rate is neither a function nor a variable!') raise ValueError def rxustr(rr,ru): rxu = ' '.join((u[1]+'*'+str(u[0]) if u[0] > 1 else u[1] for u in ru)) rxs = rxr(rr) if rxu:rxs = rxu+' '+rxs return rxs rhs,afs = {},{} for sn,sv in ss: if sn in ens: base = evs[ens.index(sn)] for fn in fns: base = base.replace(fn,rxr(fn)) else:base = '' rhs[sn] = base for rr,ru,rp,rn in rs: term = rxustr(rr,ru) uvs,uns = zip(ru) if ru else ([],[]) pvs,pns = zip(rp) if rp else ([],[]) for sn,sv in ss: m = 0 if sn in uns:m -= uvs[uns.index(sn)] if sn in pns:m += pvs[pns.index(sn)] if not m == 0: smv = str(abs(m))+' * ' if abs(m) > 1 else '' sms = ' - ' if m < 0 else ' + ' rhs[sn] += sms+smv+term for sn,sv in ss: if rhs[sn].startswith(' + '): rhs[sn] = rhs[sn].replace(' + ','',1) return rhs,afs def get_simulator(e): esp = e.simparameters etime = e.end ctime = e.capture axes = e.pspace.axes rhs,afs = convert_reactions( esp['species'],esp['reactions'], esp['variables'],esp['functions'], esp['equations']) dtargs = e.targets[:] dtargs[0] = 't' dshape = (len(dtargs),int(etime/ctime)+1) if mmpi.root(): print('\n'+'-'50) print('converted rhs:') for r in rhs:print('\t'+r+': '+rhs[r]) print('-'50+'\n') algparams = { 'atol': 1e-2, #'stiff': False, #'max_step': 0.0, ## CVODE INTERNAL USE ONLY #'min_step': 0.0, ## CVODE INTERNAL USE ONLY #'init_step': 0.01, ## DICTATES DT FOR FIXED OUTPUT MESH 'init_step':ctime0.8, ## DICTATES DT FOR FIXED OUTPUT MESH } def simf(args): DSargs = dst.args(name = 'dstoolm_test') dspars,dsics,dsvarspecs,dsfnspecs = {},{},{},{} for vn,vv in esp['variables']: if vn in axes:vv = args[axes.index(vn)+1] dspars[vn] = vv for sn,si in esp['species']: if sn in axes:si = args[axes.index(sn)+1] dsics[sn] = si dsvarspecs[sn] = rhs[sn] for fn,ft in afs:dsfnspecs[fn] = ft DSargs.algparams = algparams DSargs.pars = dspars DSargs.fnspecs = dsfnspecs DSargs.varspecs = dsvarspecs DSargs.ics = dsics DSargs.tdomain = [0,etime] ode = dst.Generator.Vode_ODEsystem(DSargs) traj = ode.compute('trajectory') pts = traj.sample(dt = ctime) data = numpy.zeros(dshape,dtype = numpy.float) for dtx in range(len(dtargs)): data[dtx] = pts[dtargs[dtx]] return data return simf	mit
kernc/scikit-learn	sklearn/cross_decomposition/pls_.py	34	30531	""" The :mod:`sklearn.pls` module implements Partial Least Squares (PLS). """ # Author: Edouard Duchesnay <[email protected]> # License: BSD 3 clause from distutils.version import LooseVersion from sklearn.utils.extmath import svd_flip from ..base import BaseEstimator, RegressorMixin, TransformerMixin from ..utils import check_array, check_consistent_length from ..externals import six import warnings from abc import ABCMeta, abstractmethod import numpy as np from scipy import linalg from ..utils import arpack from ..utils.validation import check_is_fitted, FLOAT_DTYPES __all__ = ['PLSCanonical', 'PLSRegression', 'PLSSVD'] import scipy pinv2_args = {} if LooseVersion(scipy.__version__) >= LooseVersion('0.12'): # check_finite=False is an optimization available only in scipy >=0.12 pinv2_args = {'check_finite': False} def _nipals_twoblocks_inner_loop(X, Y, mode="A", max_iter=500, tol=1e-06, norm_y_weights=False): """Inner loop of the iterative NIPALS algorithm. Provides an alternative to the svd(X'Y); returns the first left and right singular vectors of X'Y. See PLS for the meaning of the parameters. It is similar to the Power method for determining the eigenvectors and eigenvalues of a X'Y. """ y_score = Y[:, [0]] x_weights_old = 0 ite = 1 X_pinv = Y_pinv = None eps = np.finfo(X.dtype).eps # Inner loop of the Wold algo. while True: # 1.1 Update u: the X weights if mode == "B": if X_pinv is None: # We use slower pinv2 (same as np.linalg.pinv) for stability # reasons X_pinv = linalg.pinv2(X, pinv2_args) x_weights = np.dot(X_pinv, y_score) else: # mode A # Mode A regress each X column on y_score x_weights = np.dot(X.T, y_score) / np.dot(y_score.T, y_score) # 1.2 Normalize u x_weights /= np.sqrt(np.dot(x_weights.T, x_weights)) + eps # 1.3 Update x_score: the X latent scores x_score = np.dot(X, x_weights) # 2.1 Update y_weights if mode == "B": if Y_pinv is None: Y_pinv = linalg.pinv2(Y, pinv2_args) # compute once pinv(Y) y_weights = np.dot(Y_pinv, x_score) else: # Mode A regress each Y column on x_score y_weights = np.dot(Y.T, x_score) / np.dot(x_score.T, x_score) # 2.2 Normalize y_weights if norm_y_weights: y_weights /= np.sqrt(np.dot(y_weights.T, y_weights)) + eps # 2.3 Update y_score: the Y latent scores y_score = np.dot(Y, y_weights) / (np.dot(y_weights.T, y_weights) + eps) # y_score = np.dot(Y, y_weights) / np.dot(y_score.T, y_score) ## BUG x_weights_diff = x_weights - x_weights_old if np.dot(x_weights_diff.T, x_weights_diff) < tol or Y.shape[1] == 1: break if ite == max_iter: warnings.warn('Maximum number of iterations reached') break x_weights_old = x_weights ite += 1 return x_weights, y_weights, ite def _svd_cross_product(X, Y): C = np.dot(X.T, Y) U, s, Vh = linalg.svd(C, full_matrices=False) u = U[:, [0]] v = Vh.T[:, [0]] return u, v def _center_scale_xy(X, Y, scale=True): """ Center X, Y and scale if the scale parameter==True Returns ------- X, Y, x_mean, y_mean, x_std, y_std """ # center x_mean = X.mean(axis=0) X -= x_mean y_mean = Y.mean(axis=0) Y -= y_mean # scale if scale: x_std = X.std(axis=0, ddof=1) x_std[x_std == 0.0] = 1.0 X /= x_std y_std = Y.std(axis=0, ddof=1) y_std[y_std == 0.0] = 1.0 Y /= y_std else: x_std = np.ones(X.shape[1]) y_std = np.ones(Y.shape[1]) return X, Y, x_mean, y_mean, x_std, y_std class _PLS(six.with_metaclass(ABCMeta), BaseEstimator, TransformerMixin, RegressorMixin): """Partial Least Squares (PLS) This class implements the generic PLS algorithm, constructors' parameters allow to obtain a specific implementation such as: - PLS2 regression, i.e., PLS 2 blocks, mode A, with asymmetric deflation and unnormalized y weights such as defined by [Tenenhaus 1998] p. 132. With univariate response it implements PLS1. - PLS canonical, i.e., PLS 2 blocks, mode A, with symmetric deflation and normalized y weights such as defined by [Tenenhaus 1998] (p. 132) and [Wegelin et al. 2000]. This parametrization implements the original Wold algorithm. We use the terminology defined by [Wegelin et al. 2000]. This implementation uses the PLS Wold 2 blocks algorithm based on two nested loops: (i) The outer loop iterate over components. (ii) The inner loop estimates the weights vectors. This can be done with two algo. (a) the inner loop of the original NIPALS algo. or (b) a SVD on residuals cross-covariance matrices. n_components : int, number of components to keep. (default 2). scale : boolean, scale data? (default True) deflation_mode : str, "canonical" or "regression". See notes. mode : "A" classical PLS and "B" CCA. See notes. norm_y_weights: boolean, normalize Y weights to one? (default False) algorithm : string, "nipals" or "svd" The algorithm used to estimate the weights. It will be called n_components times, i.e. once for each iteration of the outer loop. max_iter : an integer, the maximum number of iterations (default 500) of the NIPALS inner loop (used only if algorithm="nipals") tol : non-negative real, default 1e-06 The tolerance used in the iterative algorithm. copy : boolean, default True Whether the deflation should be done on a copy. Let the default value to True unless you don't care about side effects. Attributes ---------- x_weights_ : array, [p, n_components] X block weights vectors. y_weights_ : array, [q, n_components] Y block weights vectors. x_loadings_ : array, [p, n_components] X block loadings vectors. y_loadings_ : array, [q, n_components] Y block loadings vectors. x_scores_ : array, [n_samples, n_components] X scores. y_scores_ : array, [n_samples, n_components] Y scores. x_rotations_ : array, [p, n_components] X block to latents rotations. y_rotations_ : array, [q, n_components] Y block to latents rotations. coef_: array, [p, q] The coefficients of the linear model: ``Y = X coef_ + Err`` n_iter_ : array-like Number of iterations of the NIPALS inner loop for each component. Not useful if the algorithm given is "svd". References ---------- Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000. In French but still a reference: Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic. See also -------- PLSCanonical PLSRegression CCA PLS_SVD """ @abstractmethod def __init__(self, n_components=2, scale=True, deflation_mode="regression", mode="A", algorithm="nipals", norm_y_weights=False, max_iter=500, tol=1e-06, copy=True): self.n_components = n_components self.deflation_mode = deflation_mode self.mode = mode self.norm_y_weights = norm_y_weights self.scale = scale self.algorithm = algorithm self.max_iter = max_iter self.tol = tol self.copy = copy def fit(self, X, Y): """Fit model to data. Parameters ---------- X : array-like, shape = [n_samples, n_features] Training vectors, where n_samples in the number of samples and n_features is the number of predictors. Y : array-like of response, shape = [n_samples, n_targets] Target vectors, where n_samples in the number of samples and n_targets is the number of response variables. """ # copy since this will contains the residuals (deflated) matrices check_consistent_length(X, Y) X = check_array(X, dtype=np.float64, copy=self.copy) Y = check_array(Y, dtype=np.float64, copy=self.copy, ensure_2d=False) if Y.ndim == 1: Y = Y.reshape(-1, 1) n = X.shape[0] p = X.shape[1] q = Y.shape[1] if self.n_components < 1 or self.n_components > p: raise ValueError('Invalid number of components: %d' % self.n_components) if self.algorithm not in ("svd", "nipals"): raise ValueError("Got algorithm %s when only 'svd' " "and 'nipals' are known" % self.algorithm) if self.algorithm == "svd" and self.mode == "B": raise ValueError('Incompatible configuration: mode B is not ' 'implemented with svd algorithm') if self.deflation_mode not in ["canonical", "regression"]: raise ValueError('The deflation mode is unknown') # Scale (in place) X, Y, self.x_mean_, self.y_mean_, self.x_std_, self.y_std_ = ( _center_scale_xy(X, Y, self.scale)) # Residuals (deflated) matrices Xk = X Yk = Y # Results matrices self.x_scores_ = np.zeros((n, self.n_components)) self.y_scores_ = np.zeros((n, self.n_components)) self.x_weights_ = np.zeros((p, self.n_components)) self.y_weights_ = np.zeros((q, self.n_components)) self.x_loadings_ = np.zeros((p, self.n_components)) self.y_loadings_ = np.zeros((q, self.n_components)) self.n_iter_ = [] # NIPALS algo: outer loop, over components for k in range(self.n_components): if np.all(np.dot(Yk.T, Yk) < np.finfo(np.double).eps): # Yk constant warnings.warn('Y residual constant at iteration %s' % k) break # 1) weights estimation (inner loop) # ----------------------------------- if self.algorithm == "nipals": x_weights, y_weights, n_iter_ = \ _nipals_twoblocks_inner_loop( X=Xk, Y=Yk, mode=self.mode, max_iter=self.max_iter, tol=self.tol, norm_y_weights=self.norm_y_weights) self.n_iter_.append(n_iter_) elif self.algorithm == "svd": x_weights, y_weights = _svd_cross_product(X=Xk, Y=Yk) # Forces sign stability of x_weights and y_weights # Sign undeterminacy issue from svd if algorithm == "svd" # and from platform dependent computation if algorithm == 'nipals' x_weights, y_weights = svd_flip(x_weights, y_weights.T) y_weights = y_weights.T # compute scores x_scores = np.dot(Xk, x_weights) if self.norm_y_weights: y_ss = 1 else: y_ss = np.dot(y_weights.T, y_weights) y_scores = np.dot(Yk, y_weights) / y_ss # test for null variance if np.dot(x_scores.T, x_scores) < np.finfo(np.double).eps: warnings.warn('X scores are null at iteration %s' % k) break # 2) Deflation (in place) # ---------------------- # Possible memory footprint reduction may done here: in order to # avoid the allocation of a data chunk for the rank-one # approximations matrix which is then subtracted to Xk, we suggest # to perform a column-wise deflation. # # - regress Xk's on x_score x_loadings = np.dot(Xk.T, x_scores) / np.dot(x_scores.T, x_scores) # - subtract rank-one approximations to obtain remainder matrix Xk -= np.dot(x_scores, x_loadings.T) if self.deflation_mode == "canonical": # - regress Yk's on y_score, then subtract rank-one approx. y_loadings = (np.dot(Yk.T, y_scores) / np.dot(y_scores.T, y_scores)) Yk -= np.dot(y_scores, y_loadings.T) if self.deflation_mode == "regression": # - regress Yk's on x_score, then subtract rank-one approx. y_loadings = (np.dot(Yk.T, x_scores) / np.dot(x_scores.T, x_scores)) Yk -= np.dot(x_scores, y_loadings.T) # 3) Store weights, scores and loadings # Notation: self.x_scores_[:, k] = x_scores.ravel() # T self.y_scores_[:, k] = y_scores.ravel() # U self.x_weights_[:, k] = x_weights.ravel() # W self.y_weights_[:, k] = y_weights.ravel() # C self.x_loadings_[:, k] = x_loadings.ravel() # P self.y_loadings_[:, k] = y_loadings.ravel() # Q # Such that: X = TP' + Err and Y = UQ' + Err # 4) rotations from input space to transformed space (scores) # T = X W(P'W)^-1 = XW* (W* : p x k matrix) # U = Y C(Q'C)^-1 = YC* (W* : q x k matrix) self.x_rotations_ = np.dot( self.x_weights_, linalg.pinv2(np.dot(self.x_loadings_.T, self.x_weights_), pinv2_args)) if Y.shape[1] > 1: self.y_rotations_ = np.dot( self.y_weights_, linalg.pinv2(np.dot(self.y_loadings_.T, self.y_weights_), pinv2_args)) else: self.y_rotations_ = np.ones(1) if True or self.deflation_mode == "regression": # FIXME what's with the if? # Estimate regression coefficient # Regress Y on T # Y = TQ' + Err, # Then express in function of X # Y = X W(P'W)^-1Q' + Err = XB + Err # => B = WQ' (p x q) self.coef_ = np.dot(self.x_rotations_, self.y_loadings_.T) self.coef_ = (1. / self.x_std_.reshape((p, 1)) self.coef_ * self.y_std_) return self def transform(self, X, Y=None, copy=True): """Apply the dimension reduction learned on the train data. Parameters ---------- X : array-like of predictors, shape = [n_samples, p] Training vectors, where n_samples in the number of samples and p is the number of predictors. Y : array-like of response, shape = [n_samples, q], optional Training vectors, where n_samples in the number of samples and q is the number of response variables. copy : boolean, default True Whether to copy X and Y, or perform in-place normalization. Returns ------- x_scores if Y is not given, (x_scores, y_scores) otherwise. """ check_is_fitted(self, 'x_mean_') X = check_array(X, copy=copy, dtype=FLOAT_DTYPES) # Normalize X -= self.x_mean_ X /= self.x_std_ # Apply rotation x_scores = np.dot(X, self.x_rotations_) if Y is not None: Y = check_array(Y, ensure_2d=False, copy=copy, dtype=FLOAT_DTYPES) if Y.ndim == 1: Y = Y.reshape(-1, 1) Y -= self.y_mean_ Y /= self.y_std_ y_scores = np.dot(Y, self.y_rotations_) return x_scores, y_scores return x_scores def predict(self, X, copy=True): """Apply the dimension reduction learned on the train data. Parameters ---------- X : array-like of predictors, shape = [n_samples, p] Training vectors, where n_samples in the number of samples and p is the number of predictors. copy : boolean, default True Whether to copy X and Y, or perform in-place normalization. Notes ----- This call requires the estimation of a p x q matrix, which may be an issue in high dimensional space. """ check_is_fitted(self, 'x_mean_') X = check_array(X, copy=copy, dtype=FLOAT_DTYPES) # Normalize X -= self.x_mean_ X /= self.x_std_ Ypred = np.dot(X, self.coef_) return Ypred + self.y_mean_ def fit_transform(self, X, y=None, fit_params): """Learn and apply the dimension reduction on the train data. Parameters ---------- X : array-like of predictors, shape = [n_samples, p] Training vectors, where n_samples in the number of samples and p is the number of predictors. Y : array-like of response, shape = [n_samples, q], optional Training vectors, where n_samples in the number of samples and q is the number of response variables. copy : boolean, default True Whether to copy X and Y, or perform in-place normalization. Returns ------- x_scores if Y is not given, (x_scores, y_scores) otherwise. """ return self.fit(X, y, fit_params).transform(X, y) class PLSRegression(_PLS): """PLS regression PLSRegression implements the PLS 2 blocks regression known as PLS2 or PLS1 in case of one dimensional response. This class inherits from _PLS with mode="A", deflation_mode="regression", norm_y_weights=False and algorithm="nipals". Read more in the :ref:`User Guide <cross_decomposition>`. Parameters ---------- n_components : int, (default 2) Number of components to keep. scale : boolean, (default True) whether to scale the data max_iter : an integer, (default 500) the maximum number of iterations of the NIPALS inner loop (used only if algorithm="nipals") tol : non-negative real Tolerance used in the iterative algorithm default 1e-06. copy : boolean, default True Whether the deflation should be done on a copy. Let the default value to True unless you don't care about side effect Attributes ---------- x_weights_ : array, [p, n_components] X block weights vectors. y_weights_ : array, [q, n_components] Y block weights vectors. x_loadings_ : array, [p, n_components] X block loadings vectors. y_loadings_ : array, [q, n_components] Y block loadings vectors. x_scores_ : array, [n_samples, n_components] X scores. y_scores_ : array, [n_samples, n_components] Y scores. x_rotations_ : array, [p, n_components] X block to latents rotations. y_rotations_ : array, [q, n_components] Y block to latents rotations. coef_: array, [p, q] The coefficients of the linear model: ``Y = X coef_ + Err`` n_iter_ : array-like Number of iterations of the NIPALS inner loop for each component. Notes ----- Matrices:: T: x_scores_ U: y_scores_ W: x_weights_ C: y_weights_ P: x_loadings_ Q: y_loadings__ Are computed such that:: X = T P.T + Err and Y = U Q.T + Err T[:, k] = Xk W[:, k] for k in range(n_components) U[:, k] = Yk C[:, k] for k in range(n_components) x_rotations_ = W (P.T W)^(-1) y_rotations_ = C (Q.T C)^(-1) where Xk and Yk are residual matrices at iteration k. `Slides explaining PLS <http://www.eigenvector.com/Docs/Wise_pls_properties.pdf>` For each component k, find weights u, v that optimizes: ``max corr(Xk u, Yk v) * std(Xk u) std(Yk u)``, such that ``\|u\| = 1`` Note that it maximizes both the correlations between the scores and the intra-block variances. The residual matrix of X (Xk+1) block is obtained by the deflation on the current X score: x_score. The residual matrix of Y (Yk+1) block is obtained by deflation on the current X score. This performs the PLS regression known as PLS2. This mode is prediction oriented. This implementation provides the same results that 3 PLS packages provided in the R language (R-project): - "mixOmics" with function pls(X, Y, mode = "regression") - "plspm " with function plsreg2(X, Y) - "pls" with function oscorespls.fit(X, Y) Examples -------- >>> from sklearn.cross_decomposition import PLSRegression >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> pls2 = PLSRegression(n_components=2) >>> pls2.fit(X, Y) ... # doctest: +NORMALIZE_WHITESPACE PLSRegression(copy=True, max_iter=500, n_components=2, scale=True, tol=1e-06) >>> Y_pred = pls2.predict(X) References ---------- Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000. In french but still a reference: Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic. """ def __init__(self, n_components=2, scale=True, max_iter=500, tol=1e-06, copy=True): super(PLSRegression, self).__init__( n_components=n_components, scale=scale, deflation_mode="regression", mode="A", norm_y_weights=False, max_iter=max_iter, tol=tol, copy=copy) class PLSCanonical(_PLS): """ PLSCanonical implements the 2 blocks canonical PLS of the original Wold algorithm [Tenenhaus 1998] p.204, referred as PLS-C2A in [Wegelin 2000]. This class inherits from PLS with mode="A" and deflation_mode="canonical", norm_y_weights=True and algorithm="nipals", but svd should provide similar results up to numerical errors. Read more in the :ref:`User Guide <cross_decomposition>`. Parameters ---------- scale : boolean, scale data? (default True) algorithm : string, "nipals" or "svd" The algorithm used to estimate the weights. It will be called n_components times, i.e. once for each iteration of the outer loop. max_iter : an integer, (default 500) the maximum number of iterations of the NIPALS inner loop (used only if algorithm="nipals") tol : non-negative real, default 1e-06 the tolerance used in the iterative algorithm copy : boolean, default True Whether the deflation should be done on a copy. Let the default value to True unless you don't care about side effect n_components : int, number of components to keep. (default 2). Attributes ---------- x_weights_ : array, shape = [p, n_components] X block weights vectors. y_weights_ : array, shape = [q, n_components] Y block weights vectors. x_loadings_ : array, shape = [p, n_components] X block loadings vectors. y_loadings_ : array, shape = [q, n_components] Y block loadings vectors. x_scores_ : array, shape = [n_samples, n_components] X scores. y_scores_ : array, shape = [n_samples, n_components] Y scores. x_rotations_ : array, shape = [p, n_components] X block to latents rotations. y_rotations_ : array, shape = [q, n_components] Y block to latents rotations. n_iter_ : array-like Number of iterations of the NIPALS inner loop for each component. Not useful if the algorithm provided is "svd". Notes ----- Matrices:: T: x_scores_ U: y_scores_ W: x_weights_ C: y_weights_ P: x_loadings_ Q: y_loadings__ Are computed such that:: X = T P.T + Err and Y = U Q.T + Err T[:, k] = Xk W[:, k] for k in range(n_components) U[:, k] = Yk C[:, k] for k in range(n_components) x_rotations_ = W (P.T W)^(-1) y_rotations_ = C (Q.T C)^(-1) where Xk and Yk are residual matrices at iteration k. `Slides explaining PLS <http://www.eigenvector.com/Docs/Wise_pls_properties.pdf>` For each component k, find weights u, v that optimize:: max corr(Xk u, Yk v) * std(Xk u) std(Yk u), such that ``\|u\| = \|v\| = 1`` Note that it maximizes both the correlations between the scores and the intra-block variances. The residual matrix of X (Xk+1) block is obtained by the deflation on the current X score: x_score. The residual matrix of Y (Yk+1) block is obtained by deflation on the current Y score. This performs a canonical symmetric version of the PLS regression. But slightly different than the CCA. This is mostly used for modeling. This implementation provides the same results that the "plspm" package provided in the R language (R-project), using the function plsca(X, Y). Results are equal or collinear with the function ``pls(..., mode = "canonical")`` of the "mixOmics" package. The difference relies in the fact that mixOmics implementation does not exactly implement the Wold algorithm since it does not normalize y_weights to one. Examples -------- >>> from sklearn.cross_decomposition import PLSCanonical >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> plsca = PLSCanonical(n_components=2) >>> plsca.fit(X, Y) ... # doctest: +NORMALIZE_WHITESPACE PLSCanonical(algorithm='nipals', copy=True, max_iter=500, n_components=2, scale=True, tol=1e-06) >>> X_c, Y_c = plsca.transform(X, Y) References ---------- Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000. Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic. See also -------- CCA PLSSVD """ def __init__(self, n_components=2, scale=True, algorithm="nipals", max_iter=500, tol=1e-06, copy=True): super(PLSCanonical, self).__init__( n_components=n_components, scale=scale, deflation_mode="canonical", mode="A", norm_y_weights=True, algorithm=algorithm, max_iter=max_iter, tol=tol, copy=copy) class PLSSVD(BaseEstimator, TransformerMixin): """Partial Least Square SVD Simply perform a svd on the crosscovariance matrix: X'Y There are no iterative deflation here. Read more in the :ref:`User Guide <cross_decomposition>`. Parameters ---------- n_components : int, default 2 Number of components to keep. scale : boolean, default True Whether to scale X and Y. copy : boolean, default True Whether to copy X and Y, or perform in-place computations. Attributes ---------- x_weights_ : array, [p, n_components] X block weights vectors. y_weights_ : array, [q, n_components] Y block weights vectors. x_scores_ : array, [n_samples, n_components] X scores. y_scores_ : array, [n_samples, n_components] Y scores. See also -------- PLSCanonical CCA """ def __init__(self, n_components=2, scale=True, copy=True): self.n_components = n_components self.scale = scale self.copy = copy def fit(self, X, Y): # copy since this will contains the centered data check_consistent_length(X, Y) X = check_array(X, dtype=np.float64, copy=self.copy) Y = check_array(Y, dtype=np.float64, copy=self.copy, ensure_2d=False) if Y.ndim == 1: Y = Y.reshape(-1, 1) if self.n_components > max(Y.shape[1], X.shape[1]): raise ValueError("Invalid number of components n_components=%d" " with X of shape %s and Y of shape %s." % (self.n_components, str(X.shape), str(Y.shape))) # Scale (in place) X, Y, self.x_mean_, self.y_mean_, self.x_std_, self.y_std_ = ( _center_scale_xy(X, Y, self.scale)) # svd(X'Y) C = np.dot(X.T, Y) # The arpack svds solver only works if the number of extracted # components is smaller than rank(X) - 1. Hence, if we want to extract # all the components (C.shape[1]), we have to use another one. Else, # let's use arpacks to compute only the interesting components. if self.n_components >= np.min(C.shape): U, s, V = linalg.svd(C, full_matrices=False) else: U, s, V = arpack.svds(C, k=self.n_components) # Deterministic output U, V = svd_flip(U, V) V = V.T self.x_scores_ = np.dot(X, U) self.y_scores_ = np.dot(Y, V) self.x_weights_ = U self.y_weights_ = V return self def transform(self, X, Y=None): """Apply the dimension reduction learned on the train data.""" check_is_fitted(self, 'x_mean_') X = check_array(X, dtype=np.float64) Xr = (X - self.x_mean_) / self.x_std_ x_scores = np.dot(Xr, self.x_weights_) if Y is not None: if Y.ndim == 1: Y = Y.reshape(-1, 1) Yr = (Y - self.y_mean_) / self.y_std_ y_scores = np.dot(Yr, self.y_weights_) return x_scores, y_scores return x_scores def fit_transform(self, X, y=None, fit_params): """Learn and apply the dimension reduction on the train data. Parameters ---------- X : array-like of predictors, shape = [n_samples, p] Training vectors, where n_samples in the number of samples and p is the number of predictors. Y : array-like of response, shape = [n_samples, q], optional Training vectors, where n_samples in the number of samples and q is the number of response variables. Returns ------- x_scores if Y is not given, (x_scores, y_scores) otherwise. """ return self.fit(X, y, fit_params).transform(X, y)	bsd-3-clause
jmargeta/scikit-learn	examples/linear_model/plot_lasso_coordinate_descent_path.py	4	2823	""" ===================== Lasso and Elastic Net ===================== Lasso and elastic net (L1 and L2 penalisation) implemented using a coordinate descent. The coefficients can be forced to be positive. """ print(__doc__) # Author: Alexandre Gramfort <[email protected]> # License: BSD Style. import numpy as np import pylab as pl from sklearn.linear_model import lasso_path, enet_path from sklearn import datasets diabetes = datasets.load_diabetes() X = diabetes.data y = diabetes.target X /= X.std(0) # Standardize data (easier to set the l1_ratio parameter) ############################################################################### # Compute paths eps = 5e-3 # the smaller it is the longer is the path print("Computing regularization path using the lasso...") models = lasso_path(X, y, eps=eps) alphas_lasso = np.array([model.alpha for model in models]) coefs_lasso = np.array([model.coef_ for model in models]) print("Computing regularization path using the positive lasso...") models = lasso_path(X, y, eps=eps, positive=True) alphas_positive_lasso = np.array([model.alpha for model in models]) coefs_positive_lasso = np.array([model.coef_ for model in models]) print("Computing regularization path using the elastic net...") models = enet_path(X, y, eps=eps, l1_ratio=0.8) alphas_enet = np.array([model.alpha for model in models]) coefs_enet = np.array([model.coef_ for model in models]) print("Computing regularization path using the positve elastic net...") models = enet_path(X, y, eps=eps, l1_ratio=0.8, positive=True) alphas_positive_enet = np.array([model.alpha for model in models]) coefs_positive_enet = np.array([model.coef_ for model in models]) ############################################################################### # Display results pl.figure(1) ax = pl.gca() ax.set_color_cycle(2 * ['b', 'r', 'g', 'c', 'k']) l1 = pl.plot(coefs_lasso) l2 = pl.plot(coefs_enet, linestyle='--') pl.xlabel('-Log(lambda)') pl.ylabel('weights') pl.title('Lasso and Elastic-Net Paths') pl.legend((l1[-1], l2[-1]), ('Lasso', 'Elastic-Net'), loc='lower left') pl.axis('tight') pl.figure(2) ax = pl.gca() ax.set_color_cycle(2 * ['b', 'r', 'g', 'c', 'k']) l1 = pl.plot(coefs_lasso) l2 = pl.plot(coefs_positive_lasso, linestyle='--') pl.xlabel('-Log(lambda)') pl.ylabel('weights') pl.title('Lasso and positive Lasso') pl.legend((l1[-1], l2[-1]), ('Lasso', 'positive Lasso'), loc='lower left') pl.axis('tight') pl.figure(3) ax = pl.gca() ax.set_color_cycle(2 * ['b', 'r', 'g', 'c', 'k']) l1 = pl.plot(coefs_enet) l2 = pl.plot(coefs_positive_enet, linestyle='--') pl.xlabel('-Log(lambda)') pl.ylabel('weights') pl.title('Elastic-Net and positive Elastic-Net') pl.legend((l1[-1], l2[-1]), ('Elastic-Net', 'positive Elastic-Net'), loc='lower left') pl.axis('tight') pl.show()	bsd-3-clause
neale/CS-program	434-MachineLearning/final_project/linearClassifier/sklearn/linear_model/tests/test_base.py	83	15089	# Author: Alexandre Gramfort <[email protected]> # Fabian Pedregosa <[email protected]> # # License: BSD 3 clause import numpy as np from scipy import sparse from scipy import linalg from itertools import product from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_equal from sklearn.utils.testing import ignore_warnings from sklearn.linear_model.base import LinearRegression from sklearn.linear_model.base import _preprocess_data from sklearn.linear_model.base import sparse_center_data, center_data from sklearn.linear_model.base import _rescale_data from sklearn.utils import check_random_state from sklearn.utils.testing import assert_greater from sklearn.datasets.samples_generator import make_sparse_uncorrelated from sklearn.datasets.samples_generator import make_regression rng = np.random.RandomState(0) def test_linear_regression(): # Test LinearRegression on a simple dataset. # a simple dataset X = [[1], [2]] Y = [1, 2] reg = LinearRegression() reg.fit(X, Y) assert_array_almost_equal(reg.coef_, [1]) assert_array_almost_equal(reg.intercept_, [0]) assert_array_almost_equal(reg.predict(X), [1, 2]) # test it also for degenerate input X = [[1]] Y = [0] reg = LinearRegression() reg.fit(X, Y) assert_array_almost_equal(reg.coef_, [0]) assert_array_almost_equal(reg.intercept_, [0]) assert_array_almost_equal(reg.predict(X), [0]) def test_linear_regression_sample_weights(): # TODO: loop over sparse data as well rng = np.random.RandomState(0) # It would not work with under-determined systems for n_samples, n_features in ((6, 5), ): y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) sample_weight = 1.0 + rng.rand(n_samples) for intercept in (True, False): # LinearRegression with explicit sample_weight reg = LinearRegression(fit_intercept=intercept) reg.fit(X, y, sample_weight=sample_weight) coefs1 = reg.coef_ inter1 = reg.intercept_ assert_equal(reg.coef_.shape, (X.shape[1], )) # sanity checks assert_greater(reg.score(X, y), 0.5) # Closed form of the weighted least square # theta = (X^T W X)^(-1) * X^T W y W = np.diag(sample_weight) if intercept is False: X_aug = X else: dummy_column = np.ones(shape=(n_samples, 1)) X_aug = np.concatenate((dummy_column, X), axis=1) coefs2 = linalg.solve(X_aug.T.dot(W).dot(X_aug), X_aug.T.dot(W).dot(y)) if intercept is False: assert_array_almost_equal(coefs1, coefs2) else: assert_array_almost_equal(coefs1, coefs2[1:]) assert_almost_equal(inter1, coefs2[0]) def test_raises_value_error_if_sample_weights_greater_than_1d(): # Sample weights must be either scalar or 1D n_sampless = [2, 3] n_featuress = [3, 2] for n_samples, n_features in zip(n_sampless, n_featuress): X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) sample_weights_OK = rng.randn(n_samples) ** 2 + 1 sample_weights_OK_1 = 1. sample_weights_OK_2 = 2. reg = LinearRegression() # make sure the "OK" sample weights actually work reg.fit(X, y, sample_weights_OK) reg.fit(X, y, sample_weights_OK_1) reg.fit(X, y, sample_weights_OK_2) def test_fit_intercept(): # Test assertions on betas shape. X2 = np.array([[0.38349978, 0.61650022], [0.58853682, 0.41146318]]) X3 = np.array([[0.27677969, 0.70693172, 0.01628859], [0.08385139, 0.20692515, 0.70922346]]) y = np.array([1, 1]) lr2_without_intercept = LinearRegression(fit_intercept=False).fit(X2, y) lr2_with_intercept = LinearRegression(fit_intercept=True).fit(X2, y) lr3_without_intercept = LinearRegression(fit_intercept=False).fit(X3, y) lr3_with_intercept = LinearRegression(fit_intercept=True).fit(X3, y) assert_equal(lr2_with_intercept.coef_.shape, lr2_without_intercept.coef_.shape) assert_equal(lr3_with_intercept.coef_.shape, lr3_without_intercept.coef_.shape) assert_equal(lr2_without_intercept.coef_.ndim, lr3_without_intercept.coef_.ndim) def test_linear_regression_sparse(random_state=0): # Test that linear regression also works with sparse data random_state = check_random_state(random_state) for i in range(10): n = 100 X = sparse.eye(n, n) beta = random_state.rand(n) y = X * beta[:, np.newaxis] ols = LinearRegression() ols.fit(X, y.ravel()) assert_array_almost_equal(beta, ols.coef_ + ols.intercept_) assert_array_almost_equal(ols.predict(X) - y.ravel(), 0) def test_linear_regression_multiple_outcome(random_state=0): # Test multiple-outcome linear regressions X, y = make_regression(random_state=random_state) Y = np.vstack((y, y)).T n_features = X.shape[1] reg = LinearRegression(fit_intercept=True) reg.fit((X), Y) assert_equal(reg.coef_.shape, (2, n_features)) Y_pred = reg.predict(X) reg.fit(X, y) y_pred = reg.predict(X) assert_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3) def test_linear_regression_sparse_multiple_outcome(random_state=0): # Test multiple-outcome linear regressions with sparse data random_state = check_random_state(random_state) X, y = make_sparse_uncorrelated(random_state=random_state) X = sparse.coo_matrix(X) Y = np.vstack((y, y)).T n_features = X.shape[1] ols = LinearRegression() ols.fit(X, Y) assert_equal(ols.coef_.shape, (2, n_features)) Y_pred = ols.predict(X) ols.fit(X, y.ravel()) y_pred = ols.predict(X) assert_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3) def test_preprocess_data(): n_samples = 200 n_features = 2 X = rng.rand(n_samples, n_features) y = rng.rand(n_samples) expected_X_mean = np.mean(X, axis=0) expected_X_norm = np.std(X, axis=0) * np.sqrt(X.shape[0]) expected_y_mean = np.mean(y, axis=0) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=False, normalize=False) assert_array_almost_equal(X_mean, np.zeros(n_features)) assert_array_almost_equal(y_mean, 0) assert_array_almost_equal(X_norm, np.ones(n_features)) assert_array_almost_equal(Xt, X) assert_array_almost_equal(yt, y) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=True, normalize=False) assert_array_almost_equal(X_mean, expected_X_mean) assert_array_almost_equal(y_mean, expected_y_mean) assert_array_almost_equal(X_norm, np.ones(n_features)) assert_array_almost_equal(Xt, X - expected_X_mean) assert_array_almost_equal(yt, y - expected_y_mean) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=True, normalize=True) assert_array_almost_equal(X_mean, expected_X_mean) assert_array_almost_equal(y_mean, expected_y_mean) assert_array_almost_equal(X_norm, expected_X_norm) assert_array_almost_equal(Xt, (X - expected_X_mean) / expected_X_norm) assert_array_almost_equal(yt, y - expected_y_mean) def test_preprocess_data_multioutput(): n_samples = 200 n_features = 3 n_outputs = 2 X = rng.rand(n_samples, n_features) y = rng.rand(n_samples, n_outputs) expected_y_mean = np.mean(y, axis=0) args = [X, sparse.csc_matrix(X)] for X in args: _, yt, _, y_mean, _ = _preprocess_data(X, y, fit_intercept=False, normalize=False) assert_array_almost_equal(y_mean, np.zeros(n_outputs)) assert_array_almost_equal(yt, y) _, yt, _, y_mean, _ = _preprocess_data(X, y, fit_intercept=True, normalize=False) assert_array_almost_equal(y_mean, expected_y_mean) assert_array_almost_equal(yt, y - y_mean) _, yt, _, y_mean, _ = _preprocess_data(X, y, fit_intercept=True, normalize=True) assert_array_almost_equal(y_mean, expected_y_mean) assert_array_almost_equal(yt, y - y_mean) def test_preprocess_data_weighted(): n_samples = 200 n_features = 2 X = rng.rand(n_samples, n_features) y = rng.rand(n_samples) sample_weight = rng.rand(n_samples) expected_X_mean = np.average(X, axis=0, weights=sample_weight) expected_y_mean = np.average(y, axis=0, weights=sample_weight) # XXX: if normalize=True, should we expect a weighted standard deviation? # Currently not weighted, but calculated with respect to weighted mean expected_X_norm = (np.sqrt(X.shape[0]) * np.mean((X - expected_X_mean) 2, axis=0) .5) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=True, normalize=False, sample_weight=sample_weight) assert_array_almost_equal(X_mean, expected_X_mean) assert_array_almost_equal(y_mean, expected_y_mean) assert_array_almost_equal(X_norm, np.ones(n_features)) assert_array_almost_equal(Xt, X - expected_X_mean) assert_array_almost_equal(yt, y - expected_y_mean) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=True, normalize=True, sample_weight=sample_weight) assert_array_almost_equal(X_mean, expected_X_mean) assert_array_almost_equal(y_mean, expected_y_mean) assert_array_almost_equal(X_norm, expected_X_norm) assert_array_almost_equal(Xt, (X - expected_X_mean) / expected_X_norm) assert_array_almost_equal(yt, y - expected_y_mean) def test_sparse_preprocess_data_with_return_mean(): n_samples = 200 n_features = 2 # random_state not supported yet in sparse.rand X = sparse.rand(n_samples, n_features, density=.5) # , random_state=rng X = X.tolil() y = rng.rand(n_samples) XA = X.toarray() expected_X_norm = np.std(XA, axis=0) * np.sqrt(X.shape[0]) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=False, normalize=False, return_mean=True) assert_array_almost_equal(X_mean, np.zeros(n_features)) assert_array_almost_equal(y_mean, 0) assert_array_almost_equal(X_norm, np.ones(n_features)) assert_array_almost_equal(Xt.A, XA) assert_array_almost_equal(yt, y) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=True, normalize=False, return_mean=True) assert_array_almost_equal(X_mean, np.mean(XA, axis=0)) assert_array_almost_equal(y_mean, np.mean(y, axis=0)) assert_array_almost_equal(X_norm, np.ones(n_features)) assert_array_almost_equal(Xt.A, XA) assert_array_almost_equal(yt, y - np.mean(y, axis=0)) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=True, normalize=True, return_mean=True) assert_array_almost_equal(X_mean, np.mean(XA, axis=0)) assert_array_almost_equal(y_mean, np.mean(y, axis=0)) assert_array_almost_equal(X_norm, expected_X_norm) assert_array_almost_equal(Xt.A, XA / expected_X_norm) assert_array_almost_equal(yt, y - np.mean(y, axis=0)) def test_csr_preprocess_data(): # Test output format of _preprocess_data, when input is csr X, y = make_regression() X[X < 2.5] = 0.0 csr = sparse.csr_matrix(X) csr_, y, _, _, _ = _preprocess_data(csr, y, True) assert_equal(csr_.getformat(), 'csr') def test_rescale_data(): n_samples = 200 n_features = 2 sample_weight = 1.0 + rng.rand(n_samples) X = rng.rand(n_samples, n_features) y = rng.rand(n_samples) rescaled_X, rescaled_y = _rescale_data(X, y, sample_weight) rescaled_X2 = X * np.sqrt(sample_weight)[:, np.newaxis] rescaled_y2 = y * np.sqrt(sample_weight) assert_array_almost_equal(rescaled_X, rescaled_X2) assert_array_almost_equal(rescaled_y, rescaled_y2) @ignore_warnings # all deprecation warnings def test_deprecation_center_data(): n_samples = 200 n_features = 2 w = 1.0 + rng.rand(n_samples) X = rng.rand(n_samples, n_features) y = rng.rand(n_samples) param_grid = product([True, False], [True, False], [True, False], [None, w]) for (fit_intercept, normalize, copy, sample_weight) in param_grid: XX = X.copy() # such that we can try copy=False as well X1, y1, X1_mean, X1_var, y1_mean = \ center_data(XX, y, fit_intercept=fit_intercept, normalize=normalize, copy=copy, sample_weight=sample_weight) XX = X.copy() X2, y2, X2_mean, X2_var, y2_mean = \ _preprocess_data(XX, y, fit_intercept=fit_intercept, normalize=normalize, copy=copy, sample_weight=sample_weight) assert_array_almost_equal(X1, X2) assert_array_almost_equal(y1, y2) assert_array_almost_equal(X1_mean, X2_mean) assert_array_almost_equal(X1_var, X2_var) assert_array_almost_equal(y1_mean, y2_mean) # Sparse cases X = sparse.csr_matrix(X) for (fit_intercept, normalize, copy, sample_weight) in param_grid: X1, y1, X1_mean, X1_var, y1_mean = \ center_data(X, y, fit_intercept=fit_intercept, normalize=normalize, copy=copy, sample_weight=sample_weight) X2, y2, X2_mean, X2_var, y2_mean = \ _preprocess_data(X, y, fit_intercept=fit_intercept, normalize=normalize, copy=copy, sample_weight=sample_weight, return_mean=False) assert_array_almost_equal(X1.toarray(), X2.toarray()) assert_array_almost_equal(y1, y2) assert_array_almost_equal(X1_mean, X2_mean) assert_array_almost_equal(X1_var, X2_var) assert_array_almost_equal(y1_mean, y2_mean) for (fit_intercept, normalize) in product([True, False], [True, False]): X1, y1, X1_mean, X1_var, y1_mean = \ sparse_center_data(X, y, fit_intercept=fit_intercept, normalize=normalize) X2, y2, X2_mean, X2_var, y2_mean = \ _preprocess_data(X, y, fit_intercept=fit_intercept, normalize=normalize, return_mean=True) assert_array_almost_equal(X1.toarray(), X2.toarray()) assert_array_almost_equal(y1, y2) assert_array_almost_equal(X1_mean, X2_mean) assert_array_almost_equal(X1_var, X2_var) assert_array_almost_equal(y1_mean, y2_mean)	unlicense
ndingwall/scikit-learn	sklearn/manifold/tests/test_mds.py	2	2566	import numpy as np from numpy.testing import assert_array_almost_equal import pytest from sklearn.manifold import _mds as mds from sklearn.utils._testing import ignore_warnings def test_smacof(): # test metric smacof using the data of "Modern Multidimensional Scaling", # Borg & Groenen, p 154 sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]]) Z = np.array([[-.266, -.539], [.451, .252], [.016, -.238], [-.200, .524]]) X, _ = mds.smacof(sim, init=Z, n_components=2, max_iter=1, n_init=1) X_true = np.array([[-1.415, -2.471], [1.633, 1.107], [.249, -.067], [-.468, 1.431]]) assert_array_almost_equal(X, X_true, decimal=3) def test_smacof_error(): # Not symmetric similarity matrix: sim = np.array([[0, 5, 9, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]]) with pytest.raises(ValueError): mds.smacof(sim) # Not squared similarity matrix: sim = np.array([[0, 5, 9, 4], [5, 0, 2, 2], [4, 2, 1, 0]]) with pytest.raises(ValueError): mds.smacof(sim) # init not None and not correct format: sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]]) Z = np.array([[-.266, -.539], [.016, -.238], [-.200, .524]]) with pytest.raises(ValueError): mds.smacof(sim, init=Z, n_init=1) def test_MDS(): sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]]) mds_clf = mds.MDS(metric=False, n_jobs=3, dissimilarity="precomputed") mds_clf.fit(sim) # TODO: Remove in 0.26 def test_MDS_pairwise_deprecated(): mds_clf = mds.MDS(metric='precomputed') msg = r"Attribute _pairwise was deprecated in version 0\.24" with pytest.warns(FutureWarning, match=msg): mds_clf._pairwise # TODO: Remove in 0.26 @ignore_warnings(category=FutureWarning) @pytest.mark.parametrize("dissimilarity, expected_pairwise", [ ("precomputed", True), ("euclidean", False), ]) def test_MDS_pairwise(dissimilarity, expected_pairwise): # _pairwise attribute is set correctly mds_clf = mds.MDS(dissimilarity=dissimilarity) assert mds_clf._pairwise == expected_pairwise	bsd-3-clause
akuefler/fovea	examples/HH_neuron/HH_simple_demo.py	1	20688	""" This is the main run script for simple demo involving Hodgkin-Huxley analysis. """ from PyDSTool.Toolbox.dssrt import * from PyDSTool.Toolbox.phaseplane import * import PyDSTool as dst import matplotlib.pyplot as plt import sys from fovea.graphics import gui from model_config import man as modelManager from model_config import name, header from HH_neuron import getHH_DSSRT, computePPlaneObjects, do_traj from fovea.common import castNull, castNullArray from math import * from scipy.optimize import fsolve ## ----- ----- ----- ----- ----- ----- ## ## BUILD GENERATOR OBJECT ## ## ----- ----- ----- ----- ----- ----- ## model = modelManager.instances[name] gen = list(model.registry.values())[0] # global define for convenience plotter = gui.plotter # ------------------------ def clip_to_pt(): """Extract clipboard point from gui to a dictionary""" pt = dst.filteredDict(gui.capturedPts['Master'], ['V', 'm', 'n']) return {'V': pt['V'], 'Na.m': pt['m'], 'K.n': pt['n']} class PPcallback(object): """ Dynamic figure axes class to support state-dependent user-interactive callbacks """ def __init__(self, xvar, num_x_points=30, num_y_points=30, nullcX_style=None, nullcY_style=None, vel_arrow_scale=1): self.nully = None self.nullx = None self.num_x_points = num_x_points self.num_y_points = num_y_points if nullcX_style is None: self.nullcX_style = 'b-' else: self.nullcX_style = nullcX_style if nullcY_style is None: self.nullcY_style = 'r-' else: self.nullcY_style = nullcY_style self.last_scale = None self.vel_arrow_scale = vel_arrow_scale self.first_call = True # is reset by __call__ def dQ_dt(self, Qstr, ix, points): """ Utility to find finite difference of any named quantity in given points at index ix """ if ix == 0: ix = 1 pt1 = points[ix] pt0 = points[ix-1] t1 = points.indepvararray[ix] t0 = points.indepvararray[ix-1] return (pt1[Qstr]-pt0[Qstr])/(t1-t0) class PPcallback_m(PPcallback): def __call__(self, time, hard_reset=False): """Callback 'function' to take care of refreshing and re-computing phase plane sub-plot when time is changed interactively. """ #print("\n\npplane call back, mouseUp =", gui._mouseUp) fig_struct = plotter.figs['Master'] # combine all layer information dynamicData = fig_struct.layers['nullclines_mV'].data.copy() dynamicData.update(fig_struct.layers['horiz_PP'].data) ax = gui.dynamicPlots['nullclines_mV'] #sc = fig_struct.layers['nullclines'].scale sc = [ax.get_xlim(), ax.get_ylim()] if hard_reset: force = True preComputed = False print("\n HARD REFRESH for phase plane") else: preComputed = False force = False # REPLACE WITH A PROPER CACHE STRUCTURE for key, val in dynamicData.items(): # dynamicData.keys are 'yNull_<time>' or 'xNull' or keys from horiz_PP etc. # re-use computed nullclines if time is in "cache", i.e. it shows up in the keys if key[6:] == str(time): # cache hit! val['display'] = True # not clear if this updates original data structure after copy # also check to see whether has been rescaled if self.last_scale == sc: preComputed = True else: force = True elif key[:5] != 'xNull': # Use != to clean up collision lines and ohter nullcline that are not for # this time value. # switch off y-nullcline (V_inf) display for the other times # yNull stays constant so keep that display=True val['display'] = False pt = gui.points[gui.ix] p = fig_struct.layers['points_mV'] p.display = True dV_dt = (pt['vinf']-pt['V'])/pt['tauv'] dm_dt = (pt['Na.minf']-pt['Na.m'])/pt['Na.taum'] dn_dt = (pt['K.ninf']-pt['K.n'])/pt['K.taun'] gui.addDataPoints([[pt['Na.m'], pt['Na.m']+dm_dtself.vel_arrow_scale], [pt['V'], pt['V']+dV_dtself.vel_arrow_scale]], layer='state_vel_mV', name='state', style=vel_vec_style, force=True) if self.first_call: gui.addDataPoints([gui.points['Na.m'], gui.points['V']], layer='vfp_mV', name='traj', style='y') # Virtual fixed point and linearized nullclines if 'fast_m' in model.name: with_jac = False do_fps = False fast_vars = ['Na.m'] else: with_jac = False do_fps = False fast_vars = None # update (or create) points try: gui.setPoint('state_pt', Point2D(pt['Na.m'], pt['V']), 'points_mV') gui.setPoint('vinf_pt', Point2D(pt['Na.m'], pt['vinf']), 'points_mV') except KeyError: gui.addDataPoints(Point2D(pt['Na.m'], pt['V']), coorddict = {'x': {'y':'y', 'style':'ko', 'layer':'points_mV', 'name':'state_pt'}}) gui.addDataPoints(Point2D(pt['Na.m'], pt['vinf']),coorddict = {'x': {'y':'y', 'style':'bx', 'layer':'points_mV', 'name':'vinf_pt'}}) d = fig_struct.layers['nullclines_mV'].data if not preComputed and gui._mouseUp: ## print("\nComputing phase plane...") ## print(" Current time = %.4f" % (time)) if self.nullx is None or force: # compute m nullcline this once only_var = None else: only_var = 'V' # refresh wait notification ax.text(0.05, 0.95, 'wait', transform=ax.transAxes, fontsize=22, color='r', fontweight='bold', va='top') gui.masterWin.canvas.draw() # comment out for testing - use surrogate below nulls = computePPlaneObjects(gen, 'Na.m', 'V', state=pt, num_x_points=self.num_x_points, num_y_points=self.num_y_points, only_var=only_var, with_jac=with_jac, do_fps=do_fps, fast_vars=fast_vars, subdomain={'V': sc[1], 'Na.m': sc[0]}) # Surrogate data - much faster to test with #self.nully = [[-100+time, -50+time/10., 0], [0.1, 0.4, 0.8]] #self.nullx = [[-130, -80, 50], [0.2, 0.3, 0.4]] self.nully = castNullArray(nulls['nullcY']) gui.addDataPoints(self.nully, layer='nullclines_mV', style=self.nullcY_style, name='yNull_'+str(time), force=force) # delete update 'wait' notice ax.texts = [] #ax.clear() gui.clearAxes(ax) if only_var is None: # nullx is added second so will be the second line self.nullx = castNullArray(nulls['nullcX']) gui.addDataPoints(self.nullx, layer='nullclines_mV', style=self.nullcX_style, name='xNull', force=force) #if force: # rescale = sc #else: # rescale = None gui.buildLayers(['nullclines_mV', 'horiz_PP', 'points_mV', 'state_vel_mV', 'vfp_mV'], ax, rescale=sc, figure='Master') self.last_scale = sc ## print(" Phase plane rebuild completed.\n") else: # just refresh display with the current selected data gui.clearAxes(ax) #if force: # rescale = sc #else: # rescale = None gui.buildLayers(['nullclines_mV', 'horiz_PP', 'points_mV', 'state_vel_mV', 'vfp_mV'], ax, rescale=sc, figure='Master') self.last_scale = sc gui.masterWin.canvas.draw() self.first_call = False class PPcallback_n(PPcallback): def __call__(self, time, hard_reset=False): """Callback 'function' to take care of refreshing and re-computing phase plane sub-plot when time is changed interactively. """ #print("\n\npplane call back, mouseUp =", gui._mouseUp) fig_struct = plotter.figs['Master'] # combine all layer information dynamicData = fig_struct.layers['nullclines_nV'].data.copy() ax = gui.dynamicPlots['nullclines_nV'] #sc = fig_struct.layers['nullclines'].scale sc = [ax.get_xlim(), ax.get_ylim()] if hard_reset: force = True preComputed = False print("\n HARD REFRESH for phase plane") else: preComputed = False force = False # REPLACE WITH A PROPER CACHE STRUCTURE for key, val in dynamicData.items(): # dynamicData.keys are 'yNull_<time>' or 'xNull' or keys from horiz_PP etc. # re-use computed nullclines if time is in "cache", i.e. it shows up in the keys if key[6:] == str(time): # cache hit! val['display'] = True # not clear if this updates original data structure after copy # also check to see whether has been rescaled if self.last_scale == sc: preComputed = True else: force = True elif key[:5] != 'xNull': # Use != to clean up collision lines and ohter nullcline that are not for # this time value. # switch off y-nullcline (V_inf) display for the other times # yNull stays constant so keep that display=True val['display'] = False pt = gui.points[gui.ix] p = fig_struct.layers['points_nV'] p.display = True dV_dt = (pt['vinf']-pt['V'])/pt['tauv'] dm_dt = (pt['Na.minf']-pt['Na.m'])/pt['Na.taum'] dn_dt = (pt['K.ninf']-pt['K.n'])/pt['K.taun'] gui.addDataPoints([[pt['K.n'], pt['K.n']+dn_dtself.vel_arrow_scale], [pt['V'], pt['V']+dV_dtself.vel_arrow_scale]], layer='state_vel_nV', name='state', style=vel_vec_style, force=True) if self.first_call: gui.addDataPoints([gui.points['K.n'], gui.points['V']], layer='vfp_nV', name='traj', style='y') gui.addDataPoints([gui.points['K.n'], gui.points['vinf']], layer='vfp_nV', name='quasiVnull', style='m--') ## vs = np.linspace(sc[1][0], sc[1][1], 50) ## x = dict(pt).copy() ## ## def vinf(n, v): ## x['K.n'] = n ## x['V'] = v ## x['Na.m'] = gen.auxfns.Na_dssrt_fn_minf(v) ## # assume autonomous system ## return model.Rhs(0, x, asarray=False)['V'] ## ## vinfs_inv_n = [fsolve(vinf, gen.auxfns.K_dssrt_fn_ninf(v), args=(v,)) for v in vs] ## plotter.addData([vinfs_inv_n, vs], layer='vfp_nV', name='vinf_fastm', style='b--') # Virtual fixed point and linearized nullclines if 'fast_m' in model.name: with_jac = False do_fps = False fast_vars = ['Na.m'] else: with_jac = False do_fps = False fast_vars = None # update (or create) points try: gui.setPoint('state_pt', Point2D(pt['K.n'], pt['V']), 'points_nV') gui.setPoint('vinf_pt', Point2D(pt['K.n'], pt['vinf']), 'points_nV') except KeyError: gui.addDataPoints(Point2D(pt['K.n'], pt['V']),coorddict = {'x': {'y':'y', 'style':'ko', 'layer':'points_nV', 'name':'state_pt'}}) gui.addDataPoints(Point2D(pt['K.n'], pt['vinf']), coorddict = {'x': {'y':'y', 'style':'bx', 'layer':'points_nV', 'name':'vinf_pt'}}) d = fig_struct.layers['nullclines_nV'].data if not preComputed and gui._mouseUp: ## print("\nComputing phase plane...") ## print(" Current time = %.4f" % (time)) if self.nullx is None or force: # compute m nullcline this once only_var = None else: only_var = 'V' # refresh wait notification ax.text(0.05, 0.95, 'wait', transform=ax.transAxes, fontsize=22, color='r', fontweight='bold', va='top') gui.masterWin.canvas.draw() # comment out for testing - use surrogate below nulls = computePPlaneObjects(gen, 'K.n', 'V', state=pt, num_x_points=self.num_x_points, num_y_points=self.num_y_points, only_var=only_var, with_jac=with_jac, do_fps=do_fps, fast_vars=fast_vars, subdomain={'V': sc[1], 'K.n': sc[0]}) # Surrogate data - much faster to test with #self.nully = [[-100+time, -50+time/10., 0], [0.1, 0.4, 0.8]] #self.nullx = [[-130, -80, 50], [0.2, 0.3, 0.4]] self.nully = castNullArray(nulls['nullcY']) gui.addDataPoints(self.nully, layer='nullclines_nV', style=self.nullcY_style, name='yNull_'+str(time), force=force) # delete update 'wait' notice ax.texts = [] #ax.clear() gui.clearAxes(ax) if only_var is None: # nullx is added second so will be the second line self.nullx = castNullArray(nulls['nullcX']) gui.addDataPoints(self.nullx, layer='nullclines_nV', style=self.nullcX_style, name='xNull', force=force) #if force: # rescale = sc #else: # rescale = None gui.buildLayers(['nullclines_nV', 'points_nV', 'state_vel_nV', 'vfp_nV'], ax, rescale=sc, figure='Master') self.last_scale = sc ## print(" Phase plane rebuild completed.\n") else: # just refresh display with the current selected data gui.clearAxes(ax) #if force: # rescale = sc #else: # rescale = None gui.buildLayers(['nullclines_nV', 'points_nV', 'state_vel_nV', 'vfp_nV'], ax, rescale=sc, figure='Master') self.last_scale = sc gui.masterWin.canvas.draw() self.first_call = False # ------------------------------------------------------------------------ ## Set dssrt_name to be for saved DSSRT data file ## Change for different parameter sets or just default to model name dssrt_name = name if 'typeI' in name and 'typeII' not in name: ### FOR TYPE I H-H ONLY Kgmax = 100 # 100 # 80 original dssrt_name = name+'_gK%i' % Kgmax model.set(pars={'K.g': Kgmax, 'Na.g': 50}) else: ### FOR TYPE II H-H ONLY Kgmax = 36 #39 or 42 with fast m # 36 original dssrt_name = name+'_gK%i' % Kgmax model.set(pars={'K.g': Kgmax, 'Ib.Ibias': 8.}) dV = 0.2 ##test_ic = {'K.n': 0.37220277852490802, ## 'Na.m': 0.080387043479386036, ## 'V': -59.5} ##model.set(ics=test_ic) ## ----- ----- ----- ----- ----- ----- ## ## GET GENERATOR TRAJECTORY ## ## ----- ----- ----- ----- ----- ----- ## orig_ics = model.query('ics') if 'no_h' in name: # no periodic orbit, just simulate for 12 ms if 'typeI' in name and 'typeII' not in name: t_end = 9 else: t_end = 12 model.set(tdata=[0, t_end]) model.compute('ref') ref_traj = model['ref'] else: # get periodic orbit t_end = 20 ref_traj, ref_pts, ref_tmin, ref_tmax = do_traj(model, t_end, do_plot=False) # re-sample traj at constant dt and declare to GUI #trajPts = ref_traj.sample(dt=0.01)[:-40] # cheap way to avoid overlap from pts not being periodic trajPts = ref_traj.sample(dt=0.01)[:len(ref_traj.sample(dt=0.01))-40] #[:-40] syntax not working in python 3 gui.addTimeFromPoints(trajPts) ## ----- ----- ----- ----- ----- ----- ## ## CREATE DIAGNOSTIC OBJECT ## ## ----- ----- ----- ----- ----- ----- ## gui.clean() gui.addFig('Master', title='Geometric Dynamic Analysis: '+dssrt_name, tdom=[0, t_end], domain=[(-100,50), (0,1)]) coorddict = {'V': {'x':'t', 'layer':'V','name':'V', 'style':'k-'}, 'vinf': {'x':'t', 'layer':'V','name':'Vinf', 'style':'k:'}, 'Na.m': {'x':'t', 'layer':'activs', 'name':'m', 'style':'g--'}, 'Na.minf': {'x':'t', 'layer':'activs', 'name':'minf', 'style':'g--'}, 'K.n': {'x':'t', 'layer':'activs', 'name':'n', 'style':'r-'}, 'K.ninf': {'x':'t', 'layer':'activs', 'name':'ninf', 'style':'r--'}, 'tauv': {'x':'t','layer':'activs','name':'tauv', 'style':'b:'}, 'Na.taum': {'x':'t', 'layer':'activs','name':'taum', 'style':'g:'}, 'K.taun': {'x':'t', 'layer':'activs','name':'taun', 'style':'r:'} } gui.addDataPoints(trajPts, coorddict = coorddict) print("Key for activations / time scales window") print(" Activations: line=activation, dashed=asymptotic") print(" Time scales: dots") print("Na: green") print("K: red") print("V: blue") ## ----- ----- ----- ----- ----- ----- ## ## COMPUTE V-m PHASE PLANE ## ## ----- ----- ----- ----- ----- ----- ## # start at t = 2ms gui.set_time(2) # global style defs vel_vec_style = {'color': 'k', 'linewidth': 2, 'linestyle': '-'} vinf_vec_style = {'color': 'b', 'linewidth': 2, 'linestyle': '-'} horiz_style = {'color': 'k', 'linestyle': '--', 'linewidth': 2} def make_layer(xvar): if xvar == 'Na.m': suffix = 'mV' else: suffix = 'nV' PP_layer_name = 'nullclines_'+suffix gui.addLayer(PP_layer_name, dynamic=True) if xvar == 'Na.m': gui.addLayer('horiz_PP') nullcX_style = 'g-' PPclass = PPcallback_m else: # no horizon layer for K.n nullcX_style = 'r-' PPclass = PPcallback_n PPplot = PPclass(xvar, nullcY_style = {'color': 'b', 'linestyle': '-', 'linewidth': 1}, nullcX_style=nullcX_style) gui.dynamicPlotFns[PP_layer_name] = PPplot gui.addLayer('points_'+suffix) gui.addLayer('state_vel_'+suffix) gui.addLayer('vfp_'+suffix) make_layer('Na.m') make_layer('K.n') # sub-plot specs: (row, col) integer coords start at top left dPlot11 = {'name': 'Trajectory', 'layers': ['V'], 'scale': [None, [-85, 45]], 'axes_vars': ['t', 'V'] } dPlot12 = {'name': 'Activations, Time scales', 'layers': ['activs'], 'scale': [None, [0,1]], 'axes_vars': ['t', 'no units, ms'] } pp1_name = 'Na-V Phaseplane' pp1_dom = [[0,1], [-75,50]] pp1_vars = ['m', 'V'] pp2_name = 'K-V Phaseplane' pp2_dom = [[0.,1], [-75,50]] pp2_vars = ['n', 'V'] # ISSUE: Rename 'scale' to 'domain' or 'extent' dPlot21 = {'name': pp1_name, 'scale': pp1_dom, 'layers': ['nullclines_mV', 'horiz_PP', 'points_mV', 'state_vel_mV', 'vfp_mV'], 'axes_vars': pp1_vars} dPlot22 = {'name': pp2_name, 'scale': pp2_dom, 'layers': ['nullclines_nV', 'points_nV', 'state_vel_nV', 'vfp_nV'], 'axes_vars': pp2_vars} dPlot_dict = {'11': dPlot11, '12': dPlot12, '21': dPlot21, '22': dPlot22} gui.setup(dPlot_dict, size=(14, 8)) gui.show_legends(subplot='Times') gui.show() gui.plus_dt(0) halt = True	bsd-3-clause
fzalkow/scikit-learn	examples/neural_networks/plot_rbm_logistic_classification.py	258	4609	""" ============================================================== Restricted Boltzmann Machine features for digit classification ============================================================== For greyscale image data where pixel values can be interpreted as degrees of blackness on a white background, like handwritten digit recognition, the Bernoulli Restricted Boltzmann machine model (:class:`BernoulliRBM <sklearn.neural_network.BernoulliRBM>`) can perform effective non-linear feature extraction. In order to learn good latent representations from a small dataset, we artificially generate more labeled data by perturbing the training data with linear shifts of 1 pixel in each direction. This example shows how to build a classification pipeline with a BernoulliRBM feature extractor and a :class:`LogisticRegression <sklearn.linear_model.LogisticRegression>` classifier. The hyperparameters of the entire model (learning rate, hidden layer size, regularization) were optimized by grid search, but the search is not reproduced here because of runtime constraints. Logistic regression on raw pixel values is presented for comparison. The example shows that the features extracted by the BernoulliRBM help improve the classification accuracy. """ from __future__ import print_function print(__doc__) # Authors: Yann N. Dauphin, Vlad Niculae, Gabriel Synnaeve # License: BSD import numpy as np import matplotlib.pyplot as plt from scipy.ndimage import convolve from sklearn import linear_model, datasets, metrics from sklearn.cross_validation import train_test_split from sklearn.neural_network import BernoulliRBM from sklearn.pipeline import Pipeline ############################################################################### # Setting up def nudge_dataset(X, Y): """ This produces a dataset 5 times bigger than the original one, by moving the 8x8 images in X around by 1px to left, right, down, up """ direction_vectors = [ [[0, 1, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [1, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 1], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 1, 0]]] shift = lambda x, w: convolve(x.reshape((8, 8)), mode='constant', weights=w).ravel() X = np.concatenate([X] + [np.apply_along_axis(shift, 1, X, vector) for vector in direction_vectors]) Y = np.concatenate([Y for _ in range(5)], axis=0) return X, Y # Load Data digits = datasets.load_digits() X = np.asarray(digits.data, 'float32') X, Y = nudge_dataset(X, digits.target) X = (X - np.min(X, 0)) / (np.max(X, 0) + 0.0001) # 0-1 scaling X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0) # Models we will use logistic = linear_model.LogisticRegression() rbm = BernoulliRBM(random_state=0, verbose=True) classifier = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)]) ############################################################################### # Training # Hyper-parameters. These were set by cross-validation, # using a GridSearchCV. Here we are not performing cross-validation to # save time. rbm.learning_rate = 0.06 rbm.n_iter = 20 # More components tend to give better prediction performance, but larger # fitting time rbm.n_components = 100 logistic.C = 6000.0 # Training RBM-Logistic Pipeline classifier.fit(X_train, Y_train) # Training Logistic regression logistic_classifier = linear_model.LogisticRegression(C=100.0) logistic_classifier.fit(X_train, Y_train) ############################################################################### # Evaluation print() print("Logistic regression using RBM features:\n%s\n" % ( metrics.classification_report( Y_test, classifier.predict(X_test)))) print("Logistic regression using raw pixel features:\n%s\n" % ( metrics.classification_report( Y_test, logistic_classifier.predict(X_test)))) ############################################################################### # Plotting plt.figure(figsize=(4.2, 4)) for i, comp in enumerate(rbm.components_): plt.subplot(10, 10, i + 1) plt.imshow(comp.reshape((8, 8)), cmap=plt.cm.gray_r, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.suptitle('100 components extracted by RBM', fontsize=16) plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23) plt.show()	bsd-3-clause

xray/xray

xarray/plot/facetgrid.py

1

21774

import functools import itertools import warnings import numpy as np from ..core.formatting import format_item from .utils import ( _infer_xy_labels, _process_cmap_cbar_kwargs, import_matplotlib_pyplot, label_from_attrs, ) # Overrides axes.labelsize, xtick.major.size, ytick.major.size # from mpl.rcParams _FONTSIZE = "small" # For major ticks on x, y axes _NTICKS = 5 def _nicetitle(coord, value, maxchar, template): """ Put coord, value in template and truncate at maxchar """ prettyvalue = format_item(value, quote_strings=False) title = template.format(coord=coord, value=prettyvalue) if len(title) > maxchar: title = title[: (maxchar - 3)] + "..." return title class FacetGrid: """ Initialize the matplotlib figure and FacetGrid object. The :class:`FacetGrid` is an object that links a xarray DataArray to a matplotlib figure with a particular structure. In particular, :class:`FacetGrid` is used to draw plots with multiple Axes where each Axes shows the same relationship conditioned on different levels of some dimension. It's possible to condition on up to two variables by assigning variables to the rows and columns of the grid. The general approach to plotting here is called "small multiples", where the same kind of plot is repeated multiple times, and the specific use of small multiples to display the same relationship conditioned on one ore more other variables is often called a "trellis plot". The basic workflow is to initialize the :class:`FacetGrid` object with the DataArray and the variable names that are used to structure the grid. Then plotting functions can be applied to each subset by calling :meth:`FacetGrid.map_dataarray` or :meth:`FacetGrid.map`. Attributes ---------- axes : numpy object array Contains axes in corresponding position, as returned from plt.subplots col_labels : list list of :class:`matplotlib.text.Text` instances corresponding to column titles. row_labels : list list of :class:`matplotlib.text.Text` instances corresponding to row titles. fig : matplotlib.Figure The figure containing all the axes name_dicts : numpy object array Contains dictionaries mapping coordinate names to values. None is used as a sentinel value for axes which should remain empty, ie. sometimes the bottom right grid """ def __init__( self, data, col=None, row=None, col_wrap=None, sharex=True, sharey=True, figsize=None, aspect=1, size=3, subplot_kws=None, ): """ Parameters ---------- data : DataArray xarray DataArray to be plotted row, col : strings Dimesion names that define subsets of the data, which will be drawn on separate facets in the grid. col_wrap : int, optional "Wrap" the column variable at this width, so that the column facets sharex : bool, optional If true, the facets will share x axes sharey : bool, optional If true, the facets will share y axes figsize : tuple, optional A tuple (width, height) of the figure in inches. If set, overrides ``size`` and ``aspect``. aspect : scalar, optional Aspect ratio of each facet, so that ``aspect * size`` gives the width of each facet in inches size : scalar, optional Height (in inches) of each facet. See also: ``aspect`` subplot_kws : dict, optional Dictionary of keyword arguments for matplotlib subplots """ plt = import_matplotlib_pyplot() # Handle corner case of nonunique coordinates rep_col = col is not None and not data[col].to_index().is_unique rep_row = row is not None and not data[row].to_index().is_unique if rep_col or rep_row: raise ValueError( "Coordinates used for faceting cannot " "contain repeated (nonunique) values." ) # single_group is the grouping variable, if there is exactly one if col and row: single_group = False nrow = len(data[row]) ncol = len(data[col]) nfacet = nrow * ncol if col_wrap is not None: warnings.warn("Ignoring col_wrap since both col and row " "were passed") elif row and not col: single_group = row elif not row and col: single_group = col else: raise ValueError("Pass a coordinate name as an argument for row or col") # Compute grid shape if single_group: nfacet = len(data[single_group]) if col: # idea - could add heuristic for nice shapes like 3x4 ncol = nfacet if row: ncol = 1 if col_wrap is not None: # Overrides previous settings ncol = col_wrap nrow = int(np.ceil(nfacet / ncol)) # Set the subplot kwargs subplot_kws = {} if subplot_kws is None else subplot_kws if figsize is None: # Calculate the base figure size with extra horizontal space for a # colorbar cbar_space = 1 figsize = (ncol * size * aspect + cbar_space, nrow * size) fig, axes = plt.subplots( nrow, ncol, sharex=sharex, sharey=sharey, squeeze=False, figsize=figsize, subplot_kw=subplot_kws, ) # Set up the lists of names for the row and column facet variables col_names = list(data[col].values) if col else [] row_names = list(data[row].values) if row else [] if single_group: full = [{single_group: x} for x in data[single_group].values] empty = [None for x in range(nrow * ncol - len(full))] name_dicts = full + empty else: rowcols = itertools.product(row_names, col_names) name_dicts = [{row: r, col: c} for r, c in rowcols] name_dicts = np.array(name_dicts).reshape(nrow, ncol) # Set up the class attributes # --------------------------- # First the public API self.data = data self.name_dicts = name_dicts self.fig = fig self.axes = axes self.row_names = row_names self.col_names = col_names self.figlegend = None # Next the private variables self._single_group = single_group self._nrow = nrow self._row_var = row self._ncol = ncol self._col_var = col self._col_wrap = col_wrap self.row_labels = [None] * nrow self.col_labels = [None] * ncol self._x_var = None self._y_var = None self._cmap_extend = None self._mappables = [] self._finalized = False @property def _left_axes(self): return self.axes[:, 0] @property def _bottom_axes(self): return self.axes[-1, :] def map_dataarray(self, func, x, y, **kwargs): """ Apply a plotting function to a 2d facet's subset of the data. This is more convenient and less general than ``FacetGrid.map`` Parameters ---------- func : callable A plotting function with the same signature as a 2d xarray plotting method such as `xarray.plot.imshow` x, y : string Names of the coordinates to plot on x, y axes kwargs : additional keyword arguments to func Returns ------- self : FacetGrid object """ if kwargs.get("cbar_ax", None) is not None: raise ValueError("cbar_ax not supported by FacetGrid.") cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs( func, self.data.values, **kwargs ) self._cmap_extend = cmap_params.get("extend") # Order is important func_kwargs = { k: v for k, v in kwargs.items() if k not in {"cmap", "colors", "cbar_kwargs", "levels"} } func_kwargs.update(cmap_params) func_kwargs.update({"add_colorbar": False, "add_labels": False}) # Get x, y labels for the first subplot x, y = _infer_xy_labels( darray=self.data.loc[self.name_dicts.flat[0]], x=x, y=y, imshow=func.__name__ == "imshow", rgb=kwargs.get("rgb", None), ) for d, ax in zip(self.name_dicts.flat, self.axes.flat): # None is the sentinel value if d is not None: subset = self.data.loc[d] mappable = func( subset, x=x, y=y, ax=ax, **func_kwargs, _is_facetgrid=True ) self._mappables.append(mappable) self._finalize_grid(x, y) if kwargs.get("add_colorbar", True): self.add_colorbar(**cbar_kwargs) return self def map_dataarray_line( self, func, x, y, hue, add_legend=True, _labels=None, **kwargs ): from .plot import _infer_line_data for d, ax in zip(self.name_dicts.flat, self.axes.flat): # None is the sentinel value if d is not None: subset = self.data.loc[d] mappable = func( subset, x=x, y=y, ax=ax, hue=hue, add_legend=False, _labels=False, **kwargs, ) self._mappables.append(mappable) _, _, hueplt, xlabel, ylabel, huelabel = _infer_line_data( darray=self.data.loc[self.name_dicts.flat[0]], x=x, y=y, hue=hue ) self._hue_var = hueplt self._hue_label = huelabel self._finalize_grid(xlabel, ylabel) if add_legend and hueplt is not None and huelabel is not None: self.add_legend() return self def map_dataset( self, func, x=None, y=None, hue=None, hue_style=None, add_guide=None, **kwargs ): from .dataset_plot import _infer_meta_data, _parse_size kwargs["add_guide"] = False kwargs["_is_facetgrid"] = True if kwargs.get("markersize", None): kwargs["size_mapping"] = _parse_size( self.data[kwargs["markersize"]], kwargs.pop("size_norm", None) ) meta_data = _infer_meta_data(self.data, x, y, hue, hue_style, add_guide) kwargs["meta_data"] = meta_data if hue and meta_data["hue_style"] == "continuous": cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs( func, self.data[hue].values, **kwargs ) kwargs["meta_data"]["cmap_params"] = cmap_params kwargs["meta_data"]["cbar_kwargs"] = cbar_kwargs for d, ax in zip(self.name_dicts.flat, self.axes.flat): # None is the sentinel value if d is not None: subset = self.data.loc[d] maybe_mappable = func( ds=subset, x=x, y=y, hue=hue, hue_style=hue_style, ax=ax, **kwargs ) # TODO: this is needed to get legends to work. # but maybe_mappable is a list in that case :/ self._mappables.append(maybe_mappable) self._finalize_grid(meta_data["xlabel"], meta_data["ylabel"]) if hue: self._hue_label = meta_data.pop("hue_label", None) if meta_data["add_legend"]: self._hue_var = meta_data["hue"] self.add_legend() elif meta_data["add_colorbar"]: self.add_colorbar(label=self._hue_label, **cbar_kwargs) return self def _finalize_grid(self, *axlabels): """Finalize the annotations and layout.""" if not self._finalized: self.set_axis_labels(*axlabels) self.set_titles() self.fig.tight_layout() for ax, namedict in zip(self.axes.flat, self.name_dicts.flat): if namedict is None: ax.set_visible(False) self._finalized = True def add_legend(self, **kwargs): figlegend = self.fig.legend( handles=self._mappables[-1], labels=list(self._hue_var.values), title=self._hue_label, loc="center right", **kwargs, ) self.figlegend = figlegend # Draw the plot to set the bounding boxes correctly self.fig.draw(self.fig.canvas.get_renderer()) # Calculate and set the new width of the figure so the legend fits legend_width = figlegend.get_window_extent().width / self.fig.dpi figure_width = self.fig.get_figwidth() self.fig.set_figwidth(figure_width + legend_width) # Draw the plot again to get the new transformations self.fig.draw(self.fig.canvas.get_renderer()) # Now calculate how much space we need on the right side legend_width = figlegend.get_window_extent().width / self.fig.dpi space_needed = legend_width / (figure_width + legend_width) + 0.02 # margin = .01 # _space_needed = margin + space_needed right = 1 - space_needed # Place the subplot axes to give space for the legend self.fig.subplots_adjust(right=right) def add_colorbar(self, **kwargs): """Draw a colorbar""" kwargs = kwargs.copy() if self._cmap_extend is not None: kwargs.setdefault("extend", self._cmap_extend) # dont pass extend as kwarg if it is in the mappable if hasattr(self._mappables[-1], "extend"): kwargs.pop("extend", None) if "label" not in kwargs: kwargs.setdefault("label", label_from_attrs(self.data)) self.cbar = self.fig.colorbar( self._mappables[-1], ax=list(self.axes.flat), **kwargs ) return self def set_axis_labels(self, x_var=None, y_var=None): """Set axis labels on the left column and bottom row of the grid.""" if x_var is not None: if x_var in self.data.coords: self._x_var = x_var self.set_xlabels(label_from_attrs(self.data[x_var])) else: # x_var is a string self.set_xlabels(x_var) if y_var is not None: if y_var in self.data.coords: self._y_var = y_var self.set_ylabels(label_from_attrs(self.data[y_var])) else: self.set_ylabels(y_var) return self def set_xlabels(self, label=None, **kwargs): """Label the x axis on the bottom row of the grid.""" if label is None: label = label_from_attrs(self.data[self._x_var]) for ax in self._bottom_axes: ax.set_xlabel(label, **kwargs) return self def set_ylabels(self, label=None, **kwargs): """Label the y axis on the left column of the grid.""" if label is None: label = label_from_attrs(self.data[self._y_var]) for ax in self._left_axes: ax.set_ylabel(label, **kwargs) return self def set_titles(self, template="{coord} = {value}", maxchar=30, size=None, **kwargs): """ Draw titles either above each facet or on the grid margins. Parameters ---------- template : string Template for plot titles containing {coord} and {value} maxchar : int Truncate titles at maxchar kwargs : keyword args additional arguments to matplotlib.text Returns ------- self: FacetGrid object """ import matplotlib as mpl if size is None: size = mpl.rcParams["axes.labelsize"] nicetitle = functools.partial(_nicetitle, maxchar=maxchar, template=template) if self._single_group: for d, ax in zip(self.name_dicts.flat, self.axes.flat): # Only label the ones with data if d is not None: coord, value = list(d.items()).pop() title = nicetitle(coord, value, maxchar=maxchar) ax.set_title(title, size=size, **kwargs) else: # The row titles on the right edge of the grid for index, (ax, row_name, handle) in enumerate( zip(self.axes[:, -1], self.row_names, self.row_labels) ): title = nicetitle(coord=self._row_var, value=row_name, maxchar=maxchar) if not handle: self.row_labels[index] = ax.annotate( title, xy=(1.02, 0.5), xycoords="axes fraction", rotation=270, ha="left", va="center", **kwargs, ) else: handle.set_text(title) # The column titles on the top row for index, (ax, col_name, handle) in enumerate( zip(self.axes[0, :], self.col_names, self.col_labels) ): title = nicetitle(coord=self._col_var, value=col_name, maxchar=maxchar) if not handle: self.col_labels[index] = ax.set_title(title, size=size, **kwargs) else: handle.set_text(title) return self def set_ticks(self, max_xticks=_NTICKS, max_yticks=_NTICKS, fontsize=_FONTSIZE): """ Set and control tick behavior Parameters ---------- max_xticks, max_yticks : int, optional Maximum number of labeled ticks to plot on x, y axes fontsize : string or int Font size as used by matplotlib text Returns ------- self : FacetGrid object """ from matplotlib.ticker import MaxNLocator # Both are necessary x_major_locator = MaxNLocator(nbins=max_xticks) y_major_locator = MaxNLocator(nbins=max_yticks) for ax in self.axes.flat: ax.xaxis.set_major_locator(x_major_locator) ax.yaxis.set_major_locator(y_major_locator) for tick in itertools.chain( ax.xaxis.get_major_ticks(), ax.yaxis.get_major_ticks() ): tick.label1.set_fontsize(fontsize) return self def map(self, func, *args, **kwargs): """ Apply a plotting function to each facet's subset of the data. Parameters ---------- func : callable A plotting function that takes data and keyword arguments. It must plot to the currently active matplotlib Axes and take a `color` keyword argument. If faceting on the `hue` dimension, it must also take a `label` keyword argument. args : strings Column names in self.data that identify variables with data to plot. The data for each variable is passed to `func` in the order the variables are specified in the call. kwargs : keyword arguments All keyword arguments are passed to the plotting function. Returns ------- self : FacetGrid object """ plt = import_matplotlib_pyplot() for ax, namedict in zip(self.axes.flat, self.name_dicts.flat): if namedict is not None: data = self.data.loc[namedict] plt.sca(ax) innerargs = [data[a].values for a in args] maybe_mappable = func(*innerargs, **kwargs) # TODO: better way to verify that an artist is mappable? # https://stackoverflow.com/questions/33023036/is-it-possible-to-detect-if-a-matplotlib-artist-is-a-mappable-suitable-for-use-w#33023522 if maybe_mappable and hasattr(maybe_mappable, "autoscale_None"): self._mappables.append(maybe_mappable) self._finalize_grid(*args[:2]) return self def _easy_facetgrid( data, plotfunc, kind, x=None, y=None, row=None, col=None, col_wrap=None, sharex=True, sharey=True, aspect=None, size=None, subplot_kws=None, ax=None, figsize=None, **kwargs, ): """ Convenience method to call xarray.plot.FacetGrid from 2d plotting methods kwargs are the arguments to 2d plotting method """ if ax is not None: raise ValueError("Can't use axes when making faceted plots.") if aspect is None: aspect = 1 if size is None: size = 3 elif figsize is not None: raise ValueError("cannot provide both `figsize` and `size` arguments") g = FacetGrid( data=data, col=col, row=row, col_wrap=col_wrap, sharex=sharex, sharey=sharey, figsize=figsize, aspect=aspect, size=size, subplot_kws=subplot_kws, ) if kind == "line": return g.map_dataarray_line(plotfunc, x, y, **kwargs) if kind == "dataarray": return g.map_dataarray(plotfunc, x, y, **kwargs) if kind == "dataset": return g.map_dataset(plotfunc, x, y, **kwargs)

apache-2.0

yunfeilu/scikit-learn

sklearn/cluster/tests/test_k_means.py

63

26190

"""Testing for K-means""" import sys import numpy as np from scipy import sparse as sp from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import SkipTest from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_raises_regexp from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_less from sklearn.utils.testing import assert_warns from sklearn.utils.testing import if_safe_multiprocessing_with_blas from sklearn.utils.validation import DataConversionWarning from sklearn.utils.extmath import row_norms from sklearn.metrics.cluster import v_measure_score from sklearn.cluster import KMeans, k_means from sklearn.cluster import MiniBatchKMeans from sklearn.cluster.k_means_ import _labels_inertia from sklearn.cluster.k_means_ import _mini_batch_step from sklearn.datasets.samples_generator import make_blobs from sklearn.externals.six.moves import cStringIO as StringIO # non centered, sparse centers to check the centers = np.array([ [0.0, 5.0, 0.0, 0.0, 0.0], [1.0, 1.0, 4.0, 0.0, 0.0], [1.0, 0.0, 0.0, 5.0, 1.0], ]) n_samples = 100 n_clusters, n_features = centers.shape X, true_labels = make_blobs(n_samples=n_samples, centers=centers, cluster_std=1., random_state=42) X_csr = sp.csr_matrix(X) def test_kmeans_dtype(): rnd = np.random.RandomState(0) X = rnd.normal(size=(40, 2)) X = (X * 10).astype(np.uint8) km = KMeans(n_init=1).fit(X) pred_x = assert_warns(DataConversionWarning, km.predict, X) assert_array_equal(km.labels_, pred_x) def test_labels_assignment_and_inertia(): # pure numpy implementation as easily auditable reference gold # implementation rng = np.random.RandomState(42) noisy_centers = centers + rng.normal(size=centers.shape) labels_gold = - np.ones(n_samples, dtype=np.int) mindist = np.empty(n_samples) mindist.fill(np.infty) for center_id in range(n_clusters): dist = np.sum((X - noisy_centers[center_id]) ** 2, axis=1) labels_gold[dist < mindist] = center_id mindist = np.minimum(dist, mindist) inertia_gold = mindist.sum() assert_true((mindist >= 0.0).all()) assert_true((labels_gold != -1).all()) # perform label assignment using the dense array input x_squared_norms = (X ** 2).sum(axis=1) labels_array, inertia_array = _labels_inertia( X, x_squared_norms, noisy_centers) assert_array_almost_equal(inertia_array, inertia_gold) assert_array_equal(labels_array, labels_gold) # perform label assignment using the sparse CSR input x_squared_norms_from_csr = row_norms(X_csr, squared=True) labels_csr, inertia_csr = _labels_inertia( X_csr, x_squared_norms_from_csr, noisy_centers) assert_array_almost_equal(inertia_csr, inertia_gold) assert_array_equal(labels_csr, labels_gold) def test_minibatch_update_consistency(): # Check that dense and sparse minibatch update give the same results rng = np.random.RandomState(42) old_centers = centers + rng.normal(size=centers.shape) new_centers = old_centers.copy() new_centers_csr = old_centers.copy() counts = np.zeros(new_centers.shape[0], dtype=np.int32) counts_csr = np.zeros(new_centers.shape[0], dtype=np.int32) x_squared_norms = (X ** 2).sum(axis=1) x_squared_norms_csr = row_norms(X_csr, squared=True) buffer = np.zeros(centers.shape[1], dtype=np.double) buffer_csr = np.zeros(centers.shape[1], dtype=np.double) # extract a small minibatch X_mb = X[:10] X_mb_csr = X_csr[:10] x_mb_squared_norms = x_squared_norms[:10] x_mb_squared_norms_csr = x_squared_norms_csr[:10] # step 1: compute the dense minibatch update old_inertia, incremental_diff = _mini_batch_step( X_mb, x_mb_squared_norms, new_centers, counts, buffer, 1, None, random_reassign=False) assert_greater(old_inertia, 0.0) # compute the new inertia on the same batch to check that it decreased labels, new_inertia = _labels_inertia( X_mb, x_mb_squared_norms, new_centers) assert_greater(new_inertia, 0.0) assert_less(new_inertia, old_inertia) # check that the incremental difference computation is matching the # final observed value effective_diff = np.sum((new_centers - old_centers) ** 2) assert_almost_equal(incremental_diff, effective_diff) # step 2: compute the sparse minibatch update old_inertia_csr, incremental_diff_csr = _mini_batch_step( X_mb_csr, x_mb_squared_norms_csr, new_centers_csr, counts_csr, buffer_csr, 1, None, random_reassign=False) assert_greater(old_inertia_csr, 0.0) # compute the new inertia on the same batch to check that it decreased labels_csr, new_inertia_csr = _labels_inertia( X_mb_csr, x_mb_squared_norms_csr, new_centers_csr) assert_greater(new_inertia_csr, 0.0) assert_less(new_inertia_csr, old_inertia_csr) # check that the incremental difference computation is matching the # final observed value effective_diff = np.sum((new_centers_csr - old_centers) ** 2) assert_almost_equal(incremental_diff_csr, effective_diff) # step 3: check that sparse and dense updates lead to the same results assert_array_equal(labels, labels_csr) assert_array_almost_equal(new_centers, new_centers_csr) assert_almost_equal(incremental_diff, incremental_diff_csr) assert_almost_equal(old_inertia, old_inertia_csr) assert_almost_equal(new_inertia, new_inertia_csr) def _check_fitted_model(km): # check that the number of clusters centers and distinct labels match # the expectation centers = km.cluster_centers_ assert_equal(centers.shape, (n_clusters, n_features)) labels = km.labels_ assert_equal(np.unique(labels).shape[0], n_clusters) # check that the labels assignment are perfect (up to a permutation) assert_equal(v_measure_score(true_labels, labels), 1.0) assert_greater(km.inertia_, 0.0) # check error on dataset being too small assert_raises(ValueError, km.fit, [[0., 1.]]) def test_k_means_plus_plus_init(): km = KMeans(init="k-means++", n_clusters=n_clusters, random_state=42).fit(X) _check_fitted_model(km) def test_k_means_new_centers(): # Explore the part of the code where a new center is reassigned X = np.array([[0, 0, 1, 1], [0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 0, 0]]) labels = [0, 1, 2, 1, 1, 2] bad_centers = np.array([[+0, 1, 0, 0], [.2, 0, .2, .2], [+0, 0, 0, 0]]) km = KMeans(n_clusters=3, init=bad_centers, n_init=1, max_iter=10, random_state=1) for this_X in (X, sp.coo_matrix(X)): km.fit(this_X) this_labels = km.labels_ # Reorder the labels so that the first instance is in cluster 0, # the second in cluster 1, ... this_labels = np.unique(this_labels, return_index=True)[1][this_labels] np.testing.assert_array_equal(this_labels, labels) @if_safe_multiprocessing_with_blas def test_k_means_plus_plus_init_2_jobs(): if sys.version_info[:2] < (3, 4): raise SkipTest( "Possible multi-process bug with some BLAS under Python < 3.4") km = KMeans(init="k-means++", n_clusters=n_clusters, n_jobs=2, random_state=42).fit(X) _check_fitted_model(km) def test_k_means_precompute_distances_flag(): # check that a warning is raised if the precompute_distances flag is not # supported km = KMeans(precompute_distances="wrong") assert_raises(ValueError, km.fit, X) def test_k_means_plus_plus_init_sparse(): km = KMeans(init="k-means++", n_clusters=n_clusters, random_state=42) km.fit(X_csr) _check_fitted_model(km) def test_k_means_random_init(): km = KMeans(init="random", n_clusters=n_clusters, random_state=42) km.fit(X) _check_fitted_model(km) def test_k_means_random_init_sparse(): km = KMeans(init="random", n_clusters=n_clusters, random_state=42) km.fit(X_csr) _check_fitted_model(km) def test_k_means_plus_plus_init_not_precomputed(): km = KMeans(init="k-means++", n_clusters=n_clusters, random_state=42, precompute_distances=False).fit(X) _check_fitted_model(km) def test_k_means_random_init_not_precomputed(): km = KMeans(init="random", n_clusters=n_clusters, random_state=42, precompute_distances=False).fit(X) _check_fitted_model(km) def test_k_means_perfect_init(): km = KMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42, n_init=1) km.fit(X) _check_fitted_model(km) def test_k_means_n_init(): rnd = np.random.RandomState(0) X = rnd.normal(size=(40, 2)) # two regression tests on bad n_init argument # previous bug: n_init <= 0 threw non-informative TypeError (#3858) assert_raises_regexp(ValueError, "n_init", KMeans(n_init=0).fit, X) assert_raises_regexp(ValueError, "n_init", KMeans(n_init=-1).fit, X) def test_k_means_fortran_aligned_data(): # Check the KMeans will work well, even if X is a fortran-aligned data. X = np.asfortranarray([[0, 0], [0, 1], [0, 1]]) centers = np.array([[0, 0], [0, 1]]) labels = np.array([0, 1, 1]) km = KMeans(n_init=1, init=centers, precompute_distances=False, random_state=42) km.fit(X) assert_array_equal(km.cluster_centers_, centers) assert_array_equal(km.labels_, labels) def test_mb_k_means_plus_plus_init_dense_array(): mb_k_means = MiniBatchKMeans(init="k-means++", n_clusters=n_clusters, random_state=42) mb_k_means.fit(X) _check_fitted_model(mb_k_means) def test_mb_kmeans_verbose(): mb_k_means = MiniBatchKMeans(init="k-means++", n_clusters=n_clusters, random_state=42, verbose=1) old_stdout = sys.stdout sys.stdout = StringIO() try: mb_k_means.fit(X) finally: sys.stdout = old_stdout def test_mb_k_means_plus_plus_init_sparse_matrix(): mb_k_means = MiniBatchKMeans(init="k-means++", n_clusters=n_clusters, random_state=42) mb_k_means.fit(X_csr) _check_fitted_model(mb_k_means) def test_minibatch_init_with_large_k(): mb_k_means = MiniBatchKMeans(init='k-means++', init_size=10, n_clusters=20) # Check that a warning is raised, as the number clusters is larger # than the init_size assert_warns(RuntimeWarning, mb_k_means.fit, X) def test_minibatch_k_means_random_init_dense_array(): # increase n_init to make random init stable enough mb_k_means = MiniBatchKMeans(init="random", n_clusters=n_clusters, random_state=42, n_init=10).fit(X) _check_fitted_model(mb_k_means) def test_minibatch_k_means_random_init_sparse_csr(): # increase n_init to make random init stable enough mb_k_means = MiniBatchKMeans(init="random", n_clusters=n_clusters, random_state=42, n_init=10).fit(X_csr) _check_fitted_model(mb_k_means) def test_minibatch_k_means_perfect_init_dense_array(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42, n_init=1).fit(X) _check_fitted_model(mb_k_means) def test_minibatch_k_means_init_multiple_runs_with_explicit_centers(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42, n_init=10) assert_warns(RuntimeWarning, mb_k_means.fit, X) def test_minibatch_k_means_perfect_init_sparse_csr(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, random_state=42, n_init=1).fit(X_csr) _check_fitted_model(mb_k_means) def test_minibatch_sensible_reassign_fit(): # check if identical initial clusters are reassigned # also a regression test for when there are more desired reassignments than # samples. zeroed_X, true_labels = make_blobs(n_samples=100, centers=5, cluster_std=1., random_state=42) zeroed_X[::2, :] = 0 mb_k_means = MiniBatchKMeans(n_clusters=20, batch_size=10, random_state=42, init="random") mb_k_means.fit(zeroed_X) # there should not be too many exact zero cluster centers assert_greater(mb_k_means.cluster_centers_.any(axis=1).sum(), 10) # do the same with batch-size > X.shape[0] (regression test) mb_k_means = MiniBatchKMeans(n_clusters=20, batch_size=201, random_state=42, init="random") mb_k_means.fit(zeroed_X) # there should not be too many exact zero cluster centers assert_greater(mb_k_means.cluster_centers_.any(axis=1).sum(), 10) def test_minibatch_sensible_reassign_partial_fit(): zeroed_X, true_labels = make_blobs(n_samples=n_samples, centers=5, cluster_std=1., random_state=42) zeroed_X[::2, :] = 0 mb_k_means = MiniBatchKMeans(n_clusters=20, random_state=42, init="random") for i in range(100): mb_k_means.partial_fit(zeroed_X) # there should not be too many exact zero cluster centers assert_greater(mb_k_means.cluster_centers_.any(axis=1).sum(), 10) def test_minibatch_reassign(): # Give a perfect initialization, but a large reassignment_ratio, # as a result all the centers should be reassigned and the model # should not longer be good for this_X in (X, X_csr): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, random_state=42) mb_k_means.fit(this_X) score_before = mb_k_means.score(this_X) try: old_stdout = sys.stdout sys.stdout = StringIO() # Turn on verbosity to smoke test the display code _mini_batch_step(this_X, (X ** 2).sum(axis=1), mb_k_means.cluster_centers_, mb_k_means.counts_, np.zeros(X.shape[1], np.double), False, distances=np.zeros(X.shape[0]), random_reassign=True, random_state=42, reassignment_ratio=1, verbose=True) finally: sys.stdout = old_stdout assert_greater(score_before, mb_k_means.score(this_X)) # Give a perfect initialization, with a small reassignment_ratio, # no center should be reassigned for this_X in (X, X_csr): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, init=centers.copy(), random_state=42, n_init=1) mb_k_means.fit(this_X) clusters_before = mb_k_means.cluster_centers_ # Turn on verbosity to smoke test the display code _mini_batch_step(this_X, (X ** 2).sum(axis=1), mb_k_means.cluster_centers_, mb_k_means.counts_, np.zeros(X.shape[1], np.double), False, distances=np.zeros(X.shape[0]), random_reassign=True, random_state=42, reassignment_ratio=1e-15) assert_array_almost_equal(clusters_before, mb_k_means.cluster_centers_) def test_minibatch_with_many_reassignments(): # Test for the case that the number of clusters to reassign is bigger # than the batch_size n_samples = 550 rnd = np.random.RandomState(42) X = rnd.uniform(size=(n_samples, 10)) # Check that the fit works if n_clusters is bigger than the batch_size. # Run the test with 550 clusters and 550 samples, because it turned out # that this values ensure that the number of clusters to reassign # is always bigger than the batch_size n_clusters = 550 MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, init_size=n_samples, random_state=42).fit(X) def test_sparse_mb_k_means_callable_init(): def test_init(X, k, random_state): return centers # Small test to check that giving the wrong number of centers # raises a meaningful error assert_raises(ValueError, MiniBatchKMeans(init=test_init, random_state=42).fit, X_csr) # Now check that the fit actually works mb_k_means = MiniBatchKMeans(n_clusters=3, init=test_init, random_state=42).fit(X_csr) _check_fitted_model(mb_k_means) def test_mini_batch_k_means_random_init_partial_fit(): km = MiniBatchKMeans(n_clusters=n_clusters, init="random", random_state=42) # use the partial_fit API for online learning for X_minibatch in np.array_split(X, 10): km.partial_fit(X_minibatch) # compute the labeling on the complete dataset labels = km.predict(X) assert_equal(v_measure_score(true_labels, labels), 1.0) def test_minibatch_default_init_size(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, batch_size=10, random_state=42, n_init=1).fit(X) assert_equal(mb_k_means.init_size_, 3 * mb_k_means.batch_size) _check_fitted_model(mb_k_means) def test_minibatch_tol(): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=10, random_state=42, tol=.01).fit(X) _check_fitted_model(mb_k_means) def test_minibatch_set_init_size(): mb_k_means = MiniBatchKMeans(init=centers.copy(), n_clusters=n_clusters, init_size=666, random_state=42, n_init=1).fit(X) assert_equal(mb_k_means.init_size, 666) assert_equal(mb_k_means.init_size_, n_samples) _check_fitted_model(mb_k_means) def test_k_means_invalid_init(): km = KMeans(init="invalid", n_init=1, n_clusters=n_clusters) assert_raises(ValueError, km.fit, X) def test_mini_match_k_means_invalid_init(): km = MiniBatchKMeans(init="invalid", n_init=1, n_clusters=n_clusters) assert_raises(ValueError, km.fit, X) def test_k_means_copyx(): # Check if copy_x=False returns nearly equal X after de-centering. my_X = X.copy() km = KMeans(copy_x=False, n_clusters=n_clusters, random_state=42) km.fit(my_X) _check_fitted_model(km) # check if my_X is centered assert_array_almost_equal(my_X, X) def test_k_means_non_collapsed(): # Check k_means with a bad initialization does not yield a singleton # Starting with bad centers that are quickly ignored should not # result in a repositioning of the centers to the center of mass that # would lead to collapsed centers which in turns make the clustering # dependent of the numerical unstabilities. my_X = np.array([[1.1, 1.1], [0.9, 1.1], [1.1, 0.9], [0.9, 1.1]]) array_init = np.array([[1.0, 1.0], [5.0, 5.0], [-5.0, -5.0]]) km = KMeans(init=array_init, n_clusters=3, random_state=42, n_init=1) km.fit(my_X) # centers must not been collapsed assert_equal(len(np.unique(km.labels_)), 3) centers = km.cluster_centers_ assert_true(np.linalg.norm(centers[0] - centers[1]) >= 0.1) assert_true(np.linalg.norm(centers[0] - centers[2]) >= 0.1) assert_true(np.linalg.norm(centers[1] - centers[2]) >= 0.1) def test_predict(): km = KMeans(n_clusters=n_clusters, random_state=42) km.fit(X) # sanity check: predict centroid labels pred = km.predict(km.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # sanity check: re-predict labeling for training set samples pred = km.predict(X) assert_array_equal(pred, km.labels_) # re-predict labels for training set using fit_predict pred = km.fit_predict(X) assert_array_equal(pred, km.labels_) def test_score(): km1 = KMeans(n_clusters=n_clusters, max_iter=1, random_state=42) s1 = km1.fit(X).score(X) km2 = KMeans(n_clusters=n_clusters, max_iter=10, random_state=42) s2 = km2.fit(X).score(X) assert_greater(s2, s1) def test_predict_minibatch_dense_input(): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, random_state=40).fit(X) # sanity check: predict centroid labels pred = mb_k_means.predict(mb_k_means.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # sanity check: re-predict labeling for training set samples pred = mb_k_means.predict(X) assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_) def test_predict_minibatch_kmeanspp_init_sparse_input(): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, init='k-means++', n_init=10).fit(X_csr) # sanity check: re-predict labeling for training set samples assert_array_equal(mb_k_means.predict(X_csr), mb_k_means.labels_) # sanity check: predict centroid labels pred = mb_k_means.predict(mb_k_means.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # check that models trained on sparse input also works for dense input at # predict time assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_) def test_predict_minibatch_random_init_sparse_input(): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, init='random', n_init=10).fit(X_csr) # sanity check: re-predict labeling for training set samples assert_array_equal(mb_k_means.predict(X_csr), mb_k_means.labels_) # sanity check: predict centroid labels pred = mb_k_means.predict(mb_k_means.cluster_centers_) assert_array_equal(pred, np.arange(n_clusters)) # check that models trained on sparse input also works for dense input at # predict time assert_array_equal(mb_k_means.predict(X), mb_k_means.labels_) def test_input_dtypes(): X_list = [[0, 0], [10, 10], [12, 9], [-1, 1], [2, 0], [8, 10]] X_int = np.array(X_list, dtype=np.int32) X_int_csr = sp.csr_matrix(X_int) init_int = X_int[:2] fitted_models = [ KMeans(n_clusters=2).fit(X_list), KMeans(n_clusters=2).fit(X_int), KMeans(n_clusters=2, init=init_int, n_init=1).fit(X_list), KMeans(n_clusters=2, init=init_int, n_init=1).fit(X_int), # mini batch kmeans is very unstable on such a small dataset hence # we use many inits MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit(X_list), MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit(X_int), MiniBatchKMeans(n_clusters=2, n_init=10, batch_size=2).fit(X_int_csr), MiniBatchKMeans(n_clusters=2, batch_size=2, init=init_int, n_init=1).fit(X_list), MiniBatchKMeans(n_clusters=2, batch_size=2, init=init_int, n_init=1).fit(X_int), MiniBatchKMeans(n_clusters=2, batch_size=2, init=init_int, n_init=1).fit(X_int_csr), ] expected_labels = [0, 1, 1, 0, 0, 1] scores = np.array([v_measure_score(expected_labels, km.labels_) for km in fitted_models]) assert_array_equal(scores, np.ones(scores.shape[0])) def test_transform(): km = KMeans(n_clusters=n_clusters) km.fit(X) X_new = km.transform(km.cluster_centers_) for c in range(n_clusters): assert_equal(X_new[c, c], 0) for c2 in range(n_clusters): if c != c2: assert_greater(X_new[c, c2], 0) def test_fit_transform(): X1 = KMeans(n_clusters=3, random_state=51).fit(X).transform(X) X2 = KMeans(n_clusters=3, random_state=51).fit_transform(X) assert_array_equal(X1, X2) def test_n_init(): # Check that increasing the number of init increases the quality n_runs = 5 n_init_range = [1, 5, 10] inertia = np.zeros((len(n_init_range), n_runs)) for i, n_init in enumerate(n_init_range): for j in range(n_runs): km = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, random_state=j).fit(X) inertia[i, j] = km.inertia_ inertia = inertia.mean(axis=1) failure_msg = ("Inertia %r should be decreasing" " when n_init is increasing.") % list(inertia) for i in range(len(n_init_range) - 1): assert_true(inertia[i] >= inertia[i + 1], failure_msg) def test_k_means_function(): # test calling the k_means function directly # catch output old_stdout = sys.stdout sys.stdout = StringIO() try: cluster_centers, labels, inertia = k_means(X, n_clusters=n_clusters, verbose=True) finally: sys.stdout = old_stdout centers = cluster_centers assert_equal(centers.shape, (n_clusters, n_features)) labels = labels assert_equal(np.unique(labels).shape[0], n_clusters) # check that the labels assignment are perfect (up to a permutation) assert_equal(v_measure_score(true_labels, labels), 1.0) assert_greater(inertia, 0.0) # check warning when centers are passed assert_warns(RuntimeWarning, k_means, X, n_clusters=n_clusters, init=centers) # to many clusters desired assert_raises(ValueError, k_means, X, n_clusters=X.shape[0] + 1) def test_x_squared_norms_init_centroids(): """Test that x_squared_norms can be None in _init_centroids""" from sklearn.cluster.k_means_ import _init_centroids X_norms = np.sum(X**2, axis=1) precompute = _init_centroids( X, 3, "k-means++", random_state=0, x_squared_norms=X_norms) assert_array_equal( precompute, _init_centroids(X, 3, "k-means++", random_state=0))

bsd-3-clause

wlamond/scikit-learn

sklearn/metrics/classification.py

4

72788

"""Metrics to assess performance on classification task given class prediction Functions named as ``*_score`` return a scalar value to maximize: the higher the better Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize: the lower the better """ # Authors: Alexandre Gramfort <[email protected]> # Mathieu Blondel <[email protected]> # Olivier Grisel <[email protected]> # Arnaud Joly <[email protected]> # Jochen Wersdorfer <[email protected]> # Lars Buitinck # Joel Nothman <[email protected]> # Noel Dawe <[email protected]> # Jatin Shah <[email protected]> # Saurabh Jha <[email protected]> # Bernardo Stein <[email protected]> # License: BSD 3 clause from __future__ import division import warnings import numpy as np from scipy.sparse import coo_matrix from scipy.sparse import csr_matrix from ..preprocessing import LabelBinarizer, label_binarize from ..preprocessing import LabelEncoder from ..utils import assert_all_finite from ..utils import check_array from ..utils import check_consistent_length from ..utils import column_or_1d from ..utils.multiclass import unique_labels from ..utils.multiclass import type_of_target from ..utils.validation import _num_samples from ..utils.sparsefuncs import count_nonzero from ..utils.fixes import bincount from ..exceptions import UndefinedMetricWarning def _check_targets(y_true, y_pred): """Check that y_true and y_pred belong to the same classification task This converts multiclass or binary types to a common shape, and raises a ValueError for a mix of multilabel and multiclass targets, a mix of multilabel formats, for the presence of continuous-valued or multioutput targets, or for targets of different lengths. Column vectors are squeezed to 1d, while multilabel formats are returned as CSR sparse label indicators. Parameters ---------- y_true : array-like y_pred : array-like Returns ------- type_true : one of {'multilabel-indicator', 'multiclass', 'binary'} The type of the true target data, as output by ``utils.multiclass.type_of_target`` y_true : array or indicator matrix y_pred : array or indicator matrix """ check_consistent_length(y_true, y_pred) type_true = type_of_target(y_true) type_pred = type_of_target(y_pred) y_type = set([type_true, type_pred]) if y_type == set(["binary", "multiclass"]): y_type = set(["multiclass"]) if len(y_type) > 1: raise ValueError("Can't handle mix of {0} and {1}" "".format(type_true, type_pred)) # We can't have more than one value on y_type => The set is no more needed y_type = y_type.pop() # No metrics support "multiclass-multioutput" format if (y_type not in ["binary", "multiclass", "multilabel-indicator"]): raise ValueError("{0} is not supported".format(y_type)) if y_type in ["binary", "multiclass"]: y_true = column_or_1d(y_true) y_pred = column_or_1d(y_pred) if y_type == "binary": unique_values = np.union1d(y_true, y_pred) if len(unique_values) > 2: y_type = "multiclass" if y_type.startswith('multilabel'): y_true = csr_matrix(y_true) y_pred = csr_matrix(y_pred) y_type = 'multilabel-indicator' return y_type, y_true, y_pred def _weighted_sum(sample_score, sample_weight, normalize=False): if normalize: return np.average(sample_score, weights=sample_weight) elif sample_weight is not None: return np.dot(sample_score, sample_weight) else: return sample_score.sum() def accuracy_score(y_true, y_pred, normalize=True, sample_weight=None): """Accuracy classification score. In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must *exactly* match the corresponding set of labels in y_true. Read more in the :ref:`User Guide <accuracy_score>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) labels. y_pred : 1d array-like, or label indicator array / sparse matrix Predicted labels, as returned by a classifier. normalize : bool, optional (default=True) If ``False``, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- score : float If ``normalize == True``, return the correctly classified samples (float), else it returns the number of correctly classified samples (int). The best performance is 1 with ``normalize == True`` and the number of samples with ``normalize == False``. See also -------- jaccard_similarity_score, hamming_loss, zero_one_loss Notes ----- In binary and multiclass classification, this function is equal to the ``jaccard_similarity_score`` function. Examples -------- >>> import numpy as np >>> from sklearn.metrics import accuracy_score >>> y_pred = [0, 2, 1, 3] >>> y_true = [0, 1, 2, 3] >>> accuracy_score(y_true, y_pred) 0.5 >>> accuracy_score(y_true, y_pred, normalize=False) 2 In the multilabel case with binary label indicators: >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2))) 0.5 """ # Compute accuracy for each possible representation y_type, y_true, y_pred = _check_targets(y_true, y_pred) if y_type.startswith('multilabel'): differing_labels = count_nonzero(y_true - y_pred, axis=1) score = differing_labels == 0 else: score = y_true == y_pred return _weighted_sum(score, sample_weight, normalize) def confusion_matrix(y_true, y_pred, labels=None, sample_weight=None): """Compute confusion matrix to evaluate the accuracy of a classification By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}` is equal to the number of observations known to be in group :math:`i` but predicted to be in group :math:`j`. Thus in binary classification, the count of true negatives is :math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is :math:`C_{1,1}` and false positives is :math:`C_{0,1}`. Read more in the :ref:`User Guide <confusion_matrix>`. Parameters ---------- y_true : array, shape = [n_samples] Ground truth (correct) target values. y_pred : array, shape = [n_samples] Estimated targets as returned by a classifier. labels : array, shape = [n_classes], optional List of labels to index the matrix. This may be used to reorder or select a subset of labels. If none is given, those that appear at least once in ``y_true`` or ``y_pred`` are used in sorted order. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- C : array, shape = [n_classes, n_classes] Confusion matrix References ---------- .. [1] `Wikipedia entry for the Confusion matrix <https://en.wikipedia.org/wiki/Confusion_matrix>`_ Examples -------- >>> from sklearn.metrics import confusion_matrix >>> y_true = [2, 0, 2, 2, 0, 1] >>> y_pred = [0, 0, 2, 2, 0, 2] >>> confusion_matrix(y_true, y_pred) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]]) >>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"] >>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"] >>> confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"]) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]]) In the binary case, we can extract true positives, etc as follows: >>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel() >>> (tn, fp, fn, tp) (0, 2, 1, 1) """ y_type, y_true, y_pred = _check_targets(y_true, y_pred) if y_type not in ("binary", "multiclass"): raise ValueError("%s is not supported" % y_type) if labels is None: labels = unique_labels(y_true, y_pred) else: labels = np.asarray(labels) if np.all([l not in y_true for l in labels]): raise ValueError("At least one label specified must be in y_true") if sample_weight is None: sample_weight = np.ones(y_true.shape[0], dtype=np.int) else: sample_weight = np.asarray(sample_weight) check_consistent_length(sample_weight, y_true, y_pred) n_labels = labels.size label_to_ind = dict((y, x) for x, y in enumerate(labels)) # convert yt, yp into index y_pred = np.array([label_to_ind.get(x, n_labels + 1) for x in y_pred]) y_true = np.array([label_to_ind.get(x, n_labels + 1) for x in y_true]) # intersect y_pred, y_true with labels, eliminate items not in labels ind = np.logical_and(y_pred < n_labels, y_true < n_labels) y_pred = y_pred[ind] y_true = y_true[ind] # also eliminate weights of eliminated items sample_weight = sample_weight[ind] CM = coo_matrix((sample_weight, (y_true, y_pred)), shape=(n_labels, n_labels) ).toarray() return CM def cohen_kappa_score(y1, y2, labels=None, weights=None, sample_weight=None): """Cohen's kappa: a statistic that measures inter-annotator agreement. This function computes Cohen's kappa [1]_, a score that expresses the level of agreement between two annotators on a classification problem. It is defined as .. math:: \kappa = (p_o - p_e) / (1 - p_e) where :math:`p_o` is the empirical probability of agreement on the label assigned to any sample (the observed agreement ratio), and :math:`p_e` is the expected agreement when both annotators assign labels randomly. :math:`p_e` is estimated using a per-annotator empirical prior over the class labels [2]_. Read more in the :ref:`User Guide <cohen_kappa>`. Parameters ---------- y1 : array, shape = [n_samples] Labels assigned by the first annotator. y2 : array, shape = [n_samples] Labels assigned by the second annotator. The kappa statistic is symmetric, so swapping ``y1`` and ``y2`` doesn't change the value. labels : array, shape = [n_classes], optional List of labels to index the matrix. This may be used to select a subset of labels. If None, all labels that appear at least once in ``y1`` or ``y2`` are used. weights : str, optional List of weighting type to calculate the score. None means no weighted; "linear" means linear weighted; "quadratic" means quadratic weighted. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- kappa : float The kappa statistic, which is a number between -1 and 1. The maximum value means complete agreement; zero or lower means chance agreement. References ---------- .. [1] J. Cohen (1960). "A coefficient of agreement for nominal scales". Educational and Psychological Measurement 20(1):37-46. doi:10.1177/001316446002000104. .. [2] `R. Artstein and M. Poesio (2008). "Inter-coder agreement for computational linguistics". Computational Linguistics 34(4):555-596. <http://www.mitpressjournals.org/doi/abs/10.1162/coli.07-034-R2#.V0J1MJMrIWo>`_ .. [3] `Wikipedia entry for the Cohen's kappa. <https://en.wikipedia.org/wiki/Cohen%27s_kappa>`_ """ confusion = confusion_matrix(y1, y2, labels=labels, sample_weight=sample_weight) n_classes = confusion.shape[0] sum0 = np.sum(confusion, axis=0) sum1 = np.sum(confusion, axis=1) expected = np.outer(sum0, sum1) / np.sum(sum0) if weights is None: w_mat = np.ones([n_classes, n_classes], dtype=np.int) w_mat.flat[:: n_classes + 1] = 0 elif weights == "linear" or weights == "quadratic": w_mat = np.zeros([n_classes, n_classes], dtype=np.int) w_mat += np.arange(n_classes) if weights == "linear": w_mat = np.abs(w_mat - w_mat.T) else: w_mat = (w_mat - w_mat.T) ** 2 else: raise ValueError("Unknown kappa weighting type.") k = np.sum(w_mat * confusion) / np.sum(w_mat * expected) return 1 - k def jaccard_similarity_score(y_true, y_pred, normalize=True, sample_weight=None): """Jaccard similarity coefficient score The Jaccard index [1], or Jaccard similarity coefficient, defined as the size of the intersection divided by the size of the union of two label sets, is used to compare set of predicted labels for a sample to the corresponding set of labels in ``y_true``. Read more in the :ref:`User Guide <jaccard_similarity_score>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) labels. y_pred : 1d array-like, or label indicator array / sparse matrix Predicted labels, as returned by a classifier. normalize : bool, optional (default=True) If ``False``, return the sum of the Jaccard similarity coefficient over the sample set. Otherwise, return the average of Jaccard similarity coefficient. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- score : float If ``normalize == True``, return the average Jaccard similarity coefficient, else it returns the sum of the Jaccard similarity coefficient over the sample set. The best performance is 1 with ``normalize == True`` and the number of samples with ``normalize == False``. See also -------- accuracy_score, hamming_loss, zero_one_loss Notes ----- In binary and multiclass classification, this function is equivalent to the ``accuracy_score``. It differs in the multilabel classification problem. References ---------- .. [1] `Wikipedia entry for the Jaccard index <https://en.wikipedia.org/wiki/Jaccard_index>`_ Examples -------- >>> import numpy as np >>> from sklearn.metrics import jaccard_similarity_score >>> y_pred = [0, 2, 1, 3] >>> y_true = [0, 1, 2, 3] >>> jaccard_similarity_score(y_true, y_pred) 0.5 >>> jaccard_similarity_score(y_true, y_pred, normalize=False) 2 In the multilabel case with binary label indicators: >>> jaccard_similarity_score(np.array([[0, 1], [1, 1]]),\ np.ones((2, 2))) 0.75 """ # Compute accuracy for each possible representation y_type, y_true, y_pred = _check_targets(y_true, y_pred) if y_type.startswith('multilabel'): with np.errstate(divide='ignore', invalid='ignore'): # oddly, we may get an "invalid" rather than a "divide" error here pred_or_true = count_nonzero(y_true + y_pred, axis=1) pred_and_true = count_nonzero(y_true.multiply(y_pred), axis=1) score = pred_and_true / pred_or_true score[pred_or_true == 0.0] = 1.0 else: score = y_true == y_pred return _weighted_sum(score, sample_weight, normalize) def matthews_corrcoef(y_true, y_pred, sample_weight=None): """Compute the Matthews correlation coefficient (MCC) for binary classes The Matthews correlation coefficient is used in machine learning as a measure of the quality of binary (two-class) classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The MCC is in essence a correlation coefficient value between -1 and +1. A coefficient of +1 represents a perfect prediction, 0 an average random prediction and -1 an inverse prediction. The statistic is also known as the phi coefficient. [source: Wikipedia] Only in the binary case does this relate to information about true and false positives and negatives. See references below. Read more in the :ref:`User Guide <matthews_corrcoef>`. Parameters ---------- y_true : array, shape = [n_samples] Ground truth (correct) target values. y_pred : array, shape = [n_samples] Estimated targets as returned by a classifier. sample_weight : array-like of shape = [n_samples], default None Sample weights. Returns ------- mcc : float The Matthews correlation coefficient (+1 represents a perfect prediction, 0 an average random prediction and -1 and inverse prediction). References ---------- .. [1] `Baldi, Brunak, Chauvin, Andersen and Nielsen, (2000). Assessing the accuracy of prediction algorithms for classification: an overview <http://dx.doi.org/10.1093/bioinformatics/16.5.412>`_ .. [2] `Wikipedia entry for the Matthews Correlation Coefficient <https://en.wikipedia.org/wiki/Matthews_correlation_coefficient>`_ Examples -------- >>> from sklearn.metrics import matthews_corrcoef >>> y_true = [+1, +1, +1, -1] >>> y_pred = [+1, -1, +1, +1] >>> matthews_corrcoef(y_true, y_pred) # doctest: +ELLIPSIS -0.33... """ y_type, y_true, y_pred = _check_targets(y_true, y_pred) if y_type != "binary": raise ValueError("%s is not supported" % y_type) lb = LabelEncoder() lb.fit(np.hstack([y_true, y_pred])) y_true = lb.transform(y_true) y_pred = lb.transform(y_pred) mean_yt = np.average(y_true, weights=sample_weight) mean_yp = np.average(y_pred, weights=sample_weight) y_true_u_cent = y_true - mean_yt y_pred_u_cent = y_pred - mean_yp cov_ytyp = np.average(y_true_u_cent * y_pred_u_cent, weights=sample_weight) var_yt = np.average(y_true_u_cent ** 2, weights=sample_weight) var_yp = np.average(y_pred_u_cent ** 2, weights=sample_weight) mcc = cov_ytyp / np.sqrt(var_yt * var_yp) if np.isnan(mcc): return 0. else: return mcc def zero_one_loss(y_true, y_pred, normalize=True, sample_weight=None): """Zero-one classification loss. If normalize is ``True``, return the fraction of misclassifications (float), else it returns the number of misclassifications (int). The best performance is 0. Read more in the :ref:`User Guide <zero_one_loss>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) labels. y_pred : 1d array-like, or label indicator array / sparse matrix Predicted labels, as returned by a classifier. normalize : bool, optional (default=True) If ``False``, return the number of misclassifications. Otherwise, return the fraction of misclassifications. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- loss : float or int, If ``normalize == True``, return the fraction of misclassifications (float), else it returns the number of misclassifications (int). Notes ----- In multilabel classification, the zero_one_loss function corresponds to the subset zero-one loss: for each sample, the entire set of labels must be correctly predicted, otherwise the loss for that sample is equal to one. See also -------- accuracy_score, hamming_loss, jaccard_similarity_score Examples -------- >>> from sklearn.metrics import zero_one_loss >>> y_pred = [1, 2, 3, 4] >>> y_true = [2, 2, 3, 4] >>> zero_one_loss(y_true, y_pred) 0.25 >>> zero_one_loss(y_true, y_pred, normalize=False) 1 In the multilabel case with binary label indicators: >>> zero_one_loss(np.array([[0, 1], [1, 1]]), np.ones((2, 2))) 0.5 """ score = accuracy_score(y_true, y_pred, normalize=normalize, sample_weight=sample_weight) if normalize: return 1 - score else: if sample_weight is not None: n_samples = np.sum(sample_weight) else: n_samples = _num_samples(y_true) return n_samples - score def f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None): """Compute the F1 score, also known as balanced F-score or F-measure The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is:: F1 = 2 * (precision * recall) / (precision + recall) In the multi-class and multi-label case, this is the weighted average of the F1 score of each class. Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels : list, optional The set of labels to include when ``average != 'binary'``, and their order if ``average is None``. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in ``y_true`` and ``y_pred`` are used in sorted order. .. versionchanged:: 0.17 parameter *labels* improved for multiclass problem. pos_label : str or int, 1 by default The class to report if ``average='binary'`` and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting ``labels=[pos_label]`` and ``average != 'binary'`` will report scores for that label only. average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', \ 'weighted'] This parameter is required for multiclass/multilabel targets. If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: ``'binary'``: Only report results for the class specified by ``pos_label``. This is applicable only if targets (``y_{true,pred}``) are binary. ``'micro'``: Calculate metrics globally by counting the total true positives, false negatives and false positives. ``'macro'``: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. ``'weighted'``: Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters 'macro' to account for label imbalance; it can result in an F-score that is not between precision and recall. ``'samples'``: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from :func:`accuracy_score`). sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- f1_score : float or array of float, shape = [n_unique_labels] F1 score of the positive class in binary classification or weighted average of the F1 scores of each class for the multiclass task. References ---------- .. [1] `Wikipedia entry for the F1-score <https://en.wikipedia.org/wiki/F1_score>`_ Examples -------- >>> from sklearn.metrics import f1_score >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> f1_score(y_true, y_pred, average='macro') # doctest: +ELLIPSIS 0.26... >>> f1_score(y_true, y_pred, average='micro') # doctest: +ELLIPSIS 0.33... >>> f1_score(y_true, y_pred, average='weighted') # doctest: +ELLIPSIS 0.26... >>> f1_score(y_true, y_pred, average=None) array([ 0.8, 0. , 0. ]) """ return fbeta_score(y_true, y_pred, 1, labels=labels, pos_label=pos_label, average=average, sample_weight=sample_weight) def fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1, average='binary', sample_weight=None): """Compute the F-beta score The F-beta score is the weighted harmonic mean of precision and recall, reaching its optimal value at 1 and its worst value at 0. The `beta` parameter determines the weight of precision in the combined score. ``beta < 1`` lends more weight to precision, while ``beta > 1`` favors recall (``beta -> 0`` considers only precision, ``beta -> inf`` only recall). Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. beta : float Weight of precision in harmonic mean. labels : list, optional The set of labels to include when ``average != 'binary'``, and their order if ``average is None``. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in ``y_true`` and ``y_pred`` are used in sorted order. .. versionchanged:: 0.17 parameter *labels* improved for multiclass problem. pos_label : str or int, 1 by default The class to report if ``average='binary'`` and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting ``labels=[pos_label]`` and ``average != 'binary'`` will report scores for that label only. average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', \ 'weighted'] This parameter is required for multiclass/multilabel targets. If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: ``'binary'``: Only report results for the class specified by ``pos_label``. This is applicable only if targets (``y_{true,pred}``) are binary. ``'micro'``: Calculate metrics globally by counting the total true positives, false negatives and false positives. ``'macro'``: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. ``'weighted'``: Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters 'macro' to account for label imbalance; it can result in an F-score that is not between precision and recall. ``'samples'``: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from :func:`accuracy_score`). sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- fbeta_score : float (if average is not None) or array of float, shape =\ [n_unique_labels] F-beta score of the positive class in binary classification or weighted average of the F-beta score of each class for the multiclass task. References ---------- .. [1] R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 327-328. .. [2] `Wikipedia entry for the F1-score <https://en.wikipedia.org/wiki/F1_score>`_ Examples -------- >>> from sklearn.metrics import fbeta_score >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> fbeta_score(y_true, y_pred, average='macro', beta=0.5) ... # doctest: +ELLIPSIS 0.23... >>> fbeta_score(y_true, y_pred, average='micro', beta=0.5) ... # doctest: +ELLIPSIS 0.33... >>> fbeta_score(y_true, y_pred, average='weighted', beta=0.5) ... # doctest: +ELLIPSIS 0.23... >>> fbeta_score(y_true, y_pred, average=None, beta=0.5) ... # doctest: +ELLIPSIS array([ 0.71..., 0. , 0. ]) """ _, _, f, _ = precision_recall_fscore_support(y_true, y_pred, beta=beta, labels=labels, pos_label=pos_label, average=average, warn_for=('f-score',), sample_weight=sample_weight) return f def _prf_divide(numerator, denominator, metric, modifier, average, warn_for): """Performs division and handles divide-by-zero. On zero-division, sets the corresponding result elements to zero and raises a warning. The metric, modifier and average arguments are used only for determining an appropriate warning. """ result = numerator / denominator mask = denominator == 0.0 if not np.any(mask): return result # remove infs result[mask] = 0.0 # build appropriate warning # E.g. "Precision and F-score are ill-defined and being set to 0.0 in # labels with no predicted samples" axis0 = 'sample' axis1 = 'label' if average == 'samples': axis0, axis1 = axis1, axis0 if metric in warn_for and 'f-score' in warn_for: msg_start = '{0} and F-score are'.format(metric.title()) elif metric in warn_for: msg_start = '{0} is'.format(metric.title()) elif 'f-score' in warn_for: msg_start = 'F-score is' else: return result msg = ('{0} ill-defined and being set to 0.0 {{0}} ' 'no {1} {2}s.'.format(msg_start, modifier, axis0)) if len(mask) == 1: msg = msg.format('due to') else: msg = msg.format('in {0}s with'.format(axis1)) warnings.warn(msg, UndefinedMetricWarning, stacklevel=2) return result def precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None, pos_label=1, average=None, warn_for=('precision', 'recall', 'f-score'), sample_weight=None): """Compute precision, recall, F-measure and support for each class The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of true positives and ``fp`` the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of true positives and ``fn`` the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples. The F-beta score can be interpreted as a weighted harmonic mean of the precision and recall, where an F-beta score reaches its best value at 1 and worst score at 0. The F-beta score weights recall more than precision by a factor of ``beta``. ``beta == 1.0`` means recall and precision are equally important. The support is the number of occurrences of each class in ``y_true``. If ``pos_label is None`` and in binary classification, this function returns the average precision, recall and F-measure if ``average`` is one of ``'micro'``, ``'macro'``, ``'weighted'`` or ``'samples'``. Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. beta : float, 1.0 by default The strength of recall versus precision in the F-score. labels : list, optional The set of labels to include when ``average != 'binary'``, and their order if ``average is None``. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in ``y_true`` and ``y_pred`` are used in sorted order. pos_label : str or int, 1 by default The class to report if ``average='binary'`` and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting ``labels=[pos_label]`` and ``average != 'binary'`` will report scores for that label only. average : string, [None (default), 'binary', 'micro', 'macro', 'samples', \ 'weighted'] If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: ``'binary'``: Only report results for the class specified by ``pos_label``. This is applicable only if targets (``y_{true,pred}``) are binary. ``'micro'``: Calculate metrics globally by counting the total true positives, false negatives and false positives. ``'macro'``: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. ``'weighted'``: Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters 'macro' to account for label imbalance; it can result in an F-score that is not between precision and recall. ``'samples'``: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from :func:`accuracy_score`). warn_for : tuple or set, for internal use This determines which warnings will be made in the case that this function is being used to return only one of its metrics. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- precision : float (if average is not None) or array of float, shape =\ [n_unique_labels] recall : float (if average is not None) or array of float, , shape =\ [n_unique_labels] fbeta_score : float (if average is not None) or array of float, shape =\ [n_unique_labels] support : int (if average is not None) or array of int, shape =\ [n_unique_labels] The number of occurrences of each label in ``y_true``. References ---------- .. [1] `Wikipedia entry for the Precision and recall <https://en.wikipedia.org/wiki/Precision_and_recall>`_ .. [2] `Wikipedia entry for the F1-score <https://en.wikipedia.org/wiki/F1_score>`_ .. [3] `Discriminative Methods for Multi-labeled Classification Advances in Knowledge Discovery and Data Mining (2004), pp. 22-30 by Shantanu Godbole, Sunita Sarawagi <http://www.godbole.net/shantanu/pubs/multilabelsvm-pakdd04.pdf>`_ Examples -------- >>> from sklearn.metrics import precision_recall_fscore_support >>> y_true = np.array(['cat', 'dog', 'pig', 'cat', 'dog', 'pig']) >>> y_pred = np.array(['cat', 'pig', 'dog', 'cat', 'cat', 'dog']) >>> precision_recall_fscore_support(y_true, y_pred, average='macro') ... # doctest: +ELLIPSIS (0.22..., 0.33..., 0.26..., None) >>> precision_recall_fscore_support(y_true, y_pred, average='micro') ... # doctest: +ELLIPSIS (0.33..., 0.33..., 0.33..., None) >>> precision_recall_fscore_support(y_true, y_pred, average='weighted') ... # doctest: +ELLIPSIS (0.22..., 0.33..., 0.26..., None) It is possible to compute per-label precisions, recalls, F1-scores and supports instead of averaging: >>> precision_recall_fscore_support(y_true, y_pred, average=None, ... labels=['pig', 'dog', 'cat']) ... # doctest: +ELLIPSIS,+NORMALIZE_WHITESPACE (array([ 0. , 0. , 0.66...]), array([ 0., 0., 1.]), array([ 0. , 0. , 0.8]), array([2, 2, 2])) """ average_options = (None, 'micro', 'macro', 'weighted', 'samples') if average not in average_options and average != 'binary': raise ValueError('average has to be one of ' + str(average_options)) if beta <= 0: raise ValueError("beta should be >0 in the F-beta score") y_type, y_true, y_pred = _check_targets(y_true, y_pred) present_labels = unique_labels(y_true, y_pred) if average == 'binary': if y_type == 'binary': if pos_label not in present_labels: if len(present_labels) < 2: # Only negative labels return (0., 0., 0., 0) else: raise ValueError("pos_label=%r is not a valid label: %r" % (pos_label, present_labels)) labels = [pos_label] else: raise ValueError("Target is %s but average='binary'. Please " "choose another average setting." % y_type) elif pos_label not in (None, 1): warnings.warn("Note that pos_label (set to %r) is ignored when " "average != 'binary' (got %r). You may use " "labels=[pos_label] to specify a single positive class." % (pos_label, average), UserWarning) if labels is None: labels = present_labels n_labels = None else: n_labels = len(labels) labels = np.hstack([labels, np.setdiff1d(present_labels, labels, assume_unique=True)]) # Calculate tp_sum, pred_sum, true_sum ### if y_type.startswith('multilabel'): sum_axis = 1 if average == 'samples' else 0 # All labels are index integers for multilabel. # Select labels: if not np.all(labels == present_labels): if np.max(labels) > np.max(present_labels): raise ValueError('All labels must be in [0, n labels). ' 'Got %d > %d' % (np.max(labels), np.max(present_labels))) if np.min(labels) < 0: raise ValueError('All labels must be in [0, n labels). ' 'Got %d < 0' % np.min(labels)) y_true = y_true[:, labels[:n_labels]] y_pred = y_pred[:, labels[:n_labels]] # calculate weighted counts true_and_pred = y_true.multiply(y_pred) tp_sum = count_nonzero(true_and_pred, axis=sum_axis, sample_weight=sample_weight) pred_sum = count_nonzero(y_pred, axis=sum_axis, sample_weight=sample_weight) true_sum = count_nonzero(y_true, axis=sum_axis, sample_weight=sample_weight) elif average == 'samples': raise ValueError("Sample-based precision, recall, fscore is " "not meaningful outside multilabel " "classification. See the accuracy_score instead.") else: le = LabelEncoder() le.fit(labels) y_true = le.transform(y_true) y_pred = le.transform(y_pred) sorted_labels = le.classes_ # labels are now from 0 to len(labels) - 1 -> use bincount tp = y_true == y_pred tp_bins = y_true[tp] if sample_weight is not None: tp_bins_weights = np.asarray(sample_weight)[tp] else: tp_bins_weights = None if len(tp_bins): tp_sum = bincount(tp_bins, weights=tp_bins_weights, minlength=len(labels)) else: # Pathological case true_sum = pred_sum = tp_sum = np.zeros(len(labels)) if len(y_pred): pred_sum = bincount(y_pred, weights=sample_weight, minlength=len(labels)) if len(y_true): true_sum = bincount(y_true, weights=sample_weight, minlength=len(labels)) # Retain only selected labels indices = np.searchsorted(sorted_labels, labels[:n_labels]) tp_sum = tp_sum[indices] true_sum = true_sum[indices] pred_sum = pred_sum[indices] if average == 'micro': tp_sum = np.array([tp_sum.sum()]) pred_sum = np.array([pred_sum.sum()]) true_sum = np.array([true_sum.sum()]) # Finally, we have all our sufficient statistics. Divide! # beta2 = beta ** 2 with np.errstate(divide='ignore', invalid='ignore'): # Divide, and on zero-division, set scores to 0 and warn: # Oddly, we may get an "invalid" rather than a "divide" error # here. precision = _prf_divide(tp_sum, pred_sum, 'precision', 'predicted', average, warn_for) recall = _prf_divide(tp_sum, true_sum, 'recall', 'true', average, warn_for) # Don't need to warn for F: either P or R warned, or tp == 0 where pos # and true are nonzero, in which case, F is well-defined and zero f_score = ((1 + beta2) * precision * recall / (beta2 * precision + recall)) f_score[tp_sum == 0] = 0.0 # Average the results if average == 'weighted': weights = true_sum if weights.sum() == 0: return 0, 0, 0, None elif average == 'samples': weights = sample_weight else: weights = None if average is not None: assert average != 'binary' or len(precision) == 1 precision = np.average(precision, weights=weights) recall = np.average(recall, weights=weights) f_score = np.average(f_score, weights=weights) true_sum = None # return no support return precision, recall, f_score, true_sum def precision_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None): """Compute the precision The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of true positives and ``fp`` the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The best value is 1 and the worst value is 0. Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels : list, optional The set of labels to include when ``average != 'binary'``, and their order if ``average is None``. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in ``y_true`` and ``y_pred`` are used in sorted order. .. versionchanged:: 0.17 parameter *labels* improved for multiclass problem. pos_label : str or int, 1 by default The class to report if ``average='binary'`` and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting ``labels=[pos_label]`` and ``average != 'binary'`` will report scores for that label only. average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', \ 'weighted'] This parameter is required for multiclass/multilabel targets. If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: ``'binary'``: Only report results for the class specified by ``pos_label``. This is applicable only if targets (``y_{true,pred}``) are binary. ``'micro'``: Calculate metrics globally by counting the total true positives, false negatives and false positives. ``'macro'``: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. ``'weighted'``: Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters 'macro' to account for label imbalance; it can result in an F-score that is not between precision and recall. ``'samples'``: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from :func:`accuracy_score`). sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- precision : float (if average is not None) or array of float, shape =\ [n_unique_labels] Precision of the positive class in binary classification or weighted average of the precision of each class for the multiclass task. Examples -------- >>> from sklearn.metrics import precision_score >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> precision_score(y_true, y_pred, average='macro') # doctest: +ELLIPSIS 0.22... >>> precision_score(y_true, y_pred, average='micro') # doctest: +ELLIPSIS 0.33... >>> precision_score(y_true, y_pred, average='weighted') ... # doctest: +ELLIPSIS 0.22... >>> precision_score(y_true, y_pred, average=None) # doctest: +ELLIPSIS array([ 0.66..., 0. , 0. ]) """ p, _, _, _ = precision_recall_fscore_support(y_true, y_pred, labels=labels, pos_label=pos_label, average=average, warn_for=('precision',), sample_weight=sample_weight) return p def recall_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None): """Compute the recall The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of true positives and ``fn`` the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples. The best value is 1 and the worst value is 0. Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels : list, optional The set of labels to include when ``average != 'binary'``, and their order if ``average is None``. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in ``y_true`` and ``y_pred`` are used in sorted order. .. versionchanged:: 0.17 parameter *labels* improved for multiclass problem. pos_label : str or int, 1 by default The class to report if ``average='binary'`` and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting ``labels=[pos_label]`` and ``average != 'binary'`` will report scores for that label only. average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', \ 'weighted'] This parameter is required for multiclass/multilabel targets. If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: ``'binary'``: Only report results for the class specified by ``pos_label``. This is applicable only if targets (``y_{true,pred}``) are binary. ``'micro'``: Calculate metrics globally by counting the total true positives, false negatives and false positives. ``'macro'``: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. ``'weighted'``: Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters 'macro' to account for label imbalance; it can result in an F-score that is not between precision and recall. ``'samples'``: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from :func:`accuracy_score`). sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- recall : float (if average is not None) or array of float, shape =\ [n_unique_labels] Recall of the positive class in binary classification or weighted average of the recall of each class for the multiclass task. Examples -------- >>> from sklearn.metrics import recall_score >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> recall_score(y_true, y_pred, average='macro') # doctest: +ELLIPSIS 0.33... >>> recall_score(y_true, y_pred, average='micro') # doctest: +ELLIPSIS 0.33... >>> recall_score(y_true, y_pred, average='weighted') # doctest: +ELLIPSIS 0.33... >>> recall_score(y_true, y_pred, average=None) array([ 1., 0., 0.]) """ _, r, _, _ = precision_recall_fscore_support(y_true, y_pred, labels=labels, pos_label=pos_label, average=average, warn_for=('recall',), sample_weight=sample_weight) return r def classification_report(y_true, y_pred, labels=None, target_names=None, sample_weight=None, digits=2): """Build a text report showing the main classification metrics Read more in the :ref:`User Guide <classification_report>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels : array, shape = [n_labels] Optional list of label indices to include in the report. target_names : list of strings Optional display names matching the labels (same order). sample_weight : array-like of shape = [n_samples], optional Sample weights. digits : int Number of digits for formatting output floating point values Returns ------- report : string Text summary of the precision, recall, F1 score for each class. The reported averages are a prevalence-weighted macro-average across classes (equivalent to :func:`precision_recall_fscore_support` with ``average='weighted'``). Note that in binary classification, recall of the positive class is also known as "sensitivity"; recall of the negative class is "specificity". Examples -------- >>> from sklearn.metrics import classification_report >>> y_true = [0, 1, 2, 2, 2] >>> y_pred = [0, 0, 2, 2, 1] >>> target_names = ['class 0', 'class 1', 'class 2'] >>> print(classification_report(y_true, y_pred, target_names=target_names)) precision recall f1-score support <BLANKLINE> class 0 0.50 1.00 0.67 1 class 1 0.00 0.00 0.00 1 class 2 1.00 0.67 0.80 3 <BLANKLINE> avg / total 0.70 0.60 0.61 5 <BLANKLINE> """ if labels is None: labels = unique_labels(y_true, y_pred) else: labels = np.asarray(labels) if target_names is not None and len(labels) != len(target_names): warnings.warn( "labels size, {0}, does not match size of target_names, {1}" .format(len(labels), len(target_names)) ) last_line_heading = 'avg / total' if target_names is None: target_names = [u'%s' % l for l in labels] name_width = max(len(cn) for cn in target_names) width = max(name_width, len(last_line_heading), digits) headers = ["precision", "recall", "f1-score", "support"] head_fmt = u'{:>{width}s} ' + u' {:>9}' * len(headers) report = head_fmt.format(u'', *headers, width=width) report += u'\n\n' p, r, f1, s = precision_recall_fscore_support(y_true, y_pred, labels=labels, average=None, sample_weight=sample_weight) row_fmt = u'{:>{width}s} ' + u' {:>9.{digits}f}' * 3 + u' {:>9}\n' rows = zip(target_names, p, r, f1, s) for row in rows: report += row_fmt.format(*row, width=width, digits=digits) report += u'\n' # compute averages report += row_fmt.format(last_line_heading, np.average(p, weights=s), np.average(r, weights=s), np.average(f1, weights=s), np.sum(s), width=width, digits=digits) return report def hamming_loss(y_true, y_pred, labels=None, sample_weight=None, classes=None): """Compute the average Hamming loss. The Hamming loss is the fraction of labels that are incorrectly predicted. Read more in the :ref:`User Guide <hamming_loss>`. Parameters ---------- y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) labels. y_pred : 1d array-like, or label indicator array / sparse matrix Predicted labels, as returned by a classifier. labels : array, shape = [n_labels], optional (default=None) Integer array of labels. If not provided, labels will be inferred from y_true and y_pred. .. versionadded:: 0.18 sample_weight : array-like of shape = [n_samples], optional Sample weights. .. versionadded:: 0.18 classes : array, shape = [n_labels], optional Integer array of labels. .. deprecated:: 0.18 This parameter has been deprecated in favor of ``labels`` in version 0.18 and will be removed in 0.20. Use ``labels`` instead. Returns ------- loss : float or int, Return the average Hamming loss between element of ``y_true`` and ``y_pred``. See Also -------- accuracy_score, jaccard_similarity_score, zero_one_loss Notes ----- In multiclass classification, the Hamming loss correspond to the Hamming distance between ``y_true`` and ``y_pred`` which is equivalent to the subset ``zero_one_loss`` function. In multilabel classification, the Hamming loss is different from the subset zero-one loss. The zero-one loss considers the entire set of labels for a given sample incorrect if it does entirely match the true set of labels. Hamming loss is more forgiving in that it penalizes the individual labels. The Hamming loss is upperbounded by the subset zero-one loss. When normalized over samples, the Hamming loss is always between 0 and 1. References ---------- .. [1] Grigorios Tsoumakas, Ioannis Katakis. Multi-Label Classification: An Overview. International Journal of Data Warehousing & Mining, 3(3), 1-13, July-September 2007. .. [2] `Wikipedia entry on the Hamming distance <https://en.wikipedia.org/wiki/Hamming_distance>`_ Examples -------- >>> from sklearn.metrics import hamming_loss >>> y_pred = [1, 2, 3, 4] >>> y_true = [2, 2, 3, 4] >>> hamming_loss(y_true, y_pred) 0.25 In the multilabel case with binary label indicators: >>> hamming_loss(np.array([[0, 1], [1, 1]]), np.zeros((2, 2))) 0.75 """ if classes is not None: warnings.warn("'classes' was renamed to 'labels' in version 0.18 and " "will be removed in 0.20.", DeprecationWarning) labels = classes y_type, y_true, y_pred = _check_targets(y_true, y_pred) if labels is None: labels = unique_labels(y_true, y_pred) else: labels = np.asarray(labels) if sample_weight is None: weight_average = 1. else: weight_average = np.mean(sample_weight) if y_type.startswith('multilabel'): n_differences = count_nonzero(y_true - y_pred, sample_weight=sample_weight) return (n_differences / (y_true.shape[0] * len(labels) * weight_average)) elif y_type in ["binary", "multiclass"]: return _weighted_sum(y_true != y_pred, sample_weight, normalize=True) else: raise ValueError("{0} is not supported".format(y_type)) def log_loss(y_true, y_pred, eps=1e-15, normalize=True, sample_weight=None, labels=None): """Log loss, aka logistic loss or cross-entropy loss. This is the loss function used in (multinomial) logistic regression and extensions of it such as neural networks, defined as the negative log-likelihood of the true labels given a probabilistic classifier's predictions. The log loss is only defined for two or more labels. For a single sample with true label yt in {0,1} and estimated probability yp that yt = 1, the log loss is -log P(yt|yp) = -(yt log(yp) + (1 - yt) log(1 - yp)) Read more in the :ref:`User Guide <log_loss>`. Parameters ---------- y_true : array-like or label indicator matrix Ground truth (correct) labels for n_samples samples. y_pred : array-like of float, shape = (n_samples, n_classes) or (n_samples,) Predicted probabilities, as returned by a classifier's predict_proba method. If ``y_pred.shape = (n_samples,)`` the probabilities provided are assumed to be that of the positive class. The labels in ``y_pred`` are assumed to be ordered alphabetically, as done by :class:`preprocessing.LabelBinarizer`. eps : float Log loss is undefined for p=0 or p=1, so probabilities are clipped to max(eps, min(1 - eps, p)). normalize : bool, optional (default=True) If true, return the mean loss per sample. Otherwise, return the sum of the per-sample losses. sample_weight : array-like of shape = [n_samples], optional Sample weights. labels : array-like, optional (default=None) If not provided, labels will be inferred from y_true. If ``labels`` is ``None`` and ``y_pred`` has shape (n_samples,) the labels are assumed to be binary and are inferred from ``y_true``. .. versionadded:: 0.18 Returns ------- loss : float Examples -------- >>> log_loss(["spam", "ham", "ham", "spam"], # doctest: +ELLIPSIS ... [[.1, .9], [.9, .1], [.8, .2], [.35, .65]]) 0.21616... References ---------- C.M. Bishop (2006). Pattern Recognition and Machine Learning. Springer, p. 209. Notes ----- The logarithm used is the natural logarithm (base-e). """ y_pred = check_array(y_pred, ensure_2d=False) check_consistent_length(y_pred, y_true) lb = LabelBinarizer() if labels is not None: lb.fit(labels) else: lb.fit(y_true) if len(lb.classes_) == 1: if labels is None: raise ValueError('y_true contains only one label ({0}). Please ' 'provide the true labels explicitly through the ' 'labels argument.'.format(lb.classes_[0])) else: raise ValueError('The labels array needs to contain at least two ' 'labels for log_loss, ' 'got {0}.'.format(lb.classes_)) transformed_labels = lb.transform(y_true) if transformed_labels.shape[1] == 1: transformed_labels = np.append(1 - transformed_labels, transformed_labels, axis=1) # Clipping y_pred = np.clip(y_pred, eps, 1 - eps) # If y_pred is of single dimension, assume y_true to be binary # and then check. if y_pred.ndim == 1: y_pred = y_pred[:, np.newaxis] if y_pred.shape[1] == 1: y_pred = np.append(1 - y_pred, y_pred, axis=1) # Check if dimensions are consistent. transformed_labels = check_array(transformed_labels) if len(lb.classes_) != y_pred.shape[1]: if labels is None: raise ValueError("y_true and y_pred contain different number of " "classes {0}, {1}. Please provide the true " "labels explicitly through the labels argument. " "Classes found in " "y_true: {2}".format(transformed_labels.shape[1], y_pred.shape[1], lb.classes_)) else: raise ValueError('The number of classes in labels is different ' 'from that in y_pred. Classes found in ' 'labels: {0}'.format(lb.classes_)) # Renormalize y_pred /= y_pred.sum(axis=1)[:, np.newaxis] loss = -(transformed_labels * np.log(y_pred)).sum(axis=1) return _weighted_sum(loss, sample_weight, normalize) def hinge_loss(y_true, pred_decision, labels=None, sample_weight=None): """Average hinge loss (non-regularized) In binary class case, assuming labels in y_true are encoded with +1 and -1, when a prediction mistake is made, ``margin = y_true * pred_decision`` is always negative (since the signs disagree), implying ``1 - margin`` is always greater than 1. The cumulated hinge loss is therefore an upper bound of the number of mistakes made by the classifier. In multiclass case, the function expects that either all the labels are included in y_true or an optional labels argument is provided which contains all the labels. The multilabel margin is calculated according to Crammer-Singer's method. As in the binary case, the cumulated hinge loss is an upper bound of the number of mistakes made by the classifier. Read more in the :ref:`User Guide <hinge_loss>`. Parameters ---------- y_true : array, shape = [n_samples] True target, consisting of integers of two values. The positive label must be greater than the negative label. pred_decision : array, shape = [n_samples] or [n_samples, n_classes] Predicted decisions, as output by decision_function (floats). labels : array, optional, default None Contains all the labels for the problem. Used in multiclass hinge loss. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- loss : float References ---------- .. [1] `Wikipedia entry on the Hinge loss <https://en.wikipedia.org/wiki/Hinge_loss>`_ .. [2] Koby Crammer, Yoram Singer. On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines. Journal of Machine Learning Research 2, (2001), 265-292 .. [3] `L1 AND L2 Regularization for Multiclass Hinge Loss Models by Robert C. Moore, John DeNero. <http://www.ttic.edu/sigml/symposium2011/papers/ Moore+DeNero_Regularization.pdf>`_ Examples -------- >>> from sklearn import svm >>> from sklearn.metrics import hinge_loss >>> X = [[0], [1]] >>> y = [-1, 1] >>> est = svm.LinearSVC(random_state=0) >>> est.fit(X, y) LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True, intercept_scaling=1, loss='squared_hinge', max_iter=1000, multi_class='ovr', penalty='l2', random_state=0, tol=0.0001, verbose=0) >>> pred_decision = est.decision_function([[-2], [3], [0.5]]) >>> pred_decision # doctest: +ELLIPSIS array([-2.18..., 2.36..., 0.09...]) >>> hinge_loss([-1, 1, 1], pred_decision) # doctest: +ELLIPSIS 0.30... In the multiclass case: >>> X = np.array([[0], [1], [2], [3]]) >>> Y = np.array([0, 1, 2, 3]) >>> labels = np.array([0, 1, 2, 3]) >>> est = svm.LinearSVC() >>> est.fit(X, Y) LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True, intercept_scaling=1, loss='squared_hinge', max_iter=1000, multi_class='ovr', penalty='l2', random_state=None, tol=0.0001, verbose=0) >>> pred_decision = est.decision_function([[-1], [2], [3]]) >>> y_true = [0, 2, 3] >>> hinge_loss(y_true, pred_decision, labels) #doctest: +ELLIPSIS 0.56... """ check_consistent_length(y_true, pred_decision, sample_weight) pred_decision = check_array(pred_decision, ensure_2d=False) y_true = column_or_1d(y_true) y_true_unique = np.unique(y_true) if y_true_unique.size > 2: if (labels is None and pred_decision.ndim > 1 and (np.size(y_true_unique) != pred_decision.shape[1])): raise ValueError("Please include all labels in y_true " "or pass labels as third argument") if labels is None: labels = y_true_unique le = LabelEncoder() le.fit(labels) y_true = le.transform(y_true) mask = np.ones_like(pred_decision, dtype=bool) mask[np.arange(y_true.shape[0]), y_true] = False margin = pred_decision[~mask] margin -= np.max(pred_decision[mask].reshape(y_true.shape[0], -1), axis=1) else: # Handles binary class case # this code assumes that positive and negative labels # are encoded as +1 and -1 respectively pred_decision = column_or_1d(pred_decision) pred_decision = np.ravel(pred_decision) lbin = LabelBinarizer(neg_label=-1) y_true = lbin.fit_transform(y_true)[:, 0] try: margin = y_true * pred_decision except TypeError: raise TypeError("pred_decision should be an array of floats.") losses = 1 - margin # The hinge_loss doesn't penalize good enough predictions. losses[losses <= 0] = 0 return np.average(losses, weights=sample_weight) def _check_binary_probabilistic_predictions(y_true, y_prob): """Check that y_true is binary and y_prob contains valid probabilities""" check_consistent_length(y_true, y_prob) labels = np.unique(y_true) if len(labels) > 2: raise ValueError("Only binary classification is supported. " "Provided labels %s." % labels) if y_prob.max() > 1: raise ValueError("y_prob contains values greater than 1.") if y_prob.min() < 0: raise ValueError("y_prob contains values less than 0.") return label_binarize(y_true, labels)[:, 0] def brier_score_loss(y_true, y_prob, sample_weight=None, pos_label=None): """Compute the Brier score. The smaller the Brier score, the better, hence the naming with "loss". Across all items in a set N predictions, the Brier score measures the mean squared difference between (1) the predicted probability assigned to the possible outcomes for item i, and (2) the actual outcome. Therefore, the lower the Brier score is for a set of predictions, the better the predictions are calibrated. Note that the Brier score always takes on a value between zero and one, since this is the largest possible difference between a predicted probability (which must be between zero and one) and the actual outcome (which can take on values of only 0 and 1). The Brier score is appropriate for binary and categorical outcomes that can be structured as true or false, but is inappropriate for ordinal variables which can take on three or more values (this is because the Brier score assumes that all possible outcomes are equivalently "distant" from one another). Which label is considered to be the positive label is controlled via the parameter pos_label, which defaults to 1. Read more in the :ref:`User Guide <calibration>`. Parameters ---------- y_true : array, shape (n_samples,) True targets. y_prob : array, shape (n_samples,) Probabilities of the positive class. sample_weight : array-like of shape = [n_samples], optional Sample weights. pos_label : int or str, default=None Label of the positive class. If None, the maximum label is used as positive class Returns ------- score : float Brier score Examples -------- >>> import numpy as np >>> from sklearn.metrics import brier_score_loss >>> y_true = np.array([0, 1, 1, 0]) >>> y_true_categorical = np.array(["spam", "ham", "ham", "spam"]) >>> y_prob = np.array([0.1, 0.9, 0.8, 0.3]) >>> brier_score_loss(y_true, y_prob) # doctest: +ELLIPSIS 0.037... >>> brier_score_loss(y_true, 1-y_prob, pos_label=0) # doctest: +ELLIPSIS 0.037... >>> brier_score_loss(y_true_categorical, y_prob, \ pos_label="ham") # doctest: +ELLIPSIS 0.037... >>> brier_score_loss(y_true, np.array(y_prob) > 0.5) 0.0 References ---------- .. [1] `Wikipedia entry for the Brier score. <https://en.wikipedia.org/wiki/Brier_score>`_ """ y_true = column_or_1d(y_true) y_prob = column_or_1d(y_prob) assert_all_finite(y_true) assert_all_finite(y_prob) if pos_label is None: pos_label = y_true.max() y_true = np.array(y_true == pos_label, int) y_true = _check_binary_probabilistic_predictions(y_true, y_prob) return np.average((y_true - y_prob) ** 2, weights=sample_weight)

bsd-3-clause

gfyoung/pandas

pandas/tests/arrays/categorical/test_sorting.py

3

5040

import numpy as np import pytest from pandas import Categorical, Index import pandas._testing as tm class TestCategoricalSort: def test_argsort(self): c = Categorical([5, 3, 1, 4, 2], ordered=True) expected = np.array([2, 4, 1, 3, 0]) tm.assert_numpy_array_equal( c.argsort(ascending=True), expected, check_dtype=False ) expected = expected[::-1] tm.assert_numpy_array_equal( c.argsort(ascending=False), expected, check_dtype=False ) def test_numpy_argsort(self): c = Categorical([5, 3, 1, 4, 2], ordered=True) expected = np.array([2, 4, 1, 3, 0]) tm.assert_numpy_array_equal(np.argsort(c), expected, check_dtype=False) tm.assert_numpy_array_equal( np.argsort(c, kind="mergesort"), expected, check_dtype=False ) msg = "the 'axis' parameter is not supported" with pytest.raises(ValueError, match=msg): np.argsort(c, axis=0) msg = "the 'order' parameter is not supported" with pytest.raises(ValueError, match=msg): np.argsort(c, order="C") def test_sort_values(self): # unordered cats are sortable cat = Categorical(["a", "b", "b", "a"], ordered=False) cat.sort_values() cat = Categorical(["a", "c", "b", "d"], ordered=True) # sort_values res = cat.sort_values() exp = np.array(["a", "b", "c", "d"], dtype=object) tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, cat.categories) cat = Categorical( ["a", "c", "b", "d"], categories=["a", "b", "c", "d"], ordered=True ) res = cat.sort_values() exp = np.array(["a", "b", "c", "d"], dtype=object) tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, cat.categories) res = cat.sort_values(ascending=False) exp = np.array(["d", "c", "b", "a"], dtype=object) tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, cat.categories) # sort (inplace order) cat1 = cat.copy() orig_codes = cat1._codes cat1.sort_values(inplace=True) assert cat1._codes is orig_codes exp = np.array(["a", "b", "c", "d"], dtype=object) tm.assert_numpy_array_equal(cat1.__array__(), exp) tm.assert_index_equal(res.categories, cat.categories) # reverse cat = Categorical(["a", "c", "c", "b", "d"], ordered=True) res = cat.sort_values(ascending=False) exp_val = np.array(["d", "c", "c", "b", "a"], dtype=object) exp_categories = Index(["a", "b", "c", "d"]) tm.assert_numpy_array_equal(res.__array__(), exp_val) tm.assert_index_equal(res.categories, exp_categories) def test_sort_values_na_position(self): # see gh-12882 cat = Categorical([5, 2, np.nan, 2, np.nan], ordered=True) exp_categories = Index([2, 5]) exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan]) res = cat.sort_values() # default arguments tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, exp_categories) exp = np.array([np.nan, np.nan, 2.0, 2.0, 5.0]) res = cat.sort_values(ascending=True, na_position="first") tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, exp_categories) exp = np.array([np.nan, np.nan, 5.0, 2.0, 2.0]) res = cat.sort_values(ascending=False, na_position="first") tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, exp_categories) exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan]) res = cat.sort_values(ascending=True, na_position="last") tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, exp_categories) exp = np.array([5.0, 2.0, 2.0, np.nan, np.nan]) res = cat.sort_values(ascending=False, na_position="last") tm.assert_numpy_array_equal(res.__array__(), exp) tm.assert_index_equal(res.categories, exp_categories) cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) res = cat.sort_values(ascending=False, na_position="last") exp_val = np.array(["d", "c", "b", "a", np.nan], dtype=object) exp_categories = Index(["a", "b", "c", "d"]) tm.assert_numpy_array_equal(res.__array__(), exp_val) tm.assert_index_equal(res.categories, exp_categories) cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) res = cat.sort_values(ascending=False, na_position="first") exp_val = np.array([np.nan, "d", "c", "b", "a"], dtype=object) exp_categories = Index(["a", "b", "c", "d"]) tm.assert_numpy_array_equal(res.__array__(), exp_val) tm.assert_index_equal(res.categories, exp_categories)

bsd-3-clause

printedheart/h2o-3

h2o-py/tests/testdir_algos/gbm/pyunit_bernoulliGBM.py

5

2569

import sys, os sys.path.insert(1, "../../../") import h2o, tests import numpy as np from sklearn import ensemble from sklearn.metrics import roc_auc_score def bernoulliGBM(): #Log.info("Importing prostate.csv data...\n") prostate_train = h2o.import_file(path=h2o.locate("smalldata/logreg/prostate_train.csv")) #Log.info("Converting CAPSULE and RACE columns to factors...\n") prostate_train["CAPSULE"] = prostate_train["CAPSULE"].asfactor() #Log.info("H2O Summary of prostate frame:\n") #prostate.summary() # Import prostate_train.csv as numpy array for scikit comparison trainData = np.loadtxt(h2o.locate("smalldata/logreg/prostate_train.csv"), delimiter=',', skiprows=1) trainDataResponse = trainData[:,0] trainDataFeatures = trainData[:,1:] ntrees = 100 learning_rate = 0.1 depth = 5 min_rows = 10 # Build H2O GBM classification model: #Log.info(paste("H2O GBM with parameters:\ndistribution = 'bernoulli', ntrees = ", ntrees, ", max_depth = 5, # min_rows = 10, learn_rate = 0.1\n", sep = "")) gbm_h2o = h2o.gbm(x=prostate_train[1:], y=prostate_train["CAPSULE"], ntrees=ntrees, learn_rate=learning_rate, max_depth=depth, min_rows=min_rows, distribution="bernoulli") # Build scikit GBM classification model #Log.info("scikit GBM with same parameters\n") gbm_sci = ensemble.GradientBoostingClassifier(learning_rate=learning_rate, n_estimators=ntrees, max_depth=depth, min_samples_leaf=min_rows, max_features=None) gbm_sci.fit(trainDataFeatures,trainDataResponse) #Log.info("Importing prostate_test.csv data...\n") prostate_test = h2o.import_file(path=h2o.locate("smalldata/logreg/prostate_test.csv")) #Log.info("Converting CAPSULE and RACE columns to factors...\n") prostate_test["CAPSULE"] = prostate_test["CAPSULE"].asfactor() # Import prostate_test.csv as numpy array for scikit comparison testData = np.loadtxt(h2o.locate("smalldata/logreg/prostate_test.csv"), delimiter=',', skiprows=1) testDataResponse = testData[:,0] testDataFeatures = testData[:,1:] # Score on the test data and compare results # scikit auc_sci = roc_auc_score(testDataResponse, gbm_sci.predict_proba(testDataFeatures)[:,1]) # h2o gbm_perf = gbm_h2o.model_performance(prostate_test) auc_h2o = gbm_perf.auc() #Log.info(paste("scikit AUC:", auc_sci, "\tH2O AUC:", auc_h2o)) assert auc_h2o >= auc_sci, "h2o (auc) performance degradation, with respect to scikit" if __name__ == "__main__": tests.run_test(sys.argv, bernoulliGBM)

apache-2.0

OSUrobotics/privacy-interfaces

filtering/probability_filters/scripts/test/test_xyz_to_rpy.py

1

2816

#!/usr/bin/env python # TEST PLATFORM for step #3 below. # 3) Convert <x, y, a> to <r, theta, yaw> # 4) Choose four corner poses (each containing one of r_min, r_max, yaw_min, yaw_max) # 5) Project our PolygonStamped onto those four poses # 6) (Run convex hull and then) plot the rectangle (polygon) import rospy import numpy import random from math import sqrt, atan2 from tf.transformations import quaternion_about_axis, quaternion_matrix from matplotlib import pyplot # Premise: object is at (0, 0) and robot has random (x, y, theta) centered at (0, 0, 0) if __name__ == '__main__': # Locate object obj = [0.0, 0.0, 0.0] # Locate robots dev = 5.0 robots_xyw = [] for i in range(1000): robot = [random.gauss(0.0, dev), random.gauss(0.0, dev), random.gauss(0.0, dev)] robots_xyw.append(robot) # Add robots for permuted min & max bounds x = [robot[0] for robot in robots_xyw] y = [robot[1] for robot in robots_xyw] w = [robot[2] for robot in robots_xyw] ranges = [[min(x), max(x)], [min(y), max(y)], [min(w), max(w)]] for x_i in ranges[0]: for y_i in ranges[1]: for w_i in ranges[2]: robots_xyw.append([x_i, y_i, w_i]) # Do conversions robots_rty = [] for robot in robots_xyw: # Calculate radius dx = obj[0] - robot[0] dy = obj[1] - robot[1] r = sqrt(dx**2 + dy**2) offset = [dx, dy, 0.0] # vector offset = [el / r for el in offset] # normalize # Calculate theta (angle of vector from object to robot) theta = atan2(-1 * offset[1], -1 * offset[0]) # positive is counter-clockwise # Calculate heading vector of robot q_robot = quaternion_about_axis(robot[2], (0, 0, 1)) R_robot = quaternion_matrix(q_robot) heading_robot = numpy.matrix([1, 0, 0, 1]) * numpy.matrix(R_robot).I heading_robot /= heading_robot[0, 3] # ensure homogeneity isn't messing stuff up heading_robot = heading_robot[0, 0:3].tolist()[0] # convert from homogeneous to...not # Calculate camera yaw (angle from gaze vector to object line-of-sight vector) cosine = numpy.dot(heading_robot, offset) cross = numpy.cross(heading_robot, offset) sine = cross[2] yaw = -1 * atan2(sine, cosine) # positive is counter-clockwise robots_rty.append([r, theta, yaw]) robots_xywrty = [xyz + rty for xyz, rty in zip(robots_xyw, robots_rty)] radii = [robot[3] for robot in robots_xywrty] yaws = [robot[5] for robot in robots_xywrty] for robot in robots_xywrty: print robot print min(radii), max(radii) print min(yaws), max(yaws) # RESULT: extremes in XYW do *not* yield the extremes in RTY

mit

HIPS/pgmult

setup.py

2

1189

from distutils.core import setup import numpy as np from Cython.Build import cythonize setup( name='pgmult', version='0.1', description= "Learning and inference for models with multinomial observations and " "underlying Gaussian correlation structure. Examples include correlated " "topic model, multinomial linear dynamical systems, and multinomial " "Gaussian processes. ", author='Scott W. Linderman and Matthew James Johnson', author_email='[email protected], [email protected]', license="MIT", url='https://github.com/HIPS/pgmult', packages=['pgmult'], install_requires=[ 'Cython >= 0.20.1', 'numpy', 'scipy', 'matplotlib', 'pybasicbayes', 'pypolyagamma', 'gslrandom', 'pylds'], ext_modules=cythonize('pgmult/**/*.pyx'), include_dirs=[np.get_include(),], classifiers=[ 'Intended Audience :: Science/Research', 'Programming Language :: Python', 'Programming Language :: C++', ], keywords=[ 'multinomial', 'polya', 'gamma', 'correlated topic model', 'ctm', 'lds', 'linear dynamical system', 'gaussian process', 'gp'], platforms="ALL" )

mit

tkaitchuck/nupic

external/darwin64/lib/python2.6/site-packages/matplotlib/fontconfig_pattern.py

72

6429

""" A module for parsing and generating fontconfig patterns. See the `fontconfig pattern specification <http://www.fontconfig.org/fontconfig-user.html>`_ for more information. """ # Author : Michael Droettboom <[email protected]> # License : matplotlib license (PSF compatible) # This class is defined here because it must be available in: # - The old-style config framework (:file:`rcsetup.py`) # - The traits-based config framework (:file:`mpltraits.py`) # - The font manager (:file:`font_manager.py`) # It probably logically belongs in :file:`font_manager.py`, but # placing it in any of these places would have created cyclical # dependency problems, or an undesired dependency on traits even # when the traits-based config framework is not used. import re from matplotlib.pyparsing import Literal, ZeroOrMore, \ Optional, Regex, StringEnd, ParseException, Suppress family_punc = r'\\\-:,' family_unescape = re.compile(r'\\([%s])' % family_punc).sub family_escape = re.compile(r'([%s])' % family_punc).sub value_punc = r'\\=_:,' value_unescape = re.compile(r'\\([%s])' % value_punc).sub value_escape = re.compile(r'([%s])' % value_punc).sub class FontconfigPatternParser: """A simple pyparsing-based parser for fontconfig-style patterns. See the `fontconfig pattern specification <http://www.fontconfig.org/fontconfig-user.html>`_ for more information. """ _constants = { 'thin' : ('weight', 'light'), 'extralight' : ('weight', 'light'), 'ultralight' : ('weight', 'light'), 'light' : ('weight', 'light'), 'book' : ('weight', 'book'), 'regular' : ('weight', 'regular'), 'normal' : ('weight', 'normal'), 'medium' : ('weight', 'medium'), 'demibold' : ('weight', 'demibold'), 'semibold' : ('weight', 'semibold'), 'bold' : ('weight', 'bold'), 'extrabold' : ('weight', 'extra bold'), 'black' : ('weight', 'black'), 'heavy' : ('weight', 'heavy'), 'roman' : ('slant', 'normal'), 'italic' : ('slant', 'italic'), 'oblique' : ('slant', 'oblique'), 'ultracondensed' : ('width', 'ultra-condensed'), 'extracondensed' : ('width', 'extra-condensed'), 'condensed' : ('width', 'condensed'), 'semicondensed' : ('width', 'semi-condensed'), 'expanded' : ('width', 'expanded'), 'extraexpanded' : ('width', 'extra-expanded'), 'ultraexpanded' : ('width', 'ultra-expanded') } def __init__(self): family = Regex(r'([^%s]|(\\[%s]))*' % (family_punc, family_punc)) \ .setParseAction(self._family) size = Regex(r"([0-9]+\.?[0-9]*|\.[0-9]+)") \ .setParseAction(self._size) name = Regex(r'[a-z]+') \ .setParseAction(self._name) value = Regex(r'([^%s]|(\\[%s]))*' % (value_punc, value_punc)) \ .setParseAction(self._value) families =(family + ZeroOrMore( Literal(',') + family) ).setParseAction(self._families) point_sizes =(size + ZeroOrMore( Literal(',') + size) ).setParseAction(self._point_sizes) property =( (name + Suppress(Literal('=')) + value + ZeroOrMore( Suppress(Literal(',')) + value) ) | name ).setParseAction(self._property) pattern =(Optional( families) + Optional( Literal('-') + point_sizes) + ZeroOrMore( Literal(':') + property) + StringEnd() ) self._parser = pattern self.ParseException = ParseException def parse(self, pattern): """ Parse the given fontconfig *pattern* and return a dictionary of key/value pairs useful for initializing a :class:`font_manager.FontProperties` object. """ props = self._properties = {} try: self._parser.parseString(pattern) except self.ParseException, e: raise ValueError("Could not parse font string: '%s'\n%s" % (pattern, e)) self._properties = None return props def _family(self, s, loc, tokens): return [family_unescape(r'\1', str(tokens[0]))] def _size(self, s, loc, tokens): return [float(tokens[0])] def _name(self, s, loc, tokens): return [str(tokens[0])] def _value(self, s, loc, tokens): return [value_unescape(r'\1', str(tokens[0]))] def _families(self, s, loc, tokens): self._properties['family'] = [str(x) for x in tokens] return [] def _point_sizes(self, s, loc, tokens): self._properties['size'] = [str(x) for x in tokens] return [] def _property(self, s, loc, tokens): if len(tokens) == 1: if tokens[0] in self._constants: key, val = self._constants[tokens[0]] self._properties.setdefault(key, []).append(val) else: key = tokens[0] val = tokens[1:] self._properties.setdefault(key, []).extend(val) return [] parse_fontconfig_pattern = FontconfigPatternParser().parse def generate_fontconfig_pattern(d): """ Given a dictionary of key/value pairs, generates a fontconfig pattern string. """ props = [] families = '' size = '' for key in 'family style variant weight stretch file size'.split(): val = getattr(d, 'get_' + key)() if val is not None and val != []: if type(val) == list: val = [value_escape(r'\\\1', str(x)) for x in val if x is not None] if val != []: val = ','.join(val) props.append(":%s=%s" % (key, val)) return ''.join(props)

gpl-3.0

aewhatley/scikit-learn

sklearn/decomposition/tests/test_incremental_pca.py

297

8265

"""Tests for Incremental PCA.""" import numpy as np from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_raises from sklearn import datasets from sklearn.decomposition import PCA, IncrementalPCA iris = datasets.load_iris() def test_incremental_pca(): # Incremental PCA on dense arrays. X = iris.data batch_size = X.shape[0] // 3 ipca = IncrementalPCA(n_components=2, batch_size=batch_size) pca = PCA(n_components=2) pca.fit_transform(X) X_transformed = ipca.fit_transform(X) np.testing.assert_equal(X_transformed.shape, (X.shape[0], 2)) assert_almost_equal(ipca.explained_variance_ratio_.sum(), pca.explained_variance_ratio_.sum(), 1) for n_components in [1, 2, X.shape[1]]: ipca = IncrementalPCA(n_components, batch_size=batch_size) ipca.fit(X) cov = ipca.get_covariance() precision = ipca.get_precision() assert_array_almost_equal(np.dot(cov, precision), np.eye(X.shape[1])) def test_incremental_pca_check_projection(): # Test that the projection of data is correct. rng = np.random.RandomState(1999) n, p = 100, 3 X = rng.randn(n, p) * .1 X[:10] += np.array([3, 4, 5]) Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5]) # Get the reconstruction of the generated data X # Note that Xt has the same "components" as X, just separated # This is what we want to ensure is recreated correctly Yt = IncrementalPCA(n_components=2).fit(X).transform(Xt) # Normalize Yt /= np.sqrt((Yt ** 2).sum()) # Make sure that the first element of Yt is ~1, this means # the reconstruction worked as expected assert_almost_equal(np.abs(Yt[0][0]), 1., 1) def test_incremental_pca_inverse(): # Test that the projection of data can be inverted. rng = np.random.RandomState(1999) n, p = 50, 3 X = rng.randn(n, p) # spherical data X[:, 1] *= .00001 # make middle component relatively small X += [5, 4, 3] # make a large mean # same check that we can find the original data from the transformed # signal (since the data is almost of rank n_components) ipca = IncrementalPCA(n_components=2, batch_size=10).fit(X) Y = ipca.transform(X) Y_inverse = ipca.inverse_transform(Y) assert_almost_equal(X, Y_inverse, decimal=3) def test_incremental_pca_validation(): # Test that n_components is >=1 and <= n_features. X = [[0, 1], [1, 0]] for n_components in [-1, 0, .99, 3]: assert_raises(ValueError, IncrementalPCA(n_components, batch_size=10).fit, X) def test_incremental_pca_set_params(): # Test that components_ sign is stable over batch sizes. rng = np.random.RandomState(1999) n_samples = 100 n_features = 20 X = rng.randn(n_samples, n_features) X2 = rng.randn(n_samples, n_features) X3 = rng.randn(n_samples, n_features) ipca = IncrementalPCA(n_components=20) ipca.fit(X) # Decreasing number of components ipca.set_params(n_components=10) assert_raises(ValueError, ipca.partial_fit, X2) # Increasing number of components ipca.set_params(n_components=15) assert_raises(ValueError, ipca.partial_fit, X3) # Returning to original setting ipca.set_params(n_components=20) ipca.partial_fit(X) def test_incremental_pca_num_features_change(): # Test that changing n_components will raise an error. rng = np.random.RandomState(1999) n_samples = 100 X = rng.randn(n_samples, 20) X2 = rng.randn(n_samples, 50) ipca = IncrementalPCA(n_components=None) ipca.fit(X) assert_raises(ValueError, ipca.partial_fit, X2) def test_incremental_pca_batch_signs(): # Test that components_ sign is stable over batch sizes. rng = np.random.RandomState(1999) n_samples = 100 n_features = 3 X = rng.randn(n_samples, n_features) all_components = [] batch_sizes = np.arange(10, 20) for batch_size in batch_sizes: ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X) all_components.append(ipca.components_) for i, j in zip(all_components[:-1], all_components[1:]): assert_almost_equal(np.sign(i), np.sign(j), decimal=6) def test_incremental_pca_batch_values(): # Test that components_ values are stable over batch sizes. rng = np.random.RandomState(1999) n_samples = 100 n_features = 3 X = rng.randn(n_samples, n_features) all_components = [] batch_sizes = np.arange(20, 40, 3) for batch_size in batch_sizes: ipca = IncrementalPCA(n_components=None, batch_size=batch_size).fit(X) all_components.append(ipca.components_) for i, j in zip(all_components[:-1], all_components[1:]): assert_almost_equal(i, j, decimal=1) def test_incremental_pca_partial_fit(): # Test that fit and partial_fit get equivalent results. rng = np.random.RandomState(1999) n, p = 50, 3 X = rng.randn(n, p) # spherical data X[:, 1] *= .00001 # make middle component relatively small X += [5, 4, 3] # make a large mean # same check that we can find the original data from the transformed # signal (since the data is almost of rank n_components) batch_size = 10 ipca = IncrementalPCA(n_components=2, batch_size=batch_size).fit(X) pipca = IncrementalPCA(n_components=2, batch_size=batch_size) # Add one to make sure endpoint is included batch_itr = np.arange(0, n + 1, batch_size) for i, j in zip(batch_itr[:-1], batch_itr[1:]): pipca.partial_fit(X[i:j, :]) assert_almost_equal(ipca.components_, pipca.components_, decimal=3) def test_incremental_pca_against_pca_iris(): # Test that IncrementalPCA and PCA are approximate (to a sign flip). X = iris.data Y_pca = PCA(n_components=2).fit_transform(X) Y_ipca = IncrementalPCA(n_components=2, batch_size=25).fit_transform(X) assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1) def test_incremental_pca_against_pca_random_data(): # Test that IncrementalPCA and PCA are approximate (to a sign flip). rng = np.random.RandomState(1999) n_samples = 100 n_features = 3 X = rng.randn(n_samples, n_features) + 5 * rng.rand(1, n_features) Y_pca = PCA(n_components=3).fit_transform(X) Y_ipca = IncrementalPCA(n_components=3, batch_size=25).fit_transform(X) assert_almost_equal(np.abs(Y_pca), np.abs(Y_ipca), 1) def test_explained_variances(): # Test that PCA and IncrementalPCA calculations match X = datasets.make_low_rank_matrix(1000, 100, tail_strength=0., effective_rank=10, random_state=1999) prec = 3 n_samples, n_features = X.shape for nc in [None, 99]: pca = PCA(n_components=nc).fit(X) ipca = IncrementalPCA(n_components=nc, batch_size=100).fit(X) assert_almost_equal(pca.explained_variance_, ipca.explained_variance_, decimal=prec) assert_almost_equal(pca.explained_variance_ratio_, ipca.explained_variance_ratio_, decimal=prec) assert_almost_equal(pca.noise_variance_, ipca.noise_variance_, decimal=prec) def test_whitening(): # Test that PCA and IncrementalPCA transforms match to sign flip. X = datasets.make_low_rank_matrix(1000, 10, tail_strength=0., effective_rank=2, random_state=1999) prec = 3 n_samples, n_features = X.shape for nc in [None, 9]: pca = PCA(whiten=True, n_components=nc).fit(X) ipca = IncrementalPCA(whiten=True, n_components=nc, batch_size=250).fit(X) Xt_pca = pca.transform(X) Xt_ipca = ipca.transform(X) assert_almost_equal(np.abs(Xt_pca), np.abs(Xt_ipca), decimal=prec) Xinv_ipca = ipca.inverse_transform(Xt_ipca) Xinv_pca = pca.inverse_transform(Xt_pca) assert_almost_equal(X, Xinv_ipca, decimal=prec) assert_almost_equal(X, Xinv_pca, decimal=prec) assert_almost_equal(Xinv_pca, Xinv_ipca, decimal=prec)

bsd-3-clause

michaelpacer/networkx

examples/drawing/atlas.py

54

2609

#!/usr/bin/env python """ Atlas of all graphs of 6 nodes or less. """ __author__ = """Aric Hagberg ([email protected])""" # Copyright (C) 2004 by # Aric Hagberg <[email protected]> # Dan Schult <[email protected]> # Pieter Swart <[email protected]> # All rights reserved. # BSD license. import networkx as nx from networkx.generators.atlas import * from networkx.algorithms.isomorphism.isomorph import graph_could_be_isomorphic as isomorphic import random def atlas6(): """ Return the atlas of all connected graphs of 6 nodes or less. Attempt to check for isomorphisms and remove. """ Atlas=graph_atlas_g()[0:208] # 208 # remove isolated nodes, only connected graphs are left U=nx.Graph() # graph for union of all graphs in atlas for G in Atlas: zerodegree=[n for n in G if G.degree(n)==0] for n in zerodegree: G.remove_node(n) U=nx.disjoint_union(U,G) # list of graphs of all connected components C=nx.connected_component_subgraphs(U) UU=nx.Graph() # do quick isomorphic-like check, not a true isomorphism checker nlist=[] # list of nonisomorphic graphs for G in C: # check against all nonisomorphic graphs so far if not iso(G,nlist): nlist.append(G) UU=nx.disjoint_union(UU,G) # union the nonisomorphic graphs return UU def iso(G1, glist): """Quick and dirty nonisomorphism checker used to check isomorphisms.""" for G2 in glist: if isomorphic(G1,G2): return True return False if __name__ == '__main__': import networkx as nx G=atlas6() print("graph has %d nodes with %d edges"\ %(nx.number_of_nodes(G),nx.number_of_edges(G))) print(nx.number_connected_components(G),"connected components") try: from networkx import graphviz_layout except ImportError: raise ImportError("This example needs Graphviz and either PyGraphviz or Pydot") import matplotlib.pyplot as plt plt.figure(1,figsize=(8,8)) # layout graphs with positions using graphviz neato pos=nx.graphviz_layout(G,prog="neato") # color nodes the same in each connected subgraph C=nx.connected_component_subgraphs(G) for g in C: c=[random.random()]*nx.number_of_nodes(g) # random color... nx.draw(g, pos, node_size=40, node_color=c, vmin=0.0, vmax=1.0, with_labels=False ) plt.savefig("atlas.png",dpi=75)

bsd-3-clause

SeonghoBaek/RealtimeCamera

openface/training/plot-loss.py

8

3032

#!/usr/bin/env python3 # # Copyright 2015-2016 Carnegie Mellon University # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import matplotlib as mpl mpl.use('Agg') import matplotlib.pyplot as plt plt.style.use('bmh') import pandas as pd import os import sys scriptDir = os.path.dirname(os.path.realpath(__file__)) plotDir = os.path.join(scriptDir, 'plots') # workDir = os.path.join(scriptDir, 'work') def plot(workDirs): trainDfs = [] testDfs = [] for d in workDirs: trainF = os.path.join(d, 'train.log') testF = os.path.join(d, 'test.log') trainDfs.append(pd.read_csv(trainF, sep='\t')) testDfs.append(pd.read_csv(testF, sep='\t')) if len(trainDfs[-1]) != len(testDfs[-1]): print("Error: Train/test dataframe shapes " "for '{}' don't match: {}, {}".format( d, trainDfs[-1].shape, testDfs[-1].shape)) sys.exit(-1) trainDf = pd.concat(trainDfs, ignore_index=True) testDf = pd.concat(testDfs, ignore_index=True) # print("train, test:") # print("\n".join(["{:0.2e}, {:0.2e}".format(x, y) for (x, y) in # zip(trainDf['avg triplet loss (train set)'].values[-5:], # testDf['avg triplet loss (test set)'].values[-5:])])) fig, ax = plt.subplots(1, 1) trainDf.index += 1 trainDf['avg triplet loss (train set)'].plot(ax=ax) plt.xlabel("Epoch") plt.ylabel("Average Triplet Loss, Training") plt.ylim(ymin=0) # plt.xlim(xmin=1) plt.grid(b=True, which='major', color='k', linestyle='-') plt.grid(b=True, which='minor', color='k', linestyle='-', alpha=0.2) plt.minorticks_on() # ax.set_yscale('log') d = os.path.join(plotDir, "train-loss.pdf") fig.savefig(d) print("Created {}".format(d)) fig, ax = plt.subplots(1, 1) testDf.index += 1 testDf['lfwAcc'].plot(ax=ax) plt.xlabel("Epoch") plt.ylabel("LFW Accuracy") plt.ylim(ymin=0, ymax=1) plt.grid(b=True, which='major', color='k', linestyle='-') plt.grid(b=True, which='minor', color='k', linestyle='-', alpha=0.2) plt.minorticks_on() # plt.xlim(xmin=1) # ax.set_yscale('log') d = os.path.join(plotDir, "lfw-accuracy.pdf") fig.savefig(d) print("Created {}".format(d)) if __name__ == '__main__': os.makedirs(plotDir, exist_ok=True) parser = argparse.ArgumentParser() parser.add_argument('workDirs', type=str, nargs='+') args = parser.parse_args() plot(args.workDirs)

apache-2.0

jereze/scikit-learn

sklearn/datasets/svmlight_format.py

79

15976

"""This module implements a loader and dumper for the svmlight format This format is a text-based format, with one sample per line. It does not store zero valued features hence is suitable for sparse dataset. The first element of each line can be used to store a target variable to predict. This format is used as the default format for both svmlight and the libsvm command line programs. """ # Authors: Mathieu Blondel <[email protected]> # Lars Buitinck <[email protected]> # Olivier Grisel <[email protected]> # License: BSD 3 clause from contextlib import closing import io import os.path import numpy as np import scipy.sparse as sp from ._svmlight_format import _load_svmlight_file from .. import __version__ from ..externals import six from ..externals.six import u, b from ..externals.six.moves import range, zip from ..utils import check_array from ..utils.fixes import frombuffer_empty def load_svmlight_file(f, n_features=None, dtype=np.float64, multilabel=False, zero_based="auto", query_id=False): """Load datasets in the svmlight / libsvm format into sparse CSR matrix This format is a text-based format, with one sample per line. It does not store zero valued features hence is suitable for sparse dataset. The first element of each line can be used to store a target variable to predict. This format is used as the default format for both svmlight and the libsvm command line programs. Parsing a text based source can be expensive. When working on repeatedly on the same dataset, it is recommended to wrap this loader with joblib.Memory.cache to store a memmapped backup of the CSR results of the first call and benefit from the near instantaneous loading of memmapped structures for the subsequent calls. In case the file contains a pairwise preference constraint (known as "qid" in the svmlight format) these are ignored unless the query_id parameter is set to True. These pairwise preference constraints can be used to constraint the combination of samples when using pairwise loss functions (as is the case in some learning to rank problems) so that only pairs with the same query_id value are considered. This implementation is written in Cython and is reasonably fast. However, a faster API-compatible loader is also available at: https://github.com/mblondel/svmlight-loader Parameters ---------- f : {str, file-like, int} (Path to) a file to load. If a path ends in ".gz" or ".bz2", it will be uncompressed on the fly. If an integer is passed, it is assumed to be a file descriptor. A file-like or file descriptor will not be closed by this function. A file-like object must be opened in binary mode. n_features : int or None The number of features to use. If None, it will be inferred. This argument is useful to load several files that are subsets of a bigger sliced dataset: each subset might not have examples of every feature, hence the inferred shape might vary from one slice to another. multilabel : boolean, optional, default False Samples may have several labels each (see http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html) zero_based : boolean or "auto", optional, default "auto" Whether column indices in f are zero-based (True) or one-based (False). If column indices are one-based, they are transformed to zero-based to match Python/NumPy conventions. If set to "auto", a heuristic check is applied to determine this from the file contents. Both kinds of files occur "in the wild", but they are unfortunately not self-identifying. Using "auto" or True should always be safe. query_id : boolean, default False If True, will return the query_id array for each file. dtype : numpy data type, default np.float64 Data type of dataset to be loaded. This will be the data type of the output numpy arrays ``X`` and ``y``. Returns ------- X: scipy.sparse matrix of shape (n_samples, n_features) y: ndarray of shape (n_samples,), or, in the multilabel a list of tuples of length n_samples. query_id: array of shape (n_samples,) query_id for each sample. Only returned when query_id is set to True. See also -------- load_svmlight_files: similar function for loading multiple files in this format, enforcing the same number of features/columns on all of them. Examples -------- To use joblib.Memory to cache the svmlight file:: from sklearn.externals.joblib import Memory from sklearn.datasets import load_svmlight_file mem = Memory("./mycache") @mem.cache def get_data(): data = load_svmlight_file("mysvmlightfile") return data[0], data[1] X, y = get_data() """ return tuple(load_svmlight_files([f], n_features, dtype, multilabel, zero_based, query_id)) def _gen_open(f): if isinstance(f, int): # file descriptor return io.open(f, "rb", closefd=False) elif not isinstance(f, six.string_types): raise TypeError("expected {str, int, file-like}, got %s" % type(f)) _, ext = os.path.splitext(f) if ext == ".gz": import gzip return gzip.open(f, "rb") elif ext == ".bz2": from bz2 import BZ2File return BZ2File(f, "rb") else: return open(f, "rb") def _open_and_load(f, dtype, multilabel, zero_based, query_id): if hasattr(f, "read"): actual_dtype, data, ind, indptr, labels, query = \ _load_svmlight_file(f, dtype, multilabel, zero_based, query_id) # XXX remove closing when Python 2.7+/3.1+ required else: with closing(_gen_open(f)) as f: actual_dtype, data, ind, indptr, labels, query = \ _load_svmlight_file(f, dtype, multilabel, zero_based, query_id) # convert from array.array, give data the right dtype if not multilabel: labels = frombuffer_empty(labels, np.float64) data = frombuffer_empty(data, actual_dtype) indices = frombuffer_empty(ind, np.intc) indptr = np.frombuffer(indptr, dtype=np.intc) # never empty query = frombuffer_empty(query, np.intc) data = np.asarray(data, dtype=dtype) # no-op for float{32,64} return data, indices, indptr, labels, query def load_svmlight_files(files, n_features=None, dtype=np.float64, multilabel=False, zero_based="auto", query_id=False): """Load dataset from multiple files in SVMlight format This function is equivalent to mapping load_svmlight_file over a list of files, except that the results are concatenated into a single, flat list and the samples vectors are constrained to all have the same number of features. In case the file contains a pairwise preference constraint (known as "qid" in the svmlight format) these are ignored unless the query_id parameter is set to True. These pairwise preference constraints can be used to constraint the combination of samples when using pairwise loss functions (as is the case in some learning to rank problems) so that only pairs with the same query_id value are considered. Parameters ---------- files : iterable over {str, file-like, int} (Paths of) files to load. If a path ends in ".gz" or ".bz2", it will be uncompressed on the fly. If an integer is passed, it is assumed to be a file descriptor. File-likes and file descriptors will not be closed by this function. File-like objects must be opened in binary mode. n_features: int or None The number of features to use. If None, it will be inferred from the maximum column index occurring in any of the files. This can be set to a higher value than the actual number of features in any of the input files, but setting it to a lower value will cause an exception to be raised. multilabel: boolean, optional Samples may have several labels each (see http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html) zero_based: boolean or "auto", optional Whether column indices in f are zero-based (True) or one-based (False). If column indices are one-based, they are transformed to zero-based to match Python/NumPy conventions. If set to "auto", a heuristic check is applied to determine this from the file contents. Both kinds of files occur "in the wild", but they are unfortunately not self-identifying. Using "auto" or True should always be safe. query_id: boolean, defaults to False If True, will return the query_id array for each file. dtype : numpy data type, default np.float64 Data type of dataset to be loaded. This will be the data type of the output numpy arrays ``X`` and ``y``. Returns ------- [X1, y1, ..., Xn, yn] where each (Xi, yi) pair is the result from load_svmlight_file(files[i]). If query_id is set to True, this will return instead [X1, y1, q1, ..., Xn, yn, qn] where (Xi, yi, qi) is the result from load_svmlight_file(files[i]) Notes ----- When fitting a model to a matrix X_train and evaluating it against a matrix X_test, it is essential that X_train and X_test have the same number of features (X_train.shape[1] == X_test.shape[1]). This may not be the case if you load the files individually with load_svmlight_file. See also -------- load_svmlight_file """ r = [_open_and_load(f, dtype, multilabel, bool(zero_based), bool(query_id)) for f in files] if (zero_based is False or zero_based == "auto" and all(np.min(tmp[1]) > 0 for tmp in r)): for ind in r: indices = ind[1] indices -= 1 n_f = max(ind[1].max() for ind in r) + 1 if n_features is None: n_features = n_f elif n_features < n_f: raise ValueError("n_features was set to {}," " but input file contains {} features" .format(n_features, n_f)) result = [] for data, indices, indptr, y, query_values in r: shape = (indptr.shape[0] - 1, n_features) X = sp.csr_matrix((data, indices, indptr), shape) X.sort_indices() result += X, y if query_id: result.append(query_values) return result def _dump_svmlight(X, y, f, multilabel, one_based, comment, query_id): is_sp = int(hasattr(X, "tocsr")) if X.dtype.kind == 'i': value_pattern = u("%d:%d") else: value_pattern = u("%d:%.16g") if y.dtype.kind == 'i': label_pattern = u("%d") else: label_pattern = u("%.16g") line_pattern = u("%s") if query_id is not None: line_pattern += u(" qid:%d") line_pattern += u(" %s\n") if comment: f.write(b("# Generated by dump_svmlight_file from scikit-learn %s\n" % __version__)) f.write(b("# Column indices are %s-based\n" % ["zero", "one"][one_based])) f.write(b("#\n")) f.writelines(b("# %s\n" % line) for line in comment.splitlines()) for i in range(X.shape[0]): if is_sp: span = slice(X.indptr[i], X.indptr[i + 1]) row = zip(X.indices[span], X.data[span]) else: nz = X[i] != 0 row = zip(np.where(nz)[0], X[i, nz]) s = " ".join(value_pattern % (j + one_based, x) for j, x in row) if multilabel: nz_labels = np.where(y[i] != 0)[0] labels_str = ",".join(label_pattern % j for j in nz_labels) else: labels_str = label_pattern % y[i] if query_id is not None: feat = (labels_str, query_id[i], s) else: feat = (labels_str, s) f.write((line_pattern % feat).encode('ascii')) def dump_svmlight_file(X, y, f, zero_based=True, comment=None, query_id=None, multilabel=False): """Dump the dataset in svmlight / libsvm file format. This format is a text-based format, with one sample per line. It does not store zero valued features hence is suitable for sparse dataset. The first element of each line can be used to store a target variable to predict. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_labels] Target values. Class labels must be an integer or float, or array-like objects of integer or float for multilabel classifications. f : string or file-like in binary mode If string, specifies the path that will contain the data. If file-like, data will be written to f. f should be opened in binary mode. zero_based : boolean, optional Whether column indices should be written zero-based (True) or one-based (False). comment : string, optional Comment to insert at the top of the file. This should be either a Unicode string, which will be encoded as UTF-8, or an ASCII byte string. If a comment is given, then it will be preceded by one that identifies the file as having been dumped by scikit-learn. Note that not all tools grok comments in SVMlight files. query_id : array-like, shape = [n_samples] Array containing pairwise preference constraints (qid in svmlight format). multilabel: boolean, optional Samples may have several labels each (see http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html) """ if comment is not None: # Convert comment string to list of lines in UTF-8. # If a byte string is passed, then check whether it's ASCII; # if a user wants to get fancy, they'll have to decode themselves. # Avoid mention of str and unicode types for Python 3.x compat. if isinstance(comment, bytes): comment.decode("ascii") # just for the exception else: comment = comment.encode("utf-8") if six.b("\0") in comment: raise ValueError("comment string contains NUL byte") y = np.asarray(y) if y.ndim != 1 and not multilabel: raise ValueError("expected y of shape (n_samples,), got %r" % (y.shape,)) Xval = check_array(X, accept_sparse='csr') if Xval.shape[0] != y.shape[0]: raise ValueError("X.shape[0] and y.shape[0] should be the same, got" " %r and %r instead." % (Xval.shape[0], y.shape[0])) # We had some issues with CSR matrices with unsorted indices (e.g. #1501), # so sort them here, but first make sure we don't modify the user's X. # TODO We can do this cheaper; sorted_indices copies the whole matrix. if Xval is X and hasattr(Xval, "sorted_indices"): X = Xval.sorted_indices() else: X = Xval if hasattr(X, "sort_indices"): X.sort_indices() if query_id is not None: query_id = np.asarray(query_id) if query_id.shape[0] != y.shape[0]: raise ValueError("expected query_id of shape (n_samples,), got %r" % (query_id.shape,)) one_based = not zero_based if hasattr(f, "write"): _dump_svmlight(X, y, f, multilabel, one_based, comment, query_id) else: with open(f, "wb") as f: _dump_svmlight(X, y, f, multilabel, one_based, comment, query_id)

bsd-3-clause

tosolveit/scikit-learn

examples/ensemble/plot_adaboost_regression.py

311

1529

""" ====================================== Decision Tree Regression with AdaBoost ====================================== A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D sinusoidal dataset with a small amount of Gaussian noise. 299 boosts (300 decision trees) is compared with a single decision tree regressor. As the number of boosts is increased the regressor can fit more detail. .. [1] H. Drucker, "Improving Regressors using Boosting Techniques", 1997. """ print(__doc__) # Author: Noel Dawe <[email protected]> # # License: BSD 3 clause # importing necessary libraries import numpy as np import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import AdaBoostRegressor # Create the dataset rng = np.random.RandomState(1) X = np.linspace(0, 6, 100)[:, np.newaxis] y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0]) # Fit regression model regr_1 = DecisionTreeRegressor(max_depth=4) regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4), n_estimators=300, random_state=rng) regr_1.fit(X, y) regr_2.fit(X, y) # Predict y_1 = regr_1.predict(X) y_2 = regr_2.predict(X) # Plot the results plt.figure() plt.scatter(X, y, c="k", label="training samples") plt.plot(X, y_1, c="g", label="n_estimators=1", linewidth=2) plt.plot(X, y_2, c="r", label="n_estimators=300", linewidth=2) plt.xlabel("data") plt.ylabel("target") plt.title("Boosted Decision Tree Regression") plt.legend() plt.show()

bsd-3-clause

vishalpant/Sentiment-analysis-of-streaming-tweets

test.py

1

1528

import load import os import Sentiment import Filter from sklearn.metrics import classification_report from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix def evaluate_model(target_true,target_predicted): print("The confusion matrix is as follows:") print(confusion_matrix(target_true,target_predicted)) print("The classification report is as follows:") print(classification_report(target_true,target_predicted)) print("The accuracy score is {:.2%}".format(accuracy_score(target_true,target_predicted))) if __name__ == "__main__": if not os.path.isfile("sentiments.pickle") or not os.path.isfile("tweets.pickle"): datafile = str(input("Enter path of training data(Should be in .csv):")) # replace 4th parameter with the column number of your tweets # replace 5th parameter with the column number of your sentiments data, target = load.load_data(datafile, ",", '"', 5, 0) load.save_sentiments(target) load.save_tweets(data) else: data = load.load_tweets() target = load.load_sentiments() tf_idf = Filter.filter(data) #test data is taken to be 40% of total data and remaining 60% is training data data_train, data_test, target_train, target_test = Sentiment.data_generate(tf_idf, 0.4, target) classifier = Sentiment.learn(data_train, target_train) prediction = Sentiment.predict(data_test, classifier) evaluate_model(target_test, prediction)

mit

james4424/nest-simulator

topology/pynest/hl_api.py

4

68298

# -*- coding: utf-8 -*- # # hl_api.py # # This file is part of NEST. # # Copyright (C) 2004 The NEST Initiative # # NEST is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 2 of the License, or # (at your option) any later version. # # NEST is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with NEST. If not, see <http://www.gnu.org/licenses/>. """ **High-level API of PyNEST Topology Module** This file defines the user-level functions of NEST's Python interface to the Topology module. The basic approach is the same as for the PyNEST interface to NEST: 1. Function names are the same as in SLI. 2. Nodes are identified by their GIDs. 3. GIDs are always given as tuples or lists of integer(s). 4. Commands returning GIDs return them as tuples. 5. Other arguments can be * single items that are applied to all entries in a GID list * a list of the same length as the given list of GID(s) where each item is matched with the pertaining GID. **Example** :: layers = CreateLayer(({...}, {...}, {...})) creates three layers and returns a tuple of three GIDs. :: ConnectLayers(layers[:2], layers[1:], {...}) connects `layers[0]` to `layers[1]` and `layers[1]` to `layers[2]` \ using the same dictionary to specify both connections. :: ConnectLayers(layers[:2], layers[1:], ({...}, {...})) connects the same layers, but the `layers[0]` to `layers[1]` connection is specified by the first dictionary, the `layers[1]` to `layers[2]` connection by the second. :Authors: Kittel Austvoll, Hans Ekkehard Plesser, Hakon Enger """ import nest def topology_func(slifunc, *args): """ Execute SLI function `slifunc` with arguments `args` in Topology namespace. Parameters ---------- slifunc : str SLI namespace expression Other parameters ---------------- args : dict An arbitrary number of arguments Returns ------- out : Values from SLI function `slifunc` See also -------- nest.sli_func """ return nest.sli_func(slifunc, *args) class Mask(object): """ Class for spatial masks. Masks are used when creating connections in the Topology module. A mask describes which area of the pool layer shall be searched for nodes to connect for any given node in the driver layer. Masks are created using the ``CreateMask`` command. """ _datum = None # The constructor should not be called by the user def __init__(self, datum): """Masks must be created using the CreateMask command.""" if not isinstance(datum, nest.SLIDatum) or datum.dtype != "masktype": raise TypeError("expected mask Datum") self._datum = datum # Generic binary operation def _binop(self, op, other): if not isinstance(other, Mask): return NotImplemented return Mask(topology_func(op, self._datum, other._datum)) def __or__(self, other): return self._binop("or", other) def __and__(self, other): return self._binop("and", other) def __sub__(self, other): return self._binop("sub", other) def Inside(self, point): """ Test if a point is inside a mask. Parameters ---------- point : tuple/list of float values Coordinate of point Returns ------- out : bool True if the point is inside the mask, False otherwise """ return topology_func("Inside", point, self._datum) def CreateMask(masktype, specs, anchor=None): """ Create a spatial mask for connections. Masks are used when creating connections in the Topology module. A mask describes the area of the pool layer that is searched for nodes to connect for any given node in the driver layer. Several mask types are available. Examples are the grid region, the rectangular, circular or doughnut region. The command ``CreateMask`` creates a Mask object which may be combined with other ``Mask`` objects using Boolean operators. The mask is specified in a dictionary. ``Mask`` objects can be passed to ``ConnectLayers`` in a connection dictionary with the key `'mask'`. Parameters ---------- masktype : str, ['rectangular' | 'circular' | 'doughnut'] for 2D masks, \ ['box' | 'spherical'] for 3D masks, ['grid'] only for grid-based layers in 2D The mask name corresponds to the geometrical shape of the mask. There are different types for 2- and 3-dimensional layers. specs : dict Dictionary specifying the parameters of the provided `masktype`, see **Notes**. anchor : [tuple/list of floats | dict with the keys `'column'` and \ `'row'` (for grid masks only)], optional, default: None By providing anchor coordinates, the location of the mask relative to the driver node can be changed. The list of coordinates has a length of 2 or 3 dependent on the number of dimensions. Returns ------- out : ``Mask`` object See also -------- ConnectLayers: Connect two (lists of) layers pairwise according to specified projections. ``Mask`` objects can be passed in a connection dictionary with the key `'mask'`. Notes ----- - **Mask types** Available mask types (`masktype`) and their corresponding parameter dictionaries: * 2D free and grid-based layers :: 'rectangular' : {'lower_left' : [float, float], 'upper_right': [float, float]} #or 'circular' : {'radius' : float} #or 'doughnut' : {'inner_radius' : float, 'outer_radius' : float} * 3D free and grid-based layers :: 'box' : {'lower_left' : [float, float, float], 'upper_right' : [float, float, float]} #or 'spherical' : {'radius' : float} * 2D grid-based layers only :: 'grid' : {'rows' : float, 'columns' : float} By default the top-left corner of a grid mask, i.e., the grid mask element with grid index [0, 0], is aligned with the driver node. It can be changed by means of the 'anchor' parameter: :: 'anchor' : {'row' : float, 'column' : float} **Example** :: import nest.topology as tp # create a grid-based layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # create a circular mask m = tp.CreateMask('circular', {'radius': 0.2}) # connectivity specifications conndict = {'connection_type': 'divergent', 'mask' : m} # connect layer l with itself according to the specifications tp.ConnectLayers(l, l, conndict) """ if anchor is None: return Mask(topology_func('CreateMask', {masktype: specs})) else: return Mask( topology_func('CreateMask', {masktype: specs, 'anchor': anchor})) class Parameter(object): """ Class for parameters for distance dependency or randomization. Parameters are spatial functions which are used when creating connections in the Topology module. A parameter may be used as a probability kernel when creating connections or as synaptic parameters (such as weight and delay). Parameters are created using the ``CreateParameter`` command. """ _datum = None # The constructor should not be called by the user def __init__(self, datum): """Parameters must be created using the CreateParameter command.""" if not isinstance(datum, nest.SLIDatum) or datum.dtype != "parametertype": raise TypeError("expected parameter datum") self._datum = datum # Generic binary operation def _binop(self, op, other): if not isinstance(other, Parameter): return NotImplemented return Parameter(topology_func(op, self._datum, other._datum)) def __add__(self, other): return self._binop("add", other) def __sub__(self, other): return self._binop("sub", other) def __mul__(self, other): return self._binop("mul", other) def __div__(self, other): return self._binop("div", other) def __truediv__(self, other): return self._binop("div", other) def GetValue(self, point): """ Compute value of parameter at a point. Parameters ---------- point : tuple/list of float values coordinate of point Returns ------- out : value The value of the parameter at the point See also -------- CreateParameter : create parameter for e.g., distance dependency Notes ----- - **Example** :: import nest.topology as tp #linear dependent parameter P = tp.CreateParameter('linear', {'a' : 2., 'c' : 0.}) #get out value P.GetValue(point=[3., 4.]) """ return topology_func("GetValue", point, self._datum) def CreateParameter(parametertype, specs): """ Create a parameter for distance dependency or randomization. Parameters are (spatial) functions which are used when creating connections in the Topology module for distance dependency or randomization. This command creates a Parameter object which may be combined with other ``Parameter`` objects using arithmetic operators. The parameter is specified in a dictionary. A parameter may be used as a probability kernel when creating connections or as synaptic parameters (such as weight and delay), i.e., for specifying the parameters `'kernel'`, `'weights'` and `'delays'` in the connection dictionary passed to ``ConnectLayers``. Parameters ---------- parametertype : {'constant', 'linear', 'exponential', 'gaussian', \ 'gaussian2D', 'uniform', 'normal', 'lognormal'} Function types with or without distance dependency specs : dict Dictionary specifying the parameters of the provided `'parametertype'`, see **Notes**. Returns ------- out : ``Parameter`` object See also -------- ConnectLayers : Connect two (lists of) layers pairwise according to specified projections. Parameters can be used to specify the parameters `'kernel'`, `'weights'` and `'delays'` in the connection dictionary. Parameters : Class for parameters for distance dependency or randomization. Notes ----- - **Parameter types** Available parameter types (`parametertype` parameter), their function and acceptable keys for their corresponding specification dictionaries * Constant :: 'constant' : {'value' : float} # constant value * With dependence on the distance `d` :: # p(d) = c + a * d 'linear' : {'a' : float, # slope, default: 1.0 'c' : float} # constant offset, default: 0.0 # or # p(d) = c + a*exp(-d/tau) 'exponential' : {'a' : float, # coefficient of exponential term, default: 1.0 'c' : float, # constant offset, default: 0.0 'tau' : float} # length scale factor, default: 1.0 # or # p(d) = c + p_center*exp(-(d-mean)^2/(2*sigma^2)) 'gaussian' : {'p_center' : float, # value at center, default: 1.0 'mean' : float, # distance to center, default: 0.0 'sigma' : float, # width of Gaussian, default: 1.0 'c' : float} # constant offset, default: 0.0 * Bivariate Gaussian parameter: :: # p(x,y) = c + p_center * # exp( -( (x-mean_x)^2/sigma_x^2 + (y-mean_y)^2/sigma_y^2 # + 2*rho*(x-mean_x)*(y-mean_y)/(sigma_x*sigma_y) ) / # (2*(1-rho^2)) ) 'gaussian2D' : {'p_center' : float, # value at center, default: 1.0 'mean_x' : float, # x-coordinate of center, default: 0.0 'mean_y' : float, # y-coordinate of center, default: 0.0 'sigma_x' : float, # width in x-direction, default: 1.0 'sigma_y' : float, # width in y-direction, default: 1.0 'rho' : float, # correlation of x and y, default: 0.0 'c' : float} # constant offset, default: 0.0 * Without distance dependency, for randomization :: # random parameter with uniform distribution in [min,max) 'uniform' : {'min' : float, # minimum value, default: 0.0 'max' : float} # maximum value, default: 1.0 # or # random parameter with normal distribution, optionally truncated # to [min,max) 'normal': {'mean' : float, # mean value, default: 0.0 'sigma': float, # standard deviation, default: 1.0 'min' : float, # minimum value, default: -inf 'max' : float} # maximum value, default: +inf # or # random parameter with lognormal distribution, # optionally truncated to [min,max) 'lognormal' : {'mu' : float, # mean value of logarithm, default: 0.0 'sigma': float, # standard deviation of log, default: 1.0 'min' : float, # minimum value, default: -inf 'max' : float} # maximum value, default: +inf **Example** :: import nest.topology as tp # create a grid-based layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # parameter for delay with linear distance dependency d = tp.CreateParameter('linear', {'a': 0.2, 'c': 0.2}) # connectivity specifications conndict = {'connection_type': 'divergent', 'delays': d} tp.ConnectLayers(l, l, conndict) """ return Parameter(topology_func('CreateParameter', {parametertype: specs})) def CreateLayer(specs): """ Create one ore more Topology layer(s) according to given specifications. The Topology module organizes neuronal networks in layers. A layer is a special type of subnet which contains information about the spatial position of its nodes (simple or composite elements) in 2 or 3 dimensions. If `specs` is a dictionary, a single layer is created. If it is a list of dictionaries, one layer is created for each dictionary. Topology distinguishes between two classes of layers: * grid-based layers in which each element is placed at a location in a regular grid * free layers in which elements can be placed arbitrarily Obligatory dictionary entries define the class of layer (grid-based layers: 'columns' and 'rows'; free layers: 'positions') and the 'elements'. Parameters ---------- specs : (tuple/list of) dict(s) Dictionary or list of dictionaries with layer specifications, see **Notes**. Returns ------- out : tuple of int(s) GID(s) of created layer(s) See also -------- ConnectLayers: Connect two (lists of) layers which were created with ``CreateLayer`` pairwise according to specified projections. Other parameters ---------------- Available parameters for the layer-specifying dictionary `specs` center : tuple/list of floats, optional, default: (0.0, 0.0) Layers are centered about the origin by default, but the center coordinates can also be changed. 'center' has length 2 or 3 dependent on the number of dimensions. columns : int, obligatory for grid-based layers Number of columns. Needs `'rows'`; mutually exclusive with `'positions'`. edge_wrap : bool, default: False Periodic boundary conditions. elements : (tuple/list of) str or str followed by int Elements of layers are NEST network nodes such as neuron models or devices. For network elements with several nodes of the same type, the number of nodes to be created must follow the model name. For composite elements, a collection of nodes can be passed as list or tuple. extent : tuple of floats, optional, default in 2D: (1.0, 1.0) Size of the layer. It has length 2 or 3 dependent on the number of dimensions. positions : tuple/list of coordinates (lists/tuples of floats), obligatory for free layers Explicit specification of the positions of all elements. The coordinates have a length 2 or 3 dependent on the number of dimensions. All element positions must be within the layer’s extent. Mutually exclusive with 'rows' and 'columns'. rows : int, obligatory for grid-based layers Number of rows. Needs `'columns'`; mutually exclusive with `'positions'`. Notes ----- - **Example** :: import nest import nest.topology as tp # grid-based layer gl = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # free layer import numpy as np pos = [[np.random.uniform(-0.5, 0.5), np.random.uniform(-0.5,0.5)] for i in range(50)] fl = tp.CreateLayer({'positions' : pos, 'elements' : 'iaf_neuron'}) # extent, center and edge_wrap el = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'extent' : [2.0, 3.0], 'center' : [1.0, 1.5], 'edge_wrap' : True, 'elements' : 'iaf_neuron'}) # composite layer with several nodes of the same type cl = tp.CreateLayer({'rows' : 1, 'columns' : 2, 'elements' : ['iaf_cond_alpha', 10, 'poisson_generator', 'noise_generator', 2]}) # investigate the status dictionary of a layer nest.GetStatus(gl)[0]['topology'] """ if isinstance(specs, dict): specs = (specs, ) elif not all(isinstance(spec, dict) for spec in specs): raise TypeError("specs must be a dictionary or a list of dictionaries") return topology_func('{ CreateLayer } Map', specs) def ConnectLayers(pre, post, projections): """ Pairwise connect of pre- and postsynaptic (lists of) layers. `pre` and `post` must be a tuple/list of GIDs of equal length. The GIDs must refer to layers created with ``CreateLayers``. Layers in the `pre` and `post` lists are connected pairwise. * If `projections` is a single dictionary, it applies to all pre-post pairs. * If `projections` is a tuple/list of dictionaries, it must have the same length as `pre` and `post` and each dictionary is matched with the proper pre-post pair. A minimal call of ``ConnectLayers`` expects a source layer `pre`, a target layer `post` and a connection dictionary `projections` containing at least the entry `'connection_type'` (either `'convergent'` or `'divergent'`). When connecting two layers, the driver layer is the one in which each node is considered in turn. The pool layer is the one from which nodes are chosen for each node in the driver layer. Parameters ---------- pre : tuple/list of int(s) List of GIDs of presynaptic layers (sources) post : tuple/list of int(s) List of GIDs of postsynaptic layers (targets) projections : (tuple/list of) dict(s) Dictionary or list of dictionaries specifying projection properties Returns ------- out : None ConnectLayers returns `None` See also -------- CreateLayer : Create one or more Topology layer(s). CreateMask : Create a ``Mask`` object. Documentation on available spatial masks. Masks can be used to specify the key `'mask'` of the connection dictionary. CreateParameter : Create a ``Parameter`` object. Documentation on available parameters for distance dependency and randomization. Parameters can be used to specify the parameters `'kernel'`, `'weights'` and `'delays'` of the connection dictionary. nest.GetConnections : Retrieve connections. Other parameters ---------------- Available keys for the layer-specifying dictionary `projections` allow_autapses : bool, optional, default: True An autapse is a synapse (connection) from a node onto itself. It is used together with the `'number_of_connections'` option. allow_multapses : bool, optional, default: True Node A is connected to node B by a multapse if there are synapses (connections) from A to B. It is used together with the `'number_of_connections'` option. connection_type : str The type of connections can be either `'convergent'` or `'divergent'`. In case of convergent connections, the target layer is considered as driver layer and the source layer as pool layer - and vice versa for divergent connections. delays : [float | dict | Parameter object], optional, default: 1.0 Delays can be constant, randomized or distance-dependent according to a provided function. Information on available functions can be found in the documentation on the function ``CreateParameter``. kernel : [float | dict | Parameter object], optional, default: 1.0 A kernel is a function mapping the distance (or displacement) between a driver and a pool node to a connection probability. The default kernel is 1.0, i.e., connections are created with certainty. Information on available functions can be found in the documentation on the function ``CreateParameter``. mask : [dict | Mask object], optional The mask defines which pool nodes are considered as potential targets for each driver node. Parameters of the different available masks in 2 and 3 dimensions are also defined in dictionaries. If no mask is specified, all neurons from the pool layer are possible targets for each driver node. Information on available masks can be found in the documentation on the function ``CreateMask``. number_of_connections : int, optional Prescribed number of connections for each driver node. The actual connections being created are picked at random from all the candidate connections. synapse_model : str, optional The default synapse model in NEST is used if not specified otherwise. weights : [float | dict | Parameter object], optional, default: 1.0 Weights can be constant, randomized or distance-dependent according to a provided function. Information on available functions can be found in the documentation on the function ``CreateParameter``. Notes ----- * In the case of free probabilistic connections (in contrast to prescribing the number of connections), each possible driver-pool pair is inspected exactly once so that there will be at most one connection between each driver-pool pair. * Periodic boundary conditions are always applied in the pool layer. It is irrelevant whether the driver layer has periodic boundary conditions or not. * By default, Topology does not accept masks that are wider than the pool layer when using periodic boundary conditions. Kernel, weight and delay functions always consider the shortest distance (displacement) between driver and pool node. **Example** :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 11, 'columns' : 11, 'extent' : [11.0, 11.0], 'elements' : 'iaf_neuron'}) # connectivity specifications with a mask conndict1 = {'connection_type': 'divergent', 'mask': {'rectangular': {'lower_left' : [-2.0, -1.0], 'upper_right' : [2.0, 1.0]}}} # connect layer l with itself according to the given # specifications tp.ConnectLayers(l, l, conndict1) # connection dictionary with distance-dependent kernel # (given as Parameter object) and randomized weights # (given as a dictionary) gauss_kernel = tp.CreateParameter('gaussian', {'p_center' : 1.0, 'sigma' : 1.0}) conndict2 = {'connection_type': 'divergent', 'mask': {'circular': {'radius': 2.0}}, 'kernel': gauss_kernel, 'weights': {'uniform': {'min': 0.2, 'max': 0.8}}} """ if not nest.is_sequence_of_gids(pre): raise TypeError("pre must be a sequence of GIDs") if not nest.is_sequence_of_gids(pre): raise TypeError("post must be a sequence of GIDs") if not len(pre) == len(post): raise nest.NESTError("pre and post must have the same length.") # ensure projections is list of full length projections = nest.broadcast(projections, len(pre), (dict, ), "projections") # Replace python classes with SLI datums def fixdict(d): d = d.copy() for k, v in d.items(): if isinstance(v, dict): d[k] = fixdict(v) elif isinstance(v, Mask) or isinstance(v, Parameter): d[k] = v._datum return d projections = [fixdict(p) for p in projections] topology_func('3 arraystore { ConnectLayers } ScanThread', pre, post, projections) def GetPosition(nodes): """ Return the spatial locations of nodes. Parameters ---------- nodes : tuple/list of int(s) List of GIDs Returns ------- out : tuple of tuple(s) List of positions as 2- or 3-element lists See also -------- Displacement : Get vector of lateral displacement between nodes. Distance : Get lateral distance between nodes. DumpLayerConnections : Write connectivity information to file. DumpLayerNodes : Write layer node positions to file. Notes ----- * The functions ``GetPosition``, ``Displacement`` and ``Distance`` now only works for nodes local to the current MPI process, if used in a MPI-parallel simulation. **Example** :: import nest import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # retrieve positions of all (local) nodes belonging to the layer gids = nest.GetNodes(l, {'local_only': True})[0] tp.GetPosition(gids) """ if not nest.is_sequence_of_gids(nodes): raise TypeError("nodes must be a sequence of GIDs") return topology_func('{ GetPosition } Map', nodes) def GetLayer(nodes): """ Return the layer to which nodes belong. Parameters ---------- nodes : tuple/list of int(s) List of neuron GIDs Returns ------- out : tuple of int(s) List of layer GIDs See also -------- GetElement : Return the node(s) at the location(s) in the given layer(s). GetPosition : Return the spatial locations of nodes. Notes ----- - **Example** :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # get layer GID of nodes in layer tp.GetLayer(nest.GetNodes(l)[0]) """ if not nest.is_sequence_of_gids(nodes): raise TypeError("nodes must be a sequence of GIDs") return topology_func('{ GetLayer } Map', nodes) def GetElement(layers, locations): """ Return the node(s) at the location(s) in the given layer(s). This function works for fixed grid layers only. * If layers contains a single GID and locations is a single 2-element array giving a grid location, return a list of GIDs of layer elements at the given location. * If layers is a list with a single GID and locations is a list of coordinates, the function returns a list of lists with GIDs of the nodes at all locations. * If layers is a list of GIDs and locations single 2-element array giving a grid location, the function returns a list of lists with the GIDs of the nodes in all layers at the given location. * If layers and locations are lists, it returns a nested list of GIDs, one list for each layer and each location. Parameters ---------- layers : tuple/list of int(s) List of layer GIDs locations : [tuple/list of floats | tuple/list of tuples/lists of floats] 2-element list with coordinates of a single grid location, or list of 2-element lists of coordinates for 2-dimensional layers, i.e., on the format [column, row] Returns ------- out : tuple of int(s) List of GIDs See also -------- GetLayer : Return the layer to which nodes belong. FindNearestElement: Return the node(s) closest to the location(s) in the given layer(s). GetPosition : Return the spatial locations of nodes. Notes ----- - **Example** :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 4, 'elements' : 'iaf_neuron'}) # get GID of element in last row and column tp.GetElement(l, [3, 4]) """ if not nest.is_sequence_of_gids(layers): raise TypeError("layers must be a sequence of GIDs") if not len(layers) > 0: raise nest.NESTError("layers cannot be empty") if not (nest.is_iterable(locations) and len(locations) > 0): raise nest.NESTError( "locations must be coordinate array or list of coordinate arrays") # ensure that all layers are grid-based, otherwise one ends up with an # incomprehensible error message try: topology_func('{ [ /topology [ /rows /columns ] ] get ; } forall', layers) except: raise nest.NESTError( "layers must contain only grid-based topology layers") # SLI GetElement returns either single GID or list def make_tuple(x): if not nest.is_iterable(x): return (x, ) else: return x if nest.is_iterable(locations[0]): # layers and locations are now lists nodes = topology_func( '/locs Set { /lyr Set locs { lyr exch GetElement } Map } Map', layers, locations) node_list = tuple( tuple(make_tuple(nodes_at_loc) for nodes_at_loc in nodes_in_lyr) for nodes_in_lyr in nodes) else: # layers is list, locations is a single location nodes = topology_func('/loc Set { loc GetElement } Map', layers, locations) node_list = tuple(make_tuple(nodes_in_lyr) for nodes_in_lyr in nodes) # If only a single layer is given, un-nest list if len(layers) == 1: node_list = node_list[0] return node_list def FindNearestElement(layers, locations, find_all=False): """ Return the node(s) closest to the location(s) in the given layer(s). This function works for fixed grid layers only. * If layers contains a single GID and locations is a single 2-element array giving a grid location, return a list of GIDs of layer elements at the given location. * If layers is a list with a single GID and locations is a list of coordinates, the function returns a list of lists with GIDs of the nodes at all locations. * If layers is a list of GIDs and locations single 2-element array giving a grid location, the function returns a list of lists with the GIDs of the nodes in all layers at the given location. * If layers and locations are lists, it returns a nested list of GIDs, one list for each layer and each location. Parameters ---------- layers : tuple/list of int(s) List of layer GIDs locations : tuple(s)/list(s) of tuple(s)/list(s) 2-element list with coordinates of a single position, or list of 2-element list of positions find_all : bool, default: False If there are several nodes with same minimal distance, return only the first found, if `False`. If `True`, instead of returning a single GID, return a list of GIDs containing all nodes with minimal distance. Returns ------- out : tuple of int(s) List of node GIDs See also -------- FindCenterElement : Return GID(s) of node closest to center of layers. GetElement : Return the node(s) at the location(s) in the given layer(s). GetPosition : Return the spatial locations of nodes. Notes ----- - **Example** :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # get GID of element closest to some location tp.FindNearestElement(l, [3.0, 4.0], True) """ import numpy if not nest.is_sequence_of_gids(layers): raise TypeError("layers must be a sequence of GIDs") if not len(layers) > 0: raise nest.NESTError("layers cannot be empty") if not nest.is_iterable(locations): raise TypeError( "locations must be coordinate array or list of coordinate arrays") # ensure locations is sequence, keeps code below simpler if not nest.is_iterable(locations[0]): locations = (locations, ) result = [] # collect one list per layer # loop over layers for lyr in layers: els = nest.GetChildren((lyr, ))[0] lyr_result = [] # loop over locations for loc in locations: d = Distance(numpy.array(loc), els) if not find_all: dx = numpy.argmin(d) # finds location of one minimum lyr_result.append(els[dx]) else: mingids = list(els[:1]) minval = d[0] for idx in range(1, len(els)): if d[idx] < minval: mingids = [els[idx]] minval = d[idx] elif numpy.abs(d[idx] - minval) <= 1e-14 * minval: mingids.append(els[idx]) lyr_result.append(tuple(mingids)) result.append(tuple(lyr_result)) # If both layers and locations are multi-element lists, result shall remain # a nested list. Otherwise, either the top or the second level is a single # element list and we flatten. assert (len(result) > 0) if len(result) == 1: assert (len(layers) == 1) return result[0] elif len(result[0]) == 1: assert (len(locations) == 1) return tuple(el[0] for el in result) else: return tuple(result) def _check_displacement_args(from_arg, to_arg, caller): """ Internal helper function to check arguments to Displacement and Distance and make them lists of equal length. """ import numpy if isinstance(from_arg, numpy.ndarray): from_arg = (from_arg, ) elif not (nest.is_iterable(from_arg) and len(from_arg) > 0): raise nest.NESTError( "%s: from_arg must be lists of GIDs or positions" % caller) # invariant: from_arg is list if not nest.is_sequence_of_gids(to_arg): raise nest.NESTError("%s: to_arg must be lists of GIDs" % caller) # invariant: from_arg and to_arg are sequences if len(from_arg) > 1 and len(to_arg) > 1 and not len(from_arg) == len( to_arg): raise nest.NESTError( "%s: If to_arg and from_arg are lists, they must have same length." % caller) # invariant: from_arg and to_arg have equal length, # or (at least) one has length 1 if len(from_arg) == 1: from_arg = from_arg * len(to_arg) # this is a no-op if len(to_arg)==1 if len(to_arg) == 1: to_arg = to_arg * len(from_arg) # this is a no-op if len(from_arg)==1 # invariant: from_arg and to_arg have equal length return from_arg, to_arg def Displacement(from_arg, to_arg): """ Get vector of lateral displacement from node(s) `from_arg` to node(s) `to_arg`. Displacement is always measured in the layer to which the `to_arg` node belongs. If a node in the `from_arg` list belongs to a different layer, its location is projected into the `to_arg` layer. If explicit positions are given in the `from_arg` list, they are interpreted in the `to_arg` layer. Displacement is the shortest displacement, taking into account periodic boundary conditions where applicable. * If one of `from_arg` or `to_arg` has length 1, and the other is longer, the displacement from/to the single item to all other items is given. * If `from_arg` and `to_arg` both have more than two elements, they have to be lists of the same length and the displacement for each pair is returned. Parameters ---------- from_arg : [tuple/list of int(s) | tuple/list of tuples/lists of floats] List of GIDs or position(s) to_arg : tuple/list of int(s) List of GIDs Returns ------- out : tuple Displacement vectors between pairs of nodes in `from_arg` and `to_arg` See also -------- Distance : Get lateral distances between nodes. DumpLayerConnections : Write connectivity information to file. GetPosition : Return the spatial locations of nodes. Notes ----- * The functions ``GetPosition``, ``Displacement`` and ``Distance`` now only works for nodes local to the current MPI process, if used in a MPI-parallel simulation. **Example** :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # displacement between node 2 and 3 print tp.Displacement([2], [3]) # displacment between the position (0.0., 0.0) and node 2 print tp.Displacement([(0.0, 0.0)], [2]) """ from_arg, to_arg = _check_displacement_args(from_arg, to_arg, 'Displacement') return topology_func('{ Displacement } MapThread', [from_arg, to_arg]) def Distance(from_arg, to_arg): """ Get lateral distances from node(s) from_arg to node(s) to_arg. The distance between two nodes is the length of its displacement. Distance is always measured in the layer to which the `to_arg` node belongs. If a node in the `from_arg` list belongs to a different layer, its location is projected into the `to_arg` layer. If explicit positions are given in the `from_arg` list, they are interpreted in the `to_arg` layer. Distance is the shortest distance, taking into account periodic boundary conditions where applicable. * If one of `from_arg` or `to_arg` has length 1, and the other is longer, the displacement from/to the single item to all other items is given. * If `from_arg` and `to_arg` both have more than two elements, they have to be lists of the same length and the distance for each pair is returned. Parameters ---------- from_arg : [tuple/list of ints | tuple/list with tuples/lists of floats] List of GIDs or position(s) to_arg : tuple/list of ints List of GIDs Returns ------- out : tuple Distances between from and to See also -------- Displacement : Get vector of lateral displacements between nodes. DumpLayerConnections : Write connectivity information to file. GetPosition : Return the spatial locations of nodes. Notes ----- * The functions ``GetPosition``, ``Displacement`` and ``Distance`` now only works for nodes local to the current MPI process, if used in a MPI-parallel simulation. **Example** :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # distance between node 2 and 3 print tp.Distance([2], [3]) # distance between the position (0.0., 0.0) and node 2 print tp.Distance([(0.0, 0.0)], [2]) """ from_arg, to_arg = _check_displacement_args(from_arg, to_arg, 'Distance') return topology_func('{ Distance } MapThread', [from_arg, to_arg]) def _rank_specific_filename(basename): """Returns file name decorated with rank.""" if nest.NumProcesses() == 1: return basename else: np = nest.NumProcesses() np_digs = len(str(np - 1)) # for pretty formatting rk = nest.Rank() dot = basename.find('.') if dot < 0: return '%s-%0*d' % (basename, np_digs, rk) else: return '%s-%0*d%s' % (basename[:dot], np_digs, rk, basename[dot:]) def DumpLayerNodes(layers, outname): """ Write GID and position data of layer(s) to file. Write GID and position data to layer(s) file. For each node in a layer, a line with the following information is written: :: GID x-position y-position [z-position] If `layers` contains several GIDs, data for all layers will be written to a single file. Parameters ---------- layers : tuple/list of int(s) List of GIDs of a Topology layer outname : str Name of file to write to (existing files are overwritten) Returns ------- out : None See also -------- DumpLayerConnections : Write connectivity information to file. GetPosition : Return the spatial locations of nodes. Notes ----- * If calling this function from a distributed simulation, this function will write to one file per MPI rank. * File names are formed by adding the MPI Rank into the file name before the file name suffix. * Each file stores data for nodes local to that file. **Example** :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # write layer node positions to file tp.DumpLayerNodes(l, 'positions.txt') """ topology_func(""" (w) file exch { DumpLayerNodes } forall close """, layers, _rank_specific_filename(outname)) def DumpLayerConnections(layers, synapse_model, outname): """ Write connectivity information to file. This function writes connection information to file for all outgoing connections from the given layers with the given synapse model. Data for all layers in the list is combined. For each connection, one line is stored, in the following format: :: source_gid target_gid weight delay dx dy [dz] where (dx, dy [, dz]) is the displacement from source to target node. If targets do not have positions (eg spike detectors outside any layer), NaN is written for each displacement coordinate. Parameters ---------- layers : tuple/list of int(s) List of GIDs of a Topology layer synapse_model : str NEST synapse model outname : str Name of file to write to (will be overwritten if it exists) Returns ------- out : None See also -------- DumpLayerNodes : Write layer node positions to file. GetPosition : Return the spatial locations of nodes. nest.GetConnections : Return connection identifiers between sources and targets Notes ----- * If calling this function from a distributed simulation, this function will write to one file per MPI rank. * File names are formed by inserting the MPI Rank into the file name before the file name suffix. * Each file stores data for local nodes. **Example** :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) tp.ConnectLayers(l,l, {'connection_type': 'divergent', 'synapse_model': 'static_synapse'}) # write connectivity information to file tp.DumpLayerConnections(l, 'static_synapse', 'connections.txt') """ topology_func(""" /oname Set cvlit /synmod Set /lyrs Set oname (w) file lyrs { synmod DumpLayerConnections } forall close """, layers, synapse_model, _rank_specific_filename(outname)) def FindCenterElement(layers): """ Return GID(s) of node closest to center of layers. Parameters ---------- layers : tuple/list of int(s) List of layer GIDs Returns ------- out : tuple of int(s) A list containing for each layer the GID of the node closest to the center of the layer, as specified in the layer parameters. If several nodes are equally close to the center, an arbitrary one of them is returned. See also -------- FindNearestElement : Return the node(s) closest to the location(s) in the given layer(s). GetElement : Return the node(s) at the location(s) in the given layer(s). GetPosition : Return the spatial locations of nodes. Notes ----- - **Example** :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 5, 'columns' : 5, 'elements' : 'iaf_neuron'}) # get GID of the element closest to the center of the layer tp.FindCenterElement(l) """ if not nest.is_sequence_of_gids(layers): raise TypeError("layers must be a sequence of GIDs") # Do each layer on its own since FindNearestElement does not thread return tuple(FindNearestElement((lyr, ), nest.GetStatus((lyr, ), 'topology')[0][ 'center'])[0] for lyr in layers) def GetTargetNodes(sources, tgt_layer, tgt_model=None, syn_model=None): """ Obtain targets of a list of sources in given target layer. Parameters ---------- sources : tuple/list of int(s) List of GID(s) of source neurons tgt_layer : tuple/list of int(s) Single-element list with GID of tgt_layer tgt_model : [None | str], optional, default: None Return only target positions for a given neuron model. syn_model : [None | str], optional, default: None Return only target positions for a given synapse model. Returns ------- out : tuple of list(s) of int(s) List of GIDs of target neurons fulfilling the given criteria. It is a list of lists, one list per source. For each neuron in `sources`, this function finds all target elements in `tgt_layer`. If `tgt_model` is not given (default), all targets are returned, otherwise only targets of specific type, and similarly for syn_model. See also -------- GetTargetPositions : Obtain positions of targets of a list of sources in a given target layer. nest.GetConnections : Return connection identifiers between sources and targets Notes ----- * For distributed simulations, this function only returns targets on the local MPI process. **Example** :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 11, 'columns' : 11, 'extent' : [11.0, 11.0], 'elements' : 'iaf_neuron'}) # connectivity specifications with a mask conndict = {'connection_type': 'divergent', 'mask': {'rectangular': {'lower_left' : [-2.0, -1.0], 'upper_right': [2.0, 1.0]}}} # connect layer l with itself according to the given # specifications tp.ConnectLayers(l, l, conndict) # get the GIDs of the targets of the source neuron with GID 5 tp.GetTargetNodes([5], l) """ if not nest.is_sequence_of_gids(sources): raise TypeError("sources must be a sequence of GIDs") if not nest.is_sequence_of_gids(tgt_layer): raise TypeError("tgt_layer must be a sequence of GIDs") if len(tgt_layer) != 1: raise nest.NESTError("tgt_layer must be a one-element list") # obtain local nodes in target layer, to pass to GetConnections tgt_nodes = nest.GetLeaves(tgt_layer, properties={ 'model': tgt_model} if tgt_model else None, local_only=True)[0] conns = nest.GetConnections(sources, tgt_nodes, synapse_model=syn_model) # conns is a flat list of connections. # Re-organize into one list per source, containing only target GIDs. src_tgt_map = dict((sgid, []) for sgid in sources) for conn in conns: src_tgt_map[conn[0]].append(conn[1]) # convert dict to nested list in same order as sources return tuple(src_tgt_map[sgid] for sgid in sources) def GetTargetPositions(sources, tgt_layer, tgt_model=None, syn_model=None): """ Obtain positions of targets of a list of sources in a given target layer. Parameters ---------- sources : tuple/list of int(s) List of GID(s) of source neurons tgt_layer : tuple/list of int(s) Single-element list with GID of tgt_layer tgt_model : [None | str], optional, default: None Return only target positions for a given neuron model. syn_type : [None | str], optional, default: None Return only target positions for a given synapse model. Returns ------- out : tuple of tuple(s) of tuple(s) of floats Positions of target neurons fulfilling the given criteria as a nested list, containing one list of positions per node in sources. For each neuron in `sources`, this function finds all target elements in `tgt_layer`. If `tgt_model` is not given (default), all targets are returned, otherwise only targets of specific type, and similarly for syn_model. See also -------- GetTargetNodes : Obtain targets of a list of sources in a given target layer. Notes ----- * For distributed simulations, this function only returns targets on the local MPI process. **Example** :: import nest.topology as tp # create a layer l = tp.CreateLayer({'rows' : 11, 'columns' : 11, 'extent' : [11.0, 11.0], 'elements' : 'iaf_neuron'}) # connectivity specifications with a mask conndict1 = {'connection_type': 'divergent', 'mask': {'rectangular': {'lower_left' : [-2.0, -1.0], 'upper_right' : [2.0, 1.0]}}} # connect layer l with itself according to the given # specifications tp.ConnectLayers(l, l, conndict1) # get the positions of the targets of the source neuron with GID 5 tp.GetTargetPositions([5], l) """ return tuple(GetPosition(nodes) for nodes in GetTargetNodes(sources, tgt_layer, tgt_model, syn_model)) def _draw_extent(ax, xctr, yctr, xext, yext): """Draw extent and set aspect ration, limits""" import matplotlib.pyplot as plt # thin gray line indicating extent llx, lly = xctr - xext / 2.0, yctr - yext / 2.0 urx, ury = llx + xext, lly + yext ax.add_patch( plt.Rectangle((llx, lly), xext, yext, fc='none', ec='0.5', lw=1, zorder=1)) # set limits slightly outside extent ax.set(aspect='equal', xlim=(llx - 0.05 * xext, urx + 0.05 * xext), ylim=(lly - 0.05 * yext, ury + 0.05 * yext), xticks=tuple(), yticks=tuple()) def PlotLayer(layer, fig=None, nodecolor='b', nodesize=20): """ Plot all nodes in a layer. This function plots only top-level nodes, not the content of composite nodes. Parameters ---------- layer : tuple/list of int(s) GID of layer to plot, must be tuple/list of length 1 fig : [None | matplotlib.figure.Figure object], optional, default: None Matplotlib figure to plot to. If not given, a new figure is created. nodecolor : [None | any matplotlib color], optional, default: 'b' Color for nodes nodesize : float, optional, default: 20 Marker size for nodes Returns ------- out : `matplotlib.figure.Figure` object See also -------- PlotKernel : Add indication of mask and kernel to axes. PlotTargets : Plot all targets of a given source. matplotlib.figure.Figure : matplotlib Figure class Notes ----- * Do not use this function in distributed simulations. **Example** :: import nest.topology as tp import matplotlib.pyplot as plt # create a layer l = tp.CreateLayer({'rows' : 11, 'columns' : 11, 'extent' : [11.0, 11.0], 'elements' : 'iaf_neuron'}) # plot layer with all its nodes tp.PlotLayer(l) plt.show() """ import matplotlib.pyplot as plt if len(layer) != 1: raise ValueError("layer must contain exactly one GID.") # get layer extent ext = nest.GetStatus(layer, 'topology')[0]['extent'] if len(ext) == 2: # 2D layer # get layer extent and center, x and y xext, yext = ext xctr, yctr = nest.GetStatus(layer, 'topology')[0]['center'] # extract position information, transpose to list of x and y positions xpos, ypos = zip(*GetPosition(nest.GetChildren(layer)[0])) if fig is None: fig = plt.figure() ax = fig.add_subplot(111) else: ax = fig.gca() ax.scatter(xpos, ypos, s=nodesize, facecolor=nodecolor, edgecolor='none') _draw_extent(ax, xctr, yctr, xext, yext) elif len(ext) == 3: # 3D layer from mpl_toolkits.mplot3d import Axes3D # extract position information, transpose to list of x,y,z positions pos = zip(*GetPosition(nest.GetChildren(layer)[0])) if fig is None: fig = plt.figure() ax = fig.add_subplot(111, projection='3d') else: ax = fig.gca() ax.scatter3D(*pos, s=nodesize, facecolor=nodecolor, edgecolor='none') plt.draw_if_interactive() else: raise nest.NESTError("unexpected dimension of layer") return fig def PlotTargets(src_nrn, tgt_layer, tgt_model=None, syn_type=None, fig=None, mask=None, kernel=None, src_color='red', src_size=50, tgt_color='blue', tgt_size=20, mask_color='red', kernel_color='red'): """ Plot all targets of source neuron `src_nrn` in a target layer `tgt_layer`. Parameters ---------- src_nrn : int GID of source neuron (as single-element list) tgt_layer : tuple/list of int(s) GID of tgt_layer (as single-element list) tgt_model : [None | str], optional, default: None Show only targets of a given model. syn_type : [None | str], optional, default: None Show only targets connected to with a given synapse type fig : [None | matplotlib.figure.Figure object], optional, default: None Matplotlib figure to plot to. If not given, a new figure is created. mask : [None | dict], optional, default: None Draw topology mask with targets; see ``PlotKernel`` for details. kernel : [None | dict], optional, default: None Draw topology kernel with targets; see ``PlotKernel`` for details. src_color : [None | any matplotlib color], optional, default: 'red' Color used to mark source node position src_size : float, optional, default: 50 Size of source marker (see scatter for details) tgt_color : [None | any matplotlib color], optional, default: 'blue' Color used to mark target node positions tgt_size : float, optional, default: 20 Size of target markers (see scatter for details) mask_color : [None | any matplotlib color], optional, default: 'red' Color used for line marking mask kernel_color : [None | any matplotlib color], optional, default: 'red' Color used for lines marking kernel Returns ------- out : matplotlib.figure.Figure object See also -------- GetTargetNodes : Obtain targets of a list of sources in a given target layer. GetTargetPositions : Obtain positions of targets of a list of sources in a given target layer. PlotKernel : Add indication of mask and kernel to axes. PlotLayer : Plot all nodes in a layer. matplotlib.pyplot.scatter : matplotlib scatter plot. Notes ----- * Do not use this function in distributed simulations. **Example** :: import nest.topology as tp import matplotlib.pyplot as plt # create a layer l = tp.CreateLayer({'rows' : 11, 'columns' : 11, 'extent' : [11.0, 11.0], 'elements' : 'iaf_neuron'}) # connectivity specifications with a mask conndict = {'connection_type': 'divergent', 'mask': {'rectangular': {'lower_left' : [-2.0, -1.0], 'upper_right' : [2.0, 1.0]}}} # connect layer l with itself according to the given # specifications tp.ConnectLayers(l, l, conndict) # plot the targets of the source neuron with GID 5 tp.PlotTargets([5], l) plt.show() """ import matplotlib.pyplot as plt # get position of source srcpos = GetPosition(src_nrn)[0] # get layer extent and center, x and y ext = nest.GetStatus(tgt_layer, 'topology')[0]['extent'] if len(ext) == 2: # 2D layer # get layer extent and center, x and y xext, yext = ext xctr, yctr = nest.GetStatus(tgt_layer, 'topology')[0]['center'] if fig is None: fig = plt.figure() ax = fig.add_subplot(111) else: ax = fig.gca() # get positions, reorganize to x and y vectors tgtpos = GetTargetPositions(src_nrn, tgt_layer, tgt_model, syn_type) if tgtpos: xpos, ypos = zip(*tgtpos[0]) ax.scatter(xpos, ypos, s=tgt_size, facecolor=tgt_color, edgecolor='none') ax.scatter(srcpos[:1], srcpos[1:], s=src_size, facecolor=src_color, edgecolor='none', alpha=0.4, zorder=-10) _draw_extent(ax, xctr, yctr, xext, yext) if mask is not None or kernel is not None: PlotKernel(ax, src_nrn, mask, kernel, mask_color, kernel_color) else: # 3D layer from mpl_toolkits.mplot3d import Axes3D if fig is None: fig = plt.figure() ax = fig.add_subplot(111, projection='3d') else: ax = fig.gca() # get positions, reorganize to x,y,z vectors tgtpos = GetTargetPositions(src_nrn, tgt_layer, tgt_model, syn_type) if tgtpos: xpos, ypos, zpos = zip(*tgtpos[0]) ax.scatter3D(xpos, ypos, zpos, s=tgt_size, facecolor=tgt_color, edgecolor='none') ax.scatter3D(srcpos[:1], srcpos[1:2], srcpos[2:], s=src_size, facecolor=src_color, edgecolor='none', alpha=0.4, zorder=-10) plt.draw_if_interactive() return fig def PlotKernel(ax, src_nrn, mask, kern=None, mask_color='red', kernel_color='red'): """ Add indication of mask and kernel to axes. Adds solid red line for mask. For doughnut mask show inner and outer line. If kern is Gaussian, add blue dashed lines marking 1, 2, 3 sigma. This function ignores periodic boundary conditions. Usually, this function is invoked by ``PlotTargets``. Parameters ---------- ax : matplotlib.axes.AxesSubplot, subplot reference returned by PlotTargets src_nrn : int GID of source neuron (as single element list), mask and kernel plotted relative to it mask : dict Mask used in creating connections. kern : [None | dict], optional, default: None Kernel used in creating connections mask_color : [None | any matplotlib color], optional, default: 'red' Color used for line marking mask kernel_color : [None | any matplotlib color], optional, default: 'red' Color used for lines marking kernel Returns ------- out : None See also -------- CreateMask : Create a ``Mask`` object. Documentation on available spatial masks. CreateParameter : Create a ``Parameter`` object. Documentation on available parameters for distance dependency and randomization. PlotLayer : Plot all nodes in a layer. Notes ----- * Do not use this function in distributed simulations. **Example** :: import nest.topology as tp import matplotlib.pyplot as plt # create a layer l = tp.CreateLayer({'rows' : 11, 'columns' : 11, 'extent' : [11.0, 11.0], 'elements' : 'iaf_neuron'}) # connectivity specifications mask_dict = {'rectangular': {'lower_left' : [-2.0, -1.0], 'upper_right' : [2.0, 1.0]}} kernel_dict = {'gaussian': {'p_center' : 1.0, 'sigma' : 1.0}} conndict = {'connection_type': 'divergent', 'mask' : mask_dict, 'kernel' : kernel_dict} # connect layer l with itself according to the given # specifications tp.ConnectLayers(l, l, conndict) # set up figure fig, ax = plt.subplots() # plot layer nodes tp.PlotLayer(l, fig) # choose center element of the layer as source node ctr_elem = tp.FindCenterElement(l) # plot mask and kernel of the center element tp.PlotKernel(ax, ctr_elem, mask=mask_dict, kern=kernel_dict) """ import matplotlib import matplotlib.pyplot as plt import numpy as np # minimal checks for ax having been created by PlotKernel if ax and not isinstance(ax, matplotlib.axes.Axes): raise ValueError('ax must be matplotlib.axes.Axes instance.') srcpos = np.array(GetPosition(src_nrn)[0]) if 'anchor' in mask: offs = np.array(mask['anchor']) else: offs = np.array([0., 0.]) if 'circular' in mask: r = mask['circular']['radius'] ax.add_patch(plt.Circle(srcpos + offs, radius=r, zorder=-1000, fc='none', ec=mask_color, lw=3)) elif 'doughnut' in mask: r_in = mask['doughnut']['inner_radius'] r_out = mask['doughnut']['outer_radius'] ax.add_patch(plt.Circle(srcpos + offs, radius=r_in, zorder=-1000, fc='none', ec=mask_color, lw=3)) ax.add_patch(plt.Circle(srcpos + offs, radius=r_out, zorder=-1000, fc='none', ec=mask_color, lw=3)) elif 'rectangular' in mask: ll = mask['rectangular']['lower_left'] ur = mask['rectangular']['upper_right'] ax.add_patch( plt.Rectangle(srcpos + ll + offs, ur[0] - ll[0], ur[1] - ll[1], zorder=-1000, fc='none', ec=mask_color, lw=3)) else: raise ValueError( 'Mask type cannot be plotted with this version of PyTopology.') if kern is not None and isinstance(kern, dict): if 'gaussian' in kern: sigma = kern['gaussian']['sigma'] for r in range(3): ax.add_patch(plt.Circle(srcpos + offs, radius=(r + 1) * sigma, zorder=-1000, fc='none', ec=kernel_color, lw=3, ls='dashed')) else: raise ValueError('Kernel type cannot be plotted with this ' + 'version of PyTopology') plt.draw()

gpl-2.0

jkbradley/spark

python/pyspark/sql/tests/test_dataframe.py

2

37690

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import os import pydoc import time import unittest from pyspark.sql import SparkSession, Row from pyspark.sql.types import * from pyspark.sql.utils import AnalysisException, IllegalArgumentException from pyspark.testing.sqlutils import ReusedSQLTestCase, SQLTestUtils, have_pyarrow, have_pandas, \ pandas_requirement_message, pyarrow_requirement_message from pyspark.testing.utils import QuietTest class DataFrameTests(ReusedSQLTestCase): def test_range(self): self.assertEqual(self.spark.range(1, 1).count(), 0) self.assertEqual(self.spark.range(1, 0, -1).count(), 1) self.assertEqual(self.spark.range(0, 1 << 40, 1 << 39).count(), 2) self.assertEqual(self.spark.range(-2).count(), 0) self.assertEqual(self.spark.range(3).count(), 3) def test_duplicated_column_names(self): df = self.spark.createDataFrame([(1, 2)], ["c", "c"]) row = df.select('*').first() self.assertEqual(1, row[0]) self.assertEqual(2, row[1]) self.assertEqual("Row(c=1, c=2)", str(row)) # Cannot access columns self.assertRaises(AnalysisException, lambda: df.select(df[0]).first()) self.assertRaises(AnalysisException, lambda: df.select(df.c).first()) self.assertRaises(AnalysisException, lambda: df.select(df["c"]).first()) def test_freqItems(self): vals = [Row(a=1, b=-2.0) if i % 2 == 0 else Row(a=i, b=i * 1.0) for i in range(100)] df = self.sc.parallelize(vals).toDF() items = df.stat.freqItems(("a", "b"), 0.4).collect()[0] self.assertTrue(1 in items[0]) self.assertTrue(-2.0 in items[1]) def test_help_command(self): # Regression test for SPARK-5464 rdd = self.sc.parallelize(['{"foo":"bar"}', '{"foo":"baz"}']) df = self.spark.read.json(rdd) # render_doc() reproduces the help() exception without printing output pydoc.render_doc(df) pydoc.render_doc(df.foo) pydoc.render_doc(df.take(1)) def test_dropna(self): schema = StructType([ StructField("name", StringType(), True), StructField("age", IntegerType(), True), StructField("height", DoubleType(), True)]) # shouldn't drop a non-null row self.assertEqual(self.spark.createDataFrame( [(u'Alice', 50, 80.1)], schema).dropna().count(), 1) # dropping rows with a single null value self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, 80.1)], schema).dropna().count(), 0) self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, 80.1)], schema).dropna(how='any').count(), 0) # if how = 'all', only drop rows if all values are null self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, 80.1)], schema).dropna(how='all').count(), 1) self.assertEqual(self.spark.createDataFrame( [(None, None, None)], schema).dropna(how='all').count(), 0) # how and subset self.assertEqual(self.spark.createDataFrame( [(u'Alice', 50, None)], schema).dropna(how='any', subset=['name', 'age']).count(), 1) self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, None)], schema).dropna(how='any', subset=['name', 'age']).count(), 0) # threshold self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, 80.1)], schema).dropna(thresh=2).count(), 1) self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, None)], schema).dropna(thresh=2).count(), 0) # threshold and subset self.assertEqual(self.spark.createDataFrame( [(u'Alice', 50, None)], schema).dropna(thresh=2, subset=['name', 'age']).count(), 1) self.assertEqual(self.spark.createDataFrame( [(u'Alice', None, 180.9)], schema).dropna(thresh=2, subset=['name', 'age']).count(), 0) # thresh should take precedence over how self.assertEqual(self.spark.createDataFrame( [(u'Alice', 50, None)], schema).dropna( how='any', thresh=2, subset=['name', 'age']).count(), 1) def test_fillna(self): schema = StructType([ StructField("name", StringType(), True), StructField("age", IntegerType(), True), StructField("height", DoubleType(), True), StructField("spy", BooleanType(), True)]) # fillna shouldn't change non-null values row = self.spark.createDataFrame([(u'Alice', 10, 80.1, True)], schema).fillna(50).first() self.assertEqual(row.age, 10) # fillna with int row = self.spark.createDataFrame([(u'Alice', None, None, None)], schema).fillna(50).first() self.assertEqual(row.age, 50) self.assertEqual(row.height, 50.0) # fillna with double row = self.spark.createDataFrame( [(u'Alice', None, None, None)], schema).fillna(50.1).first() self.assertEqual(row.age, 50) self.assertEqual(row.height, 50.1) # fillna with bool row = self.spark.createDataFrame( [(u'Alice', None, None, None)], schema).fillna(True).first() self.assertEqual(row.age, None) self.assertEqual(row.spy, True) # fillna with string row = self.spark.createDataFrame([(None, None, None, None)], schema).fillna("hello").first() self.assertEqual(row.name, u"hello") self.assertEqual(row.age, None) # fillna with subset specified for numeric cols row = self.spark.createDataFrame( [(None, None, None, None)], schema).fillna(50, subset=['name', 'age']).first() self.assertEqual(row.name, None) self.assertEqual(row.age, 50) self.assertEqual(row.height, None) self.assertEqual(row.spy, None) # fillna with subset specified for string cols row = self.spark.createDataFrame( [(None, None, None, None)], schema).fillna("haha", subset=['name', 'age']).first() self.assertEqual(row.name, "haha") self.assertEqual(row.age, None) self.assertEqual(row.height, None) self.assertEqual(row.spy, None) # fillna with subset specified for bool cols row = self.spark.createDataFrame( [(None, None, None, None)], schema).fillna(True, subset=['name', 'spy']).first() self.assertEqual(row.name, None) self.assertEqual(row.age, None) self.assertEqual(row.height, None) self.assertEqual(row.spy, True) # fillna with dictionary for boolean types row = self.spark.createDataFrame([Row(a=None), Row(a=True)]).fillna({"a": True}).first() self.assertEqual(row.a, True) def test_repartitionByRange_dataframe(self): schema = StructType([ StructField("name", StringType(), True), StructField("age", IntegerType(), True), StructField("height", DoubleType(), True)]) df1 = self.spark.createDataFrame( [(u'Bob', 27, 66.0), (u'Alice', 10, 10.0), (u'Bob', 10, 66.0)], schema) df2 = self.spark.createDataFrame( [(u'Alice', 10, 10.0), (u'Bob', 10, 66.0), (u'Bob', 27, 66.0)], schema) # test repartitionByRange(numPartitions, *cols) df3 = df1.repartitionByRange(2, "name", "age") self.assertEqual(df3.rdd.getNumPartitions(), 2) self.assertEqual(df3.rdd.first(), df2.rdd.first()) self.assertEqual(df3.rdd.take(3), df2.rdd.take(3)) # test repartitionByRange(numPartitions, *cols) df4 = df1.repartitionByRange(3, "name", "age") self.assertEqual(df4.rdd.getNumPartitions(), 3) self.assertEqual(df4.rdd.first(), df2.rdd.first()) self.assertEqual(df4.rdd.take(3), df2.rdd.take(3)) # test repartitionByRange(*cols) df5 = df1.repartitionByRange("name", "age") self.assertEqual(df5.rdd.first(), df2.rdd.first()) self.assertEqual(df5.rdd.take(3), df2.rdd.take(3)) def test_replace(self): schema = StructType([ StructField("name", StringType(), True), StructField("age", IntegerType(), True), StructField("height", DoubleType(), True)]) # replace with int row = self.spark.createDataFrame([(u'Alice', 10, 10.0)], schema).replace(10, 20).first() self.assertEqual(row.age, 20) self.assertEqual(row.height, 20.0) # replace with double row = self.spark.createDataFrame( [(u'Alice', 80, 80.0)], schema).replace(80.0, 82.1).first() self.assertEqual(row.age, 82) self.assertEqual(row.height, 82.1) # replace with string row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace(u'Alice', u'Ann').first() self.assertEqual(row.name, u"Ann") self.assertEqual(row.age, 10) # replace with subset specified by a string of a column name w/ actual change row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace(10, 20, subset='age').first() self.assertEqual(row.age, 20) # replace with subset specified by a string of a column name w/o actual change row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace(10, 20, subset='height').first() self.assertEqual(row.age, 10) # replace with subset specified with one column replaced, another column not in subset # stays unchanged. row = self.spark.createDataFrame( [(u'Alice', 10, 10.0)], schema).replace(10, 20, subset=['name', 'age']).first() self.assertEqual(row.name, u'Alice') self.assertEqual(row.age, 20) self.assertEqual(row.height, 10.0) # replace with subset specified but no column will be replaced row = self.spark.createDataFrame( [(u'Alice', 10, None)], schema).replace(10, 20, subset=['name', 'height']).first() self.assertEqual(row.name, u'Alice') self.assertEqual(row.age, 10) self.assertEqual(row.height, None) # replace with lists row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace([u'Alice'], [u'Ann']).first() self.assertTupleEqual(row, (u'Ann', 10, 80.1)) # replace with dict row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace({10: 11}).first() self.assertTupleEqual(row, (u'Alice', 11, 80.1)) # test backward compatibility with dummy value dummy_value = 1 row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace({'Alice': 'Bob'}, dummy_value).first() self.assertTupleEqual(row, (u'Bob', 10, 80.1)) # test dict with mixed numerics row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace({10: -10, 80.1: 90.5}).first() self.assertTupleEqual(row, (u'Alice', -10, 90.5)) # replace with tuples row = self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace((u'Alice', ), (u'Bob', )).first() self.assertTupleEqual(row, (u'Bob', 10, 80.1)) # replace multiple columns row = self.spark.createDataFrame( [(u'Alice', 10, 80.0)], schema).replace((10, 80.0), (20, 90)).first() self.assertTupleEqual(row, (u'Alice', 20, 90.0)) # test for mixed numerics row = self.spark.createDataFrame( [(u'Alice', 10, 80.0)], schema).replace((10, 80), (20, 90.5)).first() self.assertTupleEqual(row, (u'Alice', 20, 90.5)) row = self.spark.createDataFrame( [(u'Alice', 10, 80.0)], schema).replace({10: 20, 80: 90.5}).first() self.assertTupleEqual(row, (u'Alice', 20, 90.5)) # replace with boolean row = (self .spark.createDataFrame([(u'Alice', 10, 80.0)], schema) .selectExpr("name = 'Bob'", 'age <= 15') .replace(False, True).first()) self.assertTupleEqual(row, (True, True)) # replace string with None and then drop None rows row = self.spark.createDataFrame( [(u'Alice', 10, 80.0)], schema).replace(u'Alice', None).dropna() self.assertEqual(row.count(), 0) # replace with number and None row = self.spark.createDataFrame( [(u'Alice', 10, 80.0)], schema).replace([10, 80], [20, None]).first() self.assertTupleEqual(row, (u'Alice', 20, None)) # should fail if subset is not list, tuple or None with self.assertRaises(ValueError): self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace({10: 11}, subset=1).first() # should fail if to_replace and value have different length with self.assertRaises(ValueError): self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace(["Alice", "Bob"], ["Eve"]).first() # should fail if when received unexpected type with self.assertRaises(ValueError): from datetime import datetime self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace(datetime.now(), datetime.now()).first() # should fail if provided mixed type replacements with self.assertRaises(ValueError): self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace(["Alice", 10], ["Eve", 20]).first() with self.assertRaises(ValueError): self.spark.createDataFrame( [(u'Alice', 10, 80.1)], schema).replace({u"Alice": u"Bob", 10: 20}).first() with self.assertRaisesRegexp( TypeError, 'value argument is required when to_replace is not a dictionary.'): self.spark.createDataFrame( [(u'Alice', 10, 80.0)], schema).replace(["Alice", "Bob"]).first() def test_with_column_with_existing_name(self): keys = self.df.withColumn("key", self.df.key).select("key").collect() self.assertEqual([r.key for r in keys], list(range(100))) # regression test for SPARK-10417 def test_column_iterator(self): def foo(): for x in self.df.key: break self.assertRaises(TypeError, foo) def test_generic_hints(self): from pyspark.sql import DataFrame df1 = self.spark.range(10e10).toDF("id") df2 = self.spark.range(10e10).toDF("id") self.assertIsInstance(df1.hint("broadcast"), DataFrame) self.assertIsInstance(df1.hint("broadcast", []), DataFrame) # Dummy rules self.assertIsInstance(df1.hint("broadcast", "foo", "bar"), DataFrame) self.assertIsInstance(df1.hint("broadcast", ["foo", "bar"]), DataFrame) plan = df1.join(df2.hint("broadcast"), "id")._jdf.queryExecution().executedPlan() self.assertEqual(1, plan.toString().count("BroadcastHashJoin")) # add tests for SPARK-23647 (test more types for hint) def test_extended_hint_types(self): from pyspark.sql import DataFrame df = self.spark.range(10e10).toDF("id") such_a_nice_list = ["itworks1", "itworks2", "itworks3"] hinted_df = df.hint("my awesome hint", 1.2345, "what", such_a_nice_list) logical_plan = hinted_df._jdf.queryExecution().logical() self.assertEqual(1, logical_plan.toString().count("1.2345")) self.assertEqual(1, logical_plan.toString().count("what")) self.assertEqual(3, logical_plan.toString().count("itworks")) def test_sample(self): self.assertRaisesRegexp( TypeError, "should be a bool, float and number", lambda: self.spark.range(1).sample()) self.assertRaises( TypeError, lambda: self.spark.range(1).sample("a")) self.assertRaises( TypeError, lambda: self.spark.range(1).sample(seed="abc")) self.assertRaises( IllegalArgumentException, lambda: self.spark.range(1).sample(-1.0)) def test_toDF_with_schema_string(self): data = [Row(key=i, value=str(i)) for i in range(100)] rdd = self.sc.parallelize(data, 5) df = rdd.toDF("key: int, value: string") self.assertEqual(df.schema.simpleString(), "struct<key:int,value:string>") self.assertEqual(df.collect(), data) # different but compatible field types can be used. df = rdd.toDF("key: string, value: string") self.assertEqual(df.schema.simpleString(), "struct<key:string,value:string>") self.assertEqual(df.collect(), [Row(key=str(i), value=str(i)) for i in range(100)]) # field names can differ. df = rdd.toDF(" a: int, b: string ") self.assertEqual(df.schema.simpleString(), "struct<a:int,b:string>") self.assertEqual(df.collect(), data) # number of fields must match. self.assertRaisesRegexp(Exception, "Length of object", lambda: rdd.toDF("key: int").collect()) # field types mismatch will cause exception at runtime. self.assertRaisesRegexp(Exception, "FloatType can not accept", lambda: rdd.toDF("key: float, value: string").collect()) # flat schema values will be wrapped into row. df = rdd.map(lambda row: row.key).toDF("int") self.assertEqual(df.schema.simpleString(), "struct<value:int>") self.assertEqual(df.collect(), [Row(key=i) for i in range(100)]) # users can use DataType directly instead of data type string. df = rdd.map(lambda row: row.key).toDF(IntegerType()) self.assertEqual(df.schema.simpleString(), "struct<value:int>") self.assertEqual(df.collect(), [Row(key=i) for i in range(100)]) def test_join_without_on(self): df1 = self.spark.range(1).toDF("a") df2 = self.spark.range(1).toDF("b") with self.sql_conf({"spark.sql.crossJoin.enabled": False}): self.assertRaises(AnalysisException, lambda: df1.join(df2, how="inner").collect()) with self.sql_conf({"spark.sql.crossJoin.enabled": True}): actual = df1.join(df2, how="inner").collect() expected = [Row(a=0, b=0)] self.assertEqual(actual, expected) # Regression test for invalid join methods when on is None, Spark-14761 def test_invalid_join_method(self): df1 = self.spark.createDataFrame([("Alice", 5), ("Bob", 8)], ["name", "age"]) df2 = self.spark.createDataFrame([("Alice", 80), ("Bob", 90)], ["name", "height"]) self.assertRaises(IllegalArgumentException, lambda: df1.join(df2, how="invalid-join-type")) # Cartesian products require cross join syntax def test_require_cross(self): df1 = self.spark.createDataFrame([(1, "1")], ("key", "value")) df2 = self.spark.createDataFrame([(1, "1")], ("key", "value")) with self.sql_conf({"spark.sql.crossJoin.enabled": False}): # joins without conditions require cross join syntax self.assertRaises(AnalysisException, lambda: df1.join(df2).collect()) # works with crossJoin self.assertEqual(1, df1.crossJoin(df2).count()) def test_cache(self): spark = self.spark with self.tempView("tab1", "tab2"): spark.createDataFrame([(2, 2), (3, 3)]).createOrReplaceTempView("tab1") spark.createDataFrame([(2, 2), (3, 3)]).createOrReplaceTempView("tab2") self.assertFalse(spark.catalog.isCached("tab1")) self.assertFalse(spark.catalog.isCached("tab2")) spark.catalog.cacheTable("tab1") self.assertTrue(spark.catalog.isCached("tab1")) self.assertFalse(spark.catalog.isCached("tab2")) spark.catalog.cacheTable("tab2") spark.catalog.uncacheTable("tab1") self.assertFalse(spark.catalog.isCached("tab1")) self.assertTrue(spark.catalog.isCached("tab2")) spark.catalog.clearCache() self.assertFalse(spark.catalog.isCached("tab1")) self.assertFalse(spark.catalog.isCached("tab2")) self.assertRaisesRegexp( AnalysisException, "does_not_exist", lambda: spark.catalog.isCached("does_not_exist")) self.assertRaisesRegexp( AnalysisException, "does_not_exist", lambda: spark.catalog.cacheTable("does_not_exist")) self.assertRaisesRegexp( AnalysisException, "does_not_exist", lambda: spark.catalog.uncacheTable("does_not_exist")) def _to_pandas(self): from datetime import datetime, date schema = StructType().add("a", IntegerType()).add("b", StringType())\ .add("c", BooleanType()).add("d", FloatType())\ .add("dt", DateType()).add("ts", TimestampType()) data = [ (1, "foo", True, 3.0, date(1969, 1, 1), datetime(1969, 1, 1, 1, 1, 1)), (2, "foo", True, 5.0, None, None), (3, "bar", False, -1.0, date(2012, 3, 3), datetime(2012, 3, 3, 3, 3, 3)), (4, "bar", False, 6.0, date(2100, 4, 4), datetime(2100, 4, 4, 4, 4, 4)), ] df = self.spark.createDataFrame(data, schema) return df.toPandas() @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_to_pandas(self): import numpy as np pdf = self._to_pandas() types = pdf.dtypes self.assertEquals(types[0], np.int32) self.assertEquals(types[1], np.object) self.assertEquals(types[2], np.bool) self.assertEquals(types[3], np.float32) self.assertEquals(types[4], np.object) # datetime.date self.assertEquals(types[5], 'datetime64[ns]') @unittest.skipIf(have_pandas, "Required Pandas was found.") def test_to_pandas_required_pandas_not_found(self): with QuietTest(self.sc): with self.assertRaisesRegexp(ImportError, 'Pandas >= .* must be installed'): self._to_pandas() @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_to_pandas_avoid_astype(self): import numpy as np schema = StructType().add("a", IntegerType()).add("b", StringType())\ .add("c", IntegerType()) data = [(1, "foo", 16777220), (None, "bar", None)] df = self.spark.createDataFrame(data, schema) types = df.toPandas().dtypes self.assertEquals(types[0], np.float64) # doesn't convert to np.int32 due to NaN value. self.assertEquals(types[1], np.object) self.assertEquals(types[2], np.float64) @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_to_pandas_from_empty_dataframe(self): # SPARK-29188 test that toPandas() on an empty dataframe has the correct dtypes import numpy as np sql = """ SELECT CAST(1 AS TINYINT) AS tinyint, CAST(1 AS SMALLINT) AS smallint, CAST(1 AS INT) AS int, CAST(1 AS BIGINT) AS bigint, CAST(0 AS FLOAT) AS float, CAST(0 AS DOUBLE) AS double, CAST(1 AS BOOLEAN) AS boolean, CAST('foo' AS STRING) AS string, CAST('2019-01-01' AS TIMESTAMP) AS timestamp """ dtypes_when_nonempty_df = self.spark.sql(sql).toPandas().dtypes dtypes_when_empty_df = self.spark.sql(sql).filter("False").toPandas().dtypes self.assertTrue(np.all(dtypes_when_empty_df == dtypes_when_nonempty_df)) @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_to_pandas_from_null_dataframe(self): # SPARK-29188 test that toPandas() on a dataframe with only nulls has correct dtypes import numpy as np sql = """ SELECT CAST(NULL AS TINYINT) AS tinyint, CAST(NULL AS SMALLINT) AS smallint, CAST(NULL AS INT) AS int, CAST(NULL AS BIGINT) AS bigint, CAST(NULL AS FLOAT) AS float, CAST(NULL AS DOUBLE) AS double, CAST(NULL AS BOOLEAN) AS boolean, CAST(NULL AS STRING) AS string, CAST(NULL AS TIMESTAMP) AS timestamp """ pdf = self.spark.sql(sql).toPandas() types = pdf.dtypes self.assertEqual(types[0], np.float64) self.assertEqual(types[1], np.float64) self.assertEqual(types[2], np.float64) self.assertEqual(types[3], np.float64) self.assertEqual(types[4], np.float32) self.assertEqual(types[5], np.float64) self.assertEqual(types[6], np.object) self.assertEqual(types[7], np.object) self.assertTrue(np.can_cast(np.datetime64, types[8])) @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_to_pandas_from_mixed_dataframe(self): # SPARK-29188 test that toPandas() on a dataframe with some nulls has correct dtypes import numpy as np sql = """ SELECT CAST(col1 AS TINYINT) AS tinyint, CAST(col2 AS SMALLINT) AS smallint, CAST(col3 AS INT) AS int, CAST(col4 AS BIGINT) AS bigint, CAST(col5 AS FLOAT) AS float, CAST(col6 AS DOUBLE) AS double, CAST(col7 AS BOOLEAN) AS boolean, CAST(col8 AS STRING) AS string, CAST(col9 AS TIMESTAMP) AS timestamp FROM VALUES (1, 1, 1, 1, 1, 1, 1, 1, 1), (NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL) """ pdf_with_some_nulls = self.spark.sql(sql).toPandas() pdf_with_only_nulls = self.spark.sql(sql).filter('tinyint is null').toPandas() self.assertTrue(np.all(pdf_with_only_nulls.dtypes == pdf_with_some_nulls.dtypes)) def test_create_dataframe_from_array_of_long(self): import array data = [Row(longarray=array.array('l', [-9223372036854775808, 0, 9223372036854775807]))] df = self.spark.createDataFrame(data) self.assertEqual(df.first(), Row(longarray=[-9223372036854775808, 0, 9223372036854775807])) @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_create_dataframe_from_pandas_with_timestamp(self): import pandas as pd from datetime import datetime pdf = pd.DataFrame({"ts": [datetime(2017, 10, 31, 1, 1, 1)], "d": [pd.Timestamp.now().date()]}, columns=["d", "ts"]) # test types are inferred correctly without specifying schema df = self.spark.createDataFrame(pdf) self.assertTrue(isinstance(df.schema['ts'].dataType, TimestampType)) self.assertTrue(isinstance(df.schema['d'].dataType, DateType)) # test with schema will accept pdf as input df = self.spark.createDataFrame(pdf, schema="d date, ts timestamp") self.assertTrue(isinstance(df.schema['ts'].dataType, TimestampType)) self.assertTrue(isinstance(df.schema['d'].dataType, DateType)) @unittest.skipIf(have_pandas, "Required Pandas was found.") def test_create_dataframe_required_pandas_not_found(self): with QuietTest(self.sc): with self.assertRaisesRegexp( ImportError, "(Pandas >= .* must be installed|No module named '?pandas'?)"): import pandas as pd from datetime import datetime pdf = pd.DataFrame({"ts": [datetime(2017, 10, 31, 1, 1, 1)], "d": [pd.Timestamp.now().date()]}) self.spark.createDataFrame(pdf) # Regression test for SPARK-23360 @unittest.skipIf(not have_pandas, pandas_requirement_message) def test_create_dataframe_from_pandas_with_dst(self): import pandas as pd from pandas.util.testing import assert_frame_equal from datetime import datetime pdf = pd.DataFrame({'time': [datetime(2015, 10, 31, 22, 30)]}) df = self.spark.createDataFrame(pdf) assert_frame_equal(pdf, df.toPandas()) orig_env_tz = os.environ.get('TZ', None) try: tz = 'America/Los_Angeles' os.environ['TZ'] = tz time.tzset() with self.sql_conf({'spark.sql.session.timeZone': tz}): df = self.spark.createDataFrame(pdf) assert_frame_equal(pdf, df.toPandas()) finally: del os.environ['TZ'] if orig_env_tz is not None: os.environ['TZ'] = orig_env_tz time.tzset() def test_repr_behaviors(self): import re pattern = re.compile(r'^ *\|', re.MULTILINE) df = self.spark.createDataFrame([(1, "1"), (22222, "22222")], ("key", "value")) # test when eager evaluation is enabled and _repr_html_ will not be called with self.sql_conf({"spark.sql.repl.eagerEval.enabled": True}): expected1 = """+-----+-----+ || key|value| |+-----+-----+ || 1| 1| ||22222|22222| |+-----+-----+ |""" self.assertEquals(re.sub(pattern, '', expected1), df.__repr__()) with self.sql_conf({"spark.sql.repl.eagerEval.truncate": 3}): expected2 = """+---+-----+ ||key|value| |+---+-----+ || 1| 1| ||222| 222| |+---+-----+ |""" self.assertEquals(re.sub(pattern, '', expected2), df.__repr__()) with self.sql_conf({"spark.sql.repl.eagerEval.maxNumRows": 1}): expected3 = """+---+-----+ ||key|value| |+---+-----+ || 1| 1| |+---+-----+ |only showing top 1 row |""" self.assertEquals(re.sub(pattern, '', expected3), df.__repr__()) # test when eager evaluation is enabled and _repr_html_ will be called with self.sql_conf({"spark.sql.repl.eagerEval.enabled": True}): expected1 = """<table border='1'> |<tr><th>key</th><th>value</th></tr> |<tr><td>1</td><td>1</td></tr> |<tr><td>22222</td><td>22222</td></tr> |</table> |""" self.assertEquals(re.sub(pattern, '', expected1), df._repr_html_()) with self.sql_conf({"spark.sql.repl.eagerEval.truncate": 3}): expected2 = """<table border='1'> |<tr><th>key</th><th>value</th></tr> |<tr><td>1</td><td>1</td></tr> |<tr><td>222</td><td>222</td></tr> |</table> |""" self.assertEquals(re.sub(pattern, '', expected2), df._repr_html_()) with self.sql_conf({"spark.sql.repl.eagerEval.maxNumRows": 1}): expected3 = """<table border='1'> |<tr><th>key</th><th>value</th></tr> |<tr><td>1</td><td>1</td></tr> |</table> |only showing top 1 row |""" self.assertEquals(re.sub(pattern, '', expected3), df._repr_html_()) # test when eager evaluation is disabled and _repr_html_ will be called with self.sql_conf({"spark.sql.repl.eagerEval.enabled": False}): expected = "DataFrame[key: bigint, value: string]" self.assertEquals(None, df._repr_html_()) self.assertEquals(expected, df.__repr__()) with self.sql_conf({"spark.sql.repl.eagerEval.truncate": 3}): self.assertEquals(None, df._repr_html_()) self.assertEquals(expected, df.__repr__()) with self.sql_conf({"spark.sql.repl.eagerEval.maxNumRows": 1}): self.assertEquals(None, df._repr_html_()) self.assertEquals(expected, df.__repr__()) def test_to_local_iterator(self): df = self.spark.range(8, numPartitions=4) expected = df.collect() it = df.toLocalIterator() self.assertEqual(expected, list(it)) # Test DataFrame with empty partition df = self.spark.range(3, numPartitions=4) it = df.toLocalIterator() expected = df.collect() self.assertEqual(expected, list(it)) def test_to_local_iterator_prefetch(self): df = self.spark.range(8, numPartitions=4) expected = df.collect() it = df.toLocalIterator(prefetchPartitions=True) self.assertEqual(expected, list(it)) def test_to_local_iterator_not_fully_consumed(self): # SPARK-23961: toLocalIterator throws exception when not fully consumed # Create a DataFrame large enough so that write to socket will eventually block df = self.spark.range(1 << 20, numPartitions=2) it = df.toLocalIterator() self.assertEqual(df.take(1)[0], next(it)) with QuietTest(self.sc): it = None # remove iterator from scope, socket is closed when cleaned up # Make sure normal df operations still work result = [] for i, row in enumerate(df.toLocalIterator()): result.append(row) if i == 7: break self.assertEqual(df.take(8), result) class QueryExecutionListenerTests(unittest.TestCase, SQLTestUtils): # These tests are separate because it uses 'spark.sql.queryExecutionListeners' which is # static and immutable. This can't be set or unset, for example, via `spark.conf`. @classmethod def setUpClass(cls): import glob from pyspark.find_spark_home import _find_spark_home SPARK_HOME = _find_spark_home() filename_pattern = ( "sql/core/target/scala-*/test-classes/org/apache/spark/sql/" "TestQueryExecutionListener.class") cls.has_listener = bool(glob.glob(os.path.join(SPARK_HOME, filename_pattern))) if cls.has_listener: # Note that 'spark.sql.queryExecutionListeners' is a static immutable configuration. cls.spark = SparkSession.builder \ .master("local[4]") \ .appName(cls.__name__) \ .config( "spark.sql.queryExecutionListeners", "org.apache.spark.sql.TestQueryExecutionListener") \ .getOrCreate() def setUp(self): if not self.has_listener: raise self.skipTest( "'org.apache.spark.sql.TestQueryExecutionListener' is not " "available. Will skip the related tests.") @classmethod def tearDownClass(cls): if hasattr(cls, "spark"): cls.spark.stop() def tearDown(self): self.spark._jvm.OnSuccessCall.clear() def test_query_execution_listener_on_collect(self): self.assertFalse( self.spark._jvm.OnSuccessCall.isCalled(), "The callback from the query execution listener should not be called before 'collect'") self.spark.sql("SELECT * FROM range(1)").collect() self.spark.sparkContext._jsc.sc().listenerBus().waitUntilEmpty(10000) self.assertTrue( self.spark._jvm.OnSuccessCall.isCalled(), "The callback from the query execution listener should be called after 'collect'") @unittest.skipIf( not have_pandas or not have_pyarrow, pandas_requirement_message or pyarrow_requirement_message) def test_query_execution_listener_on_collect_with_arrow(self): with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": True}): self.assertFalse( self.spark._jvm.OnSuccessCall.isCalled(), "The callback from the query execution listener should not be " "called before 'toPandas'") self.spark.sql("SELECT * FROM range(1)").toPandas() self.spark.sparkContext._jsc.sc().listenerBus().waitUntilEmpty(10000) self.assertTrue( self.spark._jvm.OnSuccessCall.isCalled(), "The callback from the query execution listener should be called after 'toPandas'") if __name__ == "__main__": from pyspark.sql.tests.test_dataframe import * try: import xmlrunner testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2) except ImportError: testRunner = None unittest.main(testRunner=testRunner, verbosity=2)

apache-2.0

iamshang1/Projects

Advanced_ML/Text_Classification/tf_han.py

1

14525

''' hierarchical attention network for document classification https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf ''' import os os.environ["CUDA_VISIBLE_DEVICES"]="0" import numpy as np import tensorflow as tf from tensorflow.contrib.rnn import LSTMCell, GRUCell import sys import time class hierarchical_attention_network(object): ''' hierarchical attention network for document classification https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf parameters: - embedding_matrix: numpy array numpy array of word embeddings each row should represent a word embedding NOTE: the word index 0 is dropped, so the first row is ignored - num classes: int number of output classes - max_sents: int maximum number of sentences per document - max_words: int maximum number of words per sentence - rnn_type: string (default: "gru") rnn cells to use, can be "gru" or "lstm" - rnn_units: int (default: 100) number of rnn units to use for embedding layers - attention_context: int (default: 50) number of dimensions to use for attention context layer - dropout_keep: float (default: 0.5) dropout keep rate for final softmax layer methods: - train(data,labels,epochs=30,savebest=False,filepath=None) train network on given data - predict(data) return the one-hot-encoded predicted labels for given data - score(data,labels,bootstrap=False,bs_samples=100) return the accuracy of predicted labels on given data - save(filepath) save the model weights to a file - load(filepath) load model weights from a file ''' def __init__(self,embedding_matrix,num_classes,max_sents,max_words,rnn_type="gru", rnn_units=200,attention_context=300,dropout_keep=1.0): self.rnn_units = rnn_units if rnn_type == "gru": self.rnn_cell = GRUCell elif rnn_type == "lstm": self.rnn_cell = LSTMCell else: raise Exception("rnn_type parameter must be set to gru or lstm") self.dropout_keep = dropout_keep self.vocab = embedding_matrix self.embedding_size = embedding_matrix.shape[1] self.ms = max_sents self.mw = max_words #shared variables with tf.variable_scope('words'): self.word_atten_W = tf.Variable(self._ortho_weight(2*rnn_units,attention_context),name='word_atten_W') self.word_atten_b = tf.Variable(np.asarray(np.zeros(attention_context),dtype=np.float32),name='word_atten_b') self.word_softmax = tf.Variable(self._ortho_weight(attention_context,1),name='word_softmax') with tf.variable_scope('sentence'): self.sent_atten_W = tf.Variable(self._ortho_weight(2*rnn_units,attention_context),name='sent_atten_W') self.sent_atten_b = tf.Variable(np.asarray(np.zeros(attention_context),dtype=np.float32),name='sent_atten_b') self.sent_softmax = tf.Variable(self._ortho_weight(attention_context,1),name='sent_softmax') with tf.variable_scope('classify'): self.W_softmax = tf.Variable(self._ortho_weight(rnn_units*2,num_classes),name='W_softmax') self.b_softmax = tf.Variable(np.asarray(np.zeros(num_classes),dtype=np.float32),name='b_softmax') self.embeddings = tf.constant(self.vocab,tf.float32) self.dropout = tf.placeholder(tf.float32) #doc input and mask self.doc_input = tf.placeholder(tf.int32, shape=[max_sents,max_words]) self.words_per_line = tf.reduce_sum(tf.sign(self.doc_input),1) self.max_lines = tf.reduce_sum(tf.sign(self.words_per_line)) self.max_words = tf.reduce_max(self.words_per_line) self.doc_input_reduced = self.doc_input[:self.max_lines,:self.max_words] self.num_words = self.words_per_line[:self.max_lines] #word rnn layer self.word_embeds = tf.gather(tf.get_variable('embeddings',initializer=self.embeddings,dtype=tf.float32),self.doc_input_reduced) with tf.variable_scope('words'): [word_outputs_fw,word_outputs_bw],_ = \ tf.nn.bidirectional_dynamic_rnn( tf.contrib.rnn.DropoutWrapper(self.rnn_cell(self.rnn_units),state_keep_prob=self.dropout), tf.contrib.rnn.DropoutWrapper(self.rnn_cell(self.rnn_units),state_keep_prob=self.dropout), self.word_embeds,sequence_length=self.num_words,dtype=tf.float32) word_outputs = tf.concat((word_outputs_fw, word_outputs_bw),2) #word attention seq_mask = tf.reshape(tf.sequence_mask(self.num_words,self.max_words),[-1]) u = tf.nn.tanh(tf.matmul(tf.reshape(word_outputs,[-1,self.rnn_units*2]),self.word_atten_W)+self.word_atten_b) exps = tf.exp(tf.matmul(u,self.word_softmax)) exps = tf.where(seq_mask,exps,tf.ones_like(exps)*0.000000001) alpha = tf.reshape(exps,[-1,self.max_words,1]) alpha /= tf.reshape(tf.reduce_sum(alpha,1),[-1,1,1]) self.sent_embeds = tf.reduce_sum(word_outputs*alpha,1) self.sent_embeds = tf.expand_dims(self.sent_embeds,0) #sentence rnn layer with tf.variable_scope('sentence'): [self.sent_outputs_fw,self.sent_outputs_bw],_ = \ tf.nn.bidirectional_dynamic_rnn( tf.contrib.rnn.DropoutWrapper(self.rnn_cell(self.rnn_units),state_keep_prob=self.dropout), tf.contrib.rnn.DropoutWrapper(self.rnn_cell(self.rnn_units),state_keep_prob=self.dropout), self.sent_embeds,sequence_length=tf.expand_dims(self.max_lines,0),dtype=tf.float32) self.sent_outputs = tf.concat((tf.squeeze(self.sent_outputs_fw,[0]),tf.squeeze(self.sent_outputs_bw,[0])),1) #sentence attention self.sent_u = tf.nn.tanh(tf.matmul(self.sent_outputs,self.sent_atten_W) + self.sent_atten_b) self.sent_exp = tf.exp(tf.matmul(self.sent_u,self.sent_softmax)) self.sent_atten = self.sent_exp/tf.reduce_sum(self.sent_exp) self.doc_embed = tf.transpose(tf.matmul(tf.transpose(self.sent_outputs),self.sent_atten)) #classification functions self.output = tf.matmul(self.doc_embed,self.W_softmax)+self.b_softmax self.prediction = tf.nn.softmax(self.output) #loss, accuracy, and training functions self.labels = tf.placeholder(tf.float32, shape=[num_classes]) self.labels_rs = tf.expand_dims(self.labels,0) self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.output,labels=self.labels_rs)) self.optimizer = tf.train.AdamOptimizer(0.00001,0.9,0.99).minimize(self.loss) #init op self.init_op = tf.global_variables_initializer() self.saver = tf.train.Saver() self.sess = tf.Session() self.sess.run(self.init_op) def _ortho_weight(self,fan_in,fan_out): ''' generate orthogonal weight matrix ''' bound = np.sqrt(2./(fan_in+fan_out)) W = np.random.randn(fan_in,fan_out)*bound u, s, v = np.linalg.svd(W,full_matrices=False) if u.shape[0] != u.shape[1]: W = u else: W = v return W.astype(np.float32) def _list_to_numpy(self,inputval): ''' convert variable length lists of input values to zero padded numpy array ''' if type(inputval) == list: retval = np.zeros((self.ms,self.mw)) for i,line in enumerate(inputval): for j, word in enumerate(line): retval[i,j] = word return retval elif type(inputval) == np.ndarray: return inputval else: raise Exception("invalid input type") def train(self,data,labels,epochs=30,validation_data=None,savebest=False,filepath=None): ''' train network on given data parameters: - data: numpy array 3d numpy array (doc x sentence x word ids) of input data - labels: numpy array 2d numpy array of one-hot-encoded labels - epochs: int (default: 30) number of epochs to train for - validation_data: tuple (optional) tuple of numpy arrays (X,y) representing validation data - savebest: boolean (default: False) set to True to save the best model based on validation score per epoch - filepath: string (optional) path to save model if savebest is set to True outputs: None ''' if savebest==True and filepath==None: raise Exception("Please enter a path to save the network") if validation_data: validation_size = len(validation_data[0]) else: validation_size = len(data) print('training network on %i documents, validating on %i documents' \ % (len(data), validation_size)) #track best model for saving prevbest = 0 for i in range(epochs): correct = 0. start = time.time() #train for doc in range(len(data)): inputval = self._list_to_numpy(data[doc]) feed_dict = {self.doc_input:inputval,self.labels:labels[doc],self.dropout:self.dropout_keep} pred,cost,_ = self.sess.run([self.prediction,self.loss,self.optimizer],feed_dict=feed_dict) if np.argmax(pred) == np.argmax(labels[doc]): correct += 1 sys.stdout.write("epoch %i, sample %i of %i, loss: %f \r"\ % (i+1,doc+1,len(data),cost)) sys.stdout.flush() if (doc+1) % 50000 == 0: score = self.score(validation_data[0],validation_data[1]) print("iteration %i validation accuracy: %.4f%%" % (doc+1, score*100)) print() #print("training time: %.2f" % (time.time()-start)) trainscore = correct/len(data) print("epoch %i training accuracy: %.4f%%" % (i+1, trainscore*100)) #validate if validation_data: score = self.score(validation_data[0],validation_data[1]) print("epoch %i validation accuracy: %.4f%%" % (i+1, score*100)) #save if performance better than previous best if savebest and score >= prevbest: prevbest = score self.save(filepath) def predict(self,data): ''' return the one-hot-encoded predicted labels for given data parameters: - data: numpy array 3d numpy array (doc x sentence x word ids) of input data outputs: numpy array of one-hot-encoded predicted labels for input data ''' labels = [] for doc in range(len(data)): inputval = self._list_to_numpy(data[doc]) feed_dict = {self.doc_input:inputval,self.dropout:1.0} prob = self.sess.run(self.prediction,feed_dict=feed_dict) prob = np.squeeze(prob,0) one_hot = np.zeros_like(prob) one_hot[np.argmax(prob)] = 1 labels.append(one_hot) labels = np.array(labels) return labels def score(self,data,labels): ''' return the accuracy of predicted labels on given data parameters: - data: numpy array 3d numpy array (doc x sentence x word ids) of input data - labels: numpy array 2d numpy array of one-hot-encoded labels outputs: float representing accuracy of predicted labels on given data ''' correct = 0. for doc in range(len(data)): inputval = self._list_to_numpy(data[doc]) feed_dict = {self.doc_input:inputval,self.dropout:1.0} prob = self.sess.run(self.prediction,feed_dict=feed_dict) if np.argmax(prob) == np.argmax(labels[doc]): correct +=1 accuracy = correct/len(labels) return accuracy def save(self,filename): ''' save the model weights to a file parameters: - filepath: string path to save model weights outputs: None ''' self.saver.save(self.sess,filename) def load(self,filename): ''' load model weights from a file parameters: - filepath: string path from which to load model weights outputs: None ''' self.saver.restore(self.sess,filename) if __name__ == "__main__": from sklearn.preprocessing import LabelEncoder, LabelBinarizer from sklearn.model_selection import train_test_split import pickle import os #load saved files print "loading data" vocab = np.load('embeddings.npy') with open('data.pkl', 'rb') as f: data = pickle.load(f) num_docs = len(data) #convert data to numpy arrays print "converting data to arrays" max_sents = 0 max_words = 0 docs = [] labels = [] for i in range(num_docs): sys.stdout.write("processing record %i of %i \r" % (i+1,num_docs)) sys.stdout.flush() doc = data[i]['idx'] docs.append(doc) labels.append(data[i]['label']) if len(doc) > max_sents: max_sents = len(doc) if len(max(doc,key=len)) > max_words: max_words = len(max(doc,key=len)) del data print #label encoder le = LabelEncoder() y = le.fit_transform(labels) classes = len(le.classes_) lb = LabelBinarizer() y_bin = lb.fit_transform(y) del labels #test train split X_train,X_test,y_train,y_test = train_test_split(docs,y_bin,test_size=0.1, random_state=1234,stratify=y) #train nn print "building hierarchical attention network" nn = hierarchical_attention_network(vocab,classes,max_sents,max_words) nn.train(X_train,y_train,epochs=5,validation_data=(X_test,y_test))

mit

thorwhalen/ut

ml/text/topic_analysis.py

1

6849

__author__ = 'thor' from wordcloud import WordCloud import colorsys import seaborn as sns from numpy import sqrt, linspace, ceil, where, arange, array, any, floor, ceil, ndarray from pandas import Series import matplotlib.pyplot as plt class TopicExplorer(object): def __init__(self, url_vectorizer, topic_model, topic_weight_normalization=None, word_preprocessor=None, wordcloud_params={'ranks_only': True, 'width': 300, 'height': 300, 'margin': 1, 'background_color': "black"}, replace_empty_feature_with='EMPTY', word_art_params={}): self.url_vectorizer = url_vectorizer self.feature_names = self.url_vectorizer.get_feature_names() if word_preprocessor is None: self.word_preprocessor = lambda x: x else: self.word_preprocessor = word_preprocessor # some features might have empty names: Replace them with replace_empty_feature_with if replace_empty_feature_with is not None: lidx = array(self.feature_names) == '' if any(lidx): self.feature_names[lidx] = replace_empty_feature_with self.topic_model = topic_model self.wordcloud_params = wordcloud_params self.word_art_params = word_art_params self.n_topics = len(self.topic_model.components_) topic_components = self.topic_model.components_ if topic_weight_normalization is not None: if isinstance(topic_weight_normalization, str): if topic_weight_normalization == 'tf_normal': def topic_weight_normalization(topic_components): topic_components /= topic_components.sum(axis=1)[:, None] topic_components *= 1 / sqrt((topic_components ** 2).sum(axis=0)) return topic_components else: ValueError("Unknown topic_weight_normalization name") if callable(topic_weight_normalization): topic_components = topic_weight_normalization(topic_components) self.topic_word_weights = list() for topic_idx, topic in enumerate(topic_components): topic_ww = dict() for i in topic.argsort(): topic_ww[self.feature_names[i]] = topic_components[topic_idx, i] self.topic_word_weights.append(Series(topic_ww).sort_values(ascending=False, inplace=False)) self.topic_color = ["hsl(0, 100%, 100%)"] h_list = list(map(int, linspace(0, 360, len(self.topic_model.components_))))[:-1] for h in h_list: self.topic_color.append("hsl({}, 100%, 50%)".format(h)) def topic_weights(self, text_collection): if isinstance(text_collection, str): urls = [text_collection] return self.topic_model.transform(self.url_vectorizer.transform(text_collection)) def topic_word_art(self, topic_idx=None, n_words=20, save_file=None, color_func=None, random_state=1, fig_row_size=16, **kwargs): if topic_idx is None: ncols = int(floor(sqrt(self.n_topics))) nrows = int(ceil(self.n_topics / float(ncols))) ncols_to_nrows_ratio = ncols / nrows plt.figure(figsize=(fig_row_size, ncols_to_nrows_ratio * fig_row_size)) for i in range(self.n_topics): plt.subplot(nrows, ncols, i + 1) self.topic_word_art(topic_idx=i, n_words=n_words, save_file=save_file, color_func=color_func, random_state=random_state, **kwargs) plt.gcf().subplots_adjust(wspace=.1, hspace=.1) # elif isinstance(topic_idx, (list, tuple, ndarray)) and len(topic_idx) == self.n_topics: # ncols = int(floor(sqrt(self.n_topics))) # nrows = int(ceil(self.n_topics / float(ncols))) # ncols_to_nrows_ratio = ncols / nrows # plt.figure(figsize=(fig_row_size, ncols_to_nrows_ratio * fig_row_size)) # for i in range(self.n_topics): # plt.subplot(nrows, ncols, i + 1) # self.topic_word_art(topic_idx=i, n_words=n_words, save_file=save_file, # color_func=color_func, random_state=random_state, # width=int(self.wordcloud_params['width'] * topic_idx[i]), # height=int(self.wordcloud_params['height'] * topic_idx[i])) # plt.gcf().subplots_adjust(wspace=.1, hspace=.1) else: kwargs = dict(self.wordcloud_params, **kwargs) if color_func is None: color_func = self.word_art_params.get('color_func', self.topic_color[topic_idx]) if isinstance(color_func, tuple): color_func = "rgb({}, {}, {})".format(*list(map(int, color_func))) if isinstance(color_func, str): color = color_func def color_func(word, font_size, position, orientation, random_state=None, **kwargs): return color elif not callable(color_func): TypeError("Unrecognized hsl_color type ()".format(type(color_func))) # kwargs = dict(self.word_art_params, **kwargs) wc = WordCloud(random_state=random_state, **kwargs) wc.fit_words([(self.word_preprocessor(k), v) for k, v in self.topic_word_weights[topic_idx].iloc[:n_words].to_dict().items()]) # wc.recolor(color_func=kwargs['color_func'], random_state=random_state) plt.imshow(wc.recolor(color_func=color_func, random_state=random_state)) plt.grid(False) plt.xticks([]) plt.yticks([]) def plot_topic_trajectory(self, urls): _topic_weights = self.topic_weights(urls) _topic_weights = (_topic_weights.T / _topic_weights.max(axis=1)) sns.heatmap(_topic_weights, cbar=False, linewidths=1) plt.ylabel('Topic') plt.xlabel('Page view') ax = plt.gca() start, end = ax.get_xlim() if _topic_weights.shape[1] > 20: ax.xaxis.set_ticks(arange(start, end, 10)) ax.xaxis.set_ticklabels(arange(start, end, 10).astype(int)) return ax def plot_topic_trajectory_of_tcid(self, tcid, data): d = data[data.tc_id == tcid].sort_values(by='timestamp', ascending=True) urls = d.data_url_test ax = self.plot_topic_trajectory(urls) conversion_idx = where(array(d.data_env_template == 'funnel_confirmation'))[0] if len(conversion_idx): min_y, max_y = plt.ylim() for idx in conversion_idx: plt.plot((idx + 0.5, idx + 0.5), (min_y, max_y), 'b-')

mit

kkk669/mxnet

docs/mxdoc.py

13

12762

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """A sphnix-doc plugin to build mxnet docs""" import subprocess import re import os import json import sys from recommonmark import transform import pypandoc # import StringIO from io for python3 compatibility from io import StringIO import contextlib # white list to evaluate the code block output, such as ['tutorials/gluon'] _EVAL_WHILTELIST = [] # start or end of a code block _CODE_MARK = re.compile('^([ ]*)```([\w]*)') # language names and the according file extensions and comment symbol _LANGS = {'python' : ('py', '#'), 'r' : ('R','#'), 'scala' : ('scala', '#'), 'julia' : ('jl', '#'), 'perl' : ('pl', '#'), 'cpp' : ('cc', '//'), 'bash' : ('sh', '#')} _LANG_SELECTION_MARK = 'INSERT SELECTION BUTTONS' _SRC_DOWNLOAD_MARK = 'INSERT SOURCE DOWNLOAD BUTTONS' def _run_cmd(cmds): """Run commands, raise exception if failed""" if not isinstance(cmds, str): cmds = "".join(cmds) print("Execute \"%s\"" % cmds) try: subprocess.check_call(cmds, shell=True) except subprocess.CalledProcessError as err: print(err) raise err def generate_doxygen(app): """Run the doxygen make commands""" _run_cmd("cd %s/.. && make doxygen" % app.builder.srcdir) _run_cmd("cp -rf doxygen/html %s/doxygen" % app.builder.outdir) def build_mxnet(app): """Build mxnet .so lib""" _run_cmd("cd %s/.. && cp make/config.mk config.mk && make -j$(nproc) DEBUG=1" % app.builder.srcdir) def build_r_docs(app): """build r pdf""" r_root = app.builder.srcdir + '/../R-package' pdf_path = root_path + '/docs/api/r/mxnet-r-reference-manual.pdf' _run_cmd('cd ' + r_root + '; R -e "roxygen2::roxygenize()"; R CMD Rd2pdf . --no-preview -o ' + pdf_path) dest_path = app.builder.outdir + '/api/r/' _run_cmd('mkdir -p ' + dest_path + '; mv ' + pdf_path + ' ' + dest_path) def build_scala_docs(app): """build scala doc and then move the outdir""" scala_path = app.builder.srcdir + '/../scala-package/core/src/main/scala/ml/dmlc/mxnet' # scaldoc fails on some apis, so exit 0 to pass the check _run_cmd('cd ' + scala_path + '; scaladoc `find . | grep .*scala`; exit 0') dest_path = app.builder.outdir + '/api/scala/docs' _run_cmd('rm -rf ' + dest_path) _run_cmd('mkdir -p ' + dest_path) scaladocs = ['index', 'index.html', 'ml', 'lib', 'index.js', 'package.html'] for doc_file in scaladocs: _run_cmd('cd ' + scala_path + ' && mv -f ' + doc_file + ' ' + dest_path) def _convert_md_table_to_rst(table): """Convert a markdown table to rst format""" if len(table) < 3: return '' out = '```eval_rst\n.. list-table::\n :header-rows: 1\n\n' for i,l in enumerate(table): cols = l.split('|')[1:-1] if i == 0: ncol = len(cols) else: if len(cols) != ncol: return '' if i == 1: for c in cols: if len(c) is not 0 and '---' not in c: return '' else: for j,c in enumerate(cols): out += ' * - ' if j == 0 else ' - ' out += pypandoc.convert_text( c, 'rst', format='md').replace('\n', ' ').replace('\r', '') + '\n' out += '```\n' return out def convert_table(app, docname, source): """Find tables in a markdown and then convert them into the rst format""" num_tables = 0 for i,j in enumerate(source): table = [] output = '' in_table = False for l in j.split('\n'): r = l.strip() if r.startswith('|'): table.append(r) in_table = True else: if in_table is True: converted = _convert_md_table_to_rst(table) if converted is '': print("Failed to convert the markdown table") print(table) else: num_tables += 1 output += converted in_table = False table = [] output += l + '\n' source[i] = output if num_tables > 0: print('Converted %d tables in %s' % (num_tables, docname)) def _parse_code_lines(lines): """A iterator that returns if a line is within a code block Returns ------- iterator of (str, bool, str, int) - line: the line - in_code: if this line is in a code block - lang: the code block langunage - indent: the code indent """ in_code = False lang = None indent = None for l in lines: m = _CODE_MARK.match(l) if m is not None: if not in_code: if m.groups()[1].lower() in _LANGS: lang = m.groups()[1].lower() indent = len(m.groups()[0]) in_code = True yield (l, in_code, lang, indent) else: yield (l, in_code, lang, indent) lang = None indent = None in_code = False else: yield (l, in_code, lang, indent) def _get_lang_selection_btn(langs): active = True btngroup = '<div class="text-center">\n<div class="btn-group opt-group" role="group">' for l in langs: btngroup += '<button type="button" class="btn btn-default opt %s">%s</button>\n' % ( 'active' if active else '', l[0].upper()+l[1:].lower()) active = False btngroup += '</div>\n</div> <script type="text/javascript" src="../../_static/js/options.js"></script>' return btngroup def _get_blocks(lines): """split lines into code and non-code blocks Returns ------- iterator of (bool, str, list of str) - if it is a code block - source language - lines of source """ cur_block = [] pre_lang = None pre_in_code = None for (l, in_code, cur_lang, _) in _parse_code_lines(lines): if in_code != pre_in_code: if pre_in_code and len(cur_block) >= 2: cur_block = cur_block[1:-1] # remove ``` # remove empty lines at head while len(cur_block) > 0: if len(cur_block[0]) == 0: cur_block.pop(0) else: break # remove empty lines at tail while len(cur_block) > 0: if len(cur_block[-1]) == 0: cur_block.pop() else: break if len(cur_block): yield (pre_in_code, pre_lang, cur_block) cur_block = [] cur_block.append(l) pre_lang = cur_lang pre_in_code = in_code if len(cur_block): yield (pre_in_code, pre_lang, cur_block) def _get_mk_code_block(src, lang): """Return a markdown code block E.g. ```python import mxnet ```` """ if lang is None: lang = '' return '```'+lang+'\n'+src.rstrip()+'\n'+'```\n' @contextlib.contextmanager def _string_io(): oldout = sys.stdout olderr = sys.stderr strio = StringIO.StringIO() sys.stdout = strio sys.stderr = strio yield strio sys.stdout = oldout sys.stderr = olderr def _get_python_block_output(src, global_dict, local_dict): """Evaluate python source codes Returns (bool, str): - True if success - output """ src = '\n'.join([l for l in src.split('\n') if not l.startswith('%') and not 'plt.show()' in l]) ret_status = True err = '' with _string_io() as s: try: exec(src, global_dict, global_dict) except Exception as e: err = str(e) ret_status = False return (ret_status, s.getvalue()+err) def _get_jupyter_notebook(lang, lines): cells = [] for in_code, blk_lang, lines in _get_blocks(lines): if blk_lang != lang: in_code = False src = '\n'.join(lines) cell = { "cell_type": "code" if in_code else "markdown", "metadata": {}, "source": src } if in_code: cell.update({ "outputs": [], "execution_count": None, }) cells.append(cell) ipynb = {"nbformat" : 4, "nbformat_minor" : 2, "metadata" : {"language":lang, "display_name":'', "name":''}, "cells" : cells} return ipynb def _get_source(lang, lines): cmt = _LANGS[lang][1] + ' ' out = [] for in_code, lines in _get_blocks(lang, lines): if in_code: out.append('') for l in lines: if in_code: if '%matplotlib' not in l: out.append(l) else: if ('<div>' in l or '</div>' in l or '<script>' in l or '</script>' in l or '' in l or '%matplotlib' in l ): continue out.append(cmt+l) if in_code: out.append('') return out def _get_src_download_btn(out_prefix, langs, lines): btn = '<div class="btn-group" role="group">\n' for lang in langs: ipynb = out_prefix if lang == 'python': ipynb += '.ipynb' else: ipynb += '_' + lang + '.ipynb' with open(ipynb, 'w') as f: json.dump(_get_jupyter_notebook(lang, lines), f) f = ipynb.split('/')[-1] btn += '<div class="download-btn"><a href="%s" download="%s">' \ '<span class="glyphicon glyphicon-download-alt"></span> %s</a></div>' % (f, f, f) btn += '</div>\n' return btn def add_buttons(app, docname, source): out_prefix = app.builder.outdir + '/' + docname dirname = os.path.dirname(out_prefix) if not os.path.exists(dirname): os.makedirs(dirname) for i,j in enumerate(source): local_dict = {} global_dict = {} lines = j.split('\n') langs = set([l for (_, _, l, _) in _parse_code_lines(lines) if l is not None and l in _LANGS]) # first convert for k,l in enumerate(lines): if _SRC_DOWNLOAD_MARK in l: lines[k] = _get_src_download_btn( out_prefix, langs, lines) # # then add lang buttons # for k,l in enumerate(lines): # if _LANG_SELECTION_MARK in l: # lines[k] = _get_lang_selection_btn(langs) output = '' for in_code, lang, lines in _get_blocks(lines): src = '\n'.join(lines)+'\n' if in_code: output += _get_mk_code_block(src, lang) if lang == 'python' and any([w in docname for w in _EVAL_WHILTELIST]): status, blk_out = _get_python_block_output(src, global_dict, local_dict) if len(blk_out): output += '<div class=\"cell-results-header\">Output:</div>\n\n' output += _get_mk_code_block(blk_out, 'results') else: output += src source[i] = output # source[i] = '\n'.join(lines) def setup(app): app.connect("builder-inited", build_mxnet) app.connect("builder-inited", generate_doxygen) app.connect("builder-inited", build_scala_docs) # skipped to build r, it requires to install latex, which is kinds of too heavy # app.connect("builder-inited", build_r_docs) app.connect('source-read', convert_table) app.connect('source-read', add_buttons) app.add_config_value('recommonmark_config', { 'url_resolver': lambda url: 'http://mxnet.io/' + url, 'enable_eval_rst': True, }, True) app.add_transform(transform.AutoStructify)

apache-2.0

Clyde-fare/scikit-learn

examples/decomposition/plot_image_denoising.py

181

5819

""" ========================================= Image denoising using dictionary learning ========================================= An example comparing the effect of reconstructing noisy fragments of the Lena image using firstly online :ref:`DictionaryLearning` and various transform methods. The dictionary is fitted on the distorted left half of the image, and subsequently used to reconstruct the right half. Note that even better performance could be achieved by fitting to an undistorted (i.e. noiseless) image, but here we start from the assumption that it is not available. A common practice for evaluating the results of image denoising is by looking at the difference between the reconstruction and the original image. If the reconstruction is perfect this will look like Gaussian noise. It can be seen from the plots that the results of :ref:`omp` with two non-zero coefficients is a bit less biased than when keeping only one (the edges look less prominent). It is in addition closer from the ground truth in Frobenius norm. The result of :ref:`least_angle_regression` is much more strongly biased: the difference is reminiscent of the local intensity value of the original image. Thresholding is clearly not useful for denoising, but it is here to show that it can produce a suggestive output with very high speed, and thus be useful for other tasks such as object classification, where performance is not necessarily related to visualisation. """ print(__doc__) from time import time import matplotlib.pyplot as plt import numpy as np from scipy.misc import lena from sklearn.decomposition import MiniBatchDictionaryLearning from sklearn.feature_extraction.image import extract_patches_2d from sklearn.feature_extraction.image import reconstruct_from_patches_2d ############################################################################### # Load Lena image and extract patches lena = lena() / 256.0 # downsample for higher speed lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2] lena /= 4.0 height, width = lena.shape # Distort the right half of the image print('Distorting image...') distorted = lena.copy() distorted[:, height // 2:] += 0.075 * np.random.randn(width, height // 2) # Extract all reference patches from the left half of the image print('Extracting reference patches...') t0 = time() patch_size = (7, 7) data = extract_patches_2d(distorted[:, :height // 2], patch_size) data = data.reshape(data.shape[0], -1) data -= np.mean(data, axis=0) data /= np.std(data, axis=0) print('done in %.2fs.' % (time() - t0)) ############################################################################### # Learn the dictionary from reference patches print('Learning the dictionary...') t0 = time() dico = MiniBatchDictionaryLearning(n_components=100, alpha=1, n_iter=500) V = dico.fit(data).components_ dt = time() - t0 print('done in %.2fs.' % dt) plt.figure(figsize=(4.2, 4)) for i, comp in enumerate(V[:100]): plt.subplot(10, 10, i + 1) plt.imshow(comp.reshape(patch_size), cmap=plt.cm.gray_r, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.suptitle('Dictionary learned from Lena patches\n' + 'Train time %.1fs on %d patches' % (dt, len(data)), fontsize=16) plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23) ############################################################################### # Display the distorted image def show_with_diff(image, reference, title): """Helper function to display denoising""" plt.figure(figsize=(5, 3.3)) plt.subplot(1, 2, 1) plt.title('Image') plt.imshow(image, vmin=0, vmax=1, cmap=plt.cm.gray, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.subplot(1, 2, 2) difference = image - reference plt.title('Difference (norm: %.2f)' % np.sqrt(np.sum(difference ** 2))) plt.imshow(difference, vmin=-0.5, vmax=0.5, cmap=plt.cm.PuOr, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.suptitle(title, size=16) plt.subplots_adjust(0.02, 0.02, 0.98, 0.79, 0.02, 0.2) show_with_diff(distorted, lena, 'Distorted image') ############################################################################### # Extract noisy patches and reconstruct them using the dictionary print('Extracting noisy patches... ') t0 = time() data = extract_patches_2d(distorted[:, height // 2:], patch_size) data = data.reshape(data.shape[0], -1) intercept = np.mean(data, axis=0) data -= intercept print('done in %.2fs.' % (time() - t0)) transform_algorithms = [ ('Orthogonal Matching Pursuit\n1 atom', 'omp', {'transform_n_nonzero_coefs': 1}), ('Orthogonal Matching Pursuit\n2 atoms', 'omp', {'transform_n_nonzero_coefs': 2}), ('Least-angle regression\n5 atoms', 'lars', {'transform_n_nonzero_coefs': 5}), ('Thresholding\n alpha=0.1', 'threshold', {'transform_alpha': .1})] reconstructions = {} for title, transform_algorithm, kwargs in transform_algorithms: print(title + '...') reconstructions[title] = lena.copy() t0 = time() dico.set_params(transform_algorithm=transform_algorithm, **kwargs) code = dico.transform(data) patches = np.dot(code, V) if transform_algorithm == 'threshold': patches -= patches.min() patches /= patches.max() patches += intercept patches = patches.reshape(len(data), *patch_size) if transform_algorithm == 'threshold': patches -= patches.min() patches /= patches.max() reconstructions[title][:, height // 2:] = reconstruct_from_patches_2d( patches, (width, height // 2)) dt = time() - t0 print('done in %.2fs.' % dt) show_with_diff(reconstructions[title], lena, title + ' (time: %.1fs)' % dt) plt.show()

bsd-3-clause

gfyoung/pandas

pandas/tests/extension/test_sparse.py

1

15154

""" This file contains a minimal set of tests for compliance with the extension array interface test suite, and should contain no other tests. The test suite for the full functionality of the array is located in `pandas/tests/arrays/`. The tests in this file are inherited from the BaseExtensionTests, and only minimal tweaks should be applied to get the tests passing (by overwriting a parent method). Additional tests should either be added to one of the BaseExtensionTests classes (if they are relevant for the extension interface for all dtypes), or be added to the array-specific tests in `pandas/tests/arrays/`. """ import numpy as np import pytest from pandas.errors import PerformanceWarning from pandas.core.dtypes.common import is_object_dtype import pandas as pd from pandas import SparseDtype import pandas._testing as tm from pandas.arrays import SparseArray from pandas.tests.extension import base def make_data(fill_value): if np.isnan(fill_value): data = np.random.uniform(size=100) else: data = np.random.randint(1, 100, size=100) if data[0] == data[1]: data[0] += 1 data[2::3] = fill_value return data @pytest.fixture def dtype(): return SparseDtype() @pytest.fixture(params=[0, np.nan]) def data(request): """Length-100 PeriodArray for semantics test.""" res = SparseArray(make_data(request.param), fill_value=request.param) return res @pytest.fixture def data_for_twos(request): return SparseArray(np.ones(100) * 2) @pytest.fixture(params=[0, np.nan]) def data_missing(request): """Length 2 array with [NA, Valid]""" return SparseArray([np.nan, 1], fill_value=request.param) @pytest.fixture(params=[0, np.nan]) def data_repeated(request): """Return different versions of data for count times""" def gen(count): for _ in range(count): yield SparseArray(make_data(request.param), fill_value=request.param) yield gen @pytest.fixture(params=[0, np.nan]) def data_for_sorting(request): return SparseArray([2, 3, 1], fill_value=request.param) @pytest.fixture(params=[0, np.nan]) def data_missing_for_sorting(request): return SparseArray([2, np.nan, 1], fill_value=request.param) @pytest.fixture def na_value(): return np.nan @pytest.fixture def na_cmp(): return lambda left, right: pd.isna(left) and pd.isna(right) @pytest.fixture(params=[0, np.nan]) def data_for_grouping(request): return SparseArray([1, 1, np.nan, np.nan, 2, 2, 1, 3], fill_value=request.param) class BaseSparseTests: def _check_unsupported(self, data): if data.dtype == SparseDtype(int, 0): pytest.skip("Can't store nan in int array.") @pytest.mark.xfail(reason="SparseArray does not support setitem") def test_ravel(self, data): super().test_ravel(data) class TestDtype(BaseSparseTests, base.BaseDtypeTests): def test_array_type_with_arg(self, data, dtype): assert dtype.construct_array_type() is SparseArray class TestInterface(BaseSparseTests, base.BaseInterfaceTests): def test_no_values_attribute(self, data): pytest.skip("We have values") def test_copy(self, data): # __setitem__ does not work, so we only have a smoke-test data.copy() def test_view(self, data): # __setitem__ does not work, so we only have a smoke-test data.view() class TestConstructors(BaseSparseTests, base.BaseConstructorsTests): pass class TestReshaping(BaseSparseTests, base.BaseReshapingTests): def test_concat_mixed_dtypes(self, data): # https://github.com/pandas-dev/pandas/issues/20762 # This should be the same, aside from concat([sparse, float]) df1 = pd.DataFrame({"A": data[:3]}) df2 = pd.DataFrame({"A": [1, 2, 3]}) df3 = pd.DataFrame({"A": ["a", "b", "c"]}).astype("category") dfs = [df1, df2, df3] # dataframes result = pd.concat(dfs) expected = pd.concat( [x.apply(lambda s: np.asarray(s).astype(object)) for x in dfs] ) self.assert_frame_equal(result, expected) def test_concat_columns(self, data, na_value): self._check_unsupported(data) super().test_concat_columns(data, na_value) def test_concat_extension_arrays_copy_false(self, data, na_value): self._check_unsupported(data) super().test_concat_extension_arrays_copy_false(data, na_value) def test_align(self, data, na_value): self._check_unsupported(data) super().test_align(data, na_value) def test_align_frame(self, data, na_value): self._check_unsupported(data) super().test_align_frame(data, na_value) def test_align_series_frame(self, data, na_value): self._check_unsupported(data) super().test_align_series_frame(data, na_value) def test_merge(self, data, na_value): self._check_unsupported(data) super().test_merge(data, na_value) @pytest.mark.xfail(reason="SparseArray does not support setitem") def test_transpose(self, data): super().test_transpose(data) class TestGetitem(BaseSparseTests, base.BaseGetitemTests): def test_get(self, data): s = pd.Series(data, index=[2 * i for i in range(len(data))]) if np.isnan(s.values.fill_value): assert np.isnan(s.get(4)) and np.isnan(s.iloc[2]) else: assert s.get(4) == s.iloc[2] assert s.get(2) == s.iloc[1] def test_reindex(self, data, na_value): self._check_unsupported(data) super().test_reindex(data, na_value) # Skipping TestSetitem, since we don't implement it. class TestMissing(BaseSparseTests, base.BaseMissingTests): def test_isna(self, data_missing): expected_dtype = SparseDtype(bool, pd.isna(data_missing.dtype.fill_value)) expected = SparseArray([True, False], dtype=expected_dtype) result = pd.isna(data_missing) self.assert_equal(result, expected) result = pd.Series(data_missing).isna() expected = pd.Series(expected) self.assert_series_equal(result, expected) # GH 21189 result = pd.Series(data_missing).drop([0, 1]).isna() expected = pd.Series([], dtype=expected_dtype) self.assert_series_equal(result, expected) def test_fillna_limit_pad(self, data_missing): with tm.assert_produces_warning(PerformanceWarning): super().test_fillna_limit_pad(data_missing) def test_fillna_limit_backfill(self, data_missing): with tm.assert_produces_warning(PerformanceWarning): super().test_fillna_limit_backfill(data_missing) def test_fillna_series_method(self, data_missing): with tm.assert_produces_warning(PerformanceWarning): super().test_fillna_limit_backfill(data_missing) @pytest.mark.skip(reason="Unsupported") def test_fillna_series(self): # this one looks doable. pass def test_fillna_frame(self, data_missing): # Have to override to specify that fill_value will change. fill_value = data_missing[1] result = pd.DataFrame({"A": data_missing, "B": [1, 2]}).fillna(fill_value) if pd.isna(data_missing.fill_value): dtype = SparseDtype(data_missing.dtype, fill_value) else: dtype = data_missing.dtype expected = pd.DataFrame( { "A": data_missing._from_sequence([fill_value, fill_value], dtype=dtype), "B": [1, 2], } ) self.assert_frame_equal(result, expected) class TestMethods(BaseSparseTests, base.BaseMethodsTests): def test_combine_le(self, data_repeated): # We return a Series[SparseArray].__le__ returns a # Series[Sparse[bool]] # rather than Series[bool] orig_data1, orig_data2 = data_repeated(2) s1 = pd.Series(orig_data1) s2 = pd.Series(orig_data2) result = s1.combine(s2, lambda x1, x2: x1 <= x2) expected = pd.Series( SparseArray( [a <= b for (a, b) in zip(list(orig_data1), list(orig_data2))], fill_value=False, ) ) self.assert_series_equal(result, expected) val = s1.iloc[0] result = s1.combine(val, lambda x1, x2: x1 <= x2) expected = pd.Series( SparseArray([a <= val for a in list(orig_data1)], fill_value=False) ) self.assert_series_equal(result, expected) def test_fillna_copy_frame(self, data_missing): arr = data_missing.take([1, 1]) df = pd.DataFrame({"A": arr}) filled_val = df.iloc[0, 0] result = df.fillna(filled_val) assert df.values.base is not result.values.base assert df.A._values.to_dense() is arr.to_dense() def test_fillna_copy_series(self, data_missing): arr = data_missing.take([1, 1]) ser = pd.Series(arr) filled_val = ser[0] result = ser.fillna(filled_val) assert ser._values is not result._values assert ser._values.to_dense() is arr.to_dense() @pytest.mark.skip(reason="Not Applicable") def test_fillna_length_mismatch(self, data_missing): pass def test_where_series(self, data, na_value): assert data[0] != data[1] cls = type(data) a, b = data[:2] ser = pd.Series(cls._from_sequence([a, a, b, b], dtype=data.dtype)) cond = np.array([True, True, False, False]) result = ser.where(cond) new_dtype = SparseDtype("float", 0.0) expected = pd.Series( cls._from_sequence([a, a, na_value, na_value], dtype=new_dtype) ) self.assert_series_equal(result, expected) other = cls._from_sequence([a, b, a, b], dtype=data.dtype) cond = np.array([True, False, True, True]) result = ser.where(cond, other) expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype)) self.assert_series_equal(result, expected) def test_combine_first(self, data): if data.dtype.subtype == "int": # Right now this is upcasted to float, just like combine_first # for Series[int] pytest.skip("TODO(SparseArray.__setitem__ will preserve dtype.") super().test_combine_first(data) def test_searchsorted(self, data_for_sorting, as_series): with tm.assert_produces_warning(PerformanceWarning): super().test_searchsorted(data_for_sorting, as_series) def test_shift_0_periods(self, data): # GH#33856 shifting with periods=0 should return a copy, not same obj result = data.shift(0) data._sparse_values[0] = data._sparse_values[1] assert result._sparse_values[0] != result._sparse_values[1] @pytest.mark.parametrize("method", ["argmax", "argmin"]) def test_argmin_argmax_all_na(self, method, data, na_value): # overriding because Sparse[int64, 0] cannot handle na_value self._check_unsupported(data) super().test_argmin_argmax_all_na(method, data, na_value) @pytest.mark.parametrize("box", [pd.array, pd.Series, pd.DataFrame]) def test_equals(self, data, na_value, as_series, box): self._check_unsupported(data) super().test_equals(data, na_value, as_series, box) class TestCasting(BaseSparseTests, base.BaseCastingTests): def test_astype_object_series(self, all_data): # Unlike the base class, we do not expect the resulting Block # to be ObjectBlock ser = pd.Series(all_data, name="A") result = ser.astype(object) assert is_object_dtype(result._data.blocks[0].dtype) def test_astype_object_frame(self, all_data): # Unlike the base class, we do not expect the resulting Block # to be ObjectBlock df = pd.DataFrame({"A": all_data}) result = df.astype(object) assert is_object_dtype(result._data.blocks[0].dtype) # FIXME: these currently fail; dont leave commented-out # check that we can compare the dtypes # comp = result.dtypes.equals(df.dtypes) # assert not comp.any() def test_astype_str(self, data): result = pd.Series(data[:5]).astype(str) expected_dtype = SparseDtype(str, str(data.fill_value)) expected = pd.Series([str(x) for x in data[:5]], dtype=expected_dtype) self.assert_series_equal(result, expected) @pytest.mark.xfail(raises=TypeError, reason="no sparse StringDtype") def test_astype_string(self, data): super().test_astype_string(data) class TestArithmeticOps(BaseSparseTests, base.BaseArithmeticOpsTests): series_scalar_exc = None frame_scalar_exc = None divmod_exc = None series_array_exc = None def _skip_if_different_combine(self, data): if data.fill_value == 0: # arith ops call on dtype.fill_value so that the sparsity # is maintained. Combine can't be called on a dtype in # general, so we can't make the expected. This is tested elsewhere raise pytest.skip("Incorrected expected from Series.combine") def test_error(self, data, all_arithmetic_operators): pass def test_arith_series_with_scalar(self, data, all_arithmetic_operators): self._skip_if_different_combine(data) super().test_arith_series_with_scalar(data, all_arithmetic_operators) def test_arith_series_with_array(self, data, all_arithmetic_operators): self._skip_if_different_combine(data) super().test_arith_series_with_array(data, all_arithmetic_operators) class TestComparisonOps(BaseSparseTests, base.BaseComparisonOpsTests): def _compare_other(self, s, data, op_name, other): op = self.get_op_from_name(op_name) # array result = pd.Series(op(data, other)) # hard to test the fill value, since we don't know what expected # is in general. # Rely on tests in `tests/sparse` to validate that. assert isinstance(result.dtype, SparseDtype) assert result.dtype.subtype == np.dtype("bool") with np.errstate(all="ignore"): expected = pd.Series( SparseArray( op(np.asarray(data), np.asarray(other)), fill_value=result.values.fill_value, ) ) tm.assert_series_equal(result, expected) # series s = pd.Series(data) result = op(s, other) tm.assert_series_equal(result, expected) class TestPrinting(BaseSparseTests, base.BasePrintingTests): @pytest.mark.xfail(reason="Different repr", strict=True) def test_array_repr(self, data, size): super().test_array_repr(data, size) class TestParsing(BaseSparseTests, base.BaseParsingTests): @pytest.mark.parametrize("engine", ["c", "python"]) def test_EA_types(self, engine, data): expected_msg = r".*must implement _from_sequence_of_strings.*" with pytest.raises(NotImplementedError, match=expected_msg): super().test_EA_types(engine, data)

bsd-3-clause

grahesh/Stock-Market-Event-Analysis

qstkutil/tsutil.py

1

29904

''' (c) 2011, 2012 Georgia Tech Research Corporation This source code is released under the New BSD license. Please see http://wiki.quantsoftware.org/index.php?title=QSTK_License for license details. Created on Jan 1, 2011 @author:Drew Bratcher @contact: [email protected] @summary: Contains tutorial for backtester and report. ''' import math import datetime as dt import numpy as np from qstkutil import qsdateutil from math import sqrt from copy import deepcopy import random as rand from qstkutil import DataAccess as da from qstkutil import qsdateutil as du import numpy as np def daily(lfFunds): """ @summary Computes daily returns centered around 0 @param funds: A time series containing daily fund values @return an array of daily returns """ nds = np.asarray(deepcopy(lfFunds)) s= np.shape(nds) if len(s)==1: nds=np.expand_dims(nds,1) returnize0(nds) return(nds) def daily1(lfFunds): """ @summary Computes daily returns centered around 1 @param funds: A time series containing daily fund values @return an array of daily returns """ nds = np.asarray(deepcopy(lfFunds)) s= np.shape(nds) if len(s)==1: nds=np.expand_dims(nds,1) returnize1(nds) return(nds) def monthly(funds): """ @summary Computes monthly returns centered around 0 @param funds: A time series containing daily fund values @return an array of monthly returns """ funds2 = [] last_last_month = -1 years = qsdateutil.getYears(funds) for year in years: months = qsdateutil.getMonths(funds, year) for month in months: last_this_month = qsdateutil.getLastDay(funds, year, month) if last_last_month == -1 : last_last_month=qsdateutil.getFirstDay(funds, year, month) if type(funds).__name__=='TimeSeries': funds2.append(funds[last_this_month]/funds[last_last_month]-1) else: funds2.append(funds.xs(last_this_month)/funds.xs(last_last_month)-1) last_last_month = last_this_month return(funds2) def average_monthly(funds): """ @summary Computes average monthly returns centered around 0 @param funds: A time series containing daily fund values @return an array of average monthly returns """ rets = daily(funds) ret_i = 0 years = qsdateutil.getYears(funds) averages = [] for year in years: months = qsdateutil.getMonths(funds, year) for month in months: avg = 0 count = 0 days = qsdateutil.getDays(funds, year, month) for day in days: avg += rets[ret_i] ret_i += 1 count += 1 averages.append(float(avg) / count) return(averages) def fillforward(nds): """ @summary Removes NaNs from a 2D array by scanning forward in the 1st dimension. If a cell is NaN, the value above it is carried forward. @param nds: the array to fill forward @return the array is revised in place """ for col in range(nds.shape[1]): for row in range(1, nds.shape[0]): if math.isnan(nds[row, col]): nds[row, col] = nds[row-1, col] def fillbackward(nds): """ @summary Removes NaNs from a 2D array by scanning backward in the 1st dimension. If a cell is NaN, the value above it is carried backward. @param nds: the array to fill backward @return the array is revised in place """ for col in range(nds.shape[1]): for row in range(nds.shape[0] - 2, -1, -1): if math.isnan(nds[row, col]): nds[row, col] = nds[row+1, col] def returnize0(nds): """ @summary Computes stepwise (usually daily) returns relative to 0, where 0 implies no change in value. @return the array is revised in place """ s= np.shape(nds) if len(s)==1: nds=np.expand_dims(nds,1) nds[1:, :] = (nds[1:, :] / nds[0:-1]) - 1 nds[0, :] = np.zeros(nds.shape[1]) def returnize1(nds): """ @summary Computes stepwise (usually daily) returns relative to 1, where 1 implies no change in value. @param nds: the array to fill backward @return the array is revised in place """ s= np.shape(nds) if len(s)==1: nds=np.expand_dims(nds,1) nds[1:, :] = (nds[1:, :]/nds[0:-1]) nds[0, :] = np.ones(nds.shape[1]) def priceize1(nds): """ @summary Computes stepwise (usually daily) returns relative to 1, where 1 implies no change in value. @param nds: the array to fill backward @return the array is revised in place """ nds[0, :] = 100 for i in range(1, nds.shape[0]): nds[i, :] = nds[i-1, :] * nds[i, :] def logreturnize(nds): """ @summary Computes stepwise (usually daily) logarithmic returns. @param nds: the array to fill backward @return the array is revised in place """ returnize1(nds) nds = np.log(nds) return nds def get_winning_days( rets): """ @summary Returns the percentage of winning days of the returns. @param rets: 1d numpy array or fund list of daily returns (centered on 0) @return Percentage of winning days """ negative_rets = [] for i in rets: if(i<0): negative_rets.append(i) return 100 * (1 - float(len(negative_rets)) / float(len(rets))) def get_max_draw_down(ts_vals): """ @summary Returns the max draw down of the returns. @param ts_vals: 1d numpy array or fund list @return Max draw down """ MDD = 0 DD = 0 peak = -99999 for value in ts_vals: if (value > peak): peak = value else: DD = (peak - value) / peak if (DD > MDD): MDD = DD return -1*MDD def get_sortino_ratio( rets, risk_free=0.00 ): """ @summary Returns the daily Sortino ratio of the returns. @param rets: 1d numpy array or fund list of daily returns (centered on 0) @param risk_free: risk free return, default is 0% @return Sortino Ratio, computed off daily returns """ rets = np.asarray(rets) f_mean = np.mean( rets, axis=0 ) negative_rets = rets[rets < 0] f_dev = np.std( negative_rets, axis=0 ) f_sortino = (f_mean*252 - risk_free) / (f_dev * np.sqrt(252)) return f_sortino def get_sharpe_ratio( rets, risk_free=0.00 ): """ @summary Returns the daily Sharpe ratio of the returns. @param rets: 1d numpy array or fund list of daily returns (centered on 0) @param risk_free: risk free returns, default is 0% @return Annualized rate of return, not converted to percent """ f_dev = np.std( rets, axis=0 ) f_mean = np.mean( rets, axis=0 ) f_sharpe = (f_mean *252 - risk_free) / ( f_dev * np.sqrt(252) ) return f_sharpe def get_ror_annual( rets ): """ @summary Returns the rate of return annualized. Assumes len(rets) is number of days. @param rets: 1d numpy array or list of daily returns @return Annualized rate of return, not converted to percent """ f_inv = 1.0 for f_ret in rets: f_inv = f_inv * f_ret f_ror_ytd = f_inv - 1.0 #print ' RorYTD =', f_inv, 'Over days:', len(rets) return ( (1.0 + f_ror_ytd)**( 1.0/(len(rets)/252.0) ) ) - 1.0 def getPeriodicRets( dmPrice, sOffset ): """ @summary Reindexes a DataMatrix price array and returns the new periodic returns. @param dmPrice: DataMatrix of stock prices @param sOffset: Offset string to use, choose from _offsetMap in pandas/core/datetools.py e.g. 'EOM', 'WEEKDAY', 'W@FRI', 'A@JAN'. Or use a pandas DateOffset. """ # Could possibly use DataMatrix.asfreq here """ # Use pandas DateRange to create the dates we want, use 4:00 """ drNewRange = DateRange(dmPrice.index[0], dmPrice.index[-1], timeRule=sOffset) drNewRange += DateOffset(hours=16) dmPrice = dmPrice.reindex( drNewRange, method='ffill' ) returnize1( dmPrice.values ) # Do not leave return of 1.0 for first time period: not accurate """ return dmPrice[1:] def getReindexedRets( rets, l_period ): """ @summary Reindexes returns using the cumulative product. E.g. if returns are 1.5 and 1.5, a period of 2 will produce a 2-day return of 2.25. Note, these must be returns centered around 1. @param rets: Daily returns of the various stocks (using returnize1) @param l_period: New target period. @note: Note that this function does not track actual weeks or months, it only approximates with trading days. You can use 5 for week, or 21 for month, etc. """ naCumData = np.cumprod(rets, axis=0) lNewRows =(rets.shape[0]-1) / (l_period) # We compress data into height / l_period + 1 new rows """ for i in range( lNewRows ): lCurInd = -1 - i*l_period # Just hold new data in same array""" # new return is cumprod on day x / cumprod on day x-l_period """ start=naCumData[lCurInd - l_period, :] naCumData[-1 - i, :] = naCumData[lCurInd, :] / start # Select new returns from end of cumulative array """ return naCumData[-lNewRows:, ] def getOptPort(rets, f_target, l_period=1, naLower=None, naUpper=None, lNagDebug=0): """ @summary Returns the Markowitz optimum portfolio for a specific return. @param rets: Daily returns of the various stocks (using returnize1) @param f_target: Target return, i.e. 0.04 = 4% per period @param l_period: Period to compress the returns to, e.g. 7 = weekly @param naLower: List of floats which corresponds to lower portfolio% for each stock @param naUpper: List of floats which corresponds to upper portfolio% for each stock @return tuple: (weights of portfolio, min possible return, max possible return) """ # Attempt to import library """ try: pass import nagint as nag except ImportError: print 'Could not import NAG library' print 'make sure nagint.so is in your python path' return ([], 0, 0) # Get number of stocks """ lStocks = rets.shape[1] # If period != 1 we need to restructure the data """ if( l_period != 1 ): rets = getReindexedRets( rets, l_period) # Calculate means and covariance """ naAvgRets = np.average( rets, axis=0 ) naCov = np.cov( rets, rowvar=False ) # Special case for None == f_target""" # simply return average returns and cov """ if( f_target is None ): return naAvgRets, np.std(rets, axis=0) # Calculate upper and lower limits of variables as well as constraints """ if( naUpper is None ): naUpper = np.ones( lStocks ) # max portfolio % is 1 if( naLower is None ): naLower = np.zeros( lStocks ) # min is 0, set negative for shorting # Two extra constraints for linear conditions""" # result = desired return, and sum of weights = 1 """ naUpper = np.append( naUpper, [f_target, 1.0] ) naLower = np.append( naLower, [f_target, 1.0] ) # Initial estimate of portfolio """ naInitial = np.array([1.0/lStocks]*lStocks) # Set up constraints matrix""" # composed of expected returns in row one, unity row in row two """ naConstraints = np.vstack( (naAvgRets, np.ones(lStocks)) ) # Get portfolio weights, last entry in array is actually variance """ try: naReturn = nag.optPort( naConstraints, naLower, naUpper, \ naCov, naInitial, lNagDebug ) except RuntimeError: print 'NAG Runtime error with target: %.02lf'%(f_target) return ( naInitial, sqrt( naCov[0][0] ) ) #return semi-junk to not mess up the rest of the plot # Calculate stdev of entire portfolio to return""" # what NAG returns is slightly different """ fPortDev = np.std( np.dot(rets, naReturn[0,0:-1]) ) # Show difference between above stdev and sqrt NAG covariance""" # possibly not taking correlation into account """ #print fPortDev / sqrt(naReturn[0, -1]) # Return weights and stdDev of portfolio.""" # note again the last value of naReturn is NAG's reported variance """ return (naReturn[0, 0:-1], fPortDev) def OptPort( naData, fTarget, naLower=None, naUpper=None, naExpected=None, s_type = "long"): """ @summary Returns the Markowitz optimum portfolio for a specific return. @param naData: Daily returns of the various stocks (using returnize1) @param fTarget: Target return, i.e. 0.04 = 4% per period @param lPeriod: Period to compress the returns to, e.g. 7 = weekly @param naLower: List of floats which corresponds to lower portfolio% for each stock @param naUpper: List of floats which corresponds to upper portfolio% for each stock @return tuple: (weights of portfolio, min possible return, max possible return) """ ''' Attempt to import library ''' try: pass from cvxopt import matrix from cvxopt.blas import dot from cvxopt.solvers import qp, options except ImportError: print 'Could not import CVX library' return ([],0, True) ''' Get number of stocks ''' length = naData.shape[1] b_error = False naLower = deepcopy(naLower) naUpper = deepcopy(naUpper) naExpected = deepcopy(naExpected) # Assuming AvgReturns as the expected returns if parameter is not specified if (naExpected==None): naExpected = np.average( naData, axis=0 ) na_signs = np.sign(naExpected) indices, = np.where(na_signs == 0) na_signs[indices] = 1 if s_type == "long": na_signs = np.ones(len(na_signs)) elif s_type == "short": na_signs = np.ones(len(na_signs))*(-1) naData = na_signs*naData naExpected = na_signs*naExpected # Covariance matrix of the Data Set naCov=np.cov(naData, rowvar=False) # If length is one, just return 100% single symbol if length == 1: return (list(na_signs), np.std(naData, axis=0)[0], False) if length == 0: return ([], [0], False) # If we have 0/1 "free" equity we can't optimize # We just use limits since we are stuck with 0 degrees of freedom ''' Special case for None == fTarget, simply return average returns and cov ''' if( fTarget is None ): return (naExpected, np.std(naData, axis=0), b_error) # Upper bound of the Weights of a equity, If not specified, assumed to be 1. if(naUpper is None): naUpper= np.ones(length) # Lower bound of the Weights of a equity, If not specified assumed to be 0 (No shorting case) if(naLower is None): naLower= np.zeros(length) if sum(naLower) == 1: fPortDev = np.std(np.dot(naData, naLower)) return (naLower, fPortDev, False) if sum(naUpper) == 1: fPortDev = np.std(np.dot(naData, naUpper)) return (naUpper, fPortDev, False) naFree = naUpper != naLower if naFree.sum() <= 1: lnaPortfolios = naUpper.copy() # If there is 1 free we need to modify it to make the total # Add up to 1 if naFree.sum() == 1: f_rest = naUpper[~naFree].sum() lnaPortfolios[naFree] = 1.0 - f_rest lnaPortfolios = na_signs * lnaPortfolios fPortDev = np.std(np.dot(naData, lnaPortfolios)) return (lnaPortfolios, fPortDev, False) # Double the covariance of the diagonal elements for calculating risk. for i in range(length): naCov[i][i]=2*naCov[i][i] # Note, returns are modified to all be long from here on out (fMin, fMax) = getRetRange(False, naLower, naUpper, naExpected, "long") #print (fTarget, fMin, fMax) if fTarget<fMin or fTarget>fMax: print "Target not possible", fTarget, fMin, fMax b_error = True naLower = naLower*(-1) # Setting up the parameters for the CVXOPT Library, it takes inputs in Matrix format. ''' The Risk minimization problem is a standard Quadratic Programming problem according to the Markowitz Theory. ''' S=matrix(naCov) #pbar=matrix(naExpected) naLower.shape=(length,1) naUpper.shape=(length,1) naExpected.shape = (1,length) zeo=matrix(0.0,(length,1)) I = np.eye(length) minusI=-1*I G=matrix(np.vstack((I, minusI))) h=matrix(np.vstack((naUpper, naLower))) ones=matrix(1.0,(1,length)) A=matrix(np.vstack((naExpected, ones))) b=matrix([float(fTarget),1.0]) # Optional Settings for CVXOPT options['show_progress'] = False options['abstol']=1e-25 options['reltol']=1e-24 options['feastol']=1e-25 # Optimization Calls # Optimal Portfolio try: lnaPortfolios = qp(S, -zeo, G, h, A, b)['x'] except: b_error = True if b_error == True: print "Optimization not Possible" na_port = naLower*-1 if sum(na_port) < 1: if sum(naUpper) == 1: na_port = naUpper else: i=0 while(sum(na_port)<1 and i<25): naOrder = naUpper - na_port i = i+1 indices = np.where(naOrder > 0) na_port[indices]= na_port[indices] + (1-sum(na_port))/len(indices[0]) naOrder = naUpper - na_port indices = np.where(naOrder < 0) na_port[indices]= naUpper[indices] lnaPortfolios = matrix(na_port) lnaPortfolios = (na_signs.reshape(-1,1) * lnaPortfolios).reshape(-1) # Expected Return of the Portfolio # lfReturn = dot(pbar, lnaPortfolios) # Risk of the portfolio fPortDev = np.std(np.dot(naData, lnaPortfolios)) return (lnaPortfolios, fPortDev, b_error) def getRetRange( rets, naLower, naUpper, naExpected = "False", s_type = "long"): """ @summary Returns the range of possible returns with upper and lower bounds on the portfolio participation @param rets: Expected returns @param naLower: List of lower percentages by stock @param naUpper: List of upper percentages by stock @return tuple containing (fMin, fMax) """ # Calculate theoretical minimum and maximum theoretical returns """ fMin = 0 fMax = 0 rets = deepcopy(rets) if naExpected == "False": naExpected = np.average( rets, axis=0 ) na_signs = np.sign(naExpected) indices, = np.where(na_signs == 0) na_signs[indices] = 1 if s_type == "long": na_signs = np.ones(len(na_signs)) elif s_type == "short": na_signs = np.ones(len(na_signs))*(-1) rets = na_signs*rets naExpected = na_signs*naExpected naSortInd = naExpected.argsort() # First add the lower bounds on portfolio participation """ for i, fRet in enumerate(naExpected): fMin = fMin + fRet*naLower[i] fMax = fMax + fRet*naLower[i] # Now calculate minimum returns""" # allocate the max possible in worst performing equities """ # Subtract min since we have already counted it """ naUpperAdd = naUpper - naLower fTotalPercent = np.sum(naLower[:]) for i, lInd in enumerate(naSortInd): fRetAdd = naUpperAdd[lInd] * naExpected[lInd] fTotalPercent = fTotalPercent + naUpperAdd[lInd] fMin = fMin + fRetAdd # Check if this additional percent puts us over the limit """ if fTotalPercent > 1.0: fMin = fMin - naExpected[lInd] * (fTotalPercent - 1.0) break # Repeat for max, just reverse the sort, i.e. high to low """ naUpperAdd = naUpper - naLower fTotalPercent = np.sum(naLower[:]) for i, lInd in enumerate(naSortInd[::-1]): fRetAdd = naUpperAdd[lInd] * naExpected[lInd] fTotalPercent = fTotalPercent + naUpperAdd[lInd] fMax = fMax + fRetAdd # Check if this additional percent puts us over the limit """ if fTotalPercent > 1.0: fMax = fMax - naExpected[lInd] * (fTotalPercent - 1.0) break return (fMin, fMax) def _create_dict(df_rets, lnaPortfolios): allocations = {} for i, sym in enumerate(df_rets.columns): allocations[sym] = lnaPortfolios[i] return allocations def optimizePortfolio(df_rets, list_min, list_max, list_price_target, target_risk, direction="long"): naLower = np.array(list_min) naUpper = np.array(list_max) naExpected = np.array(list_price_target) b_same_flag = np.all( naExpected == naExpected[0]) if b_same_flag and (naExpected[0] == 0): naExpected = naExpected + 0.1 if b_same_flag: na_randomness = np.ones(naExpected.shape) target_risk = 0 for i in range(len(na_randomness)): if i%2 ==0: na_randomness[i] = -1 naExpected = naExpected + naExpected*0.0000001*na_randomness (fMin, fMax) = getRetRange( df_rets.values, naLower, naUpper, naExpected, direction) # Try to avoid intractible endpoints due to rounding errors """ fMin += abs(fMin) * 0.00000000001 fMax -= abs(fMax) * 0.00000000001 if target_risk == 1: (naPortWeights, fPortDev, b_error) = OptPort( df_rets.values, fMax, naLower, naUpper, naExpected, direction) allocations = _create_dict(df_rets, naPortWeights) return {'allocations': allocations, 'std_dev': fPortDev, 'expected_return': fMax, 'error': b_error} fStep = (fMax - fMin) / 50.0 lfReturn = [fMin + x * fStep for x in range(51)] lfStd = [] lnaPortfolios = [] for fTarget in lfReturn: (naWeights, fStd, b_error) = OptPort( df_rets.values, fTarget, naLower, naUpper, naExpected, direction) if b_error == False: lfStd.append(fStd) lnaPortfolios.append( naWeights ) else: # Return error on ANY failed optimization allocations = _create_dict(df_rets, np.zeros(df_rets.shape[1])) return {'allocations': allocations, 'std_dev': 0.0, 'expected_return': fMax, 'error': True} if len(lfStd) == 0: (naPortWeights, fPortDev, b_error) = OptPort( df_rets.values, fMax, naLower, naUpper, naExpected, direction) allocations = _create_dict(df_rets, naPortWeights) return {'allocations': allocations, 'std_dev': fPortDev, 'expected_return': fMax, 'error': True} f_return = lfReturn[lfStd.index(min(lfStd))] if target_risk == 0: naPortWeights=lnaPortfolios[lfStd.index(min(lfStd))] allocations = _create_dict(df_rets, naPortWeights) return {'allocations': allocations, 'std_dev': min(lfStd), 'expected_return': f_return, 'error': False} # Otherwise try to hit custom target between 0-1 min-max risk fTarget = f_return + ((fMax - f_return) * target_risk) (naPortWeights, fPortDev, b_error) = OptPort( df_rets.values, fTarget, naLower, naUpper, naExpected, direction) allocations = _create_dict(df_rets, naPortWeights) return {'allocations': allocations, 'std_dev': fPortDev, 'expected_return': fTarget, 'error': b_error} def getFrontier( rets, lRes=100, fUpper=0.2, fLower=0.00): """ @summary Generates an efficient frontier based on average returns. @param rets: Array of returns to use @param lRes: Resolution of the curve, default=100 @param fUpper: Upper bound on portfolio percentage @param fLower: Lower bound on portfolio percentage @return tuple containing (lf_ret, lfStd, lnaPortfolios) lf_ret: List of returns provided by each point lfStd: list of standard deviations provided by each point lnaPortfolios: list of numpy arrays containing weights for each portfolio """ # Limit/enforce percent participation """ naUpper = np.ones(rets.shape[1]) * fUpper naLower = np.ones(rets.shape[1]) * fLower (fMin, fMax) = getRetRange( rets, naLower, naUpper ) # Try to avoid intractible endpoints due to rounding errors """ fMin *= 1.0000001 fMax *= 0.9999999 # Calculate target returns from min and max """ lf_ret = [] for i in range(lRes): lf_ret.append( (fMax - fMin) * i / (lRes - 1) + fMin ) lfStd = [] lnaPortfolios = [] # Call the function lRes times for the given range, use 1 for period """ for f_target in lf_ret: (naWeights, fStd) = getOptPort( rets, f_target, 1, \ naUpper=naUpper, naLower=naLower ) lfStd.append(fStd) lnaPortfolios.append( naWeights ) # plot frontier """ #plt.plot( lfStd, lf_ret ) plt.plot( np.std( rets, axis=0 ), np.average( rets, axis=0 ), \ 'g+', markersize=10 ) #plt.show()""" return (lf_ret, lfStd, lnaPortfolios) def stockFilter( dmPrice, dmVolume, fNonNan=0.95, fPriceVolume=100*1000 ): """ @summary Returns the list of stocks filtered based on various criteria. @param dmPrice: DataMatrix of stock prices @param dmVolume: DataMatrix of stock volumes @param fNonNan: Optional non-nan percent, default is .95 @param fPriceVolume: Optional price*volume, default is 100,000 @return list of stocks which meet the criteria """ lsRetStocks = list( dmPrice.columns ) for sStock in dmPrice.columns: fValid = 0.0 print sStock # loop through all dates """ for dtDate in dmPrice.index: # Count null (nan/inf/etc) values """ fPrice = dmPrice[sStock][dtDate] if( not isnull(fPrice) ): fValid = fValid + 1 # else test price volume """ fVol = dmVolume[sStock][dtDate] if( not isnull(fVol) and fVol * fPrice < fPriceVolume ): lsRetStocks.remove( sStock ) break # Remove if too many nan values """ if( fValid / len(dmPrice.index) < fNonNan and sStock in lsRetStocks ): lsRetStocks.remove( sStock ) return lsRetStocks def getRandPort( lNum, dtStart=None, dtEnd=None, lsStocks=None,\ dmPrice=None, dmVolume=None, bFilter=True, fNonNan=0.95,\ fPriceVolume=100*1000, lSeed=None ): """ @summary Returns a random portfolio based on certain criteria. @param lNum: Number of stocks to be included @param dtStart: Start date for portfolio @param dtEnd: End date for portfolio @param lsStocks: Optional list of ticker symbols, if not provided all symbols will be used @param bFilter: If False, stocks are not filtered by price or volume data, simply return random Portfolio. @param dmPrice: Optional price data, if not provided, data access will be queried @param dmVolume: Optional volume data, if not provided, data access will be queried @param fNonNan: Optional non-nan percent for filter, default is .95 @param fPriceVolume: Optional price*volume for filter, default is 100,000 @warning: Does not work for all sets of optional inputs, e.g. if you don't include dtStart, dtEnd, you need to include dmPrice/dmVolume @return list of stocks which meet the criteria """ if( lsStocks is None ): if( dmPrice is None and dmVolume is None ): norObj = da.DataAccess('Norgate') lsStocks = norObj.get_all_symbols() elif( not dmPrice is None ): lsStocks = list(dmPrice.columns) else: lsStocks = list(dmVolume.columns) if( dmPrice is None and dmVolume is None and bFilter == True ): norObj = da.DataAccess('Norgate') ldtTimestamps = du.getNYSEdays( dtStart, dtEnd, dt.timedelta(hours=16) ) # if dmPrice and dmVol are provided then we don't query it every time """ bPullPrice = False bPullVol = False if( dmPrice is None ): bPullPrice = True if( dmVolume is None ): bPullVol = True # Default seed (none) uses system clock """ rand.seed(lSeed) lsRetStocks = [] # Loop until we have enough randomly selected stocks """ llRemainingIndexes = range(0,len(lsStocks)) lsValid = None while( len(lsRetStocks) != lNum ): lsCheckStocks = [] for i in range( lNum - len(lsRetStocks) ): lRemaining = len(llRemainingIndexes) if( lRemaining == 0 ): print 'Error in getRandPort: ran out of stocks' return lsRetStocks # Pick a stock and remove it from the list of remaining stocks """ lPicked = rand.randint(0, lRemaining-1) lsCheckStocks.append( lsStocks[ llRemainingIndexes.pop(lPicked) ] ) # If bFilter is false""" # simply return our first list of stocks, don't check prive/vol """ if( not bFilter ): return sorted(lsCheckStocks) # Get data if needed """ if( bPullPrice ): dmPrice = norObj.get_data( ldtTimestamps, lsCheckStocks, 'close' ) # Get data if needed """ if( bPullVol ): dmVolume = norObj.get_data(ldtTimestamps, lsCheckStocks, 'volume' ) # Only query this once if data is provided""" # else query every time with new data """ if( lsValid is None or bPullVol or bPullPrice ): lsValid = stockFilter(dmPrice, dmVolume, fNonNan, fPriceVolume) for sAdd in lsValid: if sAdd in lsCheckStocks: lsRetStocks.append( sAdd ) return sorted(lsRetStocks)

bsd-3-clause

RPGOne/Skynet

scikit-learn-0.18.1/sklearn/utils/tests/test_testing.py

13

7883

import warnings import unittest import sys from sklearn.utils.testing import ( assert_raises, _assert_less, _assert_greater, assert_less_equal, assert_greater_equal, assert_warns, assert_no_warnings, assert_equal, set_random_state, assert_raise_message, ignore_warnings) from sklearn.tree import DecisionTreeClassifier from sklearn.discriminant_analysis import LinearDiscriminantAnalysis try: from nose.tools import assert_less def test_assert_less(): # Check that the nose implementation of assert_less gives the # same thing as the scikit's assert_less(0, 1) _assert_less(0, 1) assert_raises(AssertionError, assert_less, 1, 0) assert_raises(AssertionError, _assert_less, 1, 0) except ImportError: pass try: from nose.tools import assert_greater def test_assert_greater(): # Check that the nose implementation of assert_less gives the # same thing as the scikit's assert_greater(1, 0) _assert_greater(1, 0) assert_raises(AssertionError, assert_greater, 0, 1) assert_raises(AssertionError, _assert_greater, 0, 1) except ImportError: pass def test_assert_less_equal(): assert_less_equal(0, 1) assert_less_equal(1, 1) assert_raises(AssertionError, assert_less_equal, 1, 0) def test_assert_greater_equal(): assert_greater_equal(1, 0) assert_greater_equal(1, 1) assert_raises(AssertionError, assert_greater_equal, 0, 1) def test_set_random_state(): lda = LinearDiscriminantAnalysis() tree = DecisionTreeClassifier() # Linear Discriminant Analysis doesn't have random state: smoke test set_random_state(lda, 3) set_random_state(tree, 3) assert_equal(tree.random_state, 3) def test_assert_raise_message(): def _raise_ValueError(message): raise ValueError(message) def _no_raise(): pass assert_raise_message(ValueError, "test", _raise_ValueError, "test") assert_raises(AssertionError, assert_raise_message, ValueError, "something else", _raise_ValueError, "test") assert_raises(ValueError, assert_raise_message, TypeError, "something else", _raise_ValueError, "test") assert_raises(AssertionError, assert_raise_message, ValueError, "test", _no_raise) # multiple exceptions in a tuple assert_raises(AssertionError, assert_raise_message, (ValueError, AttributeError), "test", _no_raise) def test_ignore_warning(): # This check that ignore_warning decorateur and context manager are working # as expected def _warning_function(): warnings.warn("deprecation warning", DeprecationWarning) def _multiple_warning_function(): warnings.warn("deprecation warning", DeprecationWarning) warnings.warn("deprecation warning") # Check the function directly assert_no_warnings(ignore_warnings(_warning_function)) assert_no_warnings(ignore_warnings(_warning_function, category=DeprecationWarning)) assert_warns(DeprecationWarning, ignore_warnings(_warning_function, category=UserWarning)) assert_warns(UserWarning, ignore_warnings(_multiple_warning_function, category=DeprecationWarning)) assert_warns(DeprecationWarning, ignore_warnings(_multiple_warning_function, category=UserWarning)) assert_no_warnings(ignore_warnings(_warning_function, category=(DeprecationWarning, UserWarning))) # Check the decorator @ignore_warnings def decorator_no_warning(): _warning_function() _multiple_warning_function() @ignore_warnings(category=(DeprecationWarning, UserWarning)) def decorator_no_warning_multiple(): _multiple_warning_function() @ignore_warnings(category=DeprecationWarning) def decorator_no_deprecation_warning(): _warning_function() @ignore_warnings(category=UserWarning) def decorator_no_user_warning(): _warning_function() @ignore_warnings(category=DeprecationWarning) def decorator_no_deprecation_multiple_warning(): _multiple_warning_function() @ignore_warnings(category=UserWarning) def decorator_no_user_multiple_warning(): _multiple_warning_function() assert_no_warnings(decorator_no_warning) assert_no_warnings(decorator_no_warning_multiple) assert_no_warnings(decorator_no_deprecation_warning) assert_warns(DeprecationWarning, decorator_no_user_warning) assert_warns(UserWarning, decorator_no_deprecation_multiple_warning) assert_warns(DeprecationWarning, decorator_no_user_multiple_warning) # Check the context manager def context_manager_no_warning(): with ignore_warnings(): _warning_function() def context_manager_no_warning_multiple(): with ignore_warnings(category=(DeprecationWarning, UserWarning)): _multiple_warning_function() def context_manager_no_deprecation_warning(): with ignore_warnings(category=DeprecationWarning): _warning_function() def context_manager_no_user_warning(): with ignore_warnings(category=UserWarning): _warning_function() def context_manager_no_deprecation_multiple_warning(): with ignore_warnings(category=DeprecationWarning): _multiple_warning_function() def context_manager_no_user_multiple_warning(): with ignore_warnings(category=UserWarning): _multiple_warning_function() assert_no_warnings(context_manager_no_warning) assert_no_warnings(context_manager_no_warning_multiple) assert_no_warnings(context_manager_no_deprecation_warning) assert_warns(DeprecationWarning, context_manager_no_user_warning) assert_warns(UserWarning, context_manager_no_deprecation_multiple_warning) assert_warns(DeprecationWarning, context_manager_no_user_multiple_warning) # This class is inspired from numpy 1.7 with an alteration to check # the reset warning filters after calls to assert_warns. # This assert_warns behavior is specific to scikit-learn because #`clean_warning_registry()` is called internally by assert_warns # and clears all previous filters. class TestWarns(unittest.TestCase): def test_warn(self): def f(): warnings.warn("yo") return 3 # Test that assert_warns is not impacted by externally set # filters and is reset internally. # This is because `clean_warning_registry()` is called internally by # assert_warns and clears all previous filters. warnings.simplefilter("ignore", UserWarning) assert_equal(assert_warns(UserWarning, f), 3) # Test that the warning registry is empty after assert_warns assert_equal(sys.modules['warnings'].filters, []) assert_raises(AssertionError, assert_no_warnings, f) assert_equal(assert_no_warnings(lambda x: x, 1), 1) def test_warn_wrong_warning(self): def f(): warnings.warn("yo", DeprecationWarning) failed = False filters = sys.modules['warnings'].filters[:] try: try: # Should raise an AssertionError assert_warns(UserWarning, f) failed = True except AssertionError: pass finally: sys.modules['warnings'].filters = filters if failed: raise AssertionError("wrong warning caught by assert_warn")

bsd-3-clause

nesterione/scikit-learn

sklearn/decomposition/tests/test_truncated_svd.py

240

6055

"""Test truncated SVD transformer.""" import numpy as np import scipy.sparse as sp from sklearn.decomposition import TruncatedSVD from sklearn.utils import check_random_state from sklearn.utils.testing import (assert_array_almost_equal, assert_equal, assert_raises, assert_greater, assert_array_less) # Make an X that looks somewhat like a small tf-idf matrix. # XXX newer versions of SciPy have scipy.sparse.rand for this. shape = 60, 55 n_samples, n_features = shape rng = check_random_state(42) X = rng.randint(-100, 20, np.product(shape)).reshape(shape) X = sp.csr_matrix(np.maximum(X, 0), dtype=np.float64) X.data[:] = 1 + np.log(X.data) Xdense = X.A def test_algorithms(): svd_a = TruncatedSVD(30, algorithm="arpack") svd_r = TruncatedSVD(30, algorithm="randomized", random_state=42) Xa = svd_a.fit_transform(X)[:, :6] Xr = svd_r.fit_transform(X)[:, :6] assert_array_almost_equal(Xa, Xr) comp_a = np.abs(svd_a.components_) comp_r = np.abs(svd_r.components_) # All elements are equal, but some elements are more equal than others. assert_array_almost_equal(comp_a[:9], comp_r[:9]) assert_array_almost_equal(comp_a[9:], comp_r[9:], decimal=3) def test_attributes(): for n_components in (10, 25, 41): tsvd = TruncatedSVD(n_components).fit(X) assert_equal(tsvd.n_components, n_components) assert_equal(tsvd.components_.shape, (n_components, n_features)) def test_too_many_components(): for algorithm in ["arpack", "randomized"]: for n_components in (n_features, n_features+1): tsvd = TruncatedSVD(n_components=n_components, algorithm=algorithm) assert_raises(ValueError, tsvd.fit, X) def test_sparse_formats(): for fmt in ("array", "csr", "csc", "coo", "lil"): Xfmt = Xdense if fmt == "dense" else getattr(X, "to" + fmt)() tsvd = TruncatedSVD(n_components=11) Xtrans = tsvd.fit_transform(Xfmt) assert_equal(Xtrans.shape, (n_samples, 11)) Xtrans = tsvd.transform(Xfmt) assert_equal(Xtrans.shape, (n_samples, 11)) def test_inverse_transform(): for algo in ("arpack", "randomized"): # We need a lot of components for the reconstruction to be "almost # equal" in all positions. XXX Test means or sums instead? tsvd = TruncatedSVD(n_components=52, random_state=42) Xt = tsvd.fit_transform(X) Xinv = tsvd.inverse_transform(Xt) assert_array_almost_equal(Xinv, Xdense, decimal=1) def test_integers(): Xint = X.astype(np.int64) tsvd = TruncatedSVD(n_components=6) Xtrans = tsvd.fit_transform(Xint) assert_equal(Xtrans.shape, (n_samples, tsvd.n_components)) def test_explained_variance(): # Test sparse data svd_a_10_sp = TruncatedSVD(10, algorithm="arpack") svd_r_10_sp = TruncatedSVD(10, algorithm="randomized", random_state=42) svd_a_20_sp = TruncatedSVD(20, algorithm="arpack") svd_r_20_sp = TruncatedSVD(20, algorithm="randomized", random_state=42) X_trans_a_10_sp = svd_a_10_sp.fit_transform(X) X_trans_r_10_sp = svd_r_10_sp.fit_transform(X) X_trans_a_20_sp = svd_a_20_sp.fit_transform(X) X_trans_r_20_sp = svd_r_20_sp.fit_transform(X) # Test dense data svd_a_10_de = TruncatedSVD(10, algorithm="arpack") svd_r_10_de = TruncatedSVD(10, algorithm="randomized", random_state=42) svd_a_20_de = TruncatedSVD(20, algorithm="arpack") svd_r_20_de = TruncatedSVD(20, algorithm="randomized", random_state=42) X_trans_a_10_de = svd_a_10_de.fit_transform(X.toarray()) X_trans_r_10_de = svd_r_10_de.fit_transform(X.toarray()) X_trans_a_20_de = svd_a_20_de.fit_transform(X.toarray()) X_trans_r_20_de = svd_r_20_de.fit_transform(X.toarray()) # helper arrays for tests below svds = (svd_a_10_sp, svd_r_10_sp, svd_a_20_sp, svd_r_20_sp, svd_a_10_de, svd_r_10_de, svd_a_20_de, svd_r_20_de) svds_trans = ( (svd_a_10_sp, X_trans_a_10_sp), (svd_r_10_sp, X_trans_r_10_sp), (svd_a_20_sp, X_trans_a_20_sp), (svd_r_20_sp, X_trans_r_20_sp), (svd_a_10_de, X_trans_a_10_de), (svd_r_10_de, X_trans_r_10_de), (svd_a_20_de, X_trans_a_20_de), (svd_r_20_de, X_trans_r_20_de), ) svds_10_v_20 = ( (svd_a_10_sp, svd_a_20_sp), (svd_r_10_sp, svd_r_20_sp), (svd_a_10_de, svd_a_20_de), (svd_r_10_de, svd_r_20_de), ) svds_sparse_v_dense = ( (svd_a_10_sp, svd_a_10_de), (svd_a_20_sp, svd_a_20_de), (svd_r_10_sp, svd_r_10_de), (svd_r_20_sp, svd_r_20_de), ) # Assert the 1st component is equal for svd_10, svd_20 in svds_10_v_20: assert_array_almost_equal( svd_10.explained_variance_ratio_, svd_20.explained_variance_ratio_[:10], decimal=5, ) # Assert that 20 components has higher explained variance than 10 for svd_10, svd_20 in svds_10_v_20: assert_greater( svd_20.explained_variance_ratio_.sum(), svd_10.explained_variance_ratio_.sum(), ) # Assert that all the values are greater than 0 for svd in svds: assert_array_less(0.0, svd.explained_variance_ratio_) # Assert that total explained variance is less than 1 for svd in svds: assert_array_less(svd.explained_variance_ratio_.sum(), 1.0) # Compare sparse vs. dense for svd_sparse, svd_dense in svds_sparse_v_dense: assert_array_almost_equal(svd_sparse.explained_variance_ratio_, svd_dense.explained_variance_ratio_) # Test that explained_variance is correct for svd, transformed in svds_trans: total_variance = np.var(X.toarray(), axis=0).sum() variances = np.var(transformed, axis=0) true_explained_variance_ratio = variances / total_variance assert_array_almost_equal( svd.explained_variance_ratio_, true_explained_variance_ratio, )

bsd-3-clause

FluidityStokes/fluidity

examples/tides_in_the_Mediterranean_Sea/Med-tides-probe.py

2

6073

#!/usr/bin/env python3 import vtktools import math from numpy import array u=vtktools.vtu("tidesmedsea-flat.vtu") g = open("Med-GEBCO-5m-gauges-fes2004-O1-102", "w") pts=vtktools.arr([ #[-5.3500, 36.1333, -2.00], #[-4.4500, 36.7000, 0.00], #[-3.9167, 35.2500, 0.00], #[-2.4500, 36.8333, 0.00], #[-0.5833, 38.3333, 0.00], [2.6333, 39.5833, 0.00], #[3.1000, 42.4833, 0.00], #[5.3500, 43.3000, 0.00], #[6.9167, 36.8833, 0.00], [8.0167, 43.8667, 0.00], [8.3000, 39.1500, 0.00], [8.9000, 44.4000, 0.00], [9.1667, 39.2000, 0.00], #[9.8500, 44.0667, 0.00], #[10.1167, 33.8833, 0.00], [10.3000, 43.5333, 0.00], #[10.7667, 34.7333, 0.00], #[11.1167, 33.5000, 0.00], #[11.7833, 42.1000, 0.00], [12.0000, 36.7833, 0.00], #[12.3333, 45.4167, 0.00], [12.5000, 35.5000, 0.00], [12.5833, 37.6333, 0.00], [12.8167, 36.1667, 0.00], [13.2000, 32.9000, 0.00], #[13.3333, 38.1333, 0.00], #[13.5000, 43.6167, 0.00], [13.5000, 37.2500, 0.00], [13.7500, 45.6500, 0.00], [13.9333, 40.7333, 0.00], #[14.2667, 40.8667, 0.00], [14.4000, 42.5167, 0.00], [14.5167, 35.9000, 0.00], #[14.5333, 45.3000, 0.00], [14.9667, 38.4833, 0.00], #[15.1000, 38.5000, 0.00], [15.1500, 36.6833, 0.00], #[15.2500, 38.2167, 0.00], #[15.6500, 38.1167, 0.00], [15.7667, 43.0333, 0.00], [16.1833, 41.8833, 0.00], #[16.4333, 43.5000, 0.00], #[17.2167, 40.4667, 0.00], #[17.9333, 40.6333, 0.00], #[18.5000, 40.1500, 0.00], [19.1000, 42.0667, 0.00], #[20.7000, 38.8333, 0.00], [21.3167, 37.6333, 0.00], #[22.1333, 37.0167, 0.00], #[23.0333, 40.6167, 0.00], [23.8000, 32.1833, 0.00], #[24.0500, 35.5000, 0.00], [24.9167, 37.4333, 0.00], #[25.1333, 35.3333, 0.00], [25.3833, 40.8500, 0.00], [25.7000, 31.7667, 0.00], [26.1500, 38.3833, 0.00], [26.8833, 37.0833, 0.00], [28.2333, 36.4333, 0.00], [29.8667, 31.2000, 0.00], [32.3167, 31.2667, 0.00], #[33.3167, 35.3333, 0.00], #[33.9500, 35.1167, 0.00] ]) M2amp = u.ProbeData(pts, "M2amp") (ilen, jlen) = M2amp.shape S2amp = u.ProbeData(pts, "S2amp") K1amp = u.ProbeData(pts, "K1amp") O1amp = u.ProbeData(pts, "O1amp") for i in range(ilen): g.write("%f\n" % M2amp[i][0]) ampcm=M2amp*100 M2_tideGauge_amp = array([ [29.8], [18.0], [18.0], [9.0], [2.0], [3.0], [4.6], [7.0], [5.6], [8.3], [6.5], [8.6], [7.6], [9.4], [51.1], [8.5], [41.6], [21.9], [10.9], [1.6], [23.4], [6.6], [4.3], [4.8], [11.1], [10.6], [6.6], [4.5], [26.3], [12.0], [11.1], [6.4], [6.0], [10.6], [12.0], #[6.4], [6.7], [12.0], [6.2], [6.8], [7.9], [8.0], [6.5], [8.7], [7.0], [9.2], [4.0], [3.3], [2.2], [9.0], [1.4], [1.0], [2.0], [1.5], [7.1], [2.9], [4.4], [2.1], [4.4], [7.2], [11.2], [10.1], [11.0] ]) from math import sqrt ampdiff=ampcm-M2_tideGauge_amp ampdiff2=ampdiff**2 a = sum(sum(ampdiff2))/62 RMS=sqrt(a) print("RMS difference of M2 Amp (cm):",RMS) S2_tide_guage_data_amp = array([ [10.7], [7], [7], [4], [1], [1], [1.8], [2], [2.2], [3.4], [2.6], [3.2], [2.8], [3.4], [36.4], [3.4], [26.7], [15.3], [4.1], [1.9], [14.1], [4.2], [1.8], [3.1], [5.4], [4.1], [3.6], [3.2], [15.2], [5], [4.4], [4.5], [4], [5.5], [4.5], #[3.4], [3.5], [4.7], [3.1], [4.4], [5.1], [5.6], [3.7], [5.2], [4], [5.6], [2.2], [1.6], [1.1], [6.1], [1.3], [0.8], [1], [1.1], [5], [2.9], [2.9], [1.3], [2.7], [4.1], [6.9], [6.4], [7.3] ]) S2ampcm=S2amp*100 S2ampdiff=S2ampcm-S2_tide_guage_data_amp S2ampdiff2=S2ampdiff**2 S2a = sum(sum(S2ampdiff2))/62 S2RMS=sqrt(S2a) print("RMS difference of S2 Amp (cm):",S2RMS) K1_tide_guage_data_amp= array([ [2], [3], [4], [3], [4], [4], [3.2], [3], [2.3], [3.6], [3.2], [3.3], [3.2], [3.7], [2.5], [4], [1.8], [2], [2.7], [2], [17.9], [0.9], [3.5], [0.5], [2], [3.2], [13], [1.8], [19.7], [3], [2.8], [9.7], [1], [13.8], [3.1], #[1.5], [1.9], [3.3], [1.3], [6.8], [4.2], [8.8], [1.8], [4.6], [2.3], [4.8], [1.4], [1.3], [1.2], [2.6], [0.6], [1.4], [1.9], [1.8], [0.3], [1.2], [2.3], [2], [1.8], [1.7], [2.1], [2.4], [2.1] ]) K1ampcm=K1amp*100 K1ampdiff=K1ampcm-K1_tide_guage_data_amp K1ampdiff2=K1ampdiff**2 K1a = sum(sum(K1ampdiff2))/62 K1RMS=sqrt(K1a) print("RMS difference of K1 Amp (cm):",K1RMS) O1_tide_guage_data_amp= array([ [0.9], [2], [1], [2], [2], [2], [1.9], [2], [2], [1.6], [1.9], [1.4], [1.8], [1.4], [0.5], [1.8], [0.8], [0.9], [1.2], [1.4], [5.6], [0.7], [1.6], [0.9], [0.6], [1.2], [4.2], [1.4], [6.1], [1], [1], [3.4], [1], [4.1], [1.1], #[1.1], [0.9], [1.1], [0.9], [2.5], [1.5], [2.7], [0.8], [1.5], [1], [1.4], [0.6], [0.5], [0.5], [1.3], [0.5], [0.6], [1], [0.9], [1.3], [0.8], [1.3], [1.1], [1.1], [1.3], [1.7], [1.8], [1.8] ]) O1ampcm=O1amp*100 O1ampdiff=O1ampcm-O1_tide_guage_data_amp O1ampdiff2=O1ampdiff**2 O1a = sum(sum(O1ampdiff2))/62 O1RMS=sqrt(O1a) print("RMS difference of O1 Amp (cm):",O1RMS) import fluidity_tools from matplotlib import pylab pylab.plot(ampcm,M2_tideGauge_amp) pylab.xlabel("Fluidity") pylab.ylabel("Tide Gauge") pylab.show() import matplotlib matplotlib.pyplot.scatter(M2_tideGauge_amp,ampcm,s=20, c='b', marker='o') #pylab.xlabel("Tide Gauge M2 Amplitude (cm)") #pylab.ylabel("Fluidity M2 Amplitude (cm)") #x=([0,70]) #y=([0,70]) #matplotlib.pyplot.plot(y,x, label="y=x") #pylab.ylim(ymax=70.0,ymin=0.0) #pylab.xlim(xmax=70.0,xmin=0.0) #pylab.show() #matplotlib.pyplot.scatter(S2_tide_guage_data_amp,S2ampcm,s=20, c='b', marker='o') #pylab.xlabel("Tide Gauge S2 Amplitude (cm)") #pylab.ylabel("Fluidity S2 Amplitude (cm)") #x=([0,70]) #y=([0,70]) #matplotlib.pyplot.plot(y,x, label="y=x") #pylab.ylim(ymax=70.0,ymin=0.0) #pylab.xlim(xmax=70.0,xmin=0.0) #pylab.show() #matplotlib.pyplot.scatter(K1_tide_guage_data_amp,K1ampcm,s=20, c='b', marker='o') #pylab.xlabel("Tide Gauge K1 Amplitude (cm)") #pylab.ylabel("Fluidity K1 Amplitude (cm)") #x=([0,20]) #y=([0,20]) #matplotlib.pyplot.plot(y,x, label="y=x") #pylab.ylim(ymax=20.0,ymin=0.0) #pylab.xlim(xmax=20.0,xmin=0.0) #pylab.show() #matplotlib.pyplot.scatter(O1_tide_guage_data_amp,O1ampcm,s=20, c='b', marker='o') #pylab.xlabel("Tide Gauge O1 Amplitude (cm)") #pylab.ylabel("Fluidity O1 Amplitude (cm)") #x=([0,20]) #y=([0,20]) #matplotlib.pyplot.plot(y,x, label="y=x") #pylab.ylim(ymax=20.0,ymin=0.0) #pylab.xlim(xmax=20.0,xmin=0.0) #pylab.show()

lgpl-2.1

kirangonella/BuildingMachineLearningSystemsWithPython

ch08/chapter.py

21

6372

import numpy as np # NOT IN BOOK from matplotlib import pyplot as plt # NOT IN BOOK def load(): import numpy as np from scipy import sparse data = np.loadtxt('data/ml-100k/u.data') ij = data[:, :2] ij -= 1 # original data is in 1-based system values = data[:, 2] reviews = sparse.csc_matrix((values, ij.T)).astype(float) return reviews.toarray() reviews = load() U,M = np.where(reviews) import random test_idxs = np.array(random.sample(range(len(U)), len(U)//10)) train = reviews.copy() train[U[test_idxs], M[test_idxs]] = 0 test = np.zeros_like(reviews) test[U[test_idxs], M[test_idxs]] = reviews[U[test_idxs], M[test_idxs]] class NormalizePositive(object): def __init__(self, axis=0): self.axis = axis def fit(self, features, y=None): if self.axis == 1: features = features.T # count features that are greater than zero in axis 0: binary = (features > 0) count0 = binary.sum(axis=0) # to avoid division by zero, set zero counts to one: count0[count0 == 0] = 1. # computing the mean is easy: self.mean = features.sum(axis=0)/count0 # only consider differences where binary is True: diff = (features - self.mean) * binary diff **= 2 # regularize the estimate of std by adding 0.1 self.std = np.sqrt(0.1 + diff.sum(axis=0)/count0) return self def transform(self, features): if self.axis == 1: features = features.T binary = (features > 0) features = features - self.mean features /= self.std features *= binary if self.axis == 1: features = features.T return features def inverse_transform(self, features, copy=True): if copy: features = features.copy() if self.axis == 1: features = features.T features *= self.std features += self.mean if self.axis == 1: features = features.T return features def fit_transform(self, features): return self.fit(features).transform(features) norm = NormalizePositive(axis=1) binary = (train > 0) train = norm.fit_transform(train) # plot just 200x200 area for space reasons plt.imshow(binary[:200, :200], interpolation='nearest') from scipy.spatial import distance # compute all pair-wise distances: dists = distance.pdist(binary, 'correlation') # Convert to square form, so that dists[i,j] # is distance between binary[i] and binary[j]: dists = distance.squareform(dists) neighbors = dists.argsort(axis=1) # We are going to fill this matrix with results filled = train.copy() for u in range(filled.shape[0]): # n_u is neighbors of user n_u = neighbors[u, 1:] for m in range(filled.shape[1]): # get relevant reviews in order! revs = [train[neigh, m] for neigh in n_u if binary [neigh, m]] if len(revs): # n is the number of reviews for this movie n = len(revs) # take half of the reviews plus one into consideration: n //= 2 n += 1 revs = revs[:n] filled[u,m] = np.mean(revs) predicted = norm.inverse_transform(filled) from sklearn import metrics r2 = metrics.r2_score(test[test > 0], predicted[test > 0]) print('R2 score (binary neighbors): {:.1%}'.format(r2)) reviews = reviews.T # use same code as before r2 = metrics.r2_score(test[test > 0], predicted[test > 0]) print('R2 score (binary movie neighbors): {:.1%}'.format(r2)) from sklearn.linear_model import ElasticNetCV # NOT IN BOOK reg = ElasticNetCV(alphas=[ 0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.]) filled = train.copy() # iterate over all users: for u in range(train.shape[0]): curtrain = np.delete(train, u, axis=0) bu = binary[u] reg.fit(curtrain[:,bu].T, train[u, bu]) filled[u, ~bu] = reg.predict(curtrain[:,~bu].T) predicted = norm.inverse_transform(filled) r2 = metrics.r2_score(test[test > 0], predicted[test > 0]) print('R2 score (user regression): {:.1%}'.format(r2)) # SHOPPING BASKET ANALYSIS # This is the slow version of the code, which will take a long time to # complete. from collections import defaultdict from itertools import chain # File is downloaded as a compressed file import gzip # file format is a line per transaction # of the form '12 34 342 5...' dataset = [[int(tok) for tok in line.strip().split()] for line in gzip.open('data/retail.dat.gz')] dataset = [set(d) for d in dataset] # count how often each product was purchased: counts = defaultdict(int) for elem in chain(*dataset): counts[elem] += 1 minsupport = 80 valid = set(k for k,v in counts.items() if (v >= minsupport)) itemsets = [frozenset([v]) for v in valid] freqsets = [] for i in range(16): nextsets = [] tested = set() for it in itemsets: for v in valid: if v not in it: # Create a new candidate set by adding v to it c = (it | frozenset([v])) # check If we have tested it already if c in tested: continue tested.add(c) # Count support by looping over dataset # This step is slow. # Check `apriori.py` for a better implementation. support_c = sum(1 for d in dataset if d.issuperset(c)) if support_c > minsupport: nextsets.append(c) freqsets.extend(nextsets) itemsets = nextsets if not len(itemsets): break print("Finished!") minlift = 5.0 nr_transactions = float(len(dataset)) for itemset in freqsets: for item in itemset: consequent = frozenset([item]) antecedent = itemset-consequent base = 0.0 # acount: antecedent count acount = 0.0 # ccount : consequent count ccount = 0.0 for d in dataset: if item in d: base += 1 if d.issuperset(itemset): ccount += 1 if d.issuperset(antecedent): acount += 1 base /= nr_transactions p_y_given_x = ccount/acount lift = p_y_given_x / base if lift > minlift: print('Rule {0} -> {1} has lift {2}' .format(antecedent, consequent,lift))

mit

ShawnMurd/MetPy

src/metpy/plots/skewt.py

1

36666

# Copyright (c) 2014,2015,2016,2017,2019 MetPy Developers. # Distributed under the terms of the BSD 3-Clause License. # SPDX-License-Identifier: BSD-3-Clause """Make Skew-T Log-P based plots. Contain tools for making Skew-T Log-P plots, including the base plotting class, `SkewT`, as well as a class for making a `Hodograph`. """ from contextlib import ExitStack import warnings import matplotlib from matplotlib.axes import Axes import matplotlib.axis as maxis from matplotlib.collections import LineCollection import matplotlib.colors as mcolors from matplotlib.patches import Circle from matplotlib.projections import register_projection import matplotlib.spines as mspines from matplotlib.ticker import MultipleLocator, NullFormatter, ScalarFormatter import matplotlib.transforms as transforms import numpy as np from ._util import colored_line from ..calc import dewpoint, dry_lapse, el, lcl, moist_lapse, vapor_pressure from ..calc.tools import _delete_masked_points from ..interpolate import interpolate_1d from ..package_tools import Exporter from ..units import concatenate, units exporter = Exporter(globals()) class SkewTTransform(transforms.Affine2D): """Perform Skew transform for Skew-T plotting. This works in pixel space, so is designed to be applied after the normal plotting transformations. """ def __init__(self, bbox, rot): """Initialize skew transform. This needs a reference to the parent bounding box to do the appropriate math and to register it as a child so that the transform is invalidated and regenerated if the bounding box changes. """ super().__init__() self._bbox = bbox self.set_children(bbox) self.invalidate() # We're not trying to support changing the rotation, so go ahead and convert to # the right factor for skewing here and just save that. self._rot_factor = np.tan(np.deg2rad(rot)) def get_matrix(self): """Return transformation matrix.""" if self._invalid: # The following matrix is equivalent to the following: # x0, y0 = self._bbox.xmin, self._bbox.ymin # self.translate(-x0, -y0).skew_deg(self._rot, 0).translate(x0, y0) # Setting it this way is just more efficient. self._mtx = np.array([[1.0, self._rot_factor, -self._rot_factor * self._bbox.ymin], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) # Need to clear both the invalid flag *and* reset the inverse, which is cached # by the parent class. self._invalid = 0 self._inverted = None return self._mtx class SkewXTick(maxis.XTick): r"""Make x-axis ticks for Skew-T plots. This class adds to the standard :class:`matplotlib.axis.XTick` dynamic checking for whether a top or bottom tick is actually within the data limits at that part and draw as appropriate. It also performs similar checking for gridlines. """ # Taken from matplotlib's SkewT example to update for matplotlib 3.1's changes to # state management for ticks. See matplotlib/matplotlib#10088 def draw(self, renderer): """Draw the tick.""" # When adding the callbacks with `stack.callback`, we fetch the current # visibility state of the artist with `get_visible`; the ExitStack will # restore these states (`set_visible`) at the end of the block (after # the draw). with ExitStack() as stack: for artist in [self.gridline, self.tick1line, self.tick2line, self.label1, self.label2]: stack.callback(artist.set_visible, artist.get_visible()) self.tick1line.set_visible(self.tick1line.get_visible() and self.lower_in_bounds) self.label1.set_visible(self.label1.get_visible() and self.lower_in_bounds) self.tick2line.set_visible(self.tick2line.get_visible() and self.upper_in_bounds) self.label2.set_visible(self.label2.get_visible() and self.upper_in_bounds) self.gridline.set_visible(self.gridline.get_visible() and self.grid_in_bounds) super().draw(renderer) @property def lower_in_bounds(self): """Whether the lower part of the tick is in bounds.""" return transforms.interval_contains(self.axes.lower_xlim, self.get_loc()) @property def upper_in_bounds(self): """Whether the upper part of the tick is in bounds.""" return transforms.interval_contains(self.axes.upper_xlim, self.get_loc()) @property def grid_in_bounds(self): """Whether any of the tick grid line is in bounds.""" return transforms.interval_contains(self.axes.xaxis.get_view_interval(), self.get_loc()) class SkewXAxis(maxis.XAxis): r"""Make an x-axis that works properly for Skew-T plots. This class exists to force the use of our custom :class:`SkewXTick` as well as provide a custom value for interval that combines the extents of the upper and lower x-limits from the axes. """ def _get_tick(self, major): # Warning stuff can go away when we only support Matplotlib >=3.3 with warnings.catch_warnings(): warnings.simplefilter('ignore', getattr( matplotlib, 'MatplotlibDeprecationWarning', DeprecationWarning)) return SkewXTick(self.axes, None, label=None, major=major) # Needed to properly handle tight bbox def _get_tick_bboxes(self, ticks, renderer): """Return lists of bboxes for ticks' label1's and label2's.""" return ([tick.label1.get_window_extent(renderer) for tick in ticks if tick.label1.get_visible() and tick.lower_in_bounds], [tick.label2.get_window_extent(renderer) for tick in ticks if tick.label2.get_visible() and tick.upper_in_bounds]) def get_view_interval(self): """Get the view interval.""" return self.axes.upper_xlim[0], self.axes.lower_xlim[1] class SkewSpine(mspines.Spine): r"""Make an x-axis spine that works properly for Skew-T plots. This class exists to use the separate x-limits from the axes to properly locate the spine. """ def _adjust_location(self): pts = self._path.vertices if self.spine_type == 'top': pts[:, 0] = self.axes.upper_xlim else: pts[:, 0] = self.axes.lower_xlim class SkewXAxes(Axes): r"""Make a set of axes for Skew-T plots. This class handles registration of the skew-xaxes as a projection as well as setting up the appropriate transformations. It also makes sure we use our instances for spines and x-axis: :class:`SkewSpine` and :class:`SkewXAxis`. It provides properties to facilitate finding the x-limits for the bottom and top of the plot as well. """ # The projection must specify a name. This will be used be the # user to select the projection, i.e. ``subplot(111, # projection='skewx')``. name = 'skewx' def __init__(self, *args, **kwargs): r"""Initialize `SkewXAxes`. Parameters ---------- args : Arbitrary positional arguments Passed to :class:`matplotlib.axes.Axes` position: int, optional The rotation of the x-axis against the y-axis, in degrees. kwargs : Arbitrary keyword arguments Passed to :class:`matplotlib.axes.Axes` """ # This needs to be popped and set before moving on self.rot = kwargs.pop('rotation', 30) super().__init__(*args, **kwargs) def _init_axis(self): # Taken from Axes and modified to use our modified X-axis self.xaxis = SkewXAxis(self) self.spines['top'].register_axis(self.xaxis) self.spines['bottom'].register_axis(self.xaxis) self.yaxis = maxis.YAxis(self) self.spines['left'].register_axis(self.yaxis) self.spines['right'].register_axis(self.yaxis) def _gen_axes_spines(self, locations=None, offset=0.0, units='inches'): # pylint: disable=unused-argument spines = {'top': SkewSpine.linear_spine(self, 'top'), 'bottom': mspines.Spine.linear_spine(self, 'bottom'), 'left': mspines.Spine.linear_spine(self, 'left'), 'right': mspines.Spine.linear_spine(self, 'right')} return spines def _set_lim_and_transforms(self): """Set limits and transforms. This is called once when the plot is created to set up all the transforms for the data, text and grids. """ # Get the standard transform setup from the Axes base class super()._set_lim_and_transforms() # This transformation handles the skewing skew_trans = SkewTTransform(self.bbox, self.rot) # Create the full transform from Data to Pixels self.transData += skew_trans # Blended transforms like this need to have the skewing applied using # both axes, in axes coords like before. self._xaxis_transform += skew_trans @property def lower_xlim(self): """Get the data limits for the x-axis along the bottom of the axes.""" return self.axes.viewLim.intervalx @property def upper_xlim(self): """Get the data limits for the x-axis along the top of the axes.""" return self.transData.inverted().transform([[self.bbox.xmin, self.bbox.ymax], self.bbox.max])[:, 0] # Now register the projection with matplotlib so the user can select it. register_projection(SkewXAxes) @exporter.export class SkewT(object): r"""Make Skew-T log-P plots of data. This class simplifies the process of creating Skew-T log-P plots in using matplotlib. It handles requesting the appropriate skewed projection, and provides simplified wrappers to make it easy to plot data, add wind barbs, and add other lines to the plots (e.g. dry adiabats) Attributes ---------- ax : `matplotlib.axes.Axes` The underlying Axes instance, which can be used for calling additional plot functions (e.g. `axvline`) """ def __init__(self, fig=None, rotation=30, subplot=None, rect=None, aspect=80.5): r"""Create SkewT - logP plots. Parameters ---------- fig : matplotlib.figure.Figure, optional Source figure to use for plotting. If none is given, a new :class:`matplotlib.figure.Figure` instance will be created. rotation : float or int, optional Controls the rotation of temperature relative to horizontal. Given in degrees counterclockwise from x-axis. Defaults to 30 degrees. subplot : tuple[int, int, int] or `matplotlib.gridspec.SubplotSpec` instance, optional Controls the size/position of the created subplot. This allows creating the skewT as part of a collection of subplots. If subplot is a tuple, it should conform to the specification used for :meth:`matplotlib.figure.Figure.add_subplot`. The :class:`matplotlib.gridspec.SubplotSpec` can be created by using :class:`matplotlib.gridspec.GridSpec`. rect : tuple[float, float, float, float], optional Rectangle (left, bottom, width, height) in which to place the axes. This allows the user to place the axes at an arbitrary point on the figure. aspect : float, int, or 'auto', optional Aspect ratio (i.e. ratio of y-scale to x-scale) to maintain in the plot. Defaults to 80.5. Passing the string ``'auto'`` tells matplotlib to handle the aspect ratio automatically (this is not recommended for SkewT). """ if fig is None: import matplotlib.pyplot as plt figsize = plt.rcParams.get('figure.figsize', (7, 7)) fig = plt.figure(figsize=figsize) self._fig = fig if rect and subplot: raise ValueError("Specify only one of `rect' and `subplot', but not both") elif rect: self.ax = fig.add_axes(rect, projection='skewx', rotation=rotation) else: if subplot is not None: # Handle being passed a tuple for the subplot, or a GridSpec instance try: len(subplot) except TypeError: subplot = (subplot,) else: subplot = (1, 1, 1) self.ax = fig.add_subplot(*subplot, projection='skewx', rotation=rotation) # Set the yaxis as inverted with log scaling self.ax.set_yscale('log') # Override default ticking for log scaling self.ax.yaxis.set_major_formatter(ScalarFormatter()) self.ax.yaxis.set_major_locator(MultipleLocator(100)) self.ax.yaxis.set_minor_formatter(NullFormatter()) # Needed to make sure matplotlib doesn't freak out and create a bunch of ticks # Also takes care of inverting the y-axis self.ax.set_ylim(1050, 100) self.ax.yaxis.set_units(units.hPa) # Try to make sane default temperature plotting ticks self.ax.xaxis.set_major_locator(MultipleLocator(10)) self.ax.xaxis.set_units(units.degC) self.ax.set_xlim(-40, 50) self.ax.grid(True) self.mixing_lines = None self.dry_adiabats = None self.moist_adiabats = None # Maintain a reasonable ratio of data limits. Only works on Matplotlib >= 3.2 if matplotlib.__version__[:3] > '3.1': self.ax.set_aspect(aspect, adjustable='box') def plot(self, pressure, t, *args, **kwargs): r"""Plot data. Simple wrapper around plot so that pressure is the first (independent) input. This is essentially a wrapper around `plot`. Parameters ---------- pressure : array_like pressure values t : array_like temperature values, can also be used for things like dew point args Other positional arguments to pass to :func:`~matplotlib.pyplot.plot` kwargs Other keyword arguments to pass to :func:`~matplotlib.pyplot.plot` Returns ------- list[matplotlib.lines.Line2D] lines plotted See Also -------- :func:`matplotlib.pyplot.plot` """ # Skew-T logP plotting t, pressure = _delete_masked_points(t, pressure) return self.ax.plot(t, pressure, *args, **kwargs) def plot_barbs(self, pressure, u, v, c=None, xloc=1.0, x_clip_radius=0.1, y_clip_radius=0.08, **kwargs): r"""Plot wind barbs. Adds wind barbs to the skew-T plot. This is a wrapper around the `barbs` command that adds to appropriate transform to place the barbs in a vertical line, located as a function of pressure. Parameters ---------- pressure : array_like pressure values u : array_like U (East-West) component of wind v : array_like V (North-South) component of wind c: An optional array used to map colors to the barbs xloc : float, optional Position for the barbs, in normalized axes coordinates, where 0.0 denotes far left and 1.0 denotes far right. Defaults to far right. x_clip_radius : float, optional Space, in normalized axes coordinates, to leave before clipping wind barbs in the x-direction. Defaults to 0.1. y_clip_radius : float, optional Space, in normalized axes coordinates, to leave above/below plot before clipping wind barbs in the y-direction. Defaults to 0.08. plot_units: `pint.unit` Units to plot in (performing conversion if necessary). Defaults to given units. kwargs Other keyword arguments to pass to :func:`~matplotlib.pyplot.barbs` Returns ------- matplotlib.quiver.Barbs instance created See Also -------- :func:`matplotlib.pyplot.barbs` """ # If plot_units specified, convert the data to those units plotting_units = kwargs.pop('plot_units', None) if plotting_units: if hasattr(u, 'units') and hasattr(v, 'units'): u = u.to(plotting_units) v = v.to(plotting_units) else: raise ValueError('To convert to plotting units, units must be attached to ' 'u and v wind components.') # Assemble array of x-locations in axes space x = np.empty_like(pressure) x.fill(xloc) # Do barbs plot at this location if c is not None: b = self.ax.barbs(x, pressure, u, v, c, transform=self.ax.get_yaxis_transform(which='tick2'), clip_on=True, zorder=2, **kwargs) else: b = self.ax.barbs(x, pressure, u, v, transform=self.ax.get_yaxis_transform(which='tick2'), clip_on=True, zorder=2, **kwargs) # Override the default clip box, which is the axes rectangle, so we can have # barbs that extend outside. ax_bbox = transforms.Bbox([[xloc - x_clip_radius, -y_clip_radius], [xloc + x_clip_radius, 1.0 + y_clip_radius]]) b.set_clip_box(transforms.TransformedBbox(ax_bbox, self.ax.transAxes)) return b def plot_dry_adiabats(self, t0=None, pressure=None, **kwargs): r"""Plot dry adiabats. Adds dry adiabats (lines of constant potential temperature) to the plot. The default style of these lines is dashed red lines with an alpha value of 0.5. These can be overridden using keyword arguments. Parameters ---------- t0 : array_like, optional Starting temperature values in Kelvin. If none are given, they will be generated using the current temperature range at the bottom of the plot. pressure : array_like, optional Pressure values to be included in the dry adiabats. If not specified, they will be linearly distributed across the current plotted pressure range. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.LineCollection` Returns ------- matplotlib.collections.LineCollection instance created See Also -------- :func:`~metpy.calc.thermo.dry_lapse` :meth:`plot_moist_adiabats` :class:`matplotlib.collections.LineCollection` """ # Remove old lines if self.dry_adiabats: self.dry_adiabats.remove() # Determine set of starting temps if necessary if t0 is None: xmin, xmax = self.ax.get_xlim() t0 = np.arange(xmin, xmax + 1, 10) * units.degC # Get pressure levels based on ylims if necessary if pressure is None: pressure = np.linspace(*self.ax.get_ylim()) * units.mbar # Assemble into data for plotting t = dry_lapse(pressure, t0[:, np.newaxis], 1000. * units.mbar).to(units.degC) linedata = [np.vstack((ti.m, pressure.m)).T for ti in t] # Add to plot kwargs.setdefault('colors', 'r') kwargs.setdefault('linestyles', 'dashed') kwargs.setdefault('alpha', 0.5) self.dry_adiabats = self.ax.add_collection(LineCollection(linedata, **kwargs)) return self.dry_adiabats def plot_moist_adiabats(self, t0=None, pressure=None, **kwargs): r"""Plot moist adiabats. Adds saturated pseudo-adiabats (lines of constant equivalent potential temperature) to the plot. The default style of these lines is dashed blue lines with an alpha value of 0.5. These can be overridden using keyword arguments. Parameters ---------- t0 : array_like, optional Starting temperature values in Kelvin. If none are given, they will be generated using the current temperature range at the bottom of the plot. pressure : array_like, optional Pressure values to be included in the moist adiabats. If not specified, they will be linearly distributed across the current plotted pressure range. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.LineCollection` Returns ------- matplotlib.collections.LineCollection instance created See Also -------- :func:`~metpy.calc.thermo.moist_lapse` :meth:`plot_dry_adiabats` :class:`matplotlib.collections.LineCollection` """ # Remove old lines if self.moist_adiabats: self.moist_adiabats.remove() # Determine set of starting temps if necessary if t0 is None: xmin, xmax = self.ax.get_xlim() t0 = np.concatenate((np.arange(xmin, 0, 10), np.arange(0, xmax + 1, 5))) * units.degC # Get pressure levels based on ylims if necessary if pressure is None: pressure = np.linspace(*self.ax.get_ylim()) * units.mbar # Assemble into data for plotting t = moist_lapse(pressure, t0[:, np.newaxis], 1000. * units.mbar).to(units.degC) linedata = [np.vstack((ti.m, pressure.m)).T for ti in t] # Add to plot kwargs.setdefault('colors', 'b') kwargs.setdefault('linestyles', 'dashed') kwargs.setdefault('alpha', 0.5) self.moist_adiabats = self.ax.add_collection(LineCollection(linedata, **kwargs)) return self.moist_adiabats def plot_mixing_lines(self, mixing_ratio=None, pressure=None, **kwargs): r"""Plot lines of constant mixing ratio. Adds lines of constant mixing ratio (isohumes) to the plot. The default style of these lines is dashed green lines with an alpha value of 0.8. These can be overridden using keyword arguments. Parameters ---------- mixing_ratio : array_like, optional Unitless mixing ratio values to plot. If none are given, default values are used. pressure : array_like, optional Pressure values to be included in the isohumes. If not specified, they will be linearly distributed across the current plotted pressure range up to 600 mb. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.LineCollection` Returns ------- matplotlib.collections.LineCollection instance created See Also -------- :class:`matplotlib.collections.LineCollection` """ # Remove old lines if self.mixing_lines: self.mixing_lines.remove() # Default mixing level values if necessary if mixing_ratio is None: mixing_ratio = np.array([0.0004, 0.001, 0.002, 0.004, 0.007, 0.01, 0.016, 0.024, 0.032]).reshape(-1, 1) # Set pressure range if necessary if pressure is None: pressure = np.linspace(600, max(self.ax.get_ylim())) * units.mbar # Assemble data for plotting td = dewpoint(vapor_pressure(pressure, mixing_ratio)) linedata = [np.vstack((t.m, pressure.m)).T for t in td] # Add to plot kwargs.setdefault('colors', 'g') kwargs.setdefault('linestyles', 'dashed') kwargs.setdefault('alpha', 0.8) self.mixing_lines = self.ax.add_collection(LineCollection(linedata, **kwargs)) return self.mixing_lines def shade_area(self, y, x1, x2=0, which='both', **kwargs): r"""Shade area between two curves. Shades areas between curves. Area can be where one is greater or less than the other or all areas shaded. Parameters ---------- y : array_like 1-dimensional array of numeric y-values x1 : array_like 1-dimensional array of numeric x-values x2 : array_like 1-dimensional array of numeric x-values which : string Specifies if `positive`, `negative`, or `both` areas are being shaded. Will be overridden by where. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.PolyCollection` Returns ------- :class:`matplotlib.collections.PolyCollection` See Also -------- :class:`matplotlib.collections.PolyCollection` :func:`matplotlib.axes.Axes.fill_betweenx` """ fill_properties = {'positive': {'facecolor': 'tab:red', 'alpha': 0.4, 'where': x1 > x2}, 'negative': {'facecolor': 'tab:blue', 'alpha': 0.4, 'where': x1 < x2}, 'both': {'facecolor': 'tab:green', 'alpha': 0.4, 'where': None}} try: fill_args = fill_properties[which] fill_args.update(kwargs) except KeyError: raise ValueError('Unknown option for which: {0}'.format(str(which))) arrs = y, x1, x2 if fill_args['where'] is not None: arrs = arrs + (fill_args['where'],) fill_args.pop('where', None) fill_args['interpolate'] = True arrs = _delete_masked_points(*arrs) return self.ax.fill_betweenx(*arrs, **fill_args) def shade_cape(self, pressure, t, t_parcel, **kwargs): r"""Shade areas of Convective Available Potential Energy (CAPE). Shades areas where the parcel is warmer than the environment (areas of positive buoyancy. Parameters ---------- pressure : array_like Pressure values t : array_like Temperature values dewpoint : array_like Dewpoint values t_parcel : array_like Parcel path temperature values limit_shading : bool Eliminate shading below the LCL or above the EL, default is True kwargs Other keyword arguments to pass to :class:`matplotlib.collections.PolyCollection` Returns ------- :class:`matplotlib.collections.PolyCollection` See Also -------- :class:`matplotlib.collections.PolyCollection` :func:`matplotlib.axes.Axes.fill_betweenx` """ return self.shade_area(pressure, t_parcel, t, which='positive', **kwargs) def shade_cin(self, pressure, t, t_parcel, dewpoint=None, **kwargs): r"""Shade areas of Convective INhibition (CIN). Shades areas where the parcel is cooler than the environment (areas of negative buoyancy. If `dewpoint` is passed in, negative area below the lifting condensation level or above the equilibrium level is not shaded. Parameters ---------- pressure : array_like Pressure values t : array_like Temperature values t_parcel : array_like Parcel path temperature values dewpoint : array_like Dew point values, optional kwargs Other keyword arguments to pass to :class:`matplotlib.collections.PolyCollection` Returns ------- :class:`matplotlib.collections.PolyCollection` See Also -------- :class:`matplotlib.collections.PolyCollection` :func:`matplotlib.axes.Axes.fill_betweenx` """ if dewpoint is not None: lcl_p, _ = lcl(pressure[0], t[0], dewpoint[0]) el_p, _ = el(pressure, t, dewpoint, t_parcel) idx = np.logical_and(pressure > el_p, pressure < lcl_p) else: idx = np.arange(0, len(pressure)) return self.shade_area(pressure[idx], t_parcel[idx], t[idx], which='negative', **kwargs) @exporter.export class Hodograph(object): r"""Make a hodograph of wind data. Plots the u and v components of the wind along the x and y axes, respectively. This class simplifies the process of creating a hodograph using matplotlib. It provides helpers for creating a circular grid and for plotting the wind as a line colored by another value (such as wind speed). Attributes ---------- ax : `matplotlib.axes.Axes` The underlying Axes instance used for all plotting """ def __init__(self, ax=None, component_range=80): r"""Create a Hodograph instance. Parameters ---------- ax : `matplotlib.axes.Axes`, optional The `Axes` instance used for plotting component_range : value The maximum range of the plot. Used to set plot bounds and control the maximum number of grid rings needed. """ if ax is None: import matplotlib.pyplot as plt self.ax = plt.figure().add_subplot(1, 1, 1) else: self.ax = ax self.ax.set_aspect('equal', 'box') self.ax.set_xlim(-component_range, component_range) self.ax.set_ylim(-component_range, component_range) # == sqrt(2) * max_range, which is the distance at the corner self.max_range = 1.4142135 * component_range def add_grid(self, increment=10., **kwargs): r"""Add grid lines to hodograph. Creates lines for the x- and y-axes, as well as circles denoting wind speed values. Parameters ---------- increment : value, optional The value increment between rings kwargs Other kwargs to control appearance of lines See Also -------- :class:`matplotlib.patches.Circle` :meth:`matplotlib.axes.Axes.axhline` :meth:`matplotlib.axes.Axes.axvline` """ # Some default arguments. Take those, and update with any # arguments passed in grid_args = {'color': 'grey', 'linestyle': 'dashed'} if kwargs: grid_args.update(kwargs) # Take those args and make appropriate for a Circle circle_args = grid_args.copy() color = circle_args.pop('color', None) circle_args['edgecolor'] = color circle_args['fill'] = False self.rings = [] for r in np.arange(increment, self.max_range, increment): c = Circle((0, 0), radius=r, **circle_args) self.ax.add_patch(c) self.rings.append(c) # Add lines for x=0 and y=0 self.yaxis = self.ax.axvline(0, **grid_args) self.xaxis = self.ax.axhline(0, **grid_args) @staticmethod def _form_line_args(kwargs): """Simplify taking the default line style and extending with kwargs.""" def_args = {'linewidth': 3} def_args.update(kwargs) return def_args def plot(self, u, v, **kwargs): r"""Plot u, v data. Plots the wind data on the hodograph. Parameters ---------- u : array_like u-component of wind v : array_like v-component of wind kwargs Other keyword arguments to pass to :meth:`matplotlib.axes.Axes.plot` Returns ------- list[matplotlib.lines.Line2D] lines plotted See Also -------- :meth:`Hodograph.plot_colormapped` """ line_args = self._form_line_args(kwargs) u, v = _delete_masked_points(u, v) return self.ax.plot(u, v, **line_args) def wind_vectors(self, u, v, **kwargs): r"""Plot u, v data as wind vectors. Plot the wind data as vectors for each level, beginning at the origin. Parameters ---------- u : array_like u-component of wind v : array_like v-component of wind kwargs Other keyword arguments to pass to :meth:`matplotlib.axes.Axes.quiver` Returns ------- matplotlib.quiver.Quiver arrows plotted """ quiver_args = {'units': 'xy', 'scale': 1} quiver_args.update(**kwargs) center_position = np.zeros_like(u) return self.ax.quiver(center_position, center_position, u, v, **quiver_args) def plot_colormapped(self, u, v, c, intervals=None, colors=None, **kwargs): r"""Plot u, v data, with line colored based on a third set of data. Plots the wind data on the hodograph, but with a colormapped line. Takes a third variable besides the winds (e.g. heights or pressure levels) and either a colormap to color it with or a series of contour intervals and colors to create a colormap and norm to control colormapping. The intervals must always be in increasing order. For using custom contour intervals with height data, the function will automatically interpolate to the contour intervals from the height and wind data, as well as convert the input contour intervals from height AGL to MSL to work with the provided heights. Parameters ---------- u : array_like u-component of wind v : array_like v-component of wind c : array_like data to use for colormapping (e.g. heights, pressure, wind speed) intervals: array-like, optional Array of intervals for c to use in coloring the hodograph. colors: list, optional Array of strings representing colors for the hodograph segments. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.LineCollection` Returns ------- matplotlib.collections.LineCollection instance created See Also -------- :meth:`Hodograph.plot` """ u, v, c = _delete_masked_points(u, v, c) # Plotting a color segmented hodograph if colors: cmap = mcolors.ListedColormap(colors) # If we are segmenting by height (a length), interpolate the contour intervals if intervals.dimensionality == {'[length]': 1.0}: # Find any intervals not in the data and interpolate them interpolation_heights = [bound.m for bound in intervals if bound not in c] interpolation_heights = np.array(interpolation_heights) * intervals.units interpolation_heights = (np.sort(interpolation_heights.magnitude) * interpolation_heights.units) (interpolated_heights, interpolated_u, interpolated_v) = interpolate_1d(interpolation_heights, c, c, u, v) # Combine the interpolated data with the actual data c = concatenate([c, interpolated_heights]) u = concatenate([u, interpolated_u]) v = concatenate([v, interpolated_v]) sort_inds = np.argsort(c) c = c[sort_inds] u = u[sort_inds] v = v[sort_inds] # Unit conversion required for coloring of bounds/data in dissimilar units # to work properly. c = c.to_base_units() # TODO: This shouldn't be required! intervals = intervals.to_base_units() # If segmenting by anything else, do not interpolate, just use the data else: intervals = np.asarray(intervals) * intervals.units norm = mcolors.BoundaryNorm(intervals.magnitude, cmap.N) cmap.set_over('none') cmap.set_under('none') kwargs['cmap'] = cmap kwargs['norm'] = norm line_args = self._form_line_args(kwargs) # Plotting a continuously colored line else: line_args = self._form_line_args(kwargs) # Do the plotting lc = colored_line(u, v, c, **line_args) self.ax.add_collection(lc) return lc

bsd-3-clause

coolsgupta/machine_learning_nanodegree

Supervised_Learning/naive bayes/creating_the_GNB_clf.py

1

1349

#doc: http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html from sklearn.naive_bayes import GaussianNB def classify(features_train, labels_train): ### import the sklearn module for GaussianNB ### create classifier ### fit the classifier on the training features and labels ### return the fit classifier ### your code goes here! clf = GaussianNB() clf.fit(features_train, labels_train) return clf def NBAccuracy(features_train, labels_train, features_test, labels_test): """ compute the accuracy of your Naive Bayes classifier """ ### import the sklearn module for GaussianNB from sklearn.naive_bayes import GaussianNB ### create la clf = GaussianNB() ### fit the classifier on the training features and labels #TODO clf.fit(features_train,labels_train) ### use the trained classifier to predict labels for the test features pred = clf.predict(features_test) ### calculate and return the accuracy on the test data ### this is slightly different than the example, ### where we just print the accuracy ### you might need to import an sklearn module accuracy = clf.score(features_test,labels_test) #or from sklearn.metrics import accuracy_score accuracy = accuracy_score(pred,labels_test) return accuracy

mit

dariomangoni/chrono

src/demos/python/chrono-tensorflow/PPO/value_function.py

4

7169

""" State-Value Function """ import tensorflow as tf import numpy as np from sklearn.utils import shuffle import os.path class NNValueFunction(object): """ NN-based state-value function """ def __init__(self, obs_dim, env_name, MultiGPU = False): """ Args: obs_dim: number of dimensions in observation vector (int) """ self.env_name = env_name self.multiGPU = MultiGPU self.replay_buffer_x = None self.replay_buffer_y = None self.obs_dim = obs_dim self.epochs = 10 self.lr = None # learning rate set in _build_graph() self.savedmodel = os.path.isfile("./savedmodel/"+self.env_name+"/VF/checkpoint") directory = "./savedmodel/"+self.env_name+"/VF/" if self.savedmodel : self._restore() else: if not os.path.exists(directory): os.makedirs(directory) self._build_graph() #self.sess = tf.Session(graph=self.g) #self.sess.run(self.init) def _build_graph(self): """ Construct TensorFlow graph, including loss function, init op and train op """ self.g = tf.Graph() with self.g.as_default(): self.obs_ph = tf.placeholder(tf.float32, (None, self.obs_dim), 'obs_valfunc') self.val_ph = tf.placeholder(tf.float32, (None,), 'val_valfunc') # hid1 layer size is 10x obs_dim, hid3 size is 10, and hid2 is geometric mean hid1_size = self.obs_dim * 10 # 10 chosen empirically on 'Hopper-v1' hid3_size = 5 # 5 chosen empirically on 'Hopper-v1' hid2_size = int(np.sqrt(hid1_size * hid3_size)) # heuristic to set learning rate based on NN size (tuned on 'Hopper-v1') self.lr = 1e-2 / np.sqrt(hid2_size) # 1e-3 empirically determined print('Value Params -- h1: {}, h2: {}, h3: {}, lr: {:.3g}' .format(hid1_size, hid2_size, hid3_size, self.lr)) # 3 hidden layers with tanh activations out = tf.layers.dense(self.obs_ph, hid1_size, tf.tanh, kernel_initializer=tf.random_normal_initializer( stddev=np.sqrt(1 / self.obs_dim)), name="h1VF") out = tf.layers.dense(out, hid2_size, tf.tanh, kernel_initializer=tf.random_normal_initializer( stddev=np.sqrt(1 / hid1_size)), name="h2VF") out = tf.layers.dense(out, hid3_size, tf.tanh, kernel_initializer=tf.random_normal_initializer( stddev=np.sqrt(1 / hid2_size)), name="h3VF") out = tf.layers.dense(out, 1, kernel_initializer=tf.random_normal_initializer( stddev=np.sqrt(1 / hid3_size)), name='output') self.out = tf.squeeze(out) self.loss = tf.reduce_mean(tf.square(self.out - self.val_ph), name='lossVF') # squared loss optimizer = tf.train.AdamOptimizer(self.lr) self.train_op = optimizer.minimize(self.loss, name='train_opVF') self.init = tf.global_variables_initializer() self.saverVF = tf.train.Saver() if self.multiGPU : config = tf.ConfigProto() config.gpu_options.allow_growth = True #config.gpu_options.per_process_gpu_memory_fraction = 0.1 self.sess = tf.Session(graph=self.g, config=config) else: self.sess = tf.Session(graph=self.g) self.sess.run(self.init) def _restore(self): if self.multiGPU : config = tf.ConfigProto() config.gpu_options.allow_growth = True #config.gpu_options.per_process_gpu_memory_fraction = 0.1 self.sess = tf.Session(config=config) else: self.sess = tf.Session() loader = tf.train.import_meta_graph("./savedmodel/"+self.env_name+"/VF/trained_VF.ckpt.meta") self.sess.run(tf.global_variables_initializer()) self.g = tf.get_default_graph() self.obs_ph = self.g.get_tensor_by_name('obs_valfunc:0') self.val_ph = self.g.get_tensor_by_name('val_valfunc:0') out = self.g.get_tensor_by_name('output/BiasAdd:0') self.out = tf.squeeze(out) self.loss = self.g.get_tensor_by_name('lossVF:0') self.train_op = self.g.get_operation_by_name('train_opVF') self.lr = 1e-2 / np.sqrt(int(np.sqrt(self.obs_dim * 10 * 5))) self.saverVF = tf.train.Saver() loader.restore(self.sess, tf.train.latest_checkpoint("./savedmodel/"+self.env_name+"/VF")) def fit(self, x, y, logger): """ Fit model to current data batch + previous data batch Args: x: features y: target logger: logger to save training loss and % explained variance """ # minibatches of 256 tuples. Trining set is pretty big when the episode is long (steps_perepisode*episode_per batch) num_batches = max(x.shape[0] // 256, 1) batch_size = x.shape[0] // num_batches y_hat = self.predict(x) # check explained variance prior to update old_exp_var = 1 - np.var(y - y_hat)/np.var(y) #alla prima iterazione buffer coincide con ultimo dato, a quelle seguenti lo incollo al buffer if self.replay_buffer_x is None: x_train, y_train = x, y else: x_train = np.concatenate([x, self.replay_buffer_x]) y_train = np.concatenate([y, self.replay_buffer_y]) # flush buffar. the last one wil be appended to the next self.replay_buffer_x = x self.replay_buffer_y = y for e in range(self.epochs): x_train, y_train = shuffle(x_train, y_train) for j in range(num_batches): start = j * batch_size end = (j + 1) * batch_size feed_dict = {self.obs_ph: x_train[start:end, :], self.val_ph: y_train[start:end]} _, l = self.sess.run([self.train_op, self.loss], feed_dict=feed_dict) y_hat = self.predict(x) loss = np.mean(np.square(y_hat - y)) # explained variance after update exp_var = 1 - np.var(y - y_hat) / np.var(y) # diagnose over-fitting of val func self.saverVF.save(self.sess, "./savedmodel/"+self.env_name+"/VF/trained_VF.ckpt") logger.log({'ValFuncLoss': loss, 'ExplainedVarNew': exp_var, 'ExplainedVarOld': old_exp_var}) def predict(self, x): """ Predict method """ feed_dict = {self.obs_ph: x} y_hat = self.sess.run(self.out, feed_dict=feed_dict) return np.squeeze(y_hat) def close_sess(self): """ Close TensorFlow session """ self.saverVF.save(self.sess, "./savedmodel/"+self.env_name+"/VF/trained_VF.ckpt") self.sess.close()

bsd-3-clause

kose-y/pylearn2

pylearn2/scripts/plot_monitor.py

37

10204

#!/usr/bin/env python """ usage: plot_monitor.py model_1.pkl model_2.pkl ... model_n.pkl Loads any number of .pkl files produced by train.py. Extracts all of their monitoring channels and prompts the user to select a subset of them to be plotted. """ from __future__ import print_function __authors__ = "Ian Goodfellow, Harm Aarts" __copyright__ = "Copyright 2010-2012, Universite de Montreal" __credits__ = ["Ian Goodfellow"] __license__ = "3-clause BSD" __maintainer__ = "LISA Lab" __email__ = "pylearn-dev@googlegroups" import gc import numpy as np import sys from theano.compat.six.moves import input, xrange from pylearn2.utils import serial from theano.printing import _TagGenerator from pylearn2.utils.string_utils import number_aware_alphabetical_key from pylearn2.utils import contains_nan, contains_inf import argparse channels = {} def unique_substring(s, other, min_size=1): """ .. todo:: WRITEME """ size = min(len(s), min_size) while size <= len(s): for pos in xrange(0,len(s)-size+1): rval = s[pos:pos+size] fail = False for o in other: if o.find(rval) != -1: fail = True break if not fail: return rval size += 1 # no unique substring return s def unique_substrings(l, min_size=1): """ .. todo:: WRITEME """ return [unique_substring(s, [x for x in l if x is not s], min_size) for s in l] def main(): """ .. todo:: WRITEME """ parser = argparse.ArgumentParser() parser.add_argument("--out") parser.add_argument("model_paths", nargs='+') parser.add_argument("--yrange", help='The y-range to be used for plotting, e.g. 0:1') options = parser.parse_args() model_paths = options.model_paths if options.out is not None: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt print('generating names...') model_names = [model_path.replace('.pkl', '!') for model_path in model_paths] model_names = unique_substrings(model_names, min_size=10) model_names = [model_name.replace('!','') for model_name in model_names] print('...done') for i, arg in enumerate(model_paths): try: model = serial.load(arg) except Exception: if arg.endswith('.yaml'): print(sys.stderr, arg + " is a yaml config file," + "you need to load a trained model.", file=sys.stderr) quit(-1) raise this_model_channels = model.monitor.channels if len(sys.argv) > 2: postfix = ":" + model_names[i] else: postfix = "" for channel in this_model_channels: channels[channel+postfix] = this_model_channels[channel] del model gc.collect() while True: # Make a list of short codes for each channel so user can specify them # easily tag_generator = _TagGenerator() codebook = {} sorted_codes = [] for channel_name in sorted(channels, key = number_aware_alphabetical_key): code = tag_generator.get_tag() codebook[code] = channel_name codebook['<'+channel_name+'>'] = channel_name sorted_codes.append(code) x_axis = 'example' print('set x_axis to example') if len(channels.values()) == 0: print("there are no channels to plot") break # If there is more than one channel in the monitor ask which ones to # plot prompt = len(channels.values()) > 1 if prompt: # Display the codebook for code in sorted_codes: print(code + '. ' + codebook[code]) print() print("Put e, b, s or h in the list somewhere to plot " + "epochs, batches, seconds, or hours, respectively.") response = input('Enter a list of channels to plot ' + \ '(example: A, C,F-G, h, <test_err>) or q to quit' + \ ' or o for options: ') if response == 'o': print('1: smooth all channels') print('any other response: do nothing, go back to plotting') response = input('Enter your choice: ') if response == '1': for channel in channels.values(): k = 5 new_val_record = [] for i in xrange(len(channel.val_record)): new_val = 0. count = 0. for j in xrange(max(0, i-k), i+1): new_val += channel.val_record[j] count += 1. new_val_record.append(new_val / count) channel.val_record = new_val_record continue if response == 'q': break #Remove spaces response = response.replace(' ','') #Split into list codes = response.split(',') final_codes = set([]) for code in codes: if code == 'e': x_axis = 'epoch' continue elif code == 'b': x_axis = 'batche' elif code == 's': x_axis = 'second' elif code == 'h': x_axis = 'hour' elif code.startswith('<'): assert code.endswith('>') final_codes.add(code) elif code.find('-') != -1: #The current list element is a range of codes rng = code.split('-') if len(rng) != 2: print("Input not understood: "+code) quit(-1) found = False for i in xrange(len(sorted_codes)): if sorted_codes[i] == rng[0]: found = True break if not found: print("Invalid code: "+rng[0]) quit(-1) found = False for j in xrange(i,len(sorted_codes)): if sorted_codes[j] == rng[1]: found = True break if not found: print("Invalid code: "+rng[1]) quit(-1) final_codes = final_codes.union(set(sorted_codes[i:j+1])) else: #The current list element is just a single code final_codes = final_codes.union(set([code])) # end for code in codes else: final_codes ,= set(codebook.keys()) colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k'] styles = list(colors) styles += [color+'--' for color in colors] styles += [color+':' for color in colors] fig = plt.figure() ax = plt.subplot(1,1,1) # plot the requested channels for idx, code in enumerate(sorted(final_codes)): channel_name= codebook[code] channel = channels[channel_name] y = np.asarray(channel.val_record) if contains_nan(y): print(channel_name + ' contains NaNs') if contains_inf(y): print(channel_name + 'contains infinite values') if x_axis == 'example': x = np.asarray(channel.example_record) elif x_axis == 'batche': x = np.asarray(channel.batch_record) elif x_axis == 'epoch': try: x = np.asarray(channel.epoch_record) except AttributeError: # older saved monitors won't have epoch_record x = np.arange(len(channel.batch_record)) elif x_axis == 'second': x = np.asarray(channel.time_record) elif x_axis == 'hour': x = np.asarray(channel.time_record) / 3600. else: assert False ax.plot( x, y, styles[idx % len(styles)], marker = '.', # add point margers to lines label = channel_name) plt.xlabel('# '+x_axis+'s') ax.ticklabel_format( scilimits = (-3,3), axis = 'both') handles, labels = ax.get_legend_handles_labels() lgd = ax.legend(handles, labels, loc = 'upper left', bbox_to_anchor = (1.05, 1.02)) # Get the axis positions and the height and width of the legend plt.draw() ax_pos = ax.get_position() pad_width = ax_pos.x0 * fig.get_size_inches()[0] pad_height = ax_pos.y0 * fig.get_size_inches()[1] dpi = fig.get_dpi() lgd_width = ax.get_legend().get_frame().get_width() / dpi lgd_height = ax.get_legend().get_frame().get_height() / dpi # Adjust the bounding box to encompass both legend and axis. Axis should be 3x3 inches. # I had trouble getting everything to align vertically. ax_width = 3 ax_height = 3 total_width = 2*pad_width + ax_width + lgd_width total_height = 2*pad_height + np.maximum(ax_height, lgd_height) fig.set_size_inches(total_width, total_height) ax.set_position([pad_width/total_width, 1-6*pad_height/total_height, ax_width/total_width, ax_height/total_height]) if(options.yrange is not None): ymin, ymax = map(float, options.yrange.split(':')) plt.ylim(ymin, ymax) if options.out is None: plt.show() else: plt.savefig(options.out) if not prompt: break if __name__ == "__main__": main()

bsd-3-clause

prheenan/GeneralUtil

python/Plot/Inset.py

1

1864

# force floating point division. Can still use integer with // from __future__ import division # other good compatibility recquirements for python3 from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals # This file is used for importing the common utilities classes. import numpy as np import matplotlib.pyplot as plt import sys from GeneralUtil.python import PlotUtilities from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes,mark_inset def slice_by_x(x,y,xlim): """ slices x and y by xlim: Args: <x/y>: arrays of the same shape: xlim: the limits for x-limits Returns: tuple of (sliced x, sliced y, y limits) """ x = np.array(x) y = np.array(y) # plot the data red where we will zoom in where_region = np.where( (x >= min(xlim)) & (x <= max(xlim))) assert x.shape == y.shape , "Arrays should be the same shape " zoom_x = x[where_region] zoom_y = y[where_region] ylim = [min(zoom_y),max(zoom_y)] return zoom_x,zoom_y,ylim def zoomed_axis(ax=plt.gca(),xlim=[None,None],ylim=[None,None], remove_ticks=True,zoom=1,borderpad=1,loc=4,**kw): """ Creates a (pretty) zoomed axis Args: ax: which axis to zoom on <x/y>_lim: the axes limits remove_ticks: if true, removes the x and y ticks, to reduce clutter remaining args: passed to zoomed_inset_axes Returns: the inset axis """ axins = zoomed_inset_axes(ax, zoom=zoom, loc=loc,borderpad=borderpad) axins.set_xlim(*xlim) # apply the x-limits axins.set_ylim(*ylim) # apply the y-limits if (remove_ticks): PlotUtilities.no_x_anything(axins) PlotUtilities.no_y_anything(axins) return axins

gpl-2.0

th0mmeke/toyworld

evaluators/plot_molecular_diversity.py

1

1177

""" Created on 6/05/2013 @author: thom """ from plot import Plot from evaluator import Evaluator from molecular_population import MolecularPopulation import matplotlib.colors as colors class PlotMolecularDiversity(Plot): def draw_figure(self, f1, results_filename, **kwargs): results = Evaluator.load_results(results_filename) population = MolecularPopulation(population=results['initial_population'], reactions=results['reactions']) # , size=500) diversity = [] for t in population.get_times(): slice = population.get_slice_by_time([t]) quantities = [slice.get_quantity(item) for item in slice.get_items() if slice.get_quantity(item) > 0] iteration_diversity = (1.0 * len(quantities)) / (1.0 * sum(quantities)) diversity.append(iteration_diversity) ax = f1.add_subplot(1, 1, 1) # one row, one column, first plot ax.set_title('Diversity of Molecular Types') ax.set_xlabel('Time') ax.set_ylabel('Diversity') ax.set_xlim(left=0, right=population.get_times()[-1]) ax.plot(diversity, color=colors.cnames['slategray']) ax.grid()

gpl-3.0

kezilu/pextant

pextant/analysis/loadWaypoints.py

2

5515

import json import pandas as pd from copy import deepcopy from pextant.lib.geoshapely import * import numpy as np import re import os pd.options.display.max_rows = 5 from pathlib2 import Path def loadPointsOld(filename): parsed_json = json.loads(jsonInput) waypoints = [] for element in parsed_json: # identify all of the waypoints if element["type"] == "Station": lon, lat = element["geometry"]["coordinates"] time_cost = element["userDuration"] waypoints.append(GeoPolygon(LAT_LONG, lon, lat)) return waypoints, parsed_json def get_gps_data(filename): """ Gets GPS time series gathered from a traversal :param filename: <String> csv file from GPS team in format |date|time|name|latitude|longitude|heading :return: <pandas DataFrame> time_stamp|latitude|longitude """ delimiter = r"\s+" # some of the columns are separated by a space, others by tabs, use regex to include both header_row = 0 # the first row has all the header names df = pd.read_csv(filename, sep=delimiter, header=header_row) df['date_time'] = pd.to_datetime(df['epoch timestamp'], unit='s') time_lat_long = df[['date_time', 'latitude', 'longitude']] gp = GeoPolygon(LAT_LONG, *df[['latitude', 'longitude']].as_matrix().transpose()) return gp #TODO: Need to move this over to test file #filename = '../../data/ev_tracks/20161104A_EV1.csv' #time_lat_long = get_gps_data(filename) def sextant_loader(filepath): with open(filepath) as data_file: jsondata = json.load(data_file) latlongInter = np.array(jsondata['geometry']['coordinates']).transpose() return GeoPolygon(LONG_LAT, *latlongInter) #this really is a xpjson loader class JSONloader: def __init__(self, sequence, raw, filename=None): self.extension = '_plan.json' if isinstance(filename, Path): filename = str(filename.absolute()) self.filename = filename self.raw = raw self.sequence = sequence @classmethod def from_string(cls, str): return cls(json.loads(str)) @classmethod def from_file(cls, filepath): if isinstance(filepath, Path): filepath = str(filepath.absolute()) stem = os.path.basename(filepath).split('.')[0] parent = os.path.dirname(filepath) fullfilename = os.path.join(parent, stem) with open(filepath) as data_file: jsondata = json.load(data_file) return cls(jsondata['sequence'], jsondata, fullfilename) def get_waypoints(self): #print('HI') #print(self.sequence) #print('Hi again') ways_and_segments = self.sequence s = pd.DataFrame(ways_and_segments) waypoints = s[s['type'] == 'Station']['geometry'] w = waypoints.values.tolist() latlongFull = pd.DataFrame(w) latlongInter = np.array(latlongFull['coordinates'].values.tolist()).transpose() return GeoPolygon(LONG_LAT, *latlongInter) def get_segments(self): ways_and_segments = self.sequence s = pd.DataFrame(ways_and_segments) waypoints = s[s['type'] == 'Segment']['geometry'] w = waypoints.values.tolist() latlongFull = pd.DataFrame(w) latlongInter = latlongFull['coordinates'].values.tolist() waypointslatlong = [] for elt in latlongInter: waypointslatlong.extend(elt) return GeoPolygon(LONG_LAT, *np.array(waypointslatlong).transpose()) def add_search_sol(self, segments, write_to_file=False): ways_and_segments = deepcopy(self.sequence) segment_iter = iter(segments) for i, element in enumerate(ways_and_segments): if element["type"] == "Segment": segment = segment_iter.next().tojson() ways_and_segments[i]["derivedInfo"].update(segment["derivedInfo"]) #merges our new info ways_and_segments[i]["geometry"] = segment["geometry"] raw_json = json.dumps(ways_and_segments) formatted_json = json.dumps(ways_and_segments, indent=4, sort_keys=True) if write_to_file and self.filename: rawfile = self.raw rawfile["sequence"] = ways_and_segments new_filename = self.filename + self.extension with open(new_filename, 'w') as outfile: json.dump(rawfile, outfile, indent=4, sort_keys=True) return raw_json if __name__ == '__main__': from pextant.settings import * md = JSONloader.from_file(MD_HI[6]) sextantsol = md.get_segments() test =json.dumps([{u'commands': [], u'uuid': u'ccf34b91-86f4-47ee-b03d-3dbbba6ba167', u'geometry': {u'type': u'Point', u'coordinates': [-155.20191861222781, 19.366498026755977]}, u'tolerance': 0.6, u'userDuration': 0, u'boundary': 0.6, u'type': u'Station', u'id': u'HIL11_A_WAY0'}, { u'derivedInfo': {u'durationSeconds': 28, u'straightLineDurationSeconds': 28, u'distanceMeters': 25.15366493675656}, u'commands': [], u'type': u'Segment', u'id': u'HIL11_A_SEG1', u'uuid': u'69aa6e5f-6a10-4568-bfea-5bfbc8417ba7'}, {u'commands': [], u'uuid': u'1a159ed9-77ee-4f79-9163-e3685a01a00c', u'geometry': {u'type': u'Point', u'coordinates': [-155.2016858384008, 19.36644374514718]}, u'tolerance': 0.6, u'userDuration': 0, u'boundary': 0.6, u'type': u'Station', u'id': u'HIL11_A_WAY1'}]) jloader = JSONloader(test) jloader.get_waypoints()

mit

astropy/astropy

astropy/nddata/utils.py

2

31965

# Licensed under a 3-clause BSD style license - see LICENSE.rst """ This module includes helper functions for array operations. """ from copy import deepcopy import sys import types import warnings import numpy as np from astropy import units as u from astropy.coordinates import SkyCoord from astropy.utils import lazyproperty from astropy.utils.decorators import AstropyDeprecationWarning from astropy.wcs.utils import skycoord_to_pixel, proj_plane_pixel_scales from astropy.wcs import Sip from .blocks import block_reduce as _block_reduce from .blocks import block_replicate as _block_replicate __all__ = ['extract_array', 'add_array', 'subpixel_indices', 'overlap_slices', 'NoOverlapError', 'PartialOverlapError', 'Cutout2D'] # this can be replaced with PEP562 when the minimum required Python # version is 3.7 class _ModuleWithDeprecation(types.ModuleType): def __getattribute__(self, name): deprecated = ('block_reduce', 'block_replicate') if name in deprecated: warnings.warn(f'{name} was moved to the astropy.nddata.blocks ' 'module. Please update your import statement.', AstropyDeprecationWarning) return object.__getattribute__(self, f'_{name}') return object.__getattribute__(self, name) sys.modules[__name__].__class__ = _ModuleWithDeprecation class NoOverlapError(ValueError): '''Raised when determining the overlap of non-overlapping arrays.''' pass class PartialOverlapError(ValueError): '''Raised when arrays only partially overlap.''' pass def overlap_slices(large_array_shape, small_array_shape, position, mode='partial'): """ Get slices for the overlapping part of a small and a large array. Given a certain position of the center of the small array, with respect to the large array, tuples of slices are returned which can be used to extract, add or subtract the small array at the given position. This function takes care of the correct behavior at the boundaries, where the small array is cut of appropriately. Integer positions are at the pixel centers. Parameters ---------- large_array_shape : tuple of int or int The shape of the large array (for 1D arrays, this can be an `int`). small_array_shape : int or tuple thereof The shape of the small array (for 1D arrays, this can be an `int`). See the ``mode`` keyword for additional details. position : number or tuple thereof The position of the small array's center with respect to the large array. The pixel coordinates should be in the same order as the array shape. Integer positions are at the pixel centers. For any axis where ``small_array_shape`` is even, the position is rounded up, e.g. extracting two elements with a center of ``1`` will define the extracted region as ``[0, 1]``. mode : {'partial', 'trim', 'strict'}, optional In ``'partial'`` mode, a partial overlap of the small and the large array is sufficient. The ``'trim'`` mode is similar to the ``'partial'`` mode, but ``slices_small`` will be adjusted to return only the overlapping elements. In the ``'strict'`` mode, the small array has to be fully contained in the large array, otherwise an `~astropy.nddata.utils.PartialOverlapError` is raised. In all modes, non-overlapping arrays will raise a `~astropy.nddata.utils.NoOverlapError`. Returns ------- slices_large : tuple of slice A tuple of slice objects for each axis of the large array, such that ``large_array[slices_large]`` extracts the region of the large array that overlaps with the small array. slices_small : tuple of slice A tuple of slice objects for each axis of the small array, such that ``small_array[slices_small]`` extracts the region that is inside the large array. """ if mode not in ['partial', 'trim', 'strict']: raise ValueError('Mode can be only "partial", "trim", or "strict".') if np.isscalar(small_array_shape): small_array_shape = (small_array_shape, ) if np.isscalar(large_array_shape): large_array_shape = (large_array_shape, ) if np.isscalar(position): position = (position, ) if any(~np.isfinite(position)): raise ValueError('Input position contains invalid values (NaNs or ' 'infs).') if len(small_array_shape) != len(large_array_shape): raise ValueError('"large_array_shape" and "small_array_shape" must ' 'have the same number of dimensions.') if len(small_array_shape) != len(position): raise ValueError('"position" must have the same number of dimensions ' 'as "small_array_shape".') # define the min/max pixel indices indices_min = [int(np.ceil(pos - (small_shape / 2.))) for (pos, small_shape) in zip(position, small_array_shape)] indices_max = [int(np.ceil(pos + (small_shape / 2.))) for (pos, small_shape) in zip(position, small_array_shape)] for e_max in indices_max: if e_max < 0: raise NoOverlapError('Arrays do not overlap.') for e_min, large_shape in zip(indices_min, large_array_shape): if e_min >= large_shape: raise NoOverlapError('Arrays do not overlap.') if mode == 'strict': for e_min in indices_min: if e_min < 0: raise PartialOverlapError('Arrays overlap only partially.') for e_max, large_shape in zip(indices_max, large_array_shape): if e_max > large_shape: raise PartialOverlapError('Arrays overlap only partially.') # Set up slices slices_large = tuple(slice(max(0, indices_min), min(large_shape, indices_max)) for (indices_min, indices_max, large_shape) in zip(indices_min, indices_max, large_array_shape)) if mode == 'trim': slices_small = tuple(slice(0, slc.stop - slc.start) for slc in slices_large) else: slices_small = tuple(slice(max(0, -indices_min), min(large_shape - indices_min, indices_max - indices_min)) for (indices_min, indices_max, large_shape) in zip(indices_min, indices_max, large_array_shape)) return slices_large, slices_small def extract_array(array_large, shape, position, mode='partial', fill_value=np.nan, return_position=False): """ Extract a smaller array of the given shape and position from a larger array. Parameters ---------- array_large : ndarray The array from which to extract the small array. shape : int or tuple thereof The shape of the extracted array (for 1D arrays, this can be an `int`). See the ``mode`` keyword for additional details. position : number or tuple thereof The position of the small array's center with respect to the large array. The pixel coordinates should be in the same order as the array shape. Integer positions are at the pixel centers (for 1D arrays, this can be a number). mode : {'partial', 'trim', 'strict'}, optional The mode used for extracting the small array. For the ``'partial'`` and ``'trim'`` modes, a partial overlap of the small array and the large array is sufficient. For the ``'strict'`` mode, the small array has to be fully contained within the large array, otherwise an `~astropy.nddata.utils.PartialOverlapError` is raised. In all modes, non-overlapping arrays will raise a `~astropy.nddata.utils.NoOverlapError`. In ``'partial'`` mode, positions in the small array that do not overlap with the large array will be filled with ``fill_value``. In ``'trim'`` mode only the overlapping elements are returned, thus the resulting small array may be smaller than the requested ``shape``. fill_value : number, optional If ``mode='partial'``, the value to fill pixels in the extracted small array that do not overlap with the input ``array_large``. ``fill_value`` will be changed to have the same ``dtype`` as the ``array_large`` array, with one exception. If ``array_large`` has integer type and ``fill_value`` is ``np.nan``, then a `ValueError` will be raised. return_position : bool, optional If `True`, return the coordinates of ``position`` in the coordinate system of the returned array. Returns ------- array_small : ndarray The extracted array. new_position : tuple If ``return_position`` is true, this tuple will contain the coordinates of the input ``position`` in the coordinate system of ``array_small``. Note that for partially overlapping arrays, ``new_position`` might actually be outside of the ``array_small``; ``array_small[new_position]`` might give wrong results if any element in ``new_position`` is negative. Examples -------- We consider a large array with the shape 11x10, from which we extract a small array of shape 3x5: >>> import numpy as np >>> from astropy.nddata.utils import extract_array >>> large_array = np.arange(110).reshape((11, 10)) >>> extract_array(large_array, (3, 5), (7, 7)) array([[65, 66, 67, 68, 69], [75, 76, 77, 78, 79], [85, 86, 87, 88, 89]]) """ if np.isscalar(shape): shape = (shape, ) if np.isscalar(position): position = (position, ) if mode not in ['partial', 'trim', 'strict']: raise ValueError("Valid modes are 'partial', 'trim', and 'strict'.") large_slices, small_slices = overlap_slices(array_large.shape, shape, position, mode=mode) extracted_array = array_large[large_slices] if return_position: new_position = [i - s.start for i, s in zip(position, large_slices)] # Extracting on the edges is presumably a rare case, so treat special here if (extracted_array.shape != shape) and (mode == 'partial'): extracted_array = np.zeros(shape, dtype=array_large.dtype) try: extracted_array[:] = fill_value except ValueError as exc: exc.args += ('fill_value is inconsistent with the data type of ' 'the input array (e.g., fill_value cannot be set to ' 'np.nan if the input array has integer type). Please ' 'change either the input array dtype or the ' 'fill_value.',) raise exc extracted_array[small_slices] = array_large[large_slices] if return_position: new_position = [i + s.start for i, s in zip(new_position, small_slices)] if return_position: return extracted_array, tuple(new_position) else: return extracted_array def add_array(array_large, array_small, position): """ Add a smaller array at a given position in a larger array. Parameters ---------- array_large : ndarray Large array. array_small : ndarray Small array to add. Can be equal to ``array_large`` in size in a given dimension, but not larger. position : tuple Position of the small array's center, with respect to the large array. Coordinates should be in the same order as the array shape. Returns ------- new_array : ndarray The new array formed from the sum of ``array_large`` and ``array_small``. Notes ----- The addition is done in-place. Examples -------- We consider a large array of zeros with the shape 5x5 and a small array of ones with a shape of 3x3: >>> import numpy as np >>> from astropy.nddata.utils import add_array >>> large_array = np.zeros((5, 5)) >>> small_array = np.ones((3, 3)) >>> add_array(large_array, small_array, (1, 2)) # doctest: +FLOAT_CMP array([[0., 1., 1., 1., 0.], [0., 1., 1., 1., 0.], [0., 1., 1., 1., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]]) """ # Check if large array is not smaller if all(large_shape >= small_shape for (large_shape, small_shape) in zip(array_large.shape, array_small.shape)): large_slices, small_slices = overlap_slices(array_large.shape, array_small.shape, position) array_large[large_slices] += array_small[small_slices] return array_large else: raise ValueError("Can't add array. Small array too large.") def subpixel_indices(position, subsampling): """ Convert decimal points to indices, given a subsampling factor. This discards the integer part of the position and uses only the decimal place, and converts this to a subpixel position depending on the subsampling specified. The center of a pixel corresponds to an integer position. Parameters ---------- position : ndarray or array-like Positions in pixels. subsampling : int Subsampling factor per pixel. Returns ------- indices : ndarray The integer subpixel indices corresponding to the input positions. Examples -------- If no subsampling is used, then the subpixel indices returned are always 0: >>> from astropy.nddata.utils import subpixel_indices >>> subpixel_indices([1.2, 3.4, 5.6], 1) # doctest: +FLOAT_CMP array([0., 0., 0.]) If instead we use a subsampling of 2, we see that for the two first values (1.1 and 3.4) the subpixel position is 1, while for 5.6 it is 0. This is because the values of 1, 3, and 6 lie in the center of pixels, and 1.1 and 3.4 lie in the left part of the pixels and 5.6 lies in the right part. >>> subpixel_indices([1.2, 3.4, 5.5], 2) # doctest: +FLOAT_CMP array([1., 1., 0.]) """ # Get decimal points fractions = np.modf(np.asanyarray(position) + 0.5)[0] return np.floor(fractions * subsampling) class Cutout2D: """ Create a cutout object from a 2D array. The returned object will contain a 2D cutout array. If ``copy=False`` (default), the cutout array is a view into the original ``data`` array, otherwise the cutout array will contain a copy of the original data. If a `~astropy.wcs.WCS` object is input, then the returned object will also contain a copy of the original WCS, but updated for the cutout array. For example usage, see :ref:`cutout_images`. .. warning:: The cutout WCS object does not currently handle cases where the input WCS object contains distortion lookup tables described in the `FITS WCS distortion paper <https://www.atnf.csiro.au/people/mcalabre/WCS/dcs_20040422.pdf>`__. Parameters ---------- data : ndarray The 2D data array from which to extract the cutout array. position : tuple or `~astropy.coordinates.SkyCoord` The position of the cutout array's center with respect to the ``data`` array. The position can be specified either as a ``(x, y)`` tuple of pixel coordinates or a `~astropy.coordinates.SkyCoord`, in which case ``wcs`` is a required input. size : int, array-like, or `~astropy.units.Quantity` The size of the cutout array along each axis. If ``size`` is a scalar number or a scalar `~astropy.units.Quantity`, then a square cutout of ``size`` will be created. If ``size`` has two elements, they should be in ``(ny, nx)`` order. Scalar numbers in ``size`` are assumed to be in units of pixels. ``size`` can also be a `~astropy.units.Quantity` object or contain `~astropy.units.Quantity` objects. Such `~astropy.units.Quantity` objects must be in pixel or angular units. For all cases, ``size`` will be converted to an integer number of pixels, rounding the the nearest integer. See the ``mode`` keyword for additional details on the final cutout size. .. note:: If ``size`` is in angular units, the cutout size is converted to pixels using the pixel scales along each axis of the image at the ``CRPIX`` location. Projection and other non-linear distortions are not taken into account. wcs : `~astropy.wcs.WCS`, optional A WCS object associated with the input ``data`` array. If ``wcs`` is not `None`, then the returned cutout object will contain a copy of the updated WCS for the cutout data array. mode : {'trim', 'partial', 'strict'}, optional The mode used for creating the cutout data array. For the ``'partial'`` and ``'trim'`` modes, a partial overlap of the cutout array and the input ``data`` array is sufficient. For the ``'strict'`` mode, the cutout array has to be fully contained within the ``data`` array, otherwise an `~astropy.nddata.utils.PartialOverlapError` is raised. In all modes, non-overlapping arrays will raise a `~astropy.nddata.utils.NoOverlapError`. In ``'partial'`` mode, positions in the cutout array that do not overlap with the ``data`` array will be filled with ``fill_value``. In ``'trim'`` mode only the overlapping elements are returned, thus the resulting cutout array may be smaller than the requested ``shape``. fill_value : float or int, optional If ``mode='partial'``, the value to fill pixels in the cutout array that do not overlap with the input ``data``. ``fill_value`` must have the same ``dtype`` as the input ``data`` array. copy : bool, optional If `False` (default), then the cutout data will be a view into the original ``data`` array. If `True`, then the cutout data will hold a copy of the original ``data`` array. Attributes ---------- data : 2D `~numpy.ndarray` The 2D cutout array. shape : (2,) tuple The ``(ny, nx)`` shape of the cutout array. shape_input : (2,) tuple The ``(ny, nx)`` shape of the input (original) array. input_position_cutout : (2,) tuple The (unrounded) ``(x, y)`` position with respect to the cutout array. input_position_original : (2,) tuple The original (unrounded) ``(x, y)`` input position (with respect to the original array). slices_original : (2,) tuple of slice object A tuple of slice objects for the minimal bounding box of the cutout with respect to the original array. For ``mode='partial'``, the slices are for the valid (non-filled) cutout values. slices_cutout : (2,) tuple of slice object A tuple of slice objects for the minimal bounding box of the cutout with respect to the cutout array. For ``mode='partial'``, the slices are for the valid (non-filled) cutout values. xmin_original, ymin_original, xmax_original, ymax_original : float The minimum and maximum ``x`` and ``y`` indices of the minimal rectangular region of the cutout array with respect to the original array. For ``mode='partial'``, the bounding box indices are for the valid (non-filled) cutout values. These values are the same as those in `bbox_original`. xmin_cutout, ymin_cutout, xmax_cutout, ymax_cutout : float The minimum and maximum ``x`` and ``y`` indices of the minimal rectangular region of the cutout array with respect to the cutout array. For ``mode='partial'``, the bounding box indices are for the valid (non-filled) cutout values. These values are the same as those in `bbox_cutout`. wcs : `~astropy.wcs.WCS` or None A WCS object associated with the cutout array if a ``wcs`` was input. Examples -------- >>> import numpy as np >>> from astropy.nddata.utils import Cutout2D >>> from astropy import units as u >>> data = np.arange(20.).reshape(5, 4) >>> cutout1 = Cutout2D(data, (2, 2), (3, 3)) >>> print(cutout1.data) # doctest: +FLOAT_CMP [[ 5. 6. 7.] [ 9. 10. 11.] [13. 14. 15.]] >>> print(cutout1.center_original) (2.0, 2.0) >>> print(cutout1.center_cutout) (1.0, 1.0) >>> print(cutout1.origin_original) (1, 1) >>> cutout2 = Cutout2D(data, (2, 2), 3) >>> print(cutout2.data) # doctest: +FLOAT_CMP [[ 5. 6. 7.] [ 9. 10. 11.] [13. 14. 15.]] >>> size = u.Quantity([3, 3], u.pixel) >>> cutout3 = Cutout2D(data, (0, 0), size) >>> print(cutout3.data) # doctest: +FLOAT_CMP [[0. 1.] [4. 5.]] >>> cutout4 = Cutout2D(data, (0, 0), (3 * u.pixel, 3)) >>> print(cutout4.data) # doctest: +FLOAT_CMP [[0. 1.] [4. 5.]] >>> cutout5 = Cutout2D(data, (0, 0), (3, 3), mode='partial') >>> print(cutout5.data) # doctest: +FLOAT_CMP [[nan nan nan] [nan 0. 1.] [nan 4. 5.]] """ def __init__(self, data, position, size, wcs=None, mode='trim', fill_value=np.nan, copy=False): if wcs is None: wcs = getattr(data, 'wcs', None) if isinstance(position, SkyCoord): if wcs is None: raise ValueError('wcs must be input if position is a ' 'SkyCoord') position = skycoord_to_pixel(position, wcs, mode='all') # (x, y) if np.isscalar(size): size = np.repeat(size, 2) # special handling for a scalar Quantity if isinstance(size, u.Quantity): size = np.atleast_1d(size) if len(size) == 1: size = np.repeat(size, 2) if len(size) > 2: raise ValueError('size must have at most two elements') shape = np.zeros(2).astype(int) pixel_scales = None # ``size`` can have a mixture of int and Quantity (and even units), # so evaluate each axis separately for axis, side in enumerate(size): if not isinstance(side, u.Quantity): shape[axis] = int(np.round(size[axis])) # pixels else: if side.unit == u.pixel: shape[axis] = int(np.round(side.value)) elif side.unit.physical_type == 'angle': if wcs is None: raise ValueError('wcs must be input if any element ' 'of size has angular units') if pixel_scales is None: pixel_scales = u.Quantity( proj_plane_pixel_scales(wcs), wcs.wcs.cunit[axis]) shape[axis] = int(np.round( (side / pixel_scales[axis]).decompose())) else: raise ValueError('shape can contain Quantities with only ' 'pixel or angular units') data = np.asanyarray(data) # reverse position because extract_array and overlap_slices # use (y, x), but keep the input position pos_yx = position[::-1] cutout_data, input_position_cutout = extract_array( data, tuple(shape), pos_yx, mode=mode, fill_value=fill_value, return_position=True) if copy: cutout_data = np.copy(cutout_data) self.data = cutout_data self.input_position_cutout = input_position_cutout[::-1] # (x, y) slices_original, slices_cutout = overlap_slices( data.shape, shape, pos_yx, mode=mode) self.slices_original = slices_original self.slices_cutout = slices_cutout self.shape = self.data.shape self.input_position_original = position self.shape_input = shape ((self.ymin_original, self.ymax_original), (self.xmin_original, self.xmax_original)) = self.bbox_original ((self.ymin_cutout, self.ymax_cutout), (self.xmin_cutout, self.xmax_cutout)) = self.bbox_cutout # the true origin pixel of the cutout array, including any # filled cutout values self._origin_original_true = ( self.origin_original[0] - self.slices_cutout[1].start, self.origin_original[1] - self.slices_cutout[0].start) if wcs is not None: self.wcs = deepcopy(wcs) self.wcs.wcs.crpix -= self._origin_original_true self.wcs.array_shape = self.data.shape if wcs.sip is not None: self.wcs.sip = Sip(wcs.sip.a, wcs.sip.b, wcs.sip.ap, wcs.sip.bp, wcs.sip.crpix - self._origin_original_true) else: self.wcs = None def to_original_position(self, cutout_position): """ Convert an ``(x, y)`` position in the cutout array to the original ``(x, y)`` position in the original large array. Parameters ---------- cutout_position : tuple The ``(x, y)`` pixel position in the cutout array. Returns ------- original_position : tuple The corresponding ``(x, y)`` pixel position in the original large array. """ return tuple(cutout_position[i] + self.origin_original[i] for i in [0, 1]) def to_cutout_position(self, original_position): """ Convert an ``(x, y)`` position in the original large array to the ``(x, y)`` position in the cutout array. Parameters ---------- original_position : tuple The ``(x, y)`` pixel position in the original large array. Returns ------- cutout_position : tuple The corresponding ``(x, y)`` pixel position in the cutout array. """ return tuple(original_position[i] - self.origin_original[i] for i in [0, 1]) def plot_on_original(self, ax=None, fill=False, **kwargs): """ Plot the cutout region on a matplotlib Axes instance. Parameters ---------- ax : `matplotlib.axes.Axes` instance, optional If `None`, then the current `matplotlib.axes.Axes` instance is used. fill : bool, optional Set whether to fill the cutout patch. The default is `False`. kwargs : optional Any keyword arguments accepted by `matplotlib.patches.Patch`. Returns ------- ax : `matplotlib.axes.Axes` instance The matplotlib Axes instance constructed in the method if ``ax=None``. Otherwise the output ``ax`` is the same as the input ``ax``. """ import matplotlib.pyplot as plt import matplotlib.patches as mpatches kwargs['fill'] = fill if ax is None: ax = plt.gca() height, width = self.shape hw, hh = width / 2., height / 2. pos_xy = self.position_original - np.array([hw, hh]) patch = mpatches.Rectangle(pos_xy, width, height, 0., **kwargs) ax.add_patch(patch) return ax @staticmethod def _calc_center(slices): """ Calculate the center position. The center position will be fractional for even-sized arrays. For ``mode='partial'``, the central position is calculated for the valid (non-filled) cutout values. """ return tuple(0.5 * (slices[i].start + slices[i].stop - 1) for i in [1, 0]) @staticmethod def _calc_bbox(slices): """ Calculate a minimal bounding box in the form ``((ymin, ymax), (xmin, xmax))``. Note these are pixel locations, not slice indices. For ``mode='partial'``, the bounding box indices are for the valid (non-filled) cutout values. """ # (stop - 1) to return the max pixel location, not the slice index return ((slices[0].start, slices[0].stop - 1), (slices[1].start, slices[1].stop - 1)) @lazyproperty def origin_original(self): """ The ``(x, y)`` index of the origin pixel of the cutout with respect to the original array. For ``mode='partial'``, the origin pixel is calculated for the valid (non-filled) cutout values. """ return (self.slices_original[1].start, self.slices_original[0].start) @lazyproperty def origin_cutout(self): """ The ``(x, y)`` index of the origin pixel of the cutout with respect to the cutout array. For ``mode='partial'``, the origin pixel is calculated for the valid (non-filled) cutout values. """ return (self.slices_cutout[1].start, self.slices_cutout[0].start) @staticmethod def _round(a): """ Round the input to the nearest integer. If two integers are equally close, the value is rounded up. Note that this is different from `np.round`, which rounds to the nearest even number. """ return int(np.floor(a + 0.5)) @lazyproperty def position_original(self): """ The ``(x, y)`` position index (rounded to the nearest pixel) in the original array. """ return (self._round(self.input_position_original[0]), self._round(self.input_position_original[1])) @lazyproperty def position_cutout(self): """ The ``(x, y)`` position index (rounded to the nearest pixel) in the cutout array. """ return (self._round(self.input_position_cutout[0]), self._round(self.input_position_cutout[1])) @lazyproperty def center_original(self): """ The central ``(x, y)`` position of the cutout array with respect to the original array. For ``mode='partial'``, the central position is calculated for the valid (non-filled) cutout values. """ return self._calc_center(self.slices_original) @lazyproperty def center_cutout(self): """ The central ``(x, y)`` position of the cutout array with respect to the cutout array. For ``mode='partial'``, the central position is calculated for the valid (non-filled) cutout values. """ return self._calc_center(self.slices_cutout) @lazyproperty def bbox_original(self): """ The bounding box ``((ymin, ymax), (xmin, xmax))`` of the minimal rectangular region of the cutout array with respect to the original array. For ``mode='partial'``, the bounding box indices are for the valid (non-filled) cutout values. """ return self._calc_bbox(self.slices_original) @lazyproperty def bbox_cutout(self): """ The bounding box ``((ymin, ymax), (xmin, xmax))`` of the minimal rectangular region of the cutout array with respect to the cutout array. For ``mode='partial'``, the bounding box indices are for the valid (non-filled) cutout values. """ return self._calc_bbox(self.slices_cutout)

bsd-3-clause

f3r/scikit-learn

sklearn/metrics/cluster/__init__.py

312

1322

""" The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for cluster analysis results. There are two forms of evaluation: - supervised, which uses a ground truth class values for each sample. - unsupervised, which does not and measures the 'quality' of the model itself. """ from .supervised import adjusted_mutual_info_score from .supervised import normalized_mutual_info_score from .supervised import adjusted_rand_score from .supervised import completeness_score from .supervised import contingency_matrix from .supervised import expected_mutual_information from .supervised import homogeneity_completeness_v_measure from .supervised import homogeneity_score from .supervised import mutual_info_score from .supervised import v_measure_score from .supervised import entropy from .unsupervised import silhouette_samples from .unsupervised import silhouette_score from .bicluster import consensus_score __all__ = ["adjusted_mutual_info_score", "normalized_mutual_info_score", "adjusted_rand_score", "completeness_score", "contingency_matrix", "expected_mutual_information", "homogeneity_completeness_v_measure", "homogeneity_score", "mutual_info_score", "v_measure_score", "entropy", "silhouette_samples", "silhouette_score", "consensus_score"]

bsd-3-clause

op7ic/LeakGenerator

leakme.py

1

351132

# Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. import random import string import itertools import re import sys import hashlib import binascii import optparse # Resources # https://raw.githubusercontent.com/neo/discourse_heroku/master/lib/common_passwords/10k-common-passwords.txt # https://raw.githubusercontent.com/dominictarr/random-name/master/names.txt # http://code.activestate.com/recipes/65215-e-mail-address-validation/ emailregex = "^[a-zA-Z0-9._%-]+@[a-zA-Z0-9._%-]+.[a-zA-Z]{2,6}$" common_passwords = 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inic","Dominica","Dominick","Dominik","Dominique","Dominus","Dominy","Domonic","Domph","Don","Dona","Donadee","Donaghue","Donahoe","Donahue","Donal","Donald","Donaldson","Donall","Donalt","Donata","Donatelli","Donaugh","Donavon","Donegan","Donela","Donell","Donella","Donelle","Donelson","Donelu","Doner","Donetta","Dong","Donia","Donica","Donielle","Donn","Donna","Donnamarie","Donnell","Donnelly","Donnenfeld","Donni","Donnie","Donny","Donoghue","Donoho","Donohue","Donough","Donovan","Doolittle","Doone","Dopp","Dora","Doralia","Doralin","Doralyn","Doralynn","Doralynne","Doran","Dorca","Dorcas","Dorcea","Dorcia","Dorcus","Dorcy","Dore","Doreen","Dorelia","Dorella","Dorelle","Dorena","Dorene","Doretta","Dorette","Dorey","Dorfman","Dori","Doria","Dorian","Dorice","Dorie","Dorin","Dorina","Dorinda","Dorine","Dorion","Doris","Dorisa","Dorise","Dorison","Dorita","Dorkas","Dorkus","Dorlisa","Dorman","Dorn","Doro","Dorolice","Dorolisa","Dorotea","Doroteya","Dorothea","Dorothee","Dorothi","Dorothy","Dorr","Dorran","Dorree","Dorren","Dorri","Dorrie","Dorris","Dorry","Dorsey","Dorsman","Dorsy","Dorthea","Dorthy","Dorweiler","Dorwin","Dory","Doscher","Dosh","Dosi","Dosia","Doss","Dot","Doti","Dotson","Dott","Dotti","Dottie","Dotty","Doty","Doubler","Doug","Dougal","Dougald","Dougall","Dougherty","Doughman","Doughty","Dougie","Douglas","Douglass","Dougy","Douty","Douville","Dov","Dove","Dovev","Dow","Dowd","Dowdell","Dowell","Dowlen","Dowling","Down","Downall","Downe","Downes","Downey","Downing","Downs","Dowski","Dowzall","Doxia","Doy","Doykos","Doyle","Drabeck","Dragelin","Dragon","Dragone","Dragoon","Drain","Drais","Drake","Drandell","Drape","Draper","Dray","Dre","Dream","Dreda","Dreddy","Dredi","Dreeda","Dreher","Dremann","Drescher","Dressel","Dressler","Drew","Drewett","Drews","Drexler","Dreyer","Dric","Drice","Drida","Dripps","Driscoll","Driskill","Drisko","Drislane","Drobman","Drogin","Drolet","Drona","Dronski","Drooff","Dru","Druce","Druci","Drucie","Drucill","Drucilla","Drucy","Drud","Drue","Drugge","Drugi","Drummond","Drus","Drusi","Drusie","Drusilla","Drusus","Drusy","Dry","Dryden","Drye","Dryfoos","DuBois","Duane","Duarte","Duax","Dubenko","Dublin","Ducan","Duck","Dud","Dudden","Dudley","Duer","Duester","Duff","Duffie","Duffy","Dugaid","Dugald","Dugan","Dugas","Duggan","Duhl","Duke","Dukey","Dukie","Duky","Dulce","Dulcea","Dulci","Dulcia","Dulciana","Dulcie","Dulcine","Dulcinea","Dulcle","Dulcy","Duleba","Dulla","Dulsea","Duma","Dumah","Dumanian","Dumas","Dumm","Dumond","Dun","Dunaville","Dunc","Duncan","Dunham","Dunkin","Dunlavy","Dunn","Dunning","Dunseath","Dunson","Dunstan","Dunston","Dunton","Duntson","Duong","Dupaix","Dupin","Dupre","Dupuis","Dupuy","Duquette","Dur","Durand","Durant","Durante","Durarte","Durer","Durgy","Durham","Durkee","Durkin","Durman","Durnan","Durning","Durno","Durr","Durrace","Durrell","Durrett","Durst","Durstin","Durston","Durtschi","Durward","Durware","Durwin","Durwood","Durwyn","Dusa","Dusen","Dust","Dustan","Duster","Dustie","Dustin","Dustman","Duston","Dusty","Dusza","Dutch","Dutchman","Duthie","Duval","Duvall","Duwalt","Duwe","Duyne","Dwain","Dwaine","Dwan","Dwane","Dwayne","Dweck","Dwight","Dwinnell","Dworman","Dwyer","Dyal","Dyan","Dyana","Dyane","Dyann","Dyanna","Dyanne","Dyche","Dyer","Dygal","Dygall","Dygert","Dyke","Dyl","Dylan","Dylana","Dylane","Dymoke","Dympha","Dymphia","Dyna","Dynah","Dysart","Dyson","Dyun","Dzoba","Eachelle","Eachern","Eada","Eade","Eadie","Eadith","Eadmund","Eads","Eadwina","Eadwine","Eagle","Eal","Ealasaid","Eamon","Eanore","Earl","Earla","Earle","Earleen","Earlene","Earley","Earlie","Early","Eartha","Earvin","East","Easter","Eastlake","Eastman","Easton","Eaton","Eatton","Eaves","Eb","Eba","Ebarta","Ebba","Ebbarta","Ebberta","Ebbie","Ebby","Eben","Ebeneser","Ebenezer","Eberhard","Eberhart","Eberle","Eberly","Ebert","Eberta","Eberto","Ebner","Ebneter","Eboh","Ebonee","Ebony","Ebsen","Echikson","Echo","Eckardt","Eckart","Eckblad","Eckel","Eckhardt","Eckmann","Econah","Ed","Eda","Edan","Edana","Edbert","Edd","Edda","Eddana","Eddi","Eddie","Eddina","Eddra","Eddy","Ede","Edea","Edee","Edeline","Edelman","Edelson","Edelstein","Edelsten","Eden","Edette","Edgar","Edgard","Edgardo","Edge","Edgell","Edgerton","Edholm","Edi","Edie","Edik","Edin","Edina","Edison","Edita","Edith","Editha","Edithe","Ediva","Edla","Edlin","Edlun","Edlyn","Edmanda","Edme","Edmea","Edmead","Edmee","Edmon","Edmond","Edmonda","Edmondo","Edmonds","Edmund","Edmunda","Edna","Edny","Edora","Edouard","Edra","Edrea","Edrei","Edric","Edrick","Edris","Edrock","Edroi","Edsel","Edson","Eduard","Eduardo","Eduino","Edva","Edvard","Edveh","Edward","Edwards","Edwin","Edwina","Edwine","Edwyna","Edy","Edyth","Edythe","Effie","Effy","Efram","Efrem","Efren","Efron","Efthim","Egan","Egarton","Egbert","Egerton","Eggett","Eggleston","Egide","Egidio","Egidius","Egin","Eglanteen","Eglantine","Egon","Egor","Egwan","Egwin","Ehling","Ehlke","Ehman","Ehr","Ehrenberg","Ehrlich","Ehrman","Ehrsam","Ehud","Ehudd",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aryn","Taryne","Tasha","Tasia","Tasiana","Tat","Tate","Tati","Tatia","Tatiana","Tatianas","Tatiania","Tatianna","Tatman","Tattan","Tatum","Taub","Tav","Taveda","Tavey","Tavi","Tavia","Tavie","Tavis","Tavish","Tavy","Tawney","Tawnya","Tawsha","Tay","Tayib","Tayler","Taylor","Tayyebeb","Tchao","Teador","Teagan","Teage","Teague","Teahan","Teak","Tearle","Tecla","Tecu","Ted","Tedd","Tedda","Tedder","Teddi","Teddie","Teddman","Teddy","Tedi","Tedie","Tedman","Tedmann","Tedmund","Tedra","Tedric","Teece","Teena","Teerell","Teeter","Teevens","Teferi","Tega","Tegan","Teillo","Teilo","Tekla","Telfer","Telford","Telfore","Tella","Tellford","Tem","Tema","Temp","Tempa","Tempest","Templa","Templas","Temple","Templer","Templeton","Templia","Ten","Tena","Tench","Tenenbaum","Tengdin","Tengler","Tenn","Tenner","Tennes","Tenney","Tennies","Teodoor","Teodor","Teodora","Teodorico","Teodoro","Teplica","Teplitz","Tepper","Tera","Terbecki","Terchie","Terena","Terence","Terencio","Teresa","Terese","Teresina","Teresita","Teressa","Terhune","Teri","Teria","Teriann","Terina","Terle","Ternan","Terpstra","Terr","Terra","Terrance","Terrel","Terrell","Terrena","Terrence","Terrene","Terri","Terrie","Terrijo","Terrill","Terrilyn","Terris","Terriss","Territus","Terry","Terrye","Terryl","Terryn","Tersina","Terti","Tertia","Tertias","Tertius","Teryl","Teryn","Terza","Terzas","Tesler","Tess","Tessa","Tessi","Tessie","Tessler","Tessy","Teteak","Teufert","Teuton","Tevis","Tewell","Tewfik","Tews","Thacher","Thacker","Thackeray","Thad","Thaddaus","Thaddeus","Thaddus","Thadeus","Thagard","Thain","Thaine","Thais","Thalassa","Thalia","Tham","Thamora","Thamos","Thanasi","Thane","Thanh","Thanos","Thant","Thapa","Thar","Tharp","Thatch","Thatcher","Thaxter","Thay","Thayer","Thayne","The","Thea","Theadora","Theall","Thebault","Thecla","Theda","Thedric","Thedrick","Theis","Thekla","Thelma","Thema","Themis","Thenna","Theo","Theobald","Theodor","Theodora","Theodore","Theodoric","Theodosia","Theola","Theona","Theone","Thera","Theran","Theresa","Therese","Theresina","Theresita","Theressa","Therine","Theron","Therron","Thesda","Thessa","Theta","Thetes","Thetis","Thetisa","Thetos","Theurer","Theurich","Thevenot","Thia","Thibaud","Thibault","Thibaut","Thielen","Thier","Thierry","Thilda","Thilde","Thill","Thin","Thinia","Thirion","Thirza","Thirzi","Thirzia","Thisbe","Thisbee","Thissa","Thistle","Thoer","Thom","Thoma","Thomajan","Thomas","Thomasa","Thomasin","Thomasina","Thomasine","Thomey","Thompson","Thomsen","Thomson","Thor","Thora","Thorbert","Thordia","Thordis","Thorfinn","Thorin","Thorlay","Thorley","Thorlie","Thorma","Thorman","Thormora","Thorn","Thornburg","Thorncombe","Thorndike","Thorne","Thorner","Thornie","Thornton","Thorny","Thorpe","Thorr","Thorrlow","Thorstein","Thorsten","Thorvald","Thorwald","Thrasher","Three","Threlkeld","Thrift","Thun","Thunell","Thurber","Thurlough","Thurlow","Thurman","Thurmann","Thurmond","Thurnau","Thursby","Thurstan","Thurston","Thury","Thynne","Tia","Tiana","Tibbetts","Tibbitts","Tibbs","Tibold","Tica","Tice","Tichon","Tichonn","Ticknor","Ticon","Tidwell","Tiebold","Tiebout","Tiedeman","Tiemroth","Tien","Tiena","Tierell","Tiernan","Tierney","Tiersten","Tiertza","Tierza","Tifanie","Tiff","Tiffa","Tiffani","Tiffanie","Tiffanle","Tiffany","Tiffi","Tiffie","Tiffy","Tiga","Tigges","Tila","Tilda","Tilden","Tildi","Tildie","Tildy","Tiler","Tilford","Till","Tilla","Tillford","Tillfourd","Tillie","Tillinger","Tillio","Tillion","Tillman","Tillo","Tilly","Tilney","Tiloine","Tim","Tima","Timi","Timmi","Timmie","Timmons","Timms","Timmy","Timofei","Timon","Timoteo","Timothea","Timothee","Timotheus","Timothy","Tina","Tinaret","Tindall","Tine","Tingey","Tingley","Tini","Tiny","Tinya","Tiossem","Tiphane","Tiphani","Tiphanie","Tiphany","Tippets","Tips","Tipton","Tirrell","Tirza","Tirzah","Tisbe","Tisbee","Tisdale","Tish","Tisha","Tisman","Tita","Titania","Tito","Titos","Titus","Tizes","Tjaden","Tjader","Tjon","Tletski","Toback","Tobe","Tobey","Tobi","Tobiah","Tobias","Tobie","Tobin","Tobit","Toby","Tobye","Tocci","Tod","Todd","Toddie","Toddy","Todhunter","Toffey","Toffic","Toft","Toh","Toiboid","Toinette","Tol","Toland","Tolkan","Toll","Tolland","Tolley","Tolliver","Tollman","Tollmann","Tolmach","Tolman","Tolmann","Tom","Toma","Tomas","Tomasina","Tomasine","Tomaso","Tomasz","Tombaugh","Tomchay","Tome","Tomi","Tomkiel","Tomkin","Tomkins","Tomlin","Tomlinson","Tommi","Tommie","Tommy","Tompkins","Toms","Toney","Tongue","Toni","Tonia","Tonie","Tonina","Tonjes","Tonkin","Tonl","Tonneson","Tonnie","Tonry","Tony","Tonya","Tonye","Toogood","Toole","Tooley","Toolis","Toomay","Toombs","Toomin","Toor","Tootsie","Topliffe","Topper","Topping","Tor","Torbart","Torbert","Tore","Torey","Torhert","Tori","Torie","Torin","Tormoria","Torosian","Torp","Torr","Torrance","Torras","Torray","Torre","Torrell","Torrence","Torres","Torrey","Torrie","Torrin","Torrlow","Torruella","Torry","Torto","Tortosa","Tory","Toscano","Tosch","Toshiko","Toth","Touber","Toulon","Tound"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domains = ["aol.com", "att.net", "comcast.net", "facebook.com", "gmail.com", "gmx.com", "googlemail.com","google.com", "hotmail.com", "hotmail.co.uk", "mac.com", "me.com", "mail.com", "msn.com","live.com", "sbcglobal.net", "verizon.net", "yahoo.com", "yahoo.co.uk","email.com", "games.com", "gmx.net", "hush.com", "hushmail.com", "icloud.com", "inbox.com","lavabit.com", "love.com" , "outlook.com", "pobox.com", "rocketmail.com","safe-mail.net", "wow.com", "ygm.com", "ymail.com", "zoho.com", "fastmail.fm","yandex.com","bellsouth.net", "charter.net", "comcast.net", "cox.net", "earthlink.net", "juno.com","btinternet.com", "virginmedia.com", "blueyonder.co.uk", "freeserve.co.uk", "live.co.uk","ntlworld.com", "o2.co.uk", "orange.net", "sky.com", "talktalk.co.uk", "tiscali.co.uk","virgin.net", "wanadoo.co.uk", "bt.com","sina.com", "qq.com", "naver.com", "hanmail.net", "daum.net", "nate.com", "yahoo.co.jp", "yahoo.co.kr", "yahoo.co.id", "yahoo.co.in", "yahoo.com.sg", "yahoo.com.ph","hotmail.fr", "live.fr", "laposte.net", "yahoo.fr", "wanadoo.fr", "orange.fr", "gmx.fr", "sfr.fr", "neuf.fr", "free.fr","gmx.de", "hotmail.de", "live.de", "online.de", "t-online.de", "web.de", "yahoo.de","mail.ru", "rambler.ru", "yandex.ru", "ya.ru", "list.ru","hotmail.be", "live.be", "skynet.be", "voo.be", "tvcablenet.be", "telenet.be","hotmail.com.ar", "live.com.ar", "yahoo.com.ar", "fibertel.com.ar", "speedy.com.ar", "arnet.com.ar","hotmail.com", "gmail.com", "yahoo.com.mx", "live.com.mx", "yahoo.com", "hotmail.es", "live.com", "hotmail.com.mx", "prodigy.net.mx", "msn.com"] def generate(max_num,gen_type): if max_num == 0: sys.exit(1) for x in itertools.count(start=0,step=1): password = '' if random.randrange(6,26) > 12: password = random.choice(common_passwords) else: password = ''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(2,8))) if(x==max_num): break sys.exit(1) else: name = '' if random.randrange(1,10) == 3: name = str(random.choice(english_first_names))+"."+str(random.choice(surnames))+"@"+str(random.choice(domains)) elif random.randrange(1,10) == 1: name = str.lower(random.choice(english_first_names))+"_"+''.join(random.choice(string.digits) for _ in range(random.randrange(1,5)))+"@"+random.choice(domains) elif random.randrange(1,10) == 2: name = str.lower(random.choice(surnames))+"-"+''.join(random.choice(string.digits) for _ in range(random.randrange(1,5)))+"@"+random.choice(domains) elif random.randrange(1,10) == 8: name = ''.join(random.choice(string.digits) for _ in range(random.randrange(1,5)))+str.upper(random.choice(surnames))+"@"+random.choice(domains) elif random.randrange(1,10) == 9: name = ''.join(random.choice(string.digits) for _ in range(random.randrange(1,2)))+"-"+str.upper(random.choice(surnames))+"@"+random.choice(domains) elif random.randrange(1,10) == 5: name = str.lower(random.choice(english_first_names))+"-"+''.join(random.choice(string.digits) for _ in range(random.randrange(1,5)))+"@"+random.choice(domains) elif random.randrange(1,10) == 4: name = str.upper(random.choice(english_first_names))+""+str.upper(random.choice(surnames))+"@"+random.choice(domains) elif random.randrange(1,10) == 6: name= ''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,25)))+"@"+random.choice(domains) elif random.randrange(1,10) == 7: name= ''.join(random.choice(string.ascii_letters) for _ in range(random.randrange(1,5)))+"."+str(random.choice(surnames))+"@"+random.choice(domains) elif random.randrange(1,10) == 10: name= ''.join(random.choice(string.ascii_letters + string.digits + ['.','+','-','_']) for _ in range(random.randrange(6,15)))+"."+str(random.choice(surnames))+"@"+random.choice(domains) else: name = str(random.choice(english_first_names))+"_"+str(random.choice(surnames))+"@"+str(random.choice(domains)) if (gen_type == "clear"): if (re.match(emailregex, name)): print name,":",password elif (gen_type == "sha256"): if (re.match(emailregex, name)): hash_object = hashlib.sha256(password).hexdigest() print name,":",password,":",hash_object elif (gen_type == "sha512"): if (re.match(emailregex, name)): hash_object = hashlib.sha512(password).hexdigest() print name,":",password,":",hash_object elif (gen_type == "md5"): if (re.match(emailregex, name)): hash_object = hashlib.md5(password).hexdigest() print name,":",password,":",hash_object elif (gen_type == "sha1"): if (re.match(emailregex, name)): hash_object = hashlib.sha1(password).hexdigest() print name,":",password,":",hash_object elif (gen_type == "ntlm"): if (re.match(emailregex, name)): hash_object = binascii.hexlify(hashlib.new('md4', password.encode('utf-16le')).digest()) print name,":",password,":",hash_object elif (gen_type == "sha1_r_salt"): if (re.match(emailregex, name)): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.sha1(pass_obj).hexdigest() print name,":",pass_obj,":",hash_object elif (gen_type == "sha256_r_salt"): if (re.match(emailregex, name)): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.sha256(pass_obj).hexdigest() print name,":",pass_obj,":",hash_object elif (gen_type == "sha512_r_salt"): if (re.match(emailregex, name)): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.sha512(pass_obj).hexdigest() print name,":",pass_obj,":",hash_object elif (gen_type == "md5_r_salt"): if (re.match(emailregex, name)): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.md5(pass_obj).hexdigest() print name,":",pass_obj,":",hash_object elif (gen_type == "ntlm_r_salt"): if (re.match(emailregex, name)): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = binascii.hexlify(hashlib.new('md4', pass_obj.encode('utf-16le')).digest()) print name,":",pass_obj,":",hash_object elif(gen_type == "md5_hashonly"): hash_object = hashlib.md5(password).hexdigest() print hash_object elif(gen_type == "sha1_hashonly"): hash_object = hashlib.sha1(password).hexdigest() print hash_object elif(gen_type == "sha256_hashonly"): hash_object = hashlib.sha256(password).hexdigest() print hash_object elif(gen_type == "sha512_hashonly"): hash_object = hashlib.sha512(password).hexdigest() print hash_object elif(gen_type == "ntlm_hashonly"): hash_object = binascii.hexlify(hashlib.new('md4', password.encode('utf-16le')).digest()) print hash_object elif(gen_type == "md5_r_salt_hashonly"): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.md5(pass_obj).hexdigest() print hash_object elif(gen_type == "sha1_r_salt_hashonly"): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.sha1(pass_obj).hexdigest() print hash_object elif(gen_type == "sha256_r_salt_hashonly"): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.sha256(pass_obj).hexdigest() print hash_object elif(gen_type == "sha512_r_salt_hashonly"): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits) for _ in range(random.randrange(6,16))) hash_object = hashlib.sha512(pass_obj).hexdigest() print hash_object elif(gen_type == "ntlm_r_salt_hashonly"): pass_obj = password+''.join(random.choice(string.ascii_letters + string.digits)) hash_object = binascii.hexlify(hashlib.new('md4', pass_obj.encode('utf-16le')).digest()) print hash_object elif(gen_type == "ad_compromise"): #https://technet.microsoft.com/en-us/library/active-directory-maximum-limits-scalability(v=ws.10).aspx # based on above making 6m limit on dump if (max_num is 6000000): break sys.exit(1) else: hash_object = "%s:aad3b435b51404eeaad3b435b51404ee:%s:::" % (str(x),binascii.hexlify(hashlib.new('md4', password.encode('utf-16le')).digest())) print hash_object else: print "[!] Unknown hash type, exit" sys.exit(1) def help(): types=""" -= LeakGenerator v1.1A by op7ic =- Discover your own leak with spoofed emails, random passwords and equally random hashes Supported hash types for '-t' argument: [+] Main hash alghoritms. Will print "email : password : hash" combo. md5 sha1 sha256 sha512 ntlm [+] Hash alghoritms with random salt added. Will print "email : password : hash" combo. md5_r_salt sha1_r_salt sha256_r_salt sha512_r_salt ntlm_r_salt [+] Other type. Will print "email or username : password" combo. clear ad_compromise [+] Hash only types that print only 'hash' values md5_hashonly sha1_hashonly sha256_hashonly sha512_hashonly ntlm_only md5_r_salt_hashonly sha1_r_salt_hashonly sha256_r_salt_hashonly sha512_r_salt_hashonly ntlm_r_salt_hashonly You also need to specify max passwords you want to generate in hex format e.g. 0x00FFFFFF The final command would look like this: python leakme.py -t md5_r_salt -m 0x00FFFFFF """ return types parser = optparse.OptionParser(usage=help()) parser.add_option('-m', '--max', help = "Number of hashes to generate in hex format e.g. --max=0x00FFFFFF",action="store", dest="max_dump") parser.add_option('-t', '--type', help="Hash type to print, use -h or --help to see all applicable hash types",action="store", dest="hash_type") (opts, args) = parser.parse_args() generate(opts.max_dump,opts.hash_type)

mit

jm-begon/scikit-learn

sklearn/utils/fixes.py

133

12882

"""Compatibility fixes for older version of python, numpy and scipy If you add content to this file, please give the version of the package at which the fixe is no longer needed. """ # Authors: Emmanuelle Gouillart <[email protected]> # Gael Varoquaux <[email protected]> # Fabian Pedregosa <[email protected]> # Lars Buitinck # # License: BSD 3 clause import inspect import warnings import sys import functools import os import errno import numpy as np import scipy.sparse as sp import scipy def _parse_version(version_string): version = [] for x in version_string.split('.'): try: version.append(int(x)) except ValueError: # x may be of the form dev-1ea1592 version.append(x) return tuple(version) np_version = _parse_version(np.__version__) sp_version = _parse_version(scipy.__version__) try: from scipy.special import expit # SciPy >= 0.10 with np.errstate(invalid='ignore', over='ignore'): if np.isnan(expit(1000)): # SciPy < 0.14 raise ImportError("no stable expit in scipy.special") except ImportError: def expit(x, out=None): """Logistic sigmoid function, ``1 / (1 + exp(-x))``. See sklearn.utils.extmath.log_logistic for the log of this function. """ if out is None: out = np.empty(np.atleast_1d(x).shape, dtype=np.float64) out[:] = x # 1 / (1 + exp(-x)) = (1 + tanh(x / 2)) / 2 # This way of computing the logistic is both fast and stable. out *= .5 np.tanh(out, out) out += 1 out *= .5 return out.reshape(np.shape(x)) # little danse to see if np.copy has an 'order' keyword argument if 'order' in inspect.getargspec(np.copy)[0]: def safe_copy(X): # Copy, but keep the order return np.copy(X, order='K') else: # Before an 'order' argument was introduced, numpy wouldn't muck with # the ordering safe_copy = np.copy try: if (not np.allclose(np.divide(.4, 1, casting="unsafe"), np.divide(.4, 1, casting="unsafe", dtype=np.float)) or not np.allclose(np.divide(.4, 1), .4)): raise TypeError('Divide not working with dtype: ' 'https://github.com/numpy/numpy/issues/3484') divide = np.divide except TypeError: # Compat for old versions of np.divide that do not provide support for # the dtype args def divide(x1, x2, out=None, dtype=None): out_orig = out if out is None: out = np.asarray(x1, dtype=dtype) if out is x1: out = x1.copy() else: if out is not x1: out[:] = x1 if dtype is not None and out.dtype != dtype: out = out.astype(dtype) out /= x2 if out_orig is None and np.isscalar(x1): out = np.asscalar(out) return out try: np.array(5).astype(float, copy=False) except TypeError: # Compat where astype accepted no copy argument def astype(array, dtype, copy=True): if not copy and array.dtype == dtype: return array return array.astype(dtype) else: astype = np.ndarray.astype try: with warnings.catch_warnings(record=True): # Don't raise the numpy deprecation warnings that appear in # 1.9, but avoid Python bug due to simplefilter('ignore') warnings.simplefilter('always') sp.csr_matrix([1.0, 2.0, 3.0]).max(axis=0) except (TypeError, AttributeError): # in scipy < 14.0, sparse matrix min/max doesn't accept an `axis` argument # the following code is taken from the scipy 0.14 codebase def _minor_reduce(X, ufunc): major_index = np.flatnonzero(np.diff(X.indptr)) if X.data.size == 0 and major_index.size == 0: # Numpy < 1.8.0 don't handle empty arrays in reduceat value = np.zeros_like(X.data) else: value = ufunc.reduceat(X.data, X.indptr[major_index]) return major_index, value def _min_or_max_axis(X, axis, min_or_max): N = X.shape[axis] if N == 0: raise ValueError("zero-size array to reduction operation") M = X.shape[1 - axis] mat = X.tocsc() if axis == 0 else X.tocsr() mat.sum_duplicates() major_index, value = _minor_reduce(mat, min_or_max) not_full = np.diff(mat.indptr)[major_index] < N value[not_full] = min_or_max(value[not_full], 0) mask = value != 0 major_index = np.compress(mask, major_index) value = np.compress(mask, value) from scipy.sparse import coo_matrix if axis == 0: res = coo_matrix((value, (np.zeros(len(value)), major_index)), dtype=X.dtype, shape=(1, M)) else: res = coo_matrix((value, (major_index, np.zeros(len(value)))), dtype=X.dtype, shape=(M, 1)) return res.A.ravel() def _sparse_min_or_max(X, axis, min_or_max): if axis is None: if 0 in X.shape: raise ValueError("zero-size array to reduction operation") zero = X.dtype.type(0) if X.nnz == 0: return zero m = min_or_max.reduce(X.data.ravel()) if X.nnz != np.product(X.shape): m = min_or_max(zero, m) return m if axis < 0: axis += 2 if (axis == 0) or (axis == 1): return _min_or_max_axis(X, axis, min_or_max) else: raise ValueError("invalid axis, use 0 for rows, or 1 for columns") def sparse_min_max(X, axis): return (_sparse_min_or_max(X, axis, np.minimum), _sparse_min_or_max(X, axis, np.maximum)) else: def sparse_min_max(X, axis): return (X.min(axis=axis).toarray().ravel(), X.max(axis=axis).toarray().ravel()) try: from numpy import argpartition except ImportError: # numpy.argpartition was introduced in v 1.8.0 def argpartition(a, kth, axis=-1, kind='introselect', order=None): return np.argsort(a, axis=axis, order=order) try: from itertools import combinations_with_replacement except ImportError: # Backport of itertools.combinations_with_replacement for Python 2.6, # from Python 3.4 documentation (http://tinyurl.com/comb-w-r), copyright # Python Software Foundation (https://docs.python.org/3/license.html) def combinations_with_replacement(iterable, r): # combinations_with_replacement('ABC', 2) --> AA AB AC BB BC CC pool = tuple(iterable) n = len(pool) if not n and r: return indices = [0] * r yield tuple(pool[i] for i in indices) while True: for i in reversed(range(r)): if indices[i] != n - 1: break else: return indices[i:] = [indices[i] + 1] * (r - i) yield tuple(pool[i] for i in indices) try: from numpy import isclose except ImportError: def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False): """ Returns a boolean array where two arrays are element-wise equal within a tolerance. This function was added to numpy v1.7.0, and the version you are running has been backported from numpy v1.8.1. See its documentation for more details. """ def within_tol(x, y, atol, rtol): with np.errstate(invalid='ignore'): result = np.less_equal(abs(x - y), atol + rtol * abs(y)) if np.isscalar(a) and np.isscalar(b): result = bool(result) return result x = np.array(a, copy=False, subok=True, ndmin=1) y = np.array(b, copy=False, subok=True, ndmin=1) xfin = np.isfinite(x) yfin = np.isfinite(y) if all(xfin) and all(yfin): return within_tol(x, y, atol, rtol) else: finite = xfin & yfin cond = np.zeros_like(finite, subok=True) # Since we're using boolean indexing, x & y must be the same shape. # Ideally, we'd just do x, y = broadcast_arrays(x, y). It's in # lib.stride_tricks, though, so we can't import it here. x = x * np.ones_like(cond) y = y * np.ones_like(cond) # Avoid subtraction with infinite/nan values... cond[finite] = within_tol(x[finite], y[finite], atol, rtol) # Check for equality of infinite values... cond[~finite] = (x[~finite] == y[~finite]) if equal_nan: # Make NaN == NaN cond[np.isnan(x) & np.isnan(y)] = True return cond if np_version < (1, 7): # Prior to 1.7.0, np.frombuffer wouldn't work for empty first arg. def frombuffer_empty(buf, dtype): if len(buf) == 0: return np.empty(0, dtype=dtype) else: return np.frombuffer(buf, dtype=dtype) else: frombuffer_empty = np.frombuffer if np_version < (1, 8): def in1d(ar1, ar2, assume_unique=False, invert=False): # Backport of numpy function in1d 1.8.1 to support numpy 1.6.2 # Ravel both arrays, behavior for the first array could be different ar1 = np.asarray(ar1).ravel() ar2 = np.asarray(ar2).ravel() # This code is significantly faster when the condition is satisfied. if len(ar2) < 10 * len(ar1) ** 0.145: if invert: mask = np.ones(len(ar1), dtype=np.bool) for a in ar2: mask &= (ar1 != a) else: mask = np.zeros(len(ar1), dtype=np.bool) for a in ar2: mask |= (ar1 == a) return mask # Otherwise use sorting if not assume_unique: ar1, rev_idx = np.unique(ar1, return_inverse=True) ar2 = np.unique(ar2) ar = np.concatenate((ar1, ar2)) # We need this to be a stable sort, so always use 'mergesort' # here. The values from the first array should always come before # the values from the second array. order = ar.argsort(kind='mergesort') sar = ar[order] if invert: bool_ar = (sar[1:] != sar[:-1]) else: bool_ar = (sar[1:] == sar[:-1]) flag = np.concatenate((bool_ar, [invert])) indx = order.argsort(kind='mergesort')[:len(ar1)] if assume_unique: return flag[indx] else: return flag[indx][rev_idx] else: from numpy import in1d if sp_version < (0, 15): # Backport fix for scikit-learn/scikit-learn#2986 / scipy/scipy#4142 from ._scipy_sparse_lsqr_backport import lsqr as sparse_lsqr else: from scipy.sparse.linalg import lsqr as sparse_lsqr if sys.version_info < (2, 7, 0): # partial cannot be pickled in Python 2.6 # http://bugs.python.org/issue1398 class partial(object): def __init__(self, func, *args, **keywords): functools.update_wrapper(self, func) self.func = func self.args = args self.keywords = keywords def __call__(self, *args, **keywords): args = self.args + args kwargs = self.keywords.copy() kwargs.update(keywords) return self.func(*args, **kwargs) else: from functools import partial if np_version < (1, 6, 2): # Allow bincount to accept empty arrays # https://github.com/numpy/numpy/commit/40f0844846a9d7665616b142407a3d74cb65a040 def bincount(x, weights=None, minlength=None): if len(x) > 0: return np.bincount(x, weights, minlength) else: if minlength is None: minlength = 0 minlength = np.asscalar(np.asarray(minlength, dtype=np.intp)) return np.zeros(minlength, dtype=np.intp) else: from numpy import bincount if 'exist_ok' in inspect.getargspec(os.makedirs).args: makedirs = os.makedirs else: def makedirs(name, mode=0o777, exist_ok=False): """makedirs(name [, mode=0o777][, exist_ok=False]) Super-mkdir; create a leaf directory and all intermediate ones. Works like mkdir, except that any intermediate path segment (not just the rightmost) will be created if it does not exist. If the target directory already exists, raise an OSError if exist_ok is False. Otherwise no exception is raised. This is recursive. """ try: os.makedirs(name, mode=mode) except OSError as e: if (not exist_ok or e.errno != errno.EEXIST or not os.path.isdir(name)): raise

bsd-3-clause

adamgreenhall/scikit-learn

sklearn/utils/multiclass.py

83

12343

# Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi # # License: BSD 3 clause """ Multi-class / multi-label utility function ========================================== """ from __future__ import division from collections import Sequence from itertools import chain from scipy.sparse import issparse from scipy.sparse.base import spmatrix from scipy.sparse import dok_matrix from scipy.sparse import lil_matrix import numpy as np from ..externals.six import string_types from .validation import check_array from ..utils.fixes import bincount def _unique_multiclass(y): if hasattr(y, '__array__'): return np.unique(np.asarray(y)) else: return set(y) def _unique_indicator(y): return np.arange(check_array(y, ['csr', 'csc', 'coo']).shape[1]) _FN_UNIQUE_LABELS = { 'binary': _unique_multiclass, 'multiclass': _unique_multiclass, 'multilabel-indicator': _unique_indicator, } def unique_labels(*ys): """Extract an ordered array of unique labels We don't allow: - mix of multilabel and multiclass (single label) targets - mix of label indicator matrix and anything else, because there are no explicit labels) - mix of label indicator matrices of different sizes - mix of string and integer labels At the moment, we also don't allow "multiclass-multioutput" input type. Parameters ---------- *ys : array-likes, Returns ------- out : numpy array of shape [n_unique_labels] An ordered array of unique labels. Examples -------- >>> from sklearn.utils.multiclass import unique_labels >>> unique_labels([3, 5, 5, 5, 7, 7]) array([3, 5, 7]) >>> unique_labels([1, 2, 3, 4], [2, 2, 3, 4]) array([1, 2, 3, 4]) >>> unique_labels([1, 2, 10], [5, 11]) array([ 1, 2, 5, 10, 11]) """ if not ys: raise ValueError('No argument has been passed.') # Check that we don't mix label format ys_types = set(type_of_target(x) for x in ys) if ys_types == set(["binary", "multiclass"]): ys_types = set(["multiclass"]) if len(ys_types) > 1: raise ValueError("Mix type of y not allowed, got types %s" % ys_types) label_type = ys_types.pop() # Check consistency for the indicator format if (label_type == "multilabel-indicator" and len(set(check_array(y, ['csr', 'csc', 'coo']).shape[1] for y in ys)) > 1): raise ValueError("Multi-label binary indicator input with " "different numbers of labels") # Get the unique set of labels _unique_labels = _FN_UNIQUE_LABELS.get(label_type, None) if not _unique_labels: raise ValueError("Unknown label type: %s" % repr(ys)) ys_labels = set(chain.from_iterable(_unique_labels(y) for y in ys)) # Check that we don't mix string type with number type if (len(set(isinstance(label, string_types) for label in ys_labels)) > 1): raise ValueError("Mix of label input types (string and number)") return np.array(sorted(ys_labels)) def _is_integral_float(y): return y.dtype.kind == 'f' and np.all(y.astype(int) == y) def is_multilabel(y): """ Check if ``y`` is in a multilabel format. Parameters ---------- y : numpy array of shape [n_samples] Target values. Returns ------- out : bool, Return ``True``, if ``y`` is in a multilabel format, else ```False``. Examples -------- >>> import numpy as np >>> from sklearn.utils.multiclass import is_multilabel >>> is_multilabel([0, 1, 0, 1]) False >>> is_multilabel([[1], [0, 2], []]) False >>> is_multilabel(np.array([[1, 0], [0, 0]])) True >>> is_multilabel(np.array([[1], [0], [0]])) False >>> is_multilabel(np.array([[1, 0, 0]])) True """ if hasattr(y, '__array__'): y = np.asarray(y) if not (hasattr(y, "shape") and y.ndim == 2 and y.shape[1] > 1): return False if issparse(y): if isinstance(y, (dok_matrix, lil_matrix)): y = y.tocsr() return (len(y.data) == 0 or np.ptp(y.data) == 0 and (y.dtype.kind in 'biu' or # bool, int, uint _is_integral_float(np.unique(y.data)))) else: labels = np.unique(y) return len(labels) < 3 and (y.dtype.kind in 'biu' or # bool, int, uint _is_integral_float(labels)) def type_of_target(y): """Determine the type of data indicated by target `y` Parameters ---------- y : array-like Returns ------- target_type : string One of: * 'continuous': `y` is an array-like of floats that are not all integers, and is 1d or a column vector. * 'continuous-multioutput': `y` is a 2d array of floats that are not all integers, and both dimensions are of size > 1. * 'binary': `y` contains <= 2 discrete values and is 1d or a column vector. * 'multiclass': `y` contains more than two discrete values, is not a sequence of sequences, and is 1d or a column vector. * 'multiclass-multioutput': `y` is a 2d array that contains more than two discrete values, is not a sequence of sequences, and both dimensions are of size > 1. * 'multilabel-indicator': `y` is a label indicator matrix, an array of two dimensions with at least two columns, and at most 2 unique values. * 'unknown': `y` is array-like but none of the above, such as a 3d array, sequence of sequences, or an array of non-sequence objects. Examples -------- >>> import numpy as np >>> type_of_target([0.1, 0.6]) 'continuous' >>> type_of_target([1, -1, -1, 1]) 'binary' >>> type_of_target(['a', 'b', 'a']) 'binary' >>> type_of_target([1.0, 2.0]) 'binary' >>> type_of_target([1, 0, 2]) 'multiclass' >>> type_of_target([1.0, 0.0, 3.0]) 'multiclass' >>> type_of_target(['a', 'b', 'c']) 'multiclass' >>> type_of_target(np.array([[1, 2], [3, 1]])) 'multiclass-multioutput' >>> type_of_target([[1, 2]]) 'multiclass-multioutput' >>> type_of_target(np.array([[1.5, 2.0], [3.0, 1.6]])) 'continuous-multioutput' >>> type_of_target(np.array([[0, 1], [1, 1]])) 'multilabel-indicator' """ valid = ((isinstance(y, (Sequence, spmatrix)) or hasattr(y, '__array__')) and not isinstance(y, string_types)) if not valid: raise ValueError('Expected array-like (array or non-string sequence), ' 'got %r' % y) if is_multilabel(y): return 'multilabel-indicator' try: y = np.asarray(y) except ValueError: # Known to fail in numpy 1.3 for array of arrays return 'unknown' # The old sequence of sequences format try: if (not hasattr(y[0], '__array__') and isinstance(y[0], Sequence) and not isinstance(y[0], string_types)): raise ValueError('You appear to be using a legacy multi-label data' ' representation. Sequence of sequences are no' ' longer supported; use a binary array or sparse' ' matrix instead.') except IndexError: pass # Invalid inputs if y.ndim > 2 or (y.dtype == object and len(y) and not isinstance(y.flat[0], string_types)): return 'unknown' # [[[1, 2]]] or [obj_1] and not ["label_1"] if y.ndim == 2 and y.shape[1] == 0: return 'unknown' # [[]] if y.ndim == 2 and y.shape[1] > 1: suffix = "-multioutput" # [[1, 2], [1, 2]] else: suffix = "" # [1, 2, 3] or [[1], [2], [3]] # check float and contains non-integer float values if y.dtype.kind == 'f' and np.any(y != y.astype(int)): # [.1, .2, 3] or [[.1, .2, 3]] or [[1., .2]] and not [1., 2., 3.] return 'continuous' + suffix if (len(np.unique(y)) > 2) or (y.ndim >= 2 and len(y[0]) > 1): return 'multiclass' + suffix # [1, 2, 3] or [[1., 2., 3]] or [[1, 2]] else: return 'binary' # [1, 2] or [["a"], ["b"]] def _check_partial_fit_first_call(clf, classes=None): """Private helper function for factorizing common classes param logic Estimators that implement the ``partial_fit`` API need to be provided with the list of possible classes at the first call to partial_fit. Subsequent calls to partial_fit should check that ``classes`` is still consistent with a previous value of ``clf.classes_`` when provided. This function returns True if it detects that this was the first call to ``partial_fit`` on ``clf``. In that case the ``classes_`` attribute is also set on ``clf``. """ if getattr(clf, 'classes_', None) is None and classes is None: raise ValueError("classes must be passed on the first call " "to partial_fit.") elif classes is not None: if getattr(clf, 'classes_', None) is not None: if not np.all(clf.classes_ == unique_labels(classes)): raise ValueError( "`classes=%r` is not the same as on last call " "to partial_fit, was: %r" % (classes, clf.classes_)) else: # This is the first call to partial_fit clf.classes_ = unique_labels(classes) return True # classes is None and clf.classes_ has already previously been set: # nothing to do return False def class_distribution(y, sample_weight=None): """Compute class priors from multioutput-multiclass target data Parameters ---------- y : array like or sparse matrix of size (n_samples, n_outputs) The labels for each example. sample_weight : array-like of shape = (n_samples,), optional Sample weights. Returns ------- classes : list of size n_outputs of arrays of size (n_classes,) List of classes for each column. n_classes : list of integrs of size n_outputs Number of classes in each column class_prior : list of size n_outputs of arrays of size (n_classes,) Class distribution of each column. """ classes = [] n_classes = [] class_prior = [] n_samples, n_outputs = y.shape if issparse(y): y = y.tocsc() y_nnz = np.diff(y.indptr) for k in range(n_outputs): col_nonzero = y.indices[y.indptr[k]:y.indptr[k + 1]] # separate sample weights for zero and non-zero elements if sample_weight is not None: nz_samp_weight = np.asarray(sample_weight)[col_nonzero] zeros_samp_weight_sum = (np.sum(sample_weight) - np.sum(nz_samp_weight)) else: nz_samp_weight = None zeros_samp_weight_sum = y.shape[0] - y_nnz[k] classes_k, y_k = np.unique(y.data[y.indptr[k]:y.indptr[k + 1]], return_inverse=True) class_prior_k = bincount(y_k, weights=nz_samp_weight) # An explicit zero was found, combine its wieght with the wieght # of the implicit zeros if 0 in classes_k: class_prior_k[classes_k == 0] += zeros_samp_weight_sum # If an there is an implict zero and it is not in classes and # class_prior, make an entry for it if 0 not in classes_k and y_nnz[k] < y.shape[0]: classes_k = np.insert(classes_k, 0, 0) class_prior_k = np.insert(class_prior_k, 0, zeros_samp_weight_sum) classes.append(classes_k) n_classes.append(classes_k.shape[0]) class_prior.append(class_prior_k / class_prior_k.sum()) else: for k in range(n_outputs): classes_k, y_k = np.unique(y[:, k], return_inverse=True) classes.append(classes_k) n_classes.append(classes_k.shape[0]) class_prior_k = bincount(y_k, weights=sample_weight) class_prior.append(class_prior_k / class_prior_k.sum()) return (classes, n_classes, class_prior)

bsd-3-clause

lehnertu/TEUFEL

scripts/plot_Screen_TD.py

1

4311

#!/usr/bin/env python # coding=UTF-8 import sys, time import os.path import argparse import numpy as np import h5py import matplotlib.pyplot as plt from matplotlib.ticker import NullFormatter from matplotlib.patches import Circle # magnetic field constant in N/A² mu0 = 4*np.pi*1e-7 parser = argparse.ArgumentParser() parser.add_argument('file', help='the file name of the screen output HDF5 file') parser.add_argument('-xy', help="indeces of plot point", dest="xy", type=int, nargs=2) print args = parser.parse_args() radfile = args.file radOK = os.path.isfile(radfile) if not radOK: print "file not found" sys.exit() # Open the file for reading print "reading ",radfile hdf = h5py.File(radfile, "r") print hdf print # Get the groups pos = hdf['ObservationPosition'] Nx = pos.attrs.get('Nx') Ny = pos.attrs.get('Ny') print "Nx=%d Ny=%d" % (Nx,Ny) print pos field = hdf['ElMagField'] print field t0 = field.attrs.get('t0') dt = field.attrs.get('dt') nots = field.attrs.get('NOTS') print "t0=%g dt=%g NOTS=%d" % (t0, dt, nots) pos = np.array(pos) a = np.array(field) hdf.close() print xcenter = (Nx-1)/2 ycenter = (Ny-1)/2 print "center = (",xcenter,",",ycenter,")" centerposition = pos[xcenter][ycenter] print "center position = ",centerposition onaxis = a[xcenter][ycenter] data = onaxis.transpose() Ex = data[0] Ey = data[1] Ez = data[2] Bx = data[3] By = data[4] Bz = data[5] EVec = np.array([Ex, Ey, Ez]).transpose() BVec = np.array([Bx, By, Bz]).transpose() # Poynting vector in V/m * (N/(A m)) / (N/A²) = W/m² SVec = np.cross(EVec, BVec) / mu0 # t = 1e9*np.arange(t0,t0+(nots-1)*dt,dt) t = 1e9*np.linspace(t0,t0+(nots-1)*dt,nots) print 'on axis energy flow density = ', 1e6*SVec.sum(axis=0)*dt, " µJ/m²" # first figure with the time-trace of the fields on axis left, width = 0.15, 0.80 rect1 = [left, 0.55, width, 0.40] #left, bottom, width, height rect2 = [left, 0.08, width, 0.40] fig = plt.figure(1,figsize=(12,9)) ax1 = fig.add_axes(rect1) ax4 = fig.add_axes(rect2, sharex=ax1) l1 = ax1.plot(t, Ex, "r-", label=r'$E_x$') l2 = ax1.plot(t, Ey, "b-", label=r'$E_y$') l3 = ax1.plot(t, Ez, "g-", label=r'$E_z$') ax1.set_ylabel(r'$E$ [V/m]') lines = l1 + l2 + l3 labels = [l.get_label() for l in lines] ax1.legend(lines,labels,loc='upper right') for label in ax1.get_xticklabels(): label.set_visible(False) ax1.grid(True) l4 = ax4.plot(t, Bx, "r-", label=r'$B_x$') l5 = ax4.plot(t, By, "b-", label=r'$B_y$') l6 = ax4.plot(t, Bz, "g-", label=r'$B_z$') ax4.set_ylabel(r'$B$ [T]') ax4.set_xlabel(r't [ns]') lines = l4 + l5 +l6 labels = [l.get_label() for l in lines] ax4.legend(lines,labels,loc='upper right') ax4.grid(True) if args.xy != None: xi = args.xy[0] yi = args.xy[1] print "index = (",xi,",",yi,")" position = pos[xi][yi] print "off-axis position = ",position offaxis = a[xi][yi] data = offaxis.transpose() Ex = data[0] Ey = data[1] Ez = data[2] Bx = data[3] By = data[4] Bz = data[5] EVec = np.array([Ex, Ey, Ez]).transpose() BVec = np.array([Bx, By, Bz]).transpose() # Poynting vector in V/m * (N/(A m)) / (N/A²) = W/m² SVec = np.cross(EVec, BVec) / mu0 # t = 1e9*np.arange(t0,t0+(nots-1)*dt,dt) t = 1e9*np.linspace(t0,t0+(nots-1)*dt,nots) print 'off axis energy flow density = ', 1e6*SVec.sum(axis=0)*dt, " µJ/m²" # second figure with the time-trace of the fields off axis fig2 = plt.figure(2,figsize=(12,9)) ax21 = fig2.add_axes(rect1) ax24 = fig2.add_axes(rect2, sharex=ax1) l21 = ax21.plot(t, Ex, "r-", label=r'$E_x$') l22 = ax21.plot(t, Ey, "b-", label=r'$E_y$') l23 = ax21.plot(t, Ez, "g-", label=r'$E_z$') ax21.set_ylabel(r'$E$ [V/m]') lines = l21 + l22 + l23 labels = [l.get_label() for l in lines] ax21.legend(lines,labels,loc='upper right') for label in ax21.get_xticklabels(): label.set_visible(False) ax21.grid(True) l24 = ax24.plot(t, Bx, "r-", label=r'$B_x$') l25 = ax24.plot(t, By, "b-", label=r'$B_y$') l26 = ax24.plot(t, Bz, "g-", label=r'$B_z$') ax24.set_ylabel(r'$B$ [T]') ax24.set_xlabel(r't [ns]') lines = l24 + l25 +l26 labels = [l.get_label() for l in lines] ax24.legend(lines,labels,loc='upper right') ax24.grid(True) plt.show()

gpl-3.0

henryre/shalo

shalo/model_search.py

1

5519

import numpy as np import pandas as pd import cPickle import datetime from itertools import product class Hyperparameter(object): """Base class for a grid search parameter""" def __init__(self, name): self.name = name def get_all_values(self): raise NotImplementedError() def draw_values(self, n): # Multidim parameters can't use choice directly v = self.get_all_values() return [v[int(i)] for i in np.random.choice(len(v), n)] class ListParameter(Hyperparameter): """List of parameter values for searching""" def __init__(self, name, parameter_list): self.parameter_list = np.array(parameter_list) super(ListParameter, self).__init__(name) def get_all_values(self): return self.parameter_list class RangeParameter(Hyperparameter): """ Range of parameter values for searching. min_value and max_value are the ends of the search range If log_base is specified, scale the search range in the log base step is range step size or exponent step size """ def __init__(self, name, min_value, max_value, step=1, log_base=None): self.min_value = min_value self.max_value = max_value self.step = step self.log_base = log_base super(RangeParameter, self).__init__(name) def get_all_values(self): if self.log_base: min_exp = math.log(self.min_value, self.log_base) max_exp = math.log(self.max_value, self.log_base) exps = np.arange(min_exp, max_exp + self.step, step=self.step) return np.power(self.log_base, exps) return np.arange( self.min_value, self.max_value + self.step, step=self.step ) class GridSearch(object): """ Runs hyperparameter grid search over a model object with train and score methods, training data (X), and training_marginals Selects based on maximizing F1 score on a supplied validation set Specify search space with Hyperparameter arguments """ def __init__(self, model, train_data, train_labels, parameters): self.model = model self.train_data = train_data self.train_labels = train_labels self.params = parameters self.param_names = [param.name for param in parameters] def search_space(self): return product(param.get_all_values() for param in self.params) def fit(self, dev_data, dev_labels, b=0.5, **model_hyperparams): """ Basic method to start grid search, returns DataFrame table of results b specifies the positive class threshold for calculating accuracy Non-search parameters are set using model_hyperparamters """ run_stats, score_opt, model_k = [], -1.0, 0 opt_params = None base_model_name = self.model.name # Iterate over the param values for k, param_vals in enumerate(self.search_space()): model_name = '{0}_{1}'.format(base_model_name, model_k) model_k += 1 # Set the new hyperparam configuration to test for pn, pv in zip(self.param_names, param_vals): model_hyperparams[pn] = pv print "=" * 80 print "[%d] Testing %s" % (k+1, ', '.join([ "{0} = {1}".format(pn, pv) for pn, pv in zip(self.param_names, param_vals) ])) print "=" * 80 # Train the model self.model.train( self.train_data, self.train_labels, dev_sentence_data=dev_data, dev_labels=dev_labels, **model_hyperparams ) # Test the model score = self.model.score(dev_data, dev_labels, b=b, verbose=True) run_stats.append(list(param_vals) + [score]) if score > score_opt: #self.model.save(model_name) opt_model = model_name score_opt = score opt_params = param_vals # Store optimal params optimal = {"name":opt_model, "params":param_vals} with open("optimal_params"+str(datetime.datetime.now()), 'wb') as f: cPickle.dump(optimal, f) # Set optimal parameter in the learner model #self.model.load(opt_model) for pn, pv in zip(self.param_names, opt_params): model_hyperparams[pn] = pv self.model.train( self.train_data, self.train_labels, dev_sentence_data=dev_data, dev_labels=dev_labels, **model_hyperparams ) # Return DataFrame of scores self.results = pd.DataFrame.from_records( run_stats, columns=self.param_names + ['Accuracy'] ).sort_values(by='Accuracy', ascending=False) return self.results class RandomSearch(GridSearch): def __init__(self, model, train_data, train_labels, parameters, n=10): """Search a random sample of size n from a parameter grid""" self.n = n super(RandomSearch, self).__init__( model, train_data, train_labels, parameters ) print "Initialized RandomSearch of size {0} / {1}".format( self.n, np.product([len(w) for w in GridSearch.search_space(self)]) ) def search_space(self): return zip(*[param.draw_values(self.n) for param in self.params])

apache-2.0

andyraib/data-storage

python_scripts/env/lib/python3.6/site-packages/pandas/io/tests/test_clipboard.py

7

4897

# -*- coding: utf-8 -*- import numpy as np from numpy.random import randint import nose import pandas as pd from pandas import DataFrame from pandas import read_clipboard from pandas import get_option from pandas.util import testing as tm from pandas.util.testing import makeCustomDataframe as mkdf from pandas.util.clipboard.exceptions import PyperclipException try: DataFrame({'A': [1, 2]}).to_clipboard() except PyperclipException: raise nose.SkipTest("clipboard primitives not installed") class TestClipboard(tm.TestCase): @classmethod def setUpClass(cls): super(TestClipboard, cls).setUpClass() cls.data = {} cls.data['string'] = mkdf(5, 3, c_idx_type='s', r_idx_type='i', c_idx_names=[None], r_idx_names=[None]) cls.data['int'] = mkdf(5, 3, data_gen_f=lambda *args: randint(2), c_idx_type='s', r_idx_type='i', c_idx_names=[None], r_idx_names=[None]) cls.data['float'] = mkdf(5, 3, data_gen_f=lambda r, c: float(r) + 0.01, c_idx_type='s', r_idx_type='i', c_idx_names=[None], r_idx_names=[None]) cls.data['mixed'] = DataFrame({'a': np.arange(1.0, 6.0) + 0.01, 'b': np.arange(1, 6), 'c': list('abcde')}) # Test columns exceeding "max_colwidth" (GH8305) _cw = get_option('display.max_colwidth') + 1 cls.data['colwidth'] = mkdf(5, 3, data_gen_f=lambda *args: 'x' * _cw, c_idx_type='s', r_idx_type='i', c_idx_names=[None], r_idx_names=[None]) # Test GH-5346 max_rows = get_option('display.max_rows') cls.data['longdf'] = mkdf(max_rows + 1, 3, data_gen_f=lambda *args: randint(2), c_idx_type='s', r_idx_type='i', c_idx_names=[None], r_idx_names=[None]) # Test for non-ascii text: GH9263 cls.data['nonascii'] = pd.DataFrame({'en': 'in English'.split(), 'es': 'en español'.split()}) # unicode round trip test for GH 13747, GH 12529 cls.data['utf8'] = pd.DataFrame({'a': ['µasd', 'Ωœ∑´'], 'b': ['øπ∆˚¬', 'œ∑´®']}) cls.data_types = list(cls.data.keys()) @classmethod def tearDownClass(cls): super(TestClipboard, cls).tearDownClass() del cls.data_types, cls.data def check_round_trip_frame(self, data_type, excel=None, sep=None, encoding=None): data = self.data[data_type] data.to_clipboard(excel=excel, sep=sep, encoding=encoding) if sep is not None: result = read_clipboard(sep=sep, index_col=0, encoding=encoding) else: result = read_clipboard(encoding=encoding) tm.assert_frame_equal(data, result, check_dtype=False) def test_round_trip_frame_sep(self): for dt in self.data_types: self.check_round_trip_frame(dt, sep=',') def test_round_trip_frame_string(self): for dt in self.data_types: self.check_round_trip_frame(dt, excel=False) def test_round_trip_frame(self): for dt in self.data_types: self.check_round_trip_frame(dt) def test_read_clipboard_infer_excel(self): from textwrap import dedent from pandas.util.clipboard import clipboard_set text = dedent(""" John James Charlie Mingus 1 2 4 Harry Carney """.strip()) clipboard_set(text) df = pd.read_clipboard() # excel data is parsed correctly self.assertEqual(df.iloc[1][1], 'Harry Carney') # having diff tab counts doesn't trigger it text = dedent(""" a\t b 1 2 3 4 """.strip()) clipboard_set(text) res = pd.read_clipboard() text = dedent(""" a b 1 2 3 4 """.strip()) clipboard_set(text) exp = pd.read_clipboard() tm.assert_frame_equal(res, exp) def test_invalid_encoding(self): # test case for testing invalid encoding data = self.data['string'] with tm.assertRaises(ValueError): data.to_clipboard(encoding='ascii') with tm.assertRaises(NotImplementedError): pd.read_clipboard(encoding='ascii') def test_round_trip_valid_encodings(self): for enc in ['UTF-8', 'utf-8', 'utf8']: for dt in self.data_types: self.check_round_trip_frame(dt, encoding=enc)

apache-2.0

BorisJeremic/Real-ESSI-Examples

analytic_solution/test_cases/Contact/Stress_Based_Contact_Verification/SoftContact_NonLinHardShear/Area/A_1e-4/Normal_Stress_Plot.py

72

2800

#!/usr/bin/python import h5py import matplotlib.pylab as plt import matplotlib as mpl import sys import numpy as np; import matplotlib; import math; from matplotlib.ticker import MaxNLocator plt.rcParams.update({'font.size': 28}) # set tick width mpl.rcParams['xtick.major.size'] = 10 mpl.rcParams['xtick.major.width'] = 5 mpl.rcParams['xtick.minor.size'] = 10 mpl.rcParams['xtick.minor.width'] = 5 plt.rcParams['xtick.labelsize']=24 mpl.rcParams['ytick.major.size'] = 10 mpl.rcParams['ytick.major.width'] = 5 mpl.rcParams['ytick.minor.size'] = 10 mpl.rcParams['ytick.minor.width'] = 5 plt.rcParams['ytick.labelsize']=24 ############################################################### ## Analytical Solution ############################################################### # Go over each feioutput and plot each one. thefile = "Analytical_Solution_Normal_Stress.feioutput"; finput = h5py.File(thefile) # Read the time and displacement times = finput["time"][:] normal_stress = -finput["/Model/Elements/Element_Outputs"][9,:]; normal_strain = -finput["/Model/Elements/Element_Outputs"][6,:]; # Configure the figure filename, according to the input filename. outfig=thefile.replace("_","-") outfigname=outfig.replace("h5.feioutput","pdf") # Plot the figure. Add labels and titles. plt.figure(figsize=(12,10)) plt.plot(normal_strain*100,normal_stress/1000,'-r',label='Analytical Solution', Linewidth=4, markersize=20) plt.xlabel(r"Interface Type #") plt.ylabel(r"Normal Stress $\sigma_n [kPa]$") plt.hold(True) ############################################################### ## Numerical Solution ############################################################### # Go over each feioutput and plot each one. thefile = "Monotonic_Contact_Behaviour_Adding_Normal_Load.h5.feioutput"; finput = h5py.File(thefile) # Read the time and displacement times = finput["time"][:] normal_stress = -finput["/Model/Elements/Element_Outputs"][9,:]; normal_strain = -finput["/Model/Elements/Element_Outputs"][6,:]; # Configure the figure filename, according to the input filename. outfig=thefile.replace("_","-") outfigname=outfig.replace("h5.feioutput","pdf") # Plot the figure. Add labels and titles. plt.plot(normal_strain*100,normal_stress/1000,'-k',label='Numerical Solution', Linewidth=4, markersize=20) plt.xlabel(r"Normal Strain [%]") plt.ylabel(r"Normal Stress $\sigma_n [kPa]$") ############################################################# # # # axes = plt.gca() # # # axes.set_xlim([-7,7]) # # # axes.set_ylim([-1,1]) # outfigname = "Interface_Test_Normal_Stress.pdf"; # plt.axis([0, 5.5, 90, 101]) # legend = plt.legend() # legend.get_frame().set_linewidth(0.0) # legend.get_frame().set_facecolor('none') plt.legend() plt.savefig('Normal_Stress.pdf', bbox_inches='tight') # plt.show()

cc0-1.0

trmznt/genaf

genaf/__init__.py

1

5963

import logging log = logging.getLogger(__name__) import matplotlib matplotlib.use('Agg') log.info('Setting up matplotlib to use Agg') from pyramid.config import Configurator from rhombus import includeme as rho_includeme, init_app as rhombus_init_app, add_route_view from rhombus.lib.utils import cout, cerr, cexit, generic_userid_func from rhombus.lib.fsoverlay import fsomount from rhombus.models.core import set_func_userid from genaf.lib.procmgmt import init_queue from genaf.lib.configs import set_temp_path, get_temp_path, TEMP_TOOLS import os def includeme( config ): # GenAF configuration #config.add_static_view('genaf_assets', 'genaf:static/assets/') config.add_static_view(name='genaf_static', path="genaf:static/") add_route_view( config, 'genaf.views.marker', 'genaf.marker', '/marker', '/marker/@@action', '/marker/{id}@@edit', '/marker/{id}@@save', ('/marker/{id}', 'view') ) add_route_view( config, 'genaf.views.panel', 'genaf.panel', '/panel', '/panel/@@action', '/panel/{id}@@edit', '/panel/{id}@@save', ('/panel/{id}', 'view') ) add_route_view( config, 'genaf.views.batch', 'genaf.batch', '/batch', '/batch/@@action', '/batch/{id}@@edit', '/batch/{id}@@save', ('/batch/{id}', 'view') ) add_route_view( config, 'genaf.views.sample', 'genaf.sample', '/sample', '/sample/@@action', '/sample/{id}@@edit', '/sample/{id}@@save', ('/sample/{id}', 'view') ) add_route_view( config, 'genaf.views.location', 'genaf.location', '/location', '/location/@@action', '/location/{id}@@edit', '/location/{id}@@save', ('/location/{id}', 'view') ) add_route_view( config, 'genaf.views.assay', 'genaf.assay', '/assay', '/assay/@@action', '/assay/{id}@@drawchannels', '/assay/{id}@@edit', '/assay/{id}@@save', ('/assay/{id}', 'view') ) add_route_view( config, 'genaf.views.channel', 'genaf.channel', '/channel/@@action', ('/channel/{id}', 'view'), ) add_route_view( config, 'genaf.views.uploadmgr', 'genaf.uploadmgr', '/uploadmgr', '/uploadmgr/@@action', '/uploadmgr/{id}@@edit', '/uploadmgr/{id}@@save', ('/uploadmgr/{id}@@mainpanel', 'mainpanel', 'json'), ('/uploadmgr/{id}@@rpc', 'rpc', 'json'), ('/uploadmgr/{id}@@uploaddata', 'uploaddata', 'json'), ('/uploadmgr/{id}@@uploadinfo', 'uploadinfo', 'json'), '/uploadmgr/{id}@@template', ('/uploadmgr/{id}', 'view') ) add_route_view( config, 'genaf.views.famgr', 'genaf.famgr', '/famgr', '/famgr/{id}@@process', ('/famgr/{id}', 'view') ) add_route_view( config, 'genaf.views.task', 'genaf.task', '/task', ('/task/{id}', 'view'), ) add_route_view( config, 'rhombus.views.fso', 'rhombus.fso', '/fso{path:.*}@@view', '/fso{path:.*}@@edit', '/fso{path:.*}@@save', '/fso{path:.*}@@action', ('/fso{path:.*}', 'index'), ) # tools and analysis config.add_route('tools-help', '/tools/help') config.add_view('genaf.views.tools.help.index', route_name='tools-help') config.add_route('tools-allele', '/tools/allele') config.add_view('genaf.views.tools.allele.index', route_name='tools-allele') config.add_route('tools-he', '/tools/he') config.add_view('genaf.views.tools.he.index', route_name='tools-he') config.add_route('tools-genotype', '/tools/genotype') config.add_view('genaf.views.tools.genotype.index', route_name='tools-genotype') config.add_route('tools-moi', '/tools/moi') config.add_view('genaf.views.tools.moi.index', route_name='tools-moi') config.add_route('tools-pcoa', '/tools/pcoa') config.add_view('genaf.views.tools.pcoa.index', route_name='tools-pcoa') config.add_route('tools-mca', '/tools/mca') config.add_view('genaf.views.tools.mca.index', route_name='tools-mca') config.add_route('tools-export', '/tools/export') config.add_view('genaf.views.tools.export.index', route_name='tools-export') config.add_route('tools-fst', '/tools/fst') config.add_view('genaf.views.tools.fst.index', route_name='tools-fst') config.add_route('tools-ld', '/tools/ld') config.add_view('genaf.views.tools.ld.index', route_name='tools-ld') config.add_route('tools-nj', '/tools/nj') config.add_view('genaf.views.tools.nj.index', route_name='tools-nj') config.add_route('tools-sample', '/tools/sample') config.add_view('genaf.views.tools.sample.index', route_name='tools-sample') config.add_route('tools-djost', '/tools/djost') config.add_view('genaf.views.tools.djost.index', route_name='tools-djost') # utilities config.add_route('utils-export', '/utils/export') config.add_view('genaf.views.utils.export.index', route_name='utils-export') config.add_route('utils-plot', '/utils/plot') config.add_view('genaf.views.utils.plot.index', route_name='utils-plot') def init_app( global_config, settings, prefix = '/mgr' ): # global, shared settings temp_path = settings['genaf.temp_directory'] set_temp_path( temp_path ) fsomount(TEMP_TOOLS, get_temp_path('', TEMP_TOOLS)) set_func_userid( generic_userid_func ) # preparing for multiprocessing init_queue(settings) config = rhombus_init_app( global_config, settings, prefix=prefix ) return config def main(global_config, **settings): """ This function returns a Pyramid WSGI application. """ config = Configurator(settings=settings) config.include('pyramid_chameleon') config.add_static_view('static', 'static', cache_max_age=3600) config.add_route('home', '/') config.scan() return config.make_wsgi_app()

lgpl-3.0

ChinaQuants/zipline

zipline/protocol.py

3

17052

# # Copyright 2013 Quantopian, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from copy import copy from six import iteritems, iterkeys import pandas as pd import numpy as np from . utils.protocol_utils import Enum from . utils.math_utils import nanstd, nanmean, nansum from zipline.utils.algo_instance import get_algo_instance from zipline.utils.serialization_utils import ( VERSION_LABEL ) # Datasource type should completely determine the other fields of a # message with its type. DATASOURCE_TYPE = Enum( 'AS_TRADED_EQUITY', 'MERGER', 'SPLIT', 'DIVIDEND', 'TRADE', 'TRANSACTION', 'ORDER', 'EMPTY', 'DONE', 'CUSTOM', 'BENCHMARK', 'COMMISSION', 'CLOSE_POSITION' ) # Expected fields/index values for a dividend Series. DIVIDEND_FIELDS = [ 'declared_date', 'ex_date', 'gross_amount', 'net_amount', 'pay_date', 'payment_sid', 'ratio', 'sid', ] # Expected fields/index values for a dividend payment Series. DIVIDEND_PAYMENT_FIELDS = [ 'id', 'payment_sid', 'cash_amount', 'share_count', ] def dividend_payment(data=None): """ Take a dictionary whose values are in DIVIDEND_PAYMENT_FIELDS and return a series representing the payment of a dividend. Ids are assigned to each historical dividend in PerformanceTracker.update_dividends. They are guaranteed to be unique integers with the context of a single simulation. If @data is non-empty, a id is required to identify the historical dividend associated with this payment. Additionally, if @data is non-empty, either data['cash_amount'] should be nonzero or data['payment_sid'] should be an asset identifier and data['share_count'] should be nonzero. The returned Series is given its id value as a name so that concatenating payments results in a DataFrame indexed by id. (Note, however, that the name value is not used to construct an index when this series is returned by function passed to `DataFrame.apply`. In such a case, pandas preserves the index of the DataFrame on which `apply` is being called.) """ return pd.Series( data=data, name=data['id'] if data is not None else None, index=DIVIDEND_PAYMENT_FIELDS, dtype=object, ) class Event(object): def __init__(self, initial_values=None): if initial_values: self.__dict__ = initial_values def __getitem__(self, name): return getattr(self, name) def __setitem__(self, name, value): setattr(self, name, value) def __delitem__(self, name): delattr(self, name) def keys(self): return self.__dict__.keys() def __eq__(self, other): return hasattr(other, '__dict__') and self.__dict__ == other.__dict__ def __contains__(self, name): return name in self.__dict__ def __repr__(self): return "Event({0})".format(self.__dict__) def to_series(self, index=None): return pd.Series(self.__dict__, index=index) class Order(Event): pass class Portfolio(object): def __init__(self): self.capital_used = 0.0 self.starting_cash = 0.0 self.portfolio_value = 0.0 self.pnl = 0.0 self.returns = 0.0 self.cash = 0.0 self.positions = Positions() self.start_date = None self.positions_value = 0.0 def __getitem__(self, key): return self.__dict__[key] def __repr__(self): return "Portfolio({0})".format(self.__dict__) def __getstate__(self): state_dict = copy(self.__dict__) # Have to convert to primitive dict state_dict['positions'] = dict(self.positions) STATE_VERSION = 1 state_dict[VERSION_LABEL] = STATE_VERSION return state_dict def __setstate__(self, state): OLDEST_SUPPORTED_STATE = 1 version = state.pop(VERSION_LABEL) if version < OLDEST_SUPPORTED_STATE: raise BaseException("Portfolio saved state is too old.") self.positions = Positions() self.positions.update(state.pop('positions')) self.__dict__.update(state) class Account(object): ''' The account object tracks information about the trading account. The values are updated as the algorithm runs and its keys remain unchanged. If connected to a broker, one can update these values with the trading account values as reported by the broker. ''' def __init__(self): self.settled_cash = 0.0 self.accrued_interest = 0.0 self.buying_power = float('inf') self.equity_with_loan = 0.0 self.total_positions_value = 0.0 self.regt_equity = 0.0 self.regt_margin = float('inf') self.initial_margin_requirement = 0.0 self.maintenance_margin_requirement = 0.0 self.available_funds = 0.0 self.excess_liquidity = 0.0 self.cushion = 0.0 self.day_trades_remaining = float('inf') self.leverage = 0.0 self.net_leverage = 0.0 self.net_liquidation = 0.0 def __getitem__(self, key): return self.__dict__[key] def __repr__(self): return "Account({0})".format(self.__dict__) def __getstate__(self): state_dict = copy(self.__dict__) STATE_VERSION = 1 state_dict[VERSION_LABEL] = STATE_VERSION return state_dict def __setstate__(self, state): OLDEST_SUPPORTED_STATE = 1 version = state.pop(VERSION_LABEL) if version < OLDEST_SUPPORTED_STATE: raise BaseException("Account saved state is too old.") self.__dict__.update(state) class Position(object): def __init__(self, sid): self.sid = sid self.amount = 0 self.cost_basis = 0.0 # per share self.last_sale_price = 0.0 def __getitem__(self, key): return self.__dict__[key] def __repr__(self): return "Position({0})".format(self.__dict__) def __getstate__(self): state_dict = copy(self.__dict__) STATE_VERSION = 1 state_dict[VERSION_LABEL] = STATE_VERSION return state_dict def __setstate__(self, state): OLDEST_SUPPORTED_STATE = 1 version = state.pop(VERSION_LABEL) if version < OLDEST_SUPPORTED_STATE: raise BaseException("Protocol Position saved state is too old.") self.__dict__.update(state) class Positions(dict): def __missing__(self, key): pos = Position(key) self[key] = pos return pos class SIDData(object): # Cache some data on the class so that this is shared for all instances of # siddata. # The dt where we cached the history. _history_cache_dt = None # _history_cache is a a dict mapping fields to pd.DataFrames. This is the # most data we have for a given field for the _history_cache_dt. _history_cache = {} # This is the cache that is used for returns. This will have a different # structure than the other history cache as this is always daily. _returns_cache_dt = None _returns_cache = None # The last dt that we needed to cache the number of minutes. _minute_bar_cache_dt = None # If we are in minute mode, there is some cost associated with computing # the number of minutes that we need to pass to the bar count of history. # This will remain constant for a given bar and day count. # This maps days to number of minutes. _minute_bar_cache = {} def __init__(self, sid, initial_values=None): self._sid = sid self._freqstr = None # To check if we have data, we use the __len__ which depends on the # __dict__. Because we are foward defining the attributes needed, we # need to account for their entrys in the __dict__. # We will add 1 because we need to account for the _initial_len entry # itself. self._initial_len = len(self.__dict__) + 1 if initial_values: self.__dict__.update(initial_values) @property def datetime(self): """ Provides an alias from data['foo'].datetime -> data['foo'].dt `datetime` was previously provided by adding a seperate `datetime` member of the SIDData object via a generator that wrapped the incoming data feed and added the field to each equity event. This alias is intended to be temporary, to provide backwards compatibility with existing algorithms, but should be considered deprecated, and may be removed in the future. """ return self.dt def get(self, name, default=None): return self.__dict__.get(name, default) def __getitem__(self, name): return self.__dict__[name] def __setitem__(self, name, value): self.__dict__[name] = value def __len__(self): return len(self.__dict__) - self._initial_len def __contains__(self, name): return name in self.__dict__ def __repr__(self): return "SIDData({0})".format(self.__dict__) def _get_buffer(self, bars, field='price', raw=False): """ Gets the result of history for the given number of bars and field. This will cache the results internally. """ cls = self.__class__ algo = get_algo_instance() now = algo.datetime if now != cls._history_cache_dt: # For a given dt, the history call for this field will not change. # We have a new dt, so we should reset the cache. cls._history_cache_dt = now cls._history_cache = {} if field not in self._history_cache \ or bars > len(cls._history_cache[field][0].index): # If we have never cached this field OR the amount of bars that we # need for this field is greater than the amount we have cached, # then we need to get more history. hst = algo.history( bars, self._freqstr, field, ffill=True, ) # Assert that the column holds ints, not security objects. if not isinstance(self._sid, str): hst.columns = hst.columns.astype(int) self._history_cache[field] = (hst, hst.values, hst.columns) # Slice of only the bars needed. This is because we strore the LARGEST # amount of history for the field, and we might request less than the # largest from the cache. buffer_, values, columns = cls._history_cache[field] if raw: sid_index = columns.get_loc(self._sid) return values[-bars:, sid_index] else: return buffer_[self._sid][-bars:] def _get_bars(self, days): """ Gets the number of bars needed for the current number of days. Figures this out based on the algo datafrequency and caches the result. This caches the result by replacing this function on the object. This means that after the first call to _get_bars, this method will point to a new function object. """ def daily_get_max_bars(days): return days def minute_get_max_bars(days): # max number of minute. regardless of current days or short # sessions return days * 390 def daily_get_bars(days): return days def minute_get_bars(days): cls = self.__class__ now = get_algo_instance().datetime if now != cls._minute_bar_cache_dt: cls._minute_bar_cache_dt = now cls._minute_bar_cache = {} if days not in cls._minute_bar_cache: # Cache this calculation to happen once per bar, even if we # use another transform with the same number of days. env = get_algo_instance().trading_environment prev = env.previous_trading_day(now) ds = env.days_in_range( env.add_trading_days(-days + 2, prev), prev, ) # compute the number of minutes in the (days - 1) days before # today. # 210 minutes in a an early close and 390 in a full day. ms = sum(210 if d in env.early_closes else 390 for d in ds) # Add the number of minutes for today. ms += int( (now - env.get_open_and_close(now)[0]).total_seconds() / 60 ) cls._minute_bar_cache[days] = ms + 1 # Account for this minute return cls._minute_bar_cache[days] if get_algo_instance().sim_params.data_frequency == 'daily': self._freqstr = '1d' # update this method to point to the daily variant. self._get_bars = daily_get_bars self._get_max_bars = daily_get_max_bars else: self._freqstr = '1m' # update this method to point to the minute variant. self._get_bars = minute_get_bars self._get_max_bars = minute_get_max_bars # Not actually recursive because we have already cached the new method. return self._get_bars(days) def mavg(self, days): bars = self._get_bars(days) max_bars = self._get_max_bars(days) prices = self._get_buffer(max_bars, raw=True)[-bars:] return nanmean(prices) def stddev(self, days): bars = self._get_bars(days) max_bars = self._get_max_bars(days) prices = self._get_buffer(max_bars, raw=True)[-bars:] return nanstd(prices, ddof=1) def vwap(self, days): bars = self._get_bars(days) max_bars = self._get_max_bars(days) prices = self._get_buffer(max_bars, raw=True)[-bars:] vols = self._get_buffer(max_bars, field='volume', raw=True)[-bars:] vol_sum = nansum(vols) try: ret = nansum(prices * vols) / vol_sum except ZeroDivisionError: ret = np.nan return ret def returns(self): algo = get_algo_instance() now = algo.datetime if now != self._returns_cache_dt: self._returns_cache_dt = now self._returns_cache = algo.history(2, '1d', 'price', ffill=True) hst = self._returns_cache[self._sid] return (hst.iloc[-1] - hst.iloc[0]) / hst.iloc[0] class BarData(object): """ Holds the event data for all sids for a given dt. This is what is passed as `data` to the `handle_data` function. Note: Many methods are analogues of dictionary because of historical usage of what this replaced as a dictionary subclass. """ def __init__(self, data=None): self._data = data or {} self._contains_override = None def __contains__(self, name): if self._contains_override: if self._contains_override(name): return name in self._data else: return False else: return name in self._data def has_key(self, name): """ DEPRECATED: __contains__ is preferred, but this method is for compatibility with existing algorithms. """ return name in self def __setitem__(self, name, value): self._data[name] = value def __getitem__(self, name): return self._data[name] def __delitem__(self, name): del self._data[name] def __iter__(self): for sid, data in iteritems(self._data): # Allow contains override to filter out sids. if sid in self: if len(data): yield sid def iterkeys(self): # Allow contains override to filter out sids. return (sid for sid in iterkeys(self._data) if sid in self) def keys(self): # Allow contains override to filter out sids. return list(self.iterkeys()) def itervalues(self): return (value for _sid, value in self.iteritems()) def values(self): return list(self.itervalues()) def iteritems(self): return ((sid, value) for sid, value in iteritems(self._data) if sid in self) def items(self): return list(self.iteritems()) def __len__(self): return len(self.keys()) def __repr__(self): return '{0}({1})'.format(self.__class__.__name__, self._data)

apache-2.0

eickenberg/scikit-learn

sklearn/decomposition/nmf.py

1

18931

""" Non-negative matrix factorization """ # Author: Vlad Niculae # Lars Buitinck <[email protected]> # Author: Chih-Jen Lin, National Taiwan University (original projected gradient # NMF implementation) # Author: Anthony Di Franco (original Python and NumPy port) # License: BSD 3 clause from __future__ import division from math import sqrt import warnings import numpy as np import scipy.sparse as sp from scipy.optimize import nnls from ..base import BaseEstimator, TransformerMixin from ..utils import check_random_state, check_array from ..utils.extmath import randomized_svd, safe_sparse_dot, squared_norm def safe_vstack(Xs): if any(sp.issparse(X) for X in Xs): return sp.vstack(Xs) else: return np.vstack(Xs) def norm(x): """Dot product-based Euclidean norm implementation See: http://fseoane.net/blog/2011/computing-the-vector-norm/ """ return sqrt(squared_norm(x)) def trace_dot(X, Y): """Trace of np.dot(X, Y.T).""" return np.dot(X.ravel(), Y.ravel()) def _sparseness(x): """Hoyer's measure of sparsity for a vector""" sqrt_n = np.sqrt(len(x)) return (sqrt_n - np.linalg.norm(x, 1) / norm(x)) / (sqrt_n - 1) def check_non_negative(X, whom): X = X.data if sp.issparse(X) else X if (X < 0).any(): raise ValueError("Negative values in data passed to %s" % whom) def _initialize_nmf(X, n_components, variant=None, eps=1e-6, random_state=None): """NNDSVD algorithm for NMF initialization. Computes a good initial guess for the non-negative rank k matrix approximation for X: X = WH Parameters ---------- X : array, [n_samples, n_features] The data matrix to be decomposed. n_components : array, [n_components, n_features] The number of components desired in the approximation. variant : None | 'a' | 'ar' The variant of the NNDSVD algorithm. Accepts None, 'a', 'ar' None: leaves the zero entries as zero 'a': Fills the zero entries with the average of X 'ar': Fills the zero entries with standard normal random variates. Default: None eps: float Truncate all values less then this in output to zero. random_state : numpy.RandomState | int, optional The generator used to fill in the zeros, when using variant='ar' Default: numpy.random Returns ------- (W, H) : Initial guesses for solving X ~= WH such that the number of columns in W is n_components. Remarks ------- This implements the algorithm described in C. Boutsidis, E. Gallopoulos: SVD based initialization: A head start for nonnegative matrix factorization - Pattern Recognition, 2008 http://tinyurl.com/nndsvd """ check_non_negative(X, "NMF initialization") if variant not in (None, 'a', 'ar'): raise ValueError("Invalid variant name") U, S, V = randomized_svd(X, n_components) W, H = np.zeros(U.shape), np.zeros(V.shape) # The leading singular triplet is non-negative # so it can be used as is for initialization. W[:, 0] = np.sqrt(S[0]) * np.abs(U[:, 0]) H[0, :] = np.sqrt(S[0]) * np.abs(V[0, :]) for j in range(1, n_components): x, y = U[:, j], V[j, :] # extract positive and negative parts of column vectors x_p, y_p = np.maximum(x, 0), np.maximum(y, 0) x_n, y_n = np.abs(np.minimum(x, 0)), np.abs(np.minimum(y, 0)) # and their norms x_p_nrm, y_p_nrm = norm(x_p), norm(y_p) x_n_nrm, y_n_nrm = norm(x_n), norm(y_n) m_p, m_n = x_p_nrm * y_p_nrm, x_n_nrm * y_n_nrm # choose update if m_p > m_n: u = x_p / x_p_nrm v = y_p / y_p_nrm sigma = m_p else: u = x_n / x_n_nrm v = y_n / y_n_nrm sigma = m_n lbd = np.sqrt(S[j] * sigma) W[:, j] = lbd * u H[j, :] = lbd * v W[W < eps] = 0 H[H < eps] = 0 if variant == "a": avg = X.mean() W[W == 0] = avg H[H == 0] = avg elif variant == "ar": random_state = check_random_state(random_state) avg = X.mean() W[W == 0] = abs(avg * random_state.randn(len(W[W == 0])) / 100) H[H == 0] = abs(avg * random_state.randn(len(H[H == 0])) / 100) return W, H def _nls_subproblem(V, W, H, tol, max_iter, sigma=0.01, beta=0.1): """Non-negative least square solver Solves a non-negative least squares subproblem using the projected gradient descent algorithm. min || WH - V ||_2 Parameters ---------- V, W : array-like Constant matrices. H : array-like Initial guess for the solution. tol : float Tolerance of the stopping condition. max_iter : int Maximum number of iterations before timing out. sigma : float Constant used in the sufficient decrease condition checked by the line search. Smaller values lead to a looser sufficient decrease condition, thus reducing the time taken by the line search, but potentially increasing the number of iterations of the projected gradient procedure. 0.01 is a commonly used value in the optimization literature. beta : float Factor by which the step size is decreased (resp. increased) until (resp. as long as) the sufficient decrease condition is satisfied. Larger values allow to find a better step size but lead to longer line search. 0.1 is a commonly used value in the optimization literature. Returns ------- H : array-like Solution to the non-negative least squares problem. grad : array-like The gradient. n_iter : int The number of iterations done by the algorithm. Reference --------- C.-J. Lin. Projected gradient methods for non-negative matrix factorization. Neural Computation, 19(2007), 2756-2779. http://www.csie.ntu.edu.tw/~cjlin/nmf/ """ WtV = safe_sparse_dot(W.T, V) WtW = np.dot(W.T, W) # values justified in the paper alpha = 1 for n_iter in range(1, max_iter + 1): grad = np.dot(WtW, H) - WtV # The following multiplication with a boolean array is more than twice # as fast as indexing into grad. if norm(grad * np.logical_or(grad < 0, H > 0)) < tol: break Hp = H for inner_iter in range(19): # Gradient step. Hn = H - alpha * grad # Projection step. Hn *= Hn > 0 d = Hn - H gradd = np.dot(grad.ravel(), d.ravel()) dQd = np.dot(np.dot(WtW, d).ravel(), d.ravel()) suff_decr = (1 - sigma) * gradd + 0.5 * dQd < 0 if inner_iter == 0: decr_alpha = not suff_decr if decr_alpha: if suff_decr: H = Hn break else: alpha *= beta elif not suff_decr or (Hp == Hn).all(): H = Hp break else: alpha /= beta Hp = Hn if n_iter == max_iter: warnings.warn("Iteration limit reached in nls subproblem.") return H, grad, n_iter class ProjectedGradientNMF(BaseEstimator, TransformerMixin): """Non-Negative matrix factorization by Projected Gradient (NMF) Parameters ---------- n_components : int or None Number of components, if n_components is not set all components are kept init : 'nndsvd' | 'nndsvda' | 'nndsvdar' | 'random' Method used to initialize the procedure. Default: 'nndsvdar' if n_components < n_features, otherwise random. Valid options:: 'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD) initialization (better for sparseness) 'nndsvda': NNDSVD with zeros filled with the average of X (better when sparsity is not desired) 'nndsvdar': NNDSVD with zeros filled with small random values (generally faster, less accurate alternative to NNDSVDa for when sparsity is not desired) 'random': non-negative random matrices sparseness : 'data' | 'components' | None, default: None Where to enforce sparsity in the model. beta : double, default: 1 Degree of sparseness, if sparseness is not None. Larger values mean more sparseness. eta : double, default: 0.1 Degree of correctness to maintain, if sparsity is not None. Smaller values mean larger error. tol : double, default: 1e-4 Tolerance value used in stopping conditions. max_iter : int, default: 200 Number of iterations to compute. nls_max_iter : int, default: 2000 Number of iterations in NLS subproblem. random_state : int or RandomState Random number generator seed control. Attributes ---------- `components_` : array, [n_components, n_features] Non-negative components of the data. `reconstruction_err_` : number Frobenius norm of the matrix difference between the training data and the reconstructed data from the fit produced by the model. ``|| X - WH ||_2`` Examples -------- >>> import numpy as np >>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]]) >>> from sklearn.decomposition import ProjectedGradientNMF >>> model = ProjectedGradientNMF(n_components=2, init='random', ... random_state=0) >>> model.fit(X) #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE ProjectedGradientNMF(beta=1, eta=0.1, init='random', max_iter=200, n_components=2, nls_max_iter=2000, random_state=0, sparseness=None, tol=0.0001) >>> model.components_ array([[ 0.77032744, 0.11118662], [ 0.38526873, 0.38228063]]) >>> model.reconstruction_err_ #doctest: +ELLIPSIS 0.00746... >>> model = ProjectedGradientNMF(n_components=2, ... sparseness='components', init='random', random_state=0) >>> model.fit(X) #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE ProjectedGradientNMF(beta=1, eta=0.1, init='random', max_iter=200, n_components=2, nls_max_iter=2000, random_state=0, sparseness='components', tol=0.0001) >>> model.components_ array([[ 1.67481991, 0.29614922], [ 0. , 0.4681982 ]]) >>> model.reconstruction_err_ #doctest: +ELLIPSIS 0.513... References ---------- This implements C.-J. Lin. Projected gradient methods for non-negative matrix factorization. Neural Computation, 19(2007), 2756-2779. http://www.csie.ntu.edu.tw/~cjlin/nmf/ P. Hoyer. Non-negative Matrix Factorization with Sparseness Constraints. Journal of Machine Learning Research 2004. NNDSVD is introduced in C. Boutsidis, E. Gallopoulos: SVD based initialization: A head start for nonnegative matrix factorization - Pattern Recognition, 2008 http://tinyurl.com/nndsvd """ def __init__(self, n_components=None, init=None, sparseness=None, beta=1, eta=0.1, tol=1e-4, max_iter=200, nls_max_iter=2000, random_state=None): self.n_components = n_components self.init = init self.tol = tol if sparseness not in (None, 'data', 'components'): raise ValueError( 'Invalid sparseness parameter: got %r instead of one of %r' % (sparseness, (None, 'data', 'components'))) self.sparseness = sparseness self.beta = beta self.eta = eta self.max_iter = max_iter self.nls_max_iter = nls_max_iter self.random_state = random_state def _init(self, X): n_samples, n_features = X.shape init = self.init if init is None: if self.n_components_ < n_features: init = 'nndsvd' else: init = 'random' random_state = self.random_state if init == 'nndsvd': W, H = _initialize_nmf(X, self.n_components_) elif init == 'nndsvda': W, H = _initialize_nmf(X, self.n_components_, variant='a') elif init == 'nndsvdar': W, H = _initialize_nmf(X, self.n_components_, variant='ar') elif init == "random": rng = check_random_state(random_state) W = rng.randn(n_samples, self.n_components_) # we do not write np.abs(W, out=W) to stay compatible with # numpy 1.5 and earlier where the 'out' keyword is not # supported as a kwarg on ufuncs np.abs(W, W) H = rng.randn(self.n_components_, n_features) np.abs(H, H) else: raise ValueError( 'Invalid init parameter: got %r instead of one of %r' % (init, (None, 'nndsvd', 'nndsvda', 'nndsvdar', 'random'))) return W, H def _update_W(self, X, H, W, tolW): n_samples, n_features = X.shape if self.sparseness is None: W, gradW, iterW = _nls_subproblem(X.T, H.T, W.T, tolW, self.nls_max_iter) elif self.sparseness == 'data': W, gradW, iterW = _nls_subproblem( safe_vstack([X.T, np.zeros((1, n_samples))]), safe_vstack([H.T, np.sqrt(self.beta) * np.ones((1, self.n_components_))]), W.T, tolW, self.nls_max_iter) elif self.sparseness == 'components': W, gradW, iterW = _nls_subproblem( safe_vstack([X.T, np.zeros((self.n_components_, n_samples))]), safe_vstack([H.T, np.sqrt(self.eta) * np.eye(self.n_components_)]), W.T, tolW, self.nls_max_iter) return W.T, gradW.T, iterW def _update_H(self, X, H, W, tolH): n_samples, n_features = X.shape if self.sparseness is None: H, gradH, iterH = _nls_subproblem(X, W, H, tolH, self.nls_max_iter) elif self.sparseness == 'data': H, gradH, iterH = _nls_subproblem( safe_vstack([X, np.zeros((self.n_components_, n_features))]), safe_vstack([W, np.sqrt(self.eta) * np.eye(self.n_components_)]), H, tolH, self.nls_max_iter) elif self.sparseness == 'components': H, gradH, iterH = _nls_subproblem( safe_vstack([X, np.zeros((1, n_features))]), safe_vstack([W, np.sqrt(self.beta) * np.ones((1, self.n_components_))]), H, tolH, self.nls_max_iter) return H, gradH, iterH def fit_transform(self, X, y=None): """Learn a NMF model for the data X and returns the transformed data. This is more efficient than calling fit followed by transform. Parameters ---------- X: {array-like, sparse matrix}, shape = [n_samples, n_features] Data matrix to be decomposed Returns ------- data: array, [n_samples, n_components] Transformed data """ X = check_array(X, accept_sparse='csr') check_non_negative(X, "NMF.fit") n_samples, n_features = X.shape if not self.n_components: self.n_components_ = n_features else: self.n_components_ = self.n_components W, H = self._init(X) gradW = (np.dot(W, np.dot(H, H.T)) - safe_sparse_dot(X, H.T, dense_output=True)) gradH = (np.dot(np.dot(W.T, W), H) - safe_sparse_dot(W.T, X, dense_output=True)) init_grad = norm(np.r_[gradW, gradH.T]) tolW = max(0.001, self.tol) * init_grad # why max? tolH = tolW tol = self.tol * init_grad for n_iter in range(1, self.max_iter + 1): # stopping condition # as discussed in paper proj_norm = norm(np.r_[gradW[np.logical_or(gradW < 0, W > 0)], gradH[np.logical_or(gradH < 0, H > 0)]]) if proj_norm < tol: break # update W W, gradW, iterW = self._update_W(X, H, W, tolW) if iterW == 1: tolW = 0.1 * tolW # update H H, gradH, iterH = self._update_H(X, H, W, tolH) if iterH == 1: tolH = 0.1 * tolH if not sp.issparse(X): error = norm(X - np.dot(W, H)) else: sqnorm_X = np.dot(X.data, X.data) norm_WHT = trace_dot(np.dot(np.dot(W.T, W), H), H) cross_prod = trace_dot((X * H.T), W) error = sqrt(sqnorm_X + norm_WHT - 2. * cross_prod) self.reconstruction_err_ = error self.comp_sparseness_ = _sparseness(H.ravel()) self.data_sparseness_ = _sparseness(W.ravel()) H[H == 0] = 0 # fix up negative zeros self.components_ = H if n_iter == self.max_iter: warnings.warn("Iteration limit reached during fit. Solving for W exactly.") return self.transform(X) return W def fit(self, X, y=None, **params): """Learn a NMF model for the data X. Parameters ---------- X: {array-like, sparse matrix}, shape = [n_samples, n_features] Data matrix to be decomposed Returns ------- self """ self.fit_transform(X, **params) return self def transform(self, X): """Transform the data X according to the fitted NMF model Parameters ---------- X: {array-like, sparse matrix}, shape = [n_samples, n_features] Data matrix to be transformed by the model Returns ------- data: array, [n_samples, n_components] Transformed data """ X = check_array(X, accept_sparse='csc') Wt = np.zeros((self.n_components_, X.shape[0])) check_non_negative(X, "ProjectedGradientNMF.transform") if sp.issparse(X): Wt, _, _ = _nls_subproblem(X.T, self.components_.T, Wt, tol=self.tol, max_iter=self.nls_max_iter) else: for j in range(0, X.shape[0]): Wt[:, j], _ = nnls(self.components_.T, X[j, :]) return Wt.T class NMF(ProjectedGradientNMF): __doc__ = ProjectedGradientNMF.__doc__ pass

bsd-3-clause

ndingwall/scikit-learn

doc/tutorial/text_analytics/solutions/exercise_02_sentiment.py

19

3140

"""Build a sentiment analysis / polarity model Sentiment analysis can be casted as a binary text classification problem, that is fitting a linear classifier on features extracted from the text of the user messages so as to guess whether the opinion of the author is positive or negative. In this examples we will use a movie review dataset. """ # Author: Olivier Grisel <[email protected]> # License: Simplified BSD import sys from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.svm import LinearSVC from sklearn.pipeline import Pipeline from sklearn.model_selection import GridSearchCV from sklearn.datasets import load_files from sklearn.model_selection import train_test_split from sklearn import metrics if __name__ == "__main__": # NOTE: we put the following in a 'if __name__ == "__main__"' protected # block to be able to use a multi-core grid search that also works under # Windows, see: http://docs.python.org/library/multiprocessing.html#windows # The multiprocessing module is used as the backend of joblib.Parallel # that is used when n_jobs != 1 in GridSearchCV # the training data folder must be passed as first argument movie_reviews_data_folder = sys.argv[1] dataset = load_files(movie_reviews_data_folder, shuffle=False) print("n_samples: %d" % len(dataset.data)) # split the dataset in training and test set: docs_train, docs_test, y_train, y_test = train_test_split( dataset.data, dataset.target, test_size=0.25, random_state=None) # TASK: Build a vectorizer / classifier pipeline that filters out tokens # that are too rare or too frequent pipeline = Pipeline([ ('vect', TfidfVectorizer(min_df=3, max_df=0.95)), ('clf', LinearSVC(C=1000)), ]) # TASK: Build a grid search to find out whether unigrams or bigrams are # more useful. # Fit the pipeline on the training set using grid search for the parameters parameters = { 'vect__ngram_range': [(1, 1), (1, 2)], } grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1) grid_search.fit(docs_train, y_train) # TASK: print the mean and std for each candidate along with the parameter # settings for all the candidates explored by grid search. n_candidates = len(grid_search.cv_results_['params']) for i in range(n_candidates): print(i, 'params - %s; mean - %0.2f; std - %0.2f' % (grid_search.cv_results_['params'][i], grid_search.cv_results_['mean_test_score'][i], grid_search.cv_results_['std_test_score'][i])) # TASK: Predict the outcome on the testing set and store it in a variable # named y_predicted y_predicted = grid_search.predict(docs_test) # Print the classification report print(metrics.classification_report(y_test, y_predicted, target_names=dataset.target_names)) # Print and plot the confusion matrix cm = metrics.confusion_matrix(y_test, y_predicted) print(cm) # import matplotlib.pyplot as plt # plt.matshow(cm) # plt.show()

bsd-3-clause

abhishekkrthakur/scikit-learn

sklearn/utils/estimator_checks.py

1

38107

from __future__ import print_function import warnings import sys import traceback import inspect import pickle import numpy as np from scipy import sparse import struct from sklearn.externals.six.moves import zip from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_false from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import META_ESTIMATORS from sklearn.utils.testing import set_random_state from sklearn.utils.testing import assert_greater from sklearn.utils.testing import SkipTest from sklearn.utils.testing import check_skip_travis from sklearn.utils.testing import ignore_warnings from sklearn.base import clone, ClassifierMixin from sklearn.metrics import accuracy_score, adjusted_rand_score, f1_score from sklearn.lda import LDA from sklearn.random_projection import BaseRandomProjection from sklearn.feature_selection import SelectKBest from sklearn.svm.base import BaseLibSVM from sklearn.pipeline import make_pipeline from sklearn.utils.validation import DataConversionWarning, NotFittedError from sklearn.cross_validation import train_test_split from sklearn.utils import shuffle from sklearn.preprocessing import StandardScaler from sklearn.datasets import load_iris, load_boston, make_blobs BOSTON = None CROSS_DECOMPOSITION = ['PLSCanonical', 'PLSRegression', 'CCA', 'PLSSVD'] def _boston_subset(n_samples=200): global BOSTON if BOSTON is None: boston = load_boston() X, y = boston.data, boston.target X, y = shuffle(X, y, random_state=0) X, y = X[:n_samples], y[:n_samples] X = StandardScaler().fit_transform(X) BOSTON = X, y return BOSTON def set_fast_parameters(estimator): # speed up some estimators params = estimator.get_params() if ("n_iter" in params and estimator.__class__.__name__ != "TSNE"): estimator.set_params(n_iter=5) if "max_iter" in params: # NMF if estimator.max_iter is not None: estimator.set_params(max_iter=min(5, estimator.max_iter)) # LinearSVR if estimator.__class__.__name__ == 'LinearSVR': estimator.set_params(max_iter=20) if "n_resampling" in params: # randomized lasso estimator.set_params(n_resampling=5) if "n_estimators" in params: # especially gradient boosting with default 100 estimator.set_params(n_estimators=min(5, estimator.n_estimators)) if "max_trials" in params: # RANSAC estimator.set_params(max_trials=10) if "n_init" in params: # K-Means estimator.set_params(n_init=2) if estimator.__class__.__name__ == "SelectFdr": # be tolerant of noisy datasets (not actually speed) estimator.set_params(alpha=.5) if isinstance(estimator, BaseRandomProjection): # Due to the jl lemma and often very few samples, the number # of components of the random matrix projection will be probably # greater than the number of features. # So we impose a smaller number (avoid "auto" mode) estimator.set_params(n_components=1) if isinstance(estimator, SelectKBest): # SelectKBest has a default of k=10 # which is more feature than we have in most case. estimator.set_params(k=1) class NotAnArray(object): " An object that is convertable to an array" def __init__(self, data): self.data = data def __array__(self, dtype=None): return self.data def _is_32bit(): """Detect if process is 32bit Python.""" return struct.calcsize('P') * 8 == 32 def check_estimator_sparse_data(name, Estimator): rng = np.random.RandomState(0) X = rng.rand(40, 10) X[X < .8] = 0 X = sparse.csr_matrix(X) y = (4 * rng.rand(40)).astype(np.int) # catch deprecation warnings with warnings.catch_warnings(): if name in ['Scaler', 'StandardScaler']: estimator = Estimator(with_mean=False) else: estimator = Estimator() set_fast_parameters(estimator) # fit and predict try: estimator.fit(X, y) if hasattr(estimator, "predict"): estimator.predict(X) if hasattr(estimator, 'predict_proba'): estimator.predict_proba(X) except TypeError as e: if 'sparse' not in repr(e): print("Estimator %s doesn't seem to fail gracefully on " "sparse data: error message state explicitly that " "sparse input is not supported if this is not the case." % name) raise except Exception: print("Estimator %s doesn't seem to fail gracefully on " "sparse data: it should raise a TypeError if sparse input " "is explicitly not supported." % name) raise def check_transformer(name, Transformer): X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) X = StandardScaler().fit_transform(X) X -= X.min() _check_transformer(name, Transformer, X, y) _check_transformer(name, Transformer, X.tolist(), y.tolist()) def check_transformer_data_not_an_array(name, Transformer): X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) X = StandardScaler().fit_transform(X) # We need to make sure that we have non negative data, for things # like NMF X -= X.min() - .1 this_X = NotAnArray(X) this_y = NotAnArray(np.asarray(y)) _check_transformer(name, Transformer, this_X, this_y) def check_transformers_unfitted(name, Transformer): X, y = _boston_subset() with warnings.catch_warnings(record=True): transformer = Transformer() assert_raises(NotFittedError, transformer.transform, X) def _check_transformer(name, Transformer, X, y): if name in ('CCA', 'LocallyLinearEmbedding', 'KernelPCA') and _is_32bit(): # Those transformers yield non-deterministic output when executed on # a 32bit Python. The same transformers are stable on 64bit Python. # FIXME: try to isolate a minimalistic reproduction case only depending # on numpy & scipy and/or maybe generate a test dataset that does not # cause such unstable behaviors. msg = name + ' is non deterministic on 32bit Python' raise SkipTest(msg) n_samples, n_features = np.asarray(X).shape # catch deprecation warnings with warnings.catch_warnings(record=True): transformer = Transformer() set_random_state(transformer) if name == "KernelPCA": transformer.remove_zero_eig = False set_fast_parameters(transformer) # fit if name in CROSS_DECOMPOSITION: y_ = np.c_[y, y] y_[::2, 1] *= 2 else: y_ = y transformer.fit(X, y_) X_pred = transformer.fit_transform(X, y=y_) if isinstance(X_pred, tuple): for x_pred in X_pred: assert_equal(x_pred.shape[0], n_samples) else: assert_equal(X_pred.shape[0], n_samples) if hasattr(transformer, 'transform'): if name in CROSS_DECOMPOSITION: X_pred2 = transformer.transform(X, y_) X_pred3 = transformer.fit_transform(X, y=y_) else: X_pred2 = transformer.transform(X) X_pred3 = transformer.fit_transform(X, y=y_) if isinstance(X_pred, tuple) and isinstance(X_pred2, tuple): for x_pred, x_pred2, x_pred3 in zip(X_pred, X_pred2, X_pred3): assert_array_almost_equal( x_pred, x_pred2, 2, "fit_transform and transform outcomes not consistent in %s" % Transformer) assert_array_almost_equal( x_pred, x_pred3, 2, "consecutive fit_transform outcomes not consistent in %s" % Transformer) else: assert_array_almost_equal( X_pred, X_pred2, 2, "fit_transform and transform outcomes not consistent in %s" % Transformer) assert_array_almost_equal( X_pred, X_pred3, 2, "consecutive fit_transform outcomes not consistent in %s" % Transformer) # raises error on malformed input for transform if hasattr(X, 'T'): # If it's not an array, it does not have a 'T' property assert_raises(ValueError, transformer.transform, X.T) @ignore_warnings def check_pipeline_consistency(name, Estimator): # check that make_pipeline(est) gives same score as est X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) X -= X.min() y = multioutput_estimator_convert_y_2d(name, y) estimator = Estimator() set_fast_parameters(estimator) set_random_state(estimator) pipeline = make_pipeline(estimator) estimator.fit(X, y) pipeline.fit(X, y) funcs = ["score", "fit_transform"] for func_name in funcs: func = getattr(estimator, func_name, None) if func is not None: func_pipeline = getattr(pipeline, func_name) result = func(X, y) result_pipe = func_pipeline(X, y) assert_array_almost_equal(result, result_pipe) @ignore_warnings def check_fit_score_takes_y(name, Estimator): # check that all estimators accept an optional y # in fit and score so they can be used in pipelines rnd = np.random.RandomState(0) X = rnd.uniform(size=(10, 3)) y = (X[:, 0] * 4).astype(np.int) y = multioutput_estimator_convert_y_2d(name, y) estimator = Estimator() set_fast_parameters(estimator) set_random_state(estimator) funcs = ["fit", "score", "partial_fit", "fit_predict", "fit_transform"] for func_name in funcs: func = getattr(estimator, func_name, None) if func is not None: func(X, y) args = inspect.getargspec(func).args assert_true(args[2] in ["y", "Y"]) def check_estimators_dtypes(name, Estimator): rnd = np.random.RandomState(0) X_train_32 = 4 * rnd.uniform(size=(10, 3)).astype(np.float32) X_train_64 = X_train_32.astype(np.float64) X_train_int_64 = X_train_32.astype(np.int64) X_train_int_32 = X_train_32.astype(np.int32) y = X_train_int_64[:, 0] y = multioutput_estimator_convert_y_2d(name, y) for X_train in [X_train_32, X_train_64, X_train_int_64, X_train_int_32]: with warnings.catch_warnings(record=True): estimator = Estimator() set_fast_parameters(estimator) set_random_state(estimator, 1) estimator.fit(X_train, y) for method in ["predict", "transform", "decision_function", "predict_proba"]: try: if hasattr(estimator, method): getattr(estimator, method)(X_train) except NotImplementedError: # FIXME # non-standard handling of ducktyping in BaggingEstimator pass def check_estimators_nan_inf(name, Estimator): rnd = np.random.RandomState(0) X_train_finite = rnd.uniform(size=(10, 3)) X_train_nan = rnd.uniform(size=(10, 3)) X_train_nan[0, 0] = np.nan X_train_inf = rnd.uniform(size=(10, 3)) X_train_inf[0, 0] = np.inf y = np.ones(10) y[:5] = 0 y = multioutput_estimator_convert_y_2d(name, y) error_string_fit = "Estimator doesn't check for NaN and inf in fit." error_string_predict = ("Estimator doesn't check for NaN and inf in" " predict.") error_string_transform = ("Estimator doesn't check for NaN and inf in" " transform.") for X_train in [X_train_nan, X_train_inf]: # catch deprecation warnings with warnings.catch_warnings(record=True): estimator = Estimator() set_fast_parameters(estimator) set_random_state(estimator, 1) # try to fit try: estimator.fit(X_train, y) except ValueError as e: if 'inf' not in repr(e) and 'NaN' not in repr(e): print(error_string_fit, Estimator, e) traceback.print_exc(file=sys.stdout) raise e except Exception as exc: print(error_string_fit, Estimator, exc) traceback.print_exc(file=sys.stdout) raise exc else: raise AssertionError(error_string_fit, Estimator) # actually fit estimator.fit(X_train_finite, y) # predict if hasattr(estimator, "predict"): try: estimator.predict(X_train) except ValueError as e: if 'inf' not in repr(e) and 'NaN' not in repr(e): print(error_string_predict, Estimator, e) traceback.print_exc(file=sys.stdout) raise e except Exception as exc: print(error_string_predict, Estimator, exc) traceback.print_exc(file=sys.stdout) else: raise AssertionError(error_string_predict, Estimator) # transform if hasattr(estimator, "transform"): try: estimator.transform(X_train) except ValueError as e: if 'inf' not in repr(e) and 'NaN' not in repr(e): print(error_string_transform, Estimator, e) traceback.print_exc(file=sys.stdout) raise e except Exception as exc: print(error_string_transform, Estimator, exc) traceback.print_exc(file=sys.stdout) else: raise AssertionError(error_string_transform, Estimator) def check_transformer_pickle(name, Transformer): X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) n_samples, n_features = X.shape X = StandardScaler().fit_transform(X) X -= X.min() # catch deprecation warnings with warnings.catch_warnings(record=True): transformer = Transformer() if not hasattr(transformer, 'transform'): return set_random_state(transformer) set_fast_parameters(transformer) # fit if name in CROSS_DECOMPOSITION: random_state = np.random.RandomState(seed=12345) y_ = np.vstack([y, 2 * y + random_state.randint(2, size=len(y))]) y_ = y_.T else: y_ = y transformer.fit(X, y_) X_pred = transformer.fit(X, y_).transform(X) pickled_transformer = pickle.dumps(transformer) unpickled_transformer = pickle.loads(pickled_transformer) pickled_X_pred = unpickled_transformer.transform(X) assert_array_almost_equal(pickled_X_pred, X_pred) def check_estimators_partial_fit_n_features(name, Alg): # check if number of features changes between calls to partial_fit. if not hasattr(Alg, 'partial_fit'): return X, y = make_blobs(n_samples=50, random_state=1) X -= X.min() with warnings.catch_warnings(record=True): alg = Alg() set_fast_parameters(alg) if isinstance(alg, ClassifierMixin): classes = np.unique(y) alg.partial_fit(X, y, classes=classes) else: alg.partial_fit(X, y) assert_raises(ValueError, alg.partial_fit, X[:, :-1], y) def check_clustering(name, Alg): X, y = make_blobs(n_samples=50, random_state=1) X, y = shuffle(X, y, random_state=7) X = StandardScaler().fit_transform(X) n_samples, n_features = X.shape # catch deprecation and neighbors warnings with warnings.catch_warnings(record=True): alg = Alg() set_fast_parameters(alg) if hasattr(alg, "n_clusters"): alg.set_params(n_clusters=3) set_random_state(alg) if name == 'AffinityPropagation': alg.set_params(preference=-100) alg.set_params(max_iter=100) # fit alg.fit(X) # with lists alg.fit(X.tolist()) assert_equal(alg.labels_.shape, (n_samples,)) pred = alg.labels_ assert_greater(adjusted_rand_score(pred, y), 0.4) # fit another time with ``fit_predict`` and compare results if name is 'SpectralClustering': # there is no way to make Spectral clustering deterministic :( return set_random_state(alg) with warnings.catch_warnings(record=True): pred2 = alg.fit_predict(X) assert_array_equal(pred, pred2) def check_clusterer_compute_labels_predict(name, Clusterer): """Check that predict is invariant of compute_labels""" X, y = make_blobs(n_samples=20, random_state=0) clusterer = Clusterer() if hasattr(clusterer, "compute_labels"): # MiniBatchKMeans if hasattr(clusterer, "random_state"): clusterer.set_params(random_state=0) X_pred1 = clusterer.fit(X).predict(X) clusterer.set_params(compute_labels=False) X_pred2 = clusterer.fit(X).predict(X) assert_array_equal(X_pred1, X_pred2) def check_classifiers_one_label(name, Classifier): error_string_fit = "Classifier can't train when only one class is present." error_string_predict = ("Classifier can't predict when only one class is " "present.") rnd = np.random.RandomState(0) X_train = rnd.uniform(size=(10, 3)) X_test = rnd.uniform(size=(10, 3)) y = np.ones(10) # catch deprecation warnings with warnings.catch_warnings(record=True): classifier = Classifier() set_fast_parameters(classifier) # try to fit try: classifier.fit(X_train, y) except ValueError as e: if 'class' not in repr(e): print(error_string_fit, Classifier, e) traceback.print_exc(file=sys.stdout) raise e else: return except Exception as exc: print(error_string_fit, Classifier, exc) traceback.print_exc(file=sys.stdout) raise exc # predict try: assert_array_equal(classifier.predict(X_test), y) except Exception as exc: print(error_string_predict, Classifier, exc) raise exc def check_classifiers_train(name, Classifier): X_m, y_m = make_blobs(random_state=0) X_m, y_m = shuffle(X_m, y_m, random_state=7) X_m = StandardScaler().fit_transform(X_m) # generate binary problem from multi-class one y_b = y_m[y_m != 2] X_b = X_m[y_m != 2] for (X, y) in [(X_m, y_m), (X_b, y_b)]: # catch deprecation warnings classes = np.unique(y) n_classes = len(classes) n_samples, n_features = X.shape with warnings.catch_warnings(record=True): classifier = Classifier() if name in ['BernoulliNB', 'MultinomialNB']: X -= X.min() set_fast_parameters(classifier) set_random_state(classifier) # raises error on malformed input for fit assert_raises(ValueError, classifier.fit, X, y[:-1]) # fit classifier.fit(X, y) # with lists classifier.fit(X.tolist(), y.tolist()) assert_true(hasattr(classifier, "classes_")) y_pred = classifier.predict(X) assert_equal(y_pred.shape, (n_samples,)) # training set performance if name not in ['BernoulliNB', 'MultinomialNB']: assert_greater(accuracy_score(y, y_pred), 0.85) # raises error on malformed input for predict assert_raises(ValueError, classifier.predict, X.T) if hasattr(classifier, "decision_function"): try: # decision_function agrees with predict decision = classifier.decision_function(X) if n_classes is 2: assert_equal(decision.shape, (n_samples,)) dec_pred = (decision.ravel() > 0).astype(np.int) assert_array_equal(dec_pred, y_pred) if (n_classes is 3 and not isinstance(classifier, BaseLibSVM)): # 1on1 of LibSVM works differently assert_equal(decision.shape, (n_samples, n_classes)) assert_array_equal(np.argmax(decision, axis=1), y_pred) # raises error on malformed input assert_raises(ValueError, classifier.decision_function, X.T) # raises error on malformed input for decision_function assert_raises(ValueError, classifier.decision_function, X.T) except NotImplementedError: pass if hasattr(classifier, "predict_proba"): # predict_proba agrees with predict y_prob = classifier.predict_proba(X) assert_equal(y_prob.shape, (n_samples, n_classes)) assert_array_equal(np.argmax(y_prob, axis=1), y_pred) # check that probas for all classes sum to one assert_array_almost_equal(np.sum(y_prob, axis=1), np.ones(n_samples)) # raises error on malformed input assert_raises(ValueError, classifier.predict_proba, X.T) # raises error on malformed input for predict_proba assert_raises(ValueError, classifier.predict_proba, X.T) def check_estimators_unfitted(name, Estimator): """Check if NotFittedError is raised when calling predict and related functions""" # Common test for Regressors as well as Classifiers X, y = _boston_subset() with warnings.catch_warnings(record=True): est = Estimator() assert_raises(NotFittedError, est.predict, X) if hasattr(est, 'predict'): assert_raises(NotFittedError, est.predict, X) if hasattr(est, 'decision_function'): assert_raises(NotFittedError, est.decision_function, X) if hasattr(est, 'predict_proba'): assert_raises(NotFittedError, est.predict_proba, X) if hasattr(est, 'predict_log_proba'): assert_raises(NotFittedError, est.predict_log_proba, X) def check_classifiers_input_shapes(name, Classifier): iris = load_iris() X, y = iris.data, iris.target X, y = shuffle(X, y, random_state=1) X = StandardScaler().fit_transform(X) # catch deprecation warnings with warnings.catch_warnings(record=True): classifier = Classifier() set_fast_parameters(classifier) set_random_state(classifier) # fit classifier.fit(X, y) y_pred = classifier.predict(X) set_random_state(classifier) # Check that when a 2D y is given, a DataConversionWarning is # raised with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always", DataConversionWarning) classifier.fit(X, y[:, np.newaxis]) msg = "expected 1 DataConversionWarning, got: %s" % ( ", ".join([str(w_x) for w_x in w])) assert_equal(len(w), 1, msg) assert_array_equal(y_pred, classifier.predict(X)) def check_classifiers_classes(name, Classifier): X, y = make_blobs(n_samples=30, random_state=0, cluster_std=0.1) X, y = shuffle(X, y, random_state=7) X = StandardScaler().fit_transform(X) # We need to make sure that we have non negative data, for things # like NMF X -= X.min() - .1 y_names = np.array(["one", "two", "three"])[y] for y_names in [y_names, y_names.astype('O')]: if name in ["LabelPropagation", "LabelSpreading"]: # TODO some complication with -1 label y_ = y else: y_ = y_names classes = np.unique(y_) # catch deprecation warnings with warnings.catch_warnings(record=True): classifier = Classifier() if name == 'BernoulliNB': classifier.set_params(binarize=X.mean()) set_fast_parameters(classifier) # fit classifier.fit(X, y_) y_pred = classifier.predict(X) # training set performance assert_array_equal(np.unique(y_), np.unique(y_pred)) if np.any(classifier.classes_ != classes): print("Unexpected classes_ attribute for %r: " "expected %s, got %s" % (classifier, classes, classifier.classes_)) def check_classifiers_pickle(name, Classifier): X, y = make_blobs(random_state=0) X, y = shuffle(X, y, random_state=7) X -= X.min() # catch deprecation warnings with warnings.catch_warnings(record=True): classifier = Classifier() set_fast_parameters(classifier) # raises error on malformed input for fit assert_raises(ValueError, classifier.fit, X, y[:-1]) # fit classifier.fit(X, y) y_pred = classifier.predict(X) pickled_classifier = pickle.dumps(classifier) unpickled_classifier = pickle.loads(pickled_classifier) pickled_y_pred = unpickled_classifier.predict(X) assert_array_almost_equal(pickled_y_pred, y_pred) def check_regressors_int(name, Regressor): X, _ = _boston_subset() X = X[:50] rnd = np.random.RandomState(0) y = rnd.randint(3, size=X.shape[0]) y = multioutput_estimator_convert_y_2d(name, y) if name == 'OrthogonalMatchingPursuitCV': # FIXME: This test is unstable on Travis, see issue #3190. check_skip_travis() rnd = np.random.RandomState(0) # catch deprecation warnings with warnings.catch_warnings(record=True): # separate estimators to control random seeds regressor_1 = Regressor() regressor_2 = Regressor() set_fast_parameters(regressor_1) set_fast_parameters(regressor_2) set_random_state(regressor_1) set_random_state(regressor_2) if name in CROSS_DECOMPOSITION: y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))]) y_ = y_.T else: y_ = y # fit regressor_1.fit(X, y_) pred1 = regressor_1.predict(X) regressor_2.fit(X, y_.astype(np.float)) pred2 = regressor_2.predict(X) assert_array_almost_equal(pred1, pred2, 2, name) def check_regressors_train(name, Regressor): X, y = _boston_subset() y = StandardScaler().fit_transform(y) # X is already scaled y = multioutput_estimator_convert_y_2d(name, y) if name == 'OrthogonalMatchingPursuitCV': # FIXME: This test is unstable on Travis, see issue #3190. check_skip_travis() rnd = np.random.RandomState(0) # catch deprecation warnings with warnings.catch_warnings(record=True): regressor = Regressor() set_fast_parameters(regressor) if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'): # linear regressors need to set alpha, but not generalized CV ones regressor.alpha = 0.01 # raises error on malformed input for fit assert_raises(ValueError, regressor.fit, X, y[:-1]) # fit if name in CROSS_DECOMPOSITION: y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))]) y_ = y_.T else: y_ = y set_random_state(regressor) regressor.fit(X, y_) regressor.fit(X.tolist(), y_.tolist()) regressor.predict(X) # TODO: find out why PLS and CCA fail. RANSAC is random # and furthermore assumes the presence of outliers, hence # skipped if name not in ('PLSCanonical', 'CCA', 'RANSACRegressor'): assert_greater(regressor.score(X, y_), 0.5) def check_regressors_pickle(name, Regressor): X, y = _boston_subset() y = StandardScaler().fit_transform(y) # X is already scaled y = multioutput_estimator_convert_y_2d(name, y) if name == 'OrthogonalMatchingPursuitCV': # FIXME: This test is unstable on Travis, see issue #3190. check_skip_travis() rnd = np.random.RandomState(0) # catch deprecation warnings with warnings.catch_warnings(record=True): regressor = Regressor() set_fast_parameters(regressor) if not hasattr(regressor, 'alphas') and hasattr(regressor, 'alpha'): # linear regressors need to set alpha, but not generalized CV ones regressor.alpha = 0.01 if name in CROSS_DECOMPOSITION: y_ = np.vstack([y, 2 * y + rnd.randint(2, size=len(y))]) y_ = y_.T else: y_ = y regressor.fit(X, y_) y_pred = regressor.predict(X) # store old predictions pickled_regressor = pickle.dumps(regressor) unpickled_regressor = pickle.loads(pickled_regressor) pickled_y_pred = unpickled_regressor.predict(X) assert_array_almost_equal(pickled_y_pred, y_pred) def check_class_weight_classifiers(name, Classifier): for n_centers in [2, 3]: # create a very noisy dataset X, y = make_blobs(centers=n_centers, random_state=0, cluster_std=20) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0) n_centers = len(np.unique(y_train)) if n_centers == 2: class_weight = {0: 1000, 1: 0.0001} else: class_weight = {0: 1000, 1: 0.0001, 2: 0.0001} with warnings.catch_warnings(record=True): classifier = Classifier(class_weight=class_weight) if hasattr(classifier, "n_iter"): classifier.set_params(n_iter=100) if hasattr(classifier, "min_weight_fraction_leaf"): classifier.set_params(min_weight_fraction_leaf=0.01) set_random_state(classifier) classifier.fit(X_train, y_train) y_pred = classifier.predict(X_test) assert_greater(np.mean(y_pred == 0), 0.9) def check_class_weight_auto_classifiers(name, Classifier, X_train, y_train, X_test, y_test, weights): with warnings.catch_warnings(record=True): classifier = Classifier() if hasattr(classifier, "n_iter"): classifier.set_params(n_iter=100) set_random_state(classifier) classifier.fit(X_train, y_train) y_pred = classifier.predict(X_test) classifier.set_params(class_weight='auto') classifier.fit(X_train, y_train) y_pred_auto = classifier.predict(X_test) assert_greater(f1_score(y_test, y_pred_auto, average='weighted'), f1_score(y_test, y_pred, average='weighted')) def check_class_weight_auto_linear_classifier(name, Classifier): """Test class weights with non-contiguous class labels.""" X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0], [1.0, 1.0], [1.0, 0.0]]) y = [1, 1, 1, -1, -1] with warnings.catch_warnings(record=True): classifier = Classifier() if hasattr(classifier, "n_iter"): # This is a very small dataset, default n_iter are likely to prevent # convergence classifier.set_params(n_iter=1000) set_random_state(classifier) # Let the model compute the class frequencies classifier.set_params(class_weight='auto') coef_auto = classifier.fit(X, y).coef_.copy() # Count each label occurrence to reweight manually mean_weight = (1. / 3 + 1. / 2) / 2 class_weight = { 1: 1. / 3 / mean_weight, -1: 1. / 2 / mean_weight, } classifier.set_params(class_weight=class_weight) coef_manual = classifier.fit(X, y).coef_.copy() assert_array_almost_equal(coef_auto, coef_manual) def check_estimators_overwrite_params(name, Estimator): X, y = make_blobs(random_state=0, n_samples=9) y = multioutput_estimator_convert_y_2d(name, y) # some want non-negative input X -= X.min() with warnings.catch_warnings(record=True): # catch deprecation warnings estimator = Estimator() if name == 'MiniBatchDictLearning' or name == 'MiniBatchSparsePCA': # FIXME # for MiniBatchDictLearning and MiniBatchSparsePCA estimator.batch_size = 1 set_fast_parameters(estimator) set_random_state(estimator) params = estimator.get_params() estimator.fit(X, y) new_params = estimator.get_params() for k, v in params.items(): assert_false(np.any(new_params[k] != v), "Estimator %s changes its parameter %s" " from %s to %s during fit." % (name, k, v, new_params[k])) def check_sparsify_coefficients(name, Estimator): X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [-1, -2], [2, 2], [-2, -2]]) y = [1, 1, 1, 2, 2, 2, 3, 3, 3] est = Estimator() est.fit(X, y) pred_orig = est.predict(X) # test sparsify with dense inputs est.sparsify() assert_true(sparse.issparse(est.coef_)) pred = est.predict(X) assert_array_equal(pred, pred_orig) # pickle and unpickle with sparse coef_ est = pickle.loads(pickle.dumps(est)) assert_true(sparse.issparse(est.coef_)) pred = est.predict(X) assert_array_equal(pred, pred_orig) def check_classifier_data_not_an_array(name, Estimator): X = np.array([[3, 0], [0, 1], [0, 2], [1, 1], [1, 2], [2, 1]]) y = [1, 1, 1, 2, 2, 2] y = multioutput_estimator_convert_y_2d(name, y) check_estimators_data_not_an_array(name, Estimator, X, y) def check_regressor_data_not_an_array(name, Estimator): X, y = _boston_subset(n_samples=50) y = multioutput_estimator_convert_y_2d(name, y) check_estimators_data_not_an_array(name, Estimator, X, y) def check_estimators_data_not_an_array(name, Estimator, X, y): if name in CROSS_DECOMPOSITION: raise SkipTest # catch deprecation warnings with warnings.catch_warnings(record=True): # separate estimators to control random seeds estimator_1 = Estimator() estimator_2 = Estimator() set_fast_parameters(estimator_1) set_fast_parameters(estimator_2) set_random_state(estimator_1) set_random_state(estimator_2) y_ = NotAnArray(np.asarray(y)) X_ = NotAnArray(np.asarray(X)) # fit estimator_1.fit(X_, y_) pred1 = estimator_1.predict(X_) estimator_2.fit(X, y) pred2 = estimator_2.predict(X) assert_array_almost_equal(pred1, pred2, 2, name) def check_parameters_default_constructible(name, Estimator): classifier = LDA() # test default-constructibility # get rid of deprecation warnings with warnings.catch_warnings(record=True): if name in META_ESTIMATORS: estimator = Estimator(classifier) else: estimator = Estimator() # test cloning clone(estimator) # test __repr__ repr(estimator) # test that set_params returns self assert_true(isinstance(estimator.set_params(), Estimator)) # test if init does nothing but set parameters # this is important for grid_search etc. # We get the default parameters from init and then # compare these against the actual values of the attributes. # this comes from getattr. Gets rid of deprecation decorator. init = getattr(estimator.__init__, 'deprecated_original', estimator.__init__) try: args, varargs, kws, defaults = inspect.getargspec(init) except TypeError: # init is not a python function. # true for mixins return params = estimator.get_params() if name in META_ESTIMATORS: # they need a non-default argument args = args[2:] else: args = args[1:] if args: # non-empty list assert_equal(len(args), len(defaults)) else: return for arg, default in zip(args, defaults): if arg not in params.keys(): # deprecated parameter, not in get_params assert_true(default is None) continue if isinstance(params[arg], np.ndarray): assert_array_equal(params[arg], default) else: assert_equal(params[arg], default) def multioutput_estimator_convert_y_2d(name, y): # Estimators in mono_output_task_error raise ValueError if y is of 1-D # Convert into a 2-D y for those estimators. if name in (['MultiTaskElasticNetCV', 'MultiTaskLassoCV', 'MultiTaskLasso', 'MultiTaskElasticNet']): return y[:, np.newaxis] return y def check_non_transformer_estimators_n_iter(name, estimator, multi_output=False): # Check if all iterative solvers, run for more than one iteratiom iris = load_iris() X, y_ = iris.data, iris.target if multi_output: y_ = y_[:, np.newaxis] set_random_state(estimator, 0) if name == 'AffinityPropagation': estimator.fit(X) else: estimator.fit(X, y_) assert_greater(estimator.n_iter_, 0) def check_transformer_n_iter(name, estimator): if name in CROSS_DECOMPOSITION: # Check using default data X = [[0., 0., 1.], [1., 0., 0.], [2., 2., 2.], [2., 5., 4.]] y_ = [[0.1, -0.2], [0.9, 1.1], [0.1, -0.5], [0.3, -0.2]] else: X, y_ = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, n_features=2, cluster_std=0.1) X -= X.min() - 0.1 set_random_state(estimator, 0) estimator.fit(X, y_) # These return a n_iter per component. if name in CROSS_DECOMPOSITION: for iter_ in estimator.n_iter_: assert_greater(iter_, 1) else: assert_greater(estimator.n_iter_, 1)

bsd-3-clause

mailhexu/pyDFTutils

build/lib/pyDFTutils/wannier90/pythtb.py

2

153556

from __future__ import print_function # PythTB python tight binding module. # December 22, 2016 __version__='1.7.1' # Copyright 2010, 2012, 2016 by Sinisa Coh and David Vanderbilt # # This file is part of PythTB. PythTB is free software: you can # redistribute it and/or modify it under the terms of the GNU General # Public License as published by the Free Software Foundation, either # version 3 of the License, or (at your option) any later version. # # PythTB is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY # or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public # License for more details. # # A copy of the GNU General Public License should be available # alongside this source in a file named gpl-3.0.txt. If not, # see <http://www.gnu.org/licenses/>. # # PythTB is availabe at http://www.physics.rutgers.edu/pythtb/ import numpy as np # numerics for matrices import sys # for exiting import copy # for deepcopying class tb_model(object): r""" This is the main class of the PythTB package which contains all information for the tight-binding model. :param dim_k: Dimensionality of reciprocal space, i.e., specifies how many directions are considered to be periodic. :param dim_r: Dimensionality of real space, i.e., specifies how many real space lattice vectors there are and how many coordinates are needed to specify the orbital coordinates. .. note:: Parameter *dim_r* can be larger than *dim_k*! For example, a polymer is a three-dimensional molecule (one needs three coordinates to specify orbital positions), but it is periodic along only one direction. For a polymer, therefore, we should have *dim_k* equal to 1 and *dim_r* equal to 3. See similar example here: :ref:`trestle-example`. :param lat: Array containing lattice vectors in Cartesian coordinates (in arbitrary units). In example the below, the first lattice vector has coordinates [1.0,0.5] while the second one has coordinates [0.0,2.0]. By default, lattice vectors are an identity matrix. :param orb: Array containing reduced coordinates of all tight-binding orbitals. In the example below, the first orbital is defined with reduced coordinates [0.2,0.3]. Its Cartesian coordinates are therefore 0.2 times the first lattice vector plus 0.3 times the second lattice vector. If *orb* is an integer code will assume that there are these many orbitals all at the origin of the unit cell. By default the code will assume a single orbital at the origin. :param per: This is an optional parameter giving a list of lattice vectors which are considered to be periodic. In the example below, only the vector [0.0,2.0] is considered to be periodic (since per=[1]). By default, all lattice vectors are assumed to be periodic. If dim_k is smaller than dim_r, then by default the first dim_k vectors are considered to be periodic. :param nspin: Number of explicit spin components assumed for each orbital in *orb*. Allowed values of *nspin* are *1* and *2*. If *nspin* is 1 then the model is spinless, if *nspin* is 2 then it is explicitly a spinfull model and each orbital is assumed to have two spin components. Default value of this parameter is *1*. Of course one can make spinfull calculation even with *nspin* set to 1, but then the user must keep track of which orbital corresponds to which spin component. Example usage:: # Creates model that is two-dimensional in real space but only # one-dimensional in reciprocal space. Second lattice vector is # chosen to be periodic (since per=[1]). Three orbital # coordinates are specified. tb = tb_model(1, 2, lat=[[1.0, 0.5], [0.0, 2.0]], orb=[[0.2, 0.3], [0.1, 0.1], [0.2, 0.2]], per=[1]) """ def __init__(self,dim_k,dim_r,lat=None,orb=None,per=None,nspin=1): # initialize _dim_k = dimensionality of k-space (integer) if type(dim_k).__name__!='int': raise Exception("\n\nArgument dim_k not an integer") if dim_k < 0 or dim_k > 4: raise Exception("\n\nArgument dim_k out of range. Must be between 0 and 4.") self._dim_k=dim_k # initialize _dim_r = dimensionality of r-space (integer) if type(dim_r).__name__!='int': raise Exception("\n\nArgument dim_r not an integer") if dim_r < dim_k or dim_r > 4: raise Exception("\n\nArgument dim_r out of range. Must be dim_r>=dim_k and dim_r<=4.") self._dim_r=dim_r # initialize _lat = lattice vectors, array of dim_r*dim_r # format is _lat(lat_vec_index,cartesian_index) # special option: 'unit' implies unit matrix, also default value if lat is 'unit' or lat is None: self._lat=np.identity(dim_r,float) print(" Lattice vectors not specified! I will use identity matrix.") elif type(lat).__name__ not in ['list','ndarray']: raise Exception("\n\nArgument lat is not a list.") else: self._lat=np.array(lat,dtype=float) if self._lat.shape!=(dim_r,dim_r): raise Exception("\n\nWrong lat array dimensions") # check that volume is not zero and that have right handed system if dim_r>0: if np.abs(np.linalg.det(self._lat))<1.0E-6: raise Exception("\n\nLattice vectors length/area/volume too close to zero, or zero.") if np.linalg.det(self._lat)<0.0: raise Exception("\n\nLattice vectors need to form right handed system.") # initialize _norb = number of basis orbitals per cell # and _orb = orbital locations, in reduced coordinates # format is _orb(orb_index,lat_vec_index) # special option: 'bravais' implies one atom at origin if orb is 'bravais' or orb is None: self._norb=1 self._orb=np.zeros((1,dim_r)) print(" Orbital positions not specified. I will assume a single orbital at the origin.") elif type(orb).__name__=='int': self._norb=orb self._orb=np.zeros((orb,dim_r)) print(" Orbital positions not specified. I will assume ",orb," orbitals at the origin") elif type(orb).__name__ not in ['list','ndarray']: raise Exception("\n\nArgument orb is not a list or an integer") else: self._orb=np.array(orb,dtype=float) if len(self._orb.shape)!=2: raise Exception("\n\nWrong orb array rank") self._norb=self._orb.shape[0] # number of orbitals if self._orb.shape[1]!=dim_r: raise Exception("\n\nWrong orb array dimensions") # choose which self._dim_k out of self._dim_r dimensions are # to be considered periodic. if per==None: # by default first _dim_k dimensions are periodic self._per=list(range(self._dim_k)) else: if len(per)!=self._dim_k: raise Exception("\n\nWrong choice of periodic/infinite direction!") # store which directions are the periodic ones self._per=per # remember number of spin components if nspin not in [1,2]: raise Exception("\n\nWrong value of nspin, must be 1 or 2!") self._nspin=nspin # by default, assume model did not come from w90 object and that # position operator is diagonal self._assume_position_operator_diagonal=True # compute number of electronic states at each k-point self._nsta=self._norb*self._nspin # Initialize onsite energies to zero if self._nspin==1: self._site_energies=np.zeros((self._norb),dtype=float) elif self._nspin==2: self._site_energies=np.zeros((self._norb,2,2),dtype=complex) # remember which onsite energies user has specified self._site_energies_specified=np.zeros(self._norb,dtype=bool) self._site_energies_specified[:]=False # Initialize hoppings to empty list self._hoppings=[] # The onsite energies and hoppings are not specified # when creating a 'tb_model' object. They are speficied # subsequently by separate function calls defined below. def set_onsite(self,onsite_en,ind_i=None,mode="set"): r""" Defines on-site energies for tight-binding orbitals. One can either set energy for one tight-binding orbital, or all at once. .. warning:: In previous version of PythTB this function was called *set_sites*. For backwards compatibility one can still use that name but that feature will be removed in future releases. :param onsite_en: Either a list of on-site energies (in arbitrary units) for each orbital, or a single on-site energy (in this case *ind_i* parameter must be given). In the case when *nspin* is *1* (spinless) then each on-site energy is a single number. If *nspin* is *2* then on-site energy can be given either as a single number, or as an array of four numbers, or 2x2 matrix. If a single number is given, it is interpreted as on-site energy for both up and down spin component. If an array of four numbers is given, these are the coefficients of I, sigma_x, sigma_y, and sigma_z (that is, the 2x2 identity and the three Pauli spin matrices) respectively. Finally, full 2x2 matrix can be given as well. If this function is never called, on-site energy is assumed to be zero. :param ind_i: Index of tight-binding orbital whose on-site energy you wish to change. This parameter should be specified only when *onsite_en* is a single number (not a list). :param mode: Similar to parameter *mode* in function set_hop*. Speficies way in which parameter *onsite_en* is used. It can either set value of on-site energy from scratch, reset it, or add to it. * "set" -- Default value. On-site energy is set to value of *onsite_en* parameter. One can use "set" on each tight-binding orbital only once. * "reset" -- Specifies on-site energy to given value. This function can be called multiple times for the same orbital(s). * "add" -- Adds to the previous value of on-site energy. This function can be called multiple times for the same orbital(s). Example usage:: # Defines on-site energy of first orbital to be 0.0, # second 1.0, and third 2.0 tb.set_onsite([0.0, 1.0, 2.0]) # Increases value of on-site energy for second orbital tb.set_onsite(100.0, 1, mode="add") # Changes on-site energy of second orbital to zero tb.set_onsite(0.0, 1, mode="reset") # Sets all three on-site energies at once tb.set_onsite([2.0, 3.0, 4.0], mode="reset") """ if ind_i==None: if (len(onsite_en)!=self._norb): raise Exception("\n\nWrong number of site energies") # make sure ind_i is not out of scope if ind_i!=None: if ind_i<0 or ind_i>=self._norb: raise Exception("\n\nIndex ind_i out of scope.") # make sure that onsite terms are real/hermitian if ind_i!=None: to_check=[onsite_en] else: to_check=onsite_en for ons in to_check: if np.array(ons).shape==(): if np.abs(np.array(ons)-np.array(ons).conjugate())>1.0E-8: raise Exception("\n\nOnsite energy should not have imaginary part!") elif np.array(ons).shape==(4,): if np.max(np.abs(np.array(ons)-np.array(ons).conjugate()))>1.0E-8: raise Exception("\n\nOnsite energy or Zeeman field should not have imaginary part!") elif np.array(ons).shape==(2,2): if np.max(np.abs(np.array(ons)-np.array(ons).T.conjugate()))>1.0E-8: raise Exception("\n\nOnsite matrix should be Hermitian!") # specifying onsite energies from scratch, can be called only once if mode.lower()=="set": # specifying only one site at a time if ind_i!=None: # make sure we specify things only once if self._site_energies_specified[ind_i]==True: raise Exception("\n\nOnsite energy for this site was already specified! Use mode=\"reset\" or mode=\"add\".") else: self._site_energies[ind_i]=self._val_to_block(onsite_en) self._site_energies_specified[ind_i]=True # specifying all sites at once else: # make sure we specify things only once if True in self._site_energies_specified[ind_i]: raise Exception("\n\nSome or all onsite energies were already specified! Use mode=\"reset\" or mode=\"add\".") else: for i in range(self._norb): self._site_energies[i]=self._val_to_block(onsite_en[i]) self._site_energies_specified[:]=True # reset values of onsite terms, without adding to previous value elif mode.lower()=="reset": # specifying only one site at a time if ind_i!=None: self._site_energies[ind_i]=self._val_to_block(onsite_en) self._site_energies_specified[ind_i]=True # specifying all sites at once else: for i in range(self._norb): self._site_energies[i]=self._val_to_block(onsite_en[i]) self._site_energies_specified[:]=True # add to previous value elif mode.lower()=="add": # specifying only one site at a time if ind_i!=None: self._site_energies[ind_i]+=self._val_to_block(onsite_en) self._site_energies_specified[ind_i]=True # specifying all sites at once else: for i in range(self._norb): self._site_energies[i]+=self._val_to_block(onsite_en[i]) self._site_energies_specified[:]=True else: raise Exception("\n\nWrong value of mode parameter") def set_hop(self,hop_amp,ind_i,ind_j,ind_R=None,mode="set",allow_conjugate_pair=False): r""" Defines hopping parameters between tight-binding orbitals. In the notation used in section 3.1 equation 3.6 of :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>` this function specifies the following object .. math:: H_{ij}({\bf R})= \langle \phi_{{\bf 0} i} \vert H \vert \phi_{{\bf R},j} \rangle Where :math:`\langle \phi_{{\bf 0} i} \vert` is i-th tight-binding orbital in the home unit cell and :math:`\vert \phi_{{\bf R},j} \rangle` is j-th tight-binding orbital in unit cell shifted by lattice vector :math:`{\bf R}`. :math:`H` is the Hamiltonian. (Strictly speaking, this term specifies hopping amplitude for hopping from site *j+R* to site *i*, not vice-versa.) Hopping in the opposite direction is automatically included by the code since .. math:: H_{ji}(-{\bf R})= \left[ H_{ij}({\bf R}) \right]^{*} .. warning:: There is no need to specify hoppings in both :math:`i \rightarrow j+R` direction and opposite :math:`j \rightarrow i-R` direction since that is done automatically. If you want to specifiy hoppings in both directions, see description of parameter *allow_conjugate_pair*. .. warning:: In previous version of PythTB this function was called *add_hop*. For backwards compatibility one can still use that name but that feature will be removed in future releases. :param hop_amp: Hopping amplitude; can be real or complex number, equals :math:`H_{ij}({\bf R})`. If *nspin* is *2* then hopping amplitude can be given either as a single number, or as an array of four numbers, or as 2x2 matrix. If a single number is given, it is interpreted as hopping amplitude for both up and down spin component. If an array of four numbers is given, these are the coefficients of I, sigma_x, sigma_y, and sigma_z (that is, the 2x2 identity and the three Pauli spin matrices) respectively. Finally, full 2x2 matrix can be given as well. :param ind_i: Index of bra orbital from the bracket :math:`\langle \phi_{{\bf 0} i} \vert H \vert \phi_{{\bf R},j} \rangle`. This orbital is assumed to be in the home unit cell. :param ind_j: Index of ket orbital from the bracket :math:`\langle \phi_{{\bf 0} i} \vert H \vert \phi_{{\bf R},j} \rangle`. This orbital does not have to be in the home unit cell; its unit cell position is determined by parameter *ind_R*. :param ind_R: Specifies, in reduced coordinates, the shift of the ket orbital. The number of coordinates must equal the dimensionality in real space (*dim_r* parameter) for consistency, but only the periodic directions of ind_R will be considered. If reciprocal space is zero-dimensional (as in a molecule), this parameter does not need to be specified. :param mode: Similar to parameter *mode* in function *set_onsite*. Speficies way in which parameter *hop_amp* is used. It can either set value of hopping term from scratch, reset it, or add to it. * "set" -- Default value. Hopping term is set to value of *hop_amp* parameter. One can use "set" for each triplet of *ind_i*, *ind_j*, *ind_R* only once. * "reset" -- Specifies on-site energy to given value. This function can be called multiple times for the same triplet *ind_i*, *ind_j*, *ind_R*. * "add" -- Adds to the previous value of hopping term This function can be called multiple times for the same triplet *ind_i*, *ind_j*, *ind_R*. If *set_hop* was ever called with *allow_conjugate_pair* set to True, then it is possible that user has specified both :math:`i \rightarrow j+R` and conjugate pair :math:`j \rightarrow i-R`. In this case, "set", "reset", and "add" parameters will treat triplet *ind_i*, *ind_j*, *ind_R* and conjugate triplet *ind_j*, *ind_i*, *-ind_R* as distinct. :param allow_conjugate_pair: Default value is *False*. If set to *True* code will allow user to specify hopping :math:`i \rightarrow j+R` even if conjugate-pair hopping :math:`j \rightarrow i-R` has been specified. If both terms are specified, code will still count each term two times. Example usage:: # Specifies complex hopping amplitude between first orbital in home # unit cell and third orbital in neigbouring unit cell. tb.set_hop(0.3+0.4j, 0, 2, [0, 1]) # change value of this hopping tb.set_hop(0.1+0.2j, 0, 2, [0, 1], mode="reset") # add to previous value (after this function call below, # hopping term amplitude is 100.1+0.2j) tb.set_hop(100.0, 0, 2, [0, 1], mode="add") """ # if self._dim_k!=0 and (ind_R is None): raise Exception("\n\nNeed to specify ind_R!") # if necessary convert from integer to array if self._dim_k==1 and type(ind_R).__name__=='int': tmpR=np.zeros(self._dim_r,dtype=int) tmpR[self._per]=ind_R ind_R=tmpR # check length of ind_R if self._dim_k!=0: if len(ind_R)!=self._dim_r: raise Exception("\n\nLength of input ind_R vector must equal dim_r! Even if dim_k<dim_r.") # make sure ind_i and ind_j are not out of scope if ind_i<0 or ind_i>=self._norb: raise Exception("\n\nIndex ind_i out of scope.") if ind_j<0 or ind_j>=self._norb: raise Exception("\n\nIndex ind_j out of scope.") # do not allow onsite hoppings to be specified here because then they # will be double-counted if self._dim_k==0: if ind_i==ind_j: raise Exception("\n\nDo not use set_hop for onsite terms. Use set_onsite instead!") else: if ind_i==ind_j: all_zer=True for k in self._per: if int(ind_R[k])!=0: all_zer=False if all_zer==True: raise Exception("\n\nDo not use set_hop for onsite terms. Use set_onsite instead!") # # make sure that if <i|H|j+R> is specified that <j|H|i-R> is not! if allow_conjugate_pair==False: for h in self._hoppings: if ind_i==h[2] and ind_j==h[1]: if self._dim_k==0: raise Exception(\ """\n Following matrix element was already implicitely specified: i="""+str(ind_i)+" j="+str(ind_j)+""" Remember, specifying <i|H|j> automatically specifies <j|H|i>. For consistency, specify all hoppings for a given bond in the same direction. (Or, alternatively, see the documentation on the 'allow_conjugate_pair' flag.) """) elif False not in (np.array(ind_R)[self._per]==(-1)*np.array(h[3])[self._per]): raise Exception(\ """\n Following matrix element was already implicitely specified: i="""+str(ind_i)+" j="+str(ind_j)+" R="+str(ind_R)+""" Remember,specifying <i|H|j+R> automatically specifies <j|H|i-R>. For consistency, specify all hoppings for a given bond in the same direction. (Or, alternatively, see the documentation on the 'allow_conjugate_pair' flag.) """) # convert to 2by2 matrix if needed hop_use=self._val_to_block(hop_amp) # hopping term parameters to be stored if self._dim_k==0: new_hop=[hop_use,int(ind_i),int(ind_j)] else: new_hop=[hop_use,int(ind_i),int(ind_j),np.array(ind_R)] # # see if there is a hopping term with same i,j,R use_index=None for iih,h in enumerate(self._hoppings): # check if the same same_ijR=False if ind_i==h[1] and ind_j==h[2]: if self._dim_k==0: same_ijR=True else: if False not in (np.array(ind_R)[self._per]==np.array(h[3])[self._per]): same_ijR=True # if they are the same then store index of site at which they are the same if same_ijR==True: use_index=iih # # specifying hopping terms from scratch, can be called only once if mode.lower()=="set": # make sure we specify things only once if use_index!=None: raise Exception("\n\nHopping energy for this site was already specified! Use mode=\"reset\" or mode=\"add\".") else: self._hoppings.append(new_hop) # reset value of hopping term, without adding to previous value elif mode.lower()=="reset": if use_index!=None: self._hoppings[use_index]=new_hop else: self._hoppings.append(new_hop) # add to previous value elif mode.lower()=="add": if use_index!=None: self._hoppings[use_index][0]+=new_hop[0] else: self._hoppings.append(new_hop) else: raise Exception("\n\nWrong value of mode parameter") def _val_to_block(self,val): """If nspin=2 then returns a 2 by 2 matrix from the input parameters. If only one real number is given in the input then assume that this is the diagonal term. If array with four elements is given then first one is the diagonal term, and other three are Zeeman field direction. If given a 2 by 2 matrix, just return it. If nspin=1 then just returns val.""" # spinless case if self._nspin==1: return val # spinfull case elif self._nspin==2: # matrix to return ret=np.zeros((2,2),dtype=complex) # use_val=np.array(val) # only one number is given if use_val.shape==(): ret[0,0]+=use_val ret[1,1]+=use_val # if four numbers are given elif use_val.shape==(4,): # diagonal ret[0,0]+=use_val[0] ret[1,1]+=use_val[0] # sigma_x ret[0,1]+=use_val[1] ret[1,0]+=use_val[1] # sigma_y ret[0,1]+=use_val[2]*(-1.0j) ret[1,0]+=use_val[2]*( 1.0j) # sigma_z ret[0,0]+=use_val[3] ret[1,1]+=use_val[3]*(-1.0) # if 2 by 2 matrix is given elif use_val.shape==(2,2): return use_val else: raise Exception(\ """\n Wrong format of the on-site or hopping term. Must be single number, or in the case of a spinfull model can be array of four numbers or 2x2 matrix.""") return ret def display(self): r""" Prints on the screen some information about this tight-binding model. This function doesn't take any parameters. """ print('---------------------------------------') print('report of tight-binding model') print('---------------------------------------') print('k-space dimension =',self._dim_k) print('r-space dimension =',self._dim_r) print('number of spin components =',self._nspin) print('periodic directions =',self._per) print('number of orbitals =',self._norb) print('number of electronic states =',self._nsta) print('lattice vectors:') for i,o in enumerate(self._lat): print(" #",_nice_int(i,2)," ===> [", end=' ') for j,v in enumerate(o): print(_nice_float(v,7,4), end=' ') if j!=len(o)-1: print(",", end=' ') print("]") print('positions of orbitals:') for i,o in enumerate(self._orb): print(" #",_nice_int(i,2)," ===> [", end=' ') for j,v in enumerate(o): print(_nice_float(v,7,4), end=' ') if j!=len(o)-1: print(",", end=' ') print("]") print('site energies:') for i,site in enumerate(self._site_energies): print(" #",_nice_int(i,2)," ===> ", end=' ') if self._nspin==1: print(_nice_float(site,7,4)) elif self._nspin==2: print(str(site).replace("\n"," ")) print('hoppings:') for i,hopping in enumerate(self._hoppings): print("<",_nice_int(hopping[1],2),"| H |",_nice_int(hopping[2],2), end=' ') if len(hopping)==4: print("+ [", end=' ') for j,v in enumerate(hopping[3]): print(_nice_int(v,2), end=' ') if j!=len(hopping[3])-1: print(",", end=' ') else: print("]", end=' ') print("> ===> ", end=' ') if self._nspin==1: print(_nice_complex(hopping[0],7,4)) elif self._nspin==2: print(str(hopping[0]).replace("\n"," ")) print() def visualize(self,dir_first,dir_second=None,eig_dr=None,draw_hoppings=True,ph_color="black"): r""" Rudimentary function for visualizing tight-binding model geometry, hopping between tight-binding orbitals, and electron eigenstates. If eigenvector is not drawn, then orbitals in home cell are drawn as red circles, and those in neighboring cells are drawn with different shade of red. Hopping term directions are drawn with green lines connecting two orbitals. Origin of unit cell is indicated with blue dot, while real space unit vectors are drawn with blue lines. If eigenvector is drawn, then electron eigenstate on each orbital is drawn with a circle whose size is proportional to wavefunction amplitude while its color depends on the phase. There are various coloring schemes for the phase factor; see more details under *ph_color* parameter. If eigenvector is drawn and coloring scheme is "red-blue" or "wheel", all other elements of the picture are drawn in gray or black. :param dir_first: First index of Cartesian coordinates used for plotting. :param dir_second: Second index of Cartesian coordinates used for plotting. For example if dir_first=0 and dir_second=2, and Cartesian coordinates of some orbital is [2.0,4.0,6.0] then it will be drawn at coordinate [2.0,6.0]. If dimensionality of real space (*dim_r*) is zero or one then dir_second should not be specified. :param eig_dr: Optional parameter specifying eigenstate to plot. If specified, this should be one-dimensional array of complex numbers specifying wavefunction at each orbital in the tight-binding basis. If not specified, eigenstate is not drawn. :param draw_hoppings: Optional parameter specifying whether to draw all allowed hopping terms in the tight-binding model. Default value is True. :param ph_color: Optional parameter determining the way eigenvector phase factors are translated into color. Default value is "black". Convention of the wavefunction phase is as in convention 1 in section 3.1 of :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>`. In other words, these wavefunction phases are in correspondence with cell-periodic functions :math:`u_{n {\bf k}} ({\bf r})` not :math:`\Psi_{n {\bf k}} ({\bf r})`. * "black" -- phase of eigenvectors are ignored and wavefunction is always colored in black. * "red-blue" -- zero phase is drawn red, while phases or pi or -pi are drawn blue. Phases in between are interpolated between red and blue. Some phase information is lost in this coloring becase phase of +phi and -phi have same color. * "wheel" -- each phase is given unique color. In steps of pi/3 starting from 0, colors are assigned (in increasing hue) as: red, yellow, green, cyan, blue, magenta, red. :returns: * **fig** -- Figure object from matplotlib.pyplot module that can be used to save the figure in PDF, EPS or similar format, for example using fig.savefig("name.pdf") command. * **ax** -- Axes object from matplotlib.pyplot module that can be used to tweak the plot, for example by adding a plot title ax.set_title("Title goes here"). Example usage:: # Draws x-y projection of tight-binding model # tweaks figure and saves it as a PDF. (fig, ax) = tb.visualize(0, 1) ax.set_title("Title goes here") fig.savefig("model.pdf") See also these examples: :ref:`edge-example`, :ref:`visualize-example`. """ # check the format of eig_dr if not (eig_dr is None): if eig_dr.shape!=(self._norb,): raise Exception("\n\nWrong format of eig_dr! Must be array of size norb.") # check that ph_color is correct if ph_color not in ["black","red-blue","wheel"]: raise Exception("\n\nWrong value of ph_color parameter!") # check if dir_second had to be specified if dir_second==None and self._dim_r>1: raise Exception("\n\nNeed to specify index of second coordinate for projection!") # start a new figure import pylab as plt fig=plt.figure(figsize=[plt.rcParams["figure.figsize"][0], plt.rcParams["figure.figsize"][0]]) ax=fig.add_subplot(111, aspect='equal') def proj(v): "Project vector onto drawing plane" coord_x=v[dir_first] if dir_second==None: coord_y=0.0 else: coord_y=v[dir_second] return [coord_x,coord_y] def to_cart(red): "Convert reduced to Cartesian coordinates" return np.dot(red,self._lat) # define colors to be used in plotting everything # except eigenvectors if (eig_dr is None) or ph_color=="black": c_cell="b" c_orb="r" c_nei=[0.85,0.65,0.65] c_hop="g" else: c_cell=[0.4,0.4,0.4] c_orb=[0.0,0.0,0.0] c_nei=[0.6,0.6,0.6] c_hop=[0.0,0.0,0.0] # determine color scheme for eigenvectors def color_to_phase(ph): if ph_color=="black": return "k" if ph_color=="red-blue": ph=np.abs(ph/np.pi) return [1.0-ph,0.0,ph] if ph_color=="wheel": if ph<0.0: ph=ph+2.0*np.pi ph=6.0*ph/(2.0*np.pi) x_ph=1.0-np.abs(ph%2.0-1.0) if ph>=0.0 and ph<1.0: ret_col=[1.0 ,x_ph,0.0 ] if ph>=1.0 and ph<2.0: ret_col=[x_ph,1.0 ,0.0 ] if ph>=2.0 and ph<3.0: ret_col=[0.0 ,1.0 ,x_ph] if ph>=3.0 and ph<4.0: ret_col=[0.0 ,x_ph,1.0 ] if ph>=4.0 and ph<5.0: ret_col=[x_ph,0.0 ,1.0 ] if ph>=5.0 and ph<=6.0: ret_col=[1.0 ,0.0 ,x_ph] return ret_col # draw origin ax.plot([0.0],[0.0],"o",c=c_cell,mec="w",mew=0.0,zorder=7,ms=4.5) # first draw unit cell vectors which are considered to be periodic for i in self._per: # pick a unit cell vector and project it down to the drawing plane vec=proj(self._lat[i]) ax.plot([0.0,vec[0]],[0.0,vec[1]],"-",c=c_cell,lw=1.5,zorder=7) # now draw all orbitals for i in range(self._norb): # find position of orbital in cartesian coordinates pos=to_cart(self._orb[i]) pos=proj(pos) ax.plot([pos[0]],[pos[1]],"o",c=c_orb,mec="w",mew=0.0,zorder=10,ms=4.0) # draw hopping terms if draw_hoppings==True: for h in self._hoppings: # draw both i->j+R and i-R->j hop for s in range(2): # get "from" and "to" coordinates pos_i=np.copy(self._orb[h[1]]) pos_j=np.copy(self._orb[h[2]]) # add also lattice vector if not 0-dim if self._dim_k!=0: if s==0: pos_j[self._per]=pos_j[self._per]+h[3][self._per] if s==1: pos_i[self._per]=pos_i[self._per]-h[3][self._per] # project down vector to the plane pos_i=np.array(proj(to_cart(pos_i))) pos_j=np.array(proj(to_cart(pos_j))) # add also one point in the middle to bend the curve prcnt=0.05 # bend always by this ammount pos_mid=(pos_i+pos_j)*0.5 dif=pos_j-pos_i # difference vector orth=np.array([dif[1],-1.0*dif[0]]) # orthogonal to difference vector orth=orth/np.sqrt(np.dot(orth,orth)) # normalize pos_mid=pos_mid+orth*prcnt*np.sqrt(np.dot(dif,dif)) # shift mid point in orthogonal direction # draw hopping all_pnts=np.array([pos_i,pos_mid,pos_j]).T ax.plot(all_pnts[0],all_pnts[1],"-",c=c_hop,lw=0.75,zorder=8) # draw "from" and "to" sites ax.plot([pos_i[0]],[pos_i[1]],"o",c=c_nei,zorder=9,mew=0.0,ms=4.0,mec="w") ax.plot([pos_j[0]],[pos_j[1]],"o",c=c_nei,zorder=9,mew=0.0,ms=4.0,mec="w") # now draw the eigenstate if not (eig_dr is None): for i in range(self._norb): # find position of orbital in cartesian coordinates pos=to_cart(self._orb[i]) pos=proj(pos) # find norm of eigenfunction at this point nrm=(eig_dr[i]*eig_dr[i].conjugate()).real # rescale and get size of circle nrm_rad=2.0*nrm*float(self._norb) # get color based on the phase of the eigenstate phase=np.angle(eig_dr[i]) c_ph=color_to_phase(phase) ax.plot([pos[0]],[pos[1]],"o",c=c_ph,mec="w",mew=0.0,ms=nrm_rad,zorder=11,alpha=0.8) # center the image # first get the current limit, which is probably tight xl=ax.set_xlim() yl=ax.set_ylim() # now get the center of current limit centx=(xl[1]+xl[0])*0.5 centy=(yl[1]+yl[0])*0.5 # now get the maximal size (lengthwise or heightwise) mx=max([xl[1]-xl[0],yl[1]-yl[0]]) # set new limits extr=0.05 # add some boundary as well ax.set_xlim(centx-mx*(0.5+extr),centx+mx*(0.5+extr)) ax.set_ylim(centy-mx*(0.5+extr),centy+mx*(0.5+extr)) # return a figure and axes to the user return (fig,ax) def get_num_orbitals(self): "Returns number of orbitals in the model." return self._norb def get_orb(self): "Returns reduced coordinates of orbitals in format [orbital,coordinate.]" return self._orb.copy() def get_lat(self): "Returns lattice vectors in format [vector,coordinate]." return self._lat.copy() def _gen_ham(self,k_input=None): """Generate Hamiltonian for a certain k-point, K-point is given in reduced coordinates!""" kpnt=np.array(k_input) if not (k_input is None): # if kpnt is just a number then convert it to an array if len(kpnt.shape)==0: kpnt=np.array([kpnt]) # check that k-vector is of corect size if kpnt.shape!=(self._dim_k,): raise Exception("\n\nk-vector of wrong shape!") else: if self._dim_k!=0: raise Exception("\n\nHave to provide a k-vector!") # zero the Hamiltonian matrix if self._nspin==1: ham=np.zeros((self._norb,self._norb),dtype=complex) elif self._nspin==2: ham=np.zeros((self._norb,2,self._norb,2),dtype=complex) # modify diagonal elements for i in range(self._norb): if self._nspin==1: ham[i,i]=self._site_energies[i] elif self._nspin==2: ham[i,:,i,:]=self._site_energies[i] # go over all hoppings for hopping in self._hoppings: # get all data for the hopping parameter if self._nspin==1: amp=complex(hopping[0]) elif self._nspin==2: amp=np.array(hopping[0],dtype=complex) i=hopping[1] j=hopping[2] # in 0-dim case there is no phase factor if self._dim_k>0: ind_R=np.array(hopping[3],dtype=float) # vector from one site to another rv=-self._orb[i,:]+self._orb[j,:]+ind_R # Take only components of vector which are periodic rv=rv[self._per] # Calculate the hopping, see details in info/tb/tb.pdf phase=np.exp((2.0j)*np.pi*np.dot(kpnt,rv)) amp=amp*phase # add this hopping into a matrix and also its conjugate if self._nspin==1: ham[i,j]+=amp ham[j,i]+=amp.conjugate() elif self._nspin==2: ham[i,:,j,:]+=amp ham[j,:,i,:]+=amp.T.conjugate() return ham def _sol_ham(self,ham,eig_vectors=False): """Solves Hamiltonian and returns eigenvectors, eigenvalues""" # reshape matrix first if self._nspin==1: ham_use=ham elif self._nspin==2: ham_use=ham.reshape((2*self._norb,2*self._norb)) # check that matrix is hermitian if np.max(ham_use-ham_use.T.conj())>1.0E-9: raise Exception("\n\nHamiltonian matrix is not hermitian?!") #solve matrix if eig_vectors==False: # only find eigenvalues eval=np.linalg.eigvalsh(ham_use) # sort eigenvalues and convert to real numbers eval=_nicefy_eig(eval) return np.array(eval,dtype=float) else: # find eigenvalues and eigenvectors (eval,eig)=np.linalg.eigh(ham_use) # transpose matrix eig since otherwise it is confusing # now eig[i,:] is eigenvector for eval[i]-th eigenvalue eig=eig.T # sort evectors, eigenvalues and convert to real numbers (eval,eig)=_nicefy_eig(eval,eig) # reshape eigenvectors if doing a spinfull calculation if self._nspin==2: eig=eig.reshape((self._nsta,self._norb,2)) return (eval,eig) def solve_all(self,k_list=None,eig_vectors=False): r""" Solves for eigenvalues and (optionally) eigenvectors of the tight-binding model on a given one-dimensional list of k-vectors. .. note:: Eigenvectors (wavefunctions) returned by this function and used throughout the code are exclusively given in convention 1 as described in section 3.1 of :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>`. In other words, they are in correspondence with cell-periodic functions :math:`u_{n {\bf k}} ({\bf r})` not :math:`\Psi_{n {\bf k}} ({\bf r})`. .. note:: In some cases class :class:`pythtb.wf_array` provides a more elegant way to deal with eigensolutions on a regular mesh of k-vectors. :param k_list: One-dimensional array of k-vectors. Each k-vector is given in reduced coordinates of the reciprocal space unit cell. For example, for real space unit cell vectors [1.0,0.0] and [0.0,2.0] and associated reciprocal space unit vectors [2.0*pi,0.0] and [0.0,pi], k-vector with reduced coordinates [0.25,0.25] corresponds to k-vector [0.5*pi,0.25*pi]. Dimensionality of each vector must equal to the number of periodic directions (i.e. dimensionality of reciprocal space, *dim_k*). This parameter shouldn't be specified for system with zero-dimensional k-space (*dim_k* =0). :param eig_vectors: Optional boolean parameter, specifying whether eigenvectors should be returned. If *eig_vectors* is True, then both eigenvalues and eigenvectors are returned, otherwise only eigenvalues are returned. :returns: * **eval** -- Two dimensional array of eigenvalues for all bands for all kpoints. Format is eval[band,kpoint] where first index (band) corresponds to the electron band in question and second index (kpoint) corresponds to the k-point as listed in the input parameter *k_list*. Eigenvalues are sorted from smallest to largest at each k-point seperately. In the case when reciprocal space is zero-dimensional (as in a molecule) kpoint index is dropped and *eval* is of the format eval[band]. * **evec** -- Three dimensional array of eigenvectors for all bands and all kpoints. If *nspin* equals 1 the format of *evec* is evec[band,kpoint,orbital] where "band" is the electron band in question, "kpoint" is index of k-vector as given in input parameter *k_list*. Finally, "orbital" refers to the tight-binding orbital basis function. Ordering of bands is the same as in *eval*. Eigenvectors evec[n,k,j] correspond to :math:`C^{n {\bf k}}_{j}` from section 3.1 equation 3.5 and 3.7 of the :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>`. In the case when reciprocal space is zero-dimensional (as in a molecule) kpoint index is dropped and *evec* is of the format evec[band,orbital]. In the spinfull calculation (*nspin* equals 2) evec has additional component evec[...,spin] corresponding to the spin component of the wavefunction. Example usage:: # Returns eigenvalues for three k-vectors eval = tb.solve_all([[0.0, 0.0], [0.0, 0.2], [0.0, 0.5]]) # Returns eigenvalues and eigenvectors for two k-vectors (eval, evec) = tb.solve_all([[0.0, 0.0], [0.0, 0.2]], eig_vectors=True) """ # if not 0-dim case if not (k_list is None): nkp=len(k_list) # number of k points # first initialize matrices for all return data # indices are [band,kpoint] ret_eval=np.zeros((self._nsta,nkp),dtype=float) # indices are [band,kpoint,orbital,spin] if self._nspin==1: ret_evec=np.zeros((self._nsta,nkp,self._norb),dtype=complex) elif self._nspin==2: ret_evec=np.zeros((self._nsta,nkp,self._norb,2),dtype=complex) # go over all kpoints for i,k in enumerate(k_list): # generate Hamiltonian at that point ham=self._gen_ham(k) # solve Hamiltonian if eig_vectors==False: eval=self._sol_ham(ham,eig_vectors=eig_vectors) ret_eval[:,i]=eval[:] else: (eval,evec)=self._sol_ham(ham,eig_vectors=eig_vectors) ret_eval[:,i]=eval[:] if self._nspin==1: ret_evec[:,i,:]=evec[:,:] elif self._nspin==2: ret_evec[:,i,:,:]=evec[:,:,:] # return stuff if eig_vectors==False: # indices of eval are [band,kpoint] return ret_eval else: # indices of eval are [band,kpoint] for evec are [band,kpoint,orbital,(spin)] return (ret_eval,ret_evec) else: # 0 dim case # generate Hamiltonian ham=self._gen_ham() # solve if eig_vectors==False: eval=self._sol_ham(ham,eig_vectors=eig_vectors) # indices of eval are [band] return eval else: (eval,evec)=self._sol_ham(ham,eig_vectors=eig_vectors) # indices of eval are [band] and of evec are [band,orbital,spin] return (eval,evec) def solve_one(self,k_point=None,eig_vectors=False): r""" Similar to :func:`pythtb.tb_model.solve_all` but solves tight-binding model for only one k-vector. """ # if not 0-dim case if not (k_point is None): if eig_vectors==False: eval=self.solve_all([k_point],eig_vectors=eig_vectors) # indices of eval are [band] return eval[:,0] else: (eval,evec)=self.solve_all([k_point],eig_vectors=eig_vectors) # indices of eval are [band] for evec are [band,orbital,spin] if self._nspin==1: return (eval[:,0],evec[:,0,:]) elif self._nspin==2: return (eval[:,0],evec[:,0,:,:]) else: # do the same as solve_all return self.solve_all(eig_vectors=eig_vectors) def cut_piece(self,num,fin_dir,glue_edgs=False): r""" Constructs a (d-1)-dimensional tight-binding model out of a d-dimensional one by repeating the unit cell a given number of times along one of the periodic lattice vectors. The real-space lattice vectors of the returned model are the same as those of the original model; only the dimensionality of reciprocal space is reduced. :param num: How many times to repeat the unit cell. :param fin_dir: Index of the real space lattice vector along which you no longer wish to maintain periodicity. :param glue_edgs: Optional boolean parameter specifying whether to allow hoppings from one edge to the other of a cut model. :returns: * **fin_model** -- Object of type :class:`pythtb.tb_model` representing a cutout tight-binding model. Orbitals in *fin_model* are numbered so that the i-th orbital of the n-th unit cell has index i+norb*n (here norb is the number of orbitals in the original model). Example usage:: A = tb_model(3, 3, ...) # Construct two-dimensional model B out of three-dimensional # model A by repeating model along second lattice vector ten times B = A.cut_piece(10, 1) # Further cut two-dimensional model B into one-dimensional model # A by repeating unit cell twenty times along third lattice # vector and allow hoppings from one edge to the other C = B.cut_piece(20, 2, glue_edgs=True) See also these examples: :ref:`haldane_fin-example`, :ref:`edge-example`. """ if self._dim_k ==0: raise Exception("\n\nModel is already finite") if type(num).__name__!='int': raise Exception("\n\nArgument num not an integer") # check value of num if num<1: raise Exception("\n\nArgument num must be positive!") if num==1 and glue_edgs==True: raise Exception("\n\nCan't have num==1 and glueing of the edges!") # generate orbitals of a finite model fin_orb=[] onsite=[] # store also onsite energies for i in range(num): # go over all cells in finite direction for j in range(self._norb): # go over all orbitals in one cell # make a copy of j-th orbital orb_tmp=np.copy(self._orb[j,:]) # change coordinate along finite direction orb_tmp[fin_dir]+=float(i) # add to the list fin_orb.append(orb_tmp) # do the onsite energies at the same time onsite.append(self._site_energies[j]) onsite=np.array(onsite) fin_orb=np.array(fin_orb) # generate periodic directions of a finite model fin_per=copy.deepcopy(self._per) # find if list of periodic directions contains the one you # want to make finite if fin_per.count(fin_dir)!=1: raise Exception("\n\nCan not make model finite along this direction!") # remove index which is no longer periodic fin_per.remove(fin_dir) # generate object of tb_model type that will correspond to a cutout fin_model=tb_model(self._dim_k-1, self._dim_r, copy.deepcopy(self._lat), fin_orb, fin_per, self._nspin) # remember if came from w90 fin_model._assume_position_operator_diagonal=self._assume_position_operator_diagonal # now put all onsite terms for the finite model fin_model.set_onsite(onsite,mode="reset") # put all hopping terms for c in range(num): # go over all cells in finite direction for h in range(len(self._hoppings)): # go over all hoppings in one cell # amplitude of the hop is the same amp=self._hoppings[h][0] # lattice vector of the hopping ind_R=copy.deepcopy(self._hoppings[h][3]) jump_fin=ind_R[fin_dir] # store by how many cells is the hopping in finite direction if fin_model._dim_k!=0: ind_R[fin_dir]=0 # one of the directions now becomes finite # index of "from" and "to" hopping indices hi=self._hoppings[h][1] + c*self._norb # have to compensate for the fact that ind_R in finite direction # will not be used in the finite model hj=self._hoppings[h][2] + (c + jump_fin)*self._norb # decide whether this hopping should be added or not to_add=True # if edges are not glued then neglect all jumps that spill out if glue_edgs==False: if hj<0 or hj>=self._norb*num: to_add=False # if edges are glued then do mod division to wrap up the hopping else: hj=int(hj)%int(self._norb*num) # add hopping to a finite model if to_add==True: if fin_model._dim_k==0: fin_model.set_hop(amp,hi,hj,mode="add",allow_conjugate_pair=True) else: fin_model.set_hop(amp,hi,hj,ind_R,mode="add",allow_conjugate_pair=True) return fin_model def reduce_dim(self,remove_k,value_k): r""" Reduces dimensionality of the model by taking a reciprocal-space slice of the Bloch Hamiltonian :math:`{\cal H}_{\bf k}`. The Bloch Hamiltonian (defined in :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>` in section 3.1 equation 3.7) of a d-dimensional model is a function of d-dimensional k-vector. This function returns a d-1 dimensional tight-binding model obtained by constraining one of k-vector components in :math:`{\cal H}_{\bf k}` to be a constant. :param remove_k: Which reciprocal space unit vector component you wish to keep constant. :param value_k: Value of the k-vector component to which you are constraining this model. Must be given in reduced coordinates. :returns: * **red_tb** -- Object of type :class:`pythtb.tb_model` representing a reduced tight-binding model. Example usage:: # Constrains second k-vector component to equal 0.3 red_tb = tb.reduce_dim(1, 0.3) """ # if self._dim_k==0: raise Exception("\n\nCan not reduce dimensionality even further!") # make a copy red_tb=copy.deepcopy(self) # make one of the directions not periodic red_tb._per.remove(remove_k) red_tb._dim_k=len(red_tb._per) # check that really removed one and only one direction if red_tb._dim_k!=self._dim_k-1: raise Exception("\n\nSpecified wrong dimension to reduce!") # specify hopping terms from scratch red_tb._hoppings=[] # set all hopping parameters for this value of value_k for h in range(len(self._hoppings)): hop=self._hoppings[h] if self._nspin==1: amp=complex(hop[0]) elif self._nspin==2: amp=np.array(hop[0],dtype=complex) i=hop[1]; j=hop[2] ind_R=np.array(hop[3],dtype=int) # vector from one site to another rv=-red_tb._orb[i,:]+red_tb._orb[j,:]+np.array(ind_R,dtype=float) # take only r-vector component along direction you are not making periodic rv=rv[remove_k] # Calculate the part of hopping phase, only for this direction phase=np.exp((2.0j)*np.pi*(value_k*rv)) # store modified version of the hop # Since we are getting rid of one dimension, it could be that now # one of the hopping terms became onsite term because one direction # is no longer periodic if i==j and (False not in (np.array(ind_R[red_tb._per],dtype=int)==0)): if ind_R[remove_k]==0: # in this case this is really an onsite term red_tb.set_onsite(amp*phase,i,mode="add") else: # in this case must treat both R and -R because that term would # have been counted twice without dimensional reduction if self._nspin==1: red_tb.set_onsite(amp*phase+(amp*phase).conj(),i,mode="add") elif self._nspin==2: red_tb.set_onsite(amp*phase+(amp.T*phase).conj(),i,mode="add") else: # just in case make the R vector zero along the reduction dimension ind_R[remove_k]=0 # add hopping term red_tb.set_hop(amp*phase,i,j,ind_R,mode="add",allow_conjugate_pair=True) return red_tb def make_supercell(self, sc_red_lat, return_sc_vectors=False, to_home=True): r""" Returns tight-binding model :class:`pythtb.tb_model` representing a super-cell of a current object. This function can be used together with *cut_piece* in order to create slabs with arbitrary surfaces. By default all orbitals will be shifted to the home cell after unit cell has been created. That way all orbitals will have reduced coordinates between 0 and 1. If you wish to avoid this behavior, you need to set, *to_home* argument to *False*. :param sc_red_lat: Array of integers with size *dim_r*dim_r* defining a super-cell lattice vectors in terms of reduced coordinates of the original tight-binding model. First index in the array specifies super-cell vector, while second index specifies coordinate of that super-cell vector. If *dim_k<dim_r* then still need to specify full array with size *dim_r*dim_r* for consistency, but non-periodic directions must have 0 on off-diagonal elemets s and 1 on diagonal. :param return_sc_vectors: Optional parameter. Default value is *False*. If *True* returns also lattice vectors inside the super-cell. Internally, super-cell tight-binding model will have orbitals repeated in the same order in which these super-cell vectors are given, but if argument *to_home* is set *True* (which it is by default) then additionally, orbitals will be shifted to the home cell. :param to_home: Optional parameter, if *True* will shift all orbitals to the home cell. Default value is *True*. :returns: * **sc_tb** -- Object of type :class:`pythtb.tb_model` representing a tight-binding model in a super-cell. * **sc_vectors** -- Super-cell vectors, returned only if *return_sc_vectors* is set to *True* (default value is *False*). Example usage:: # Creates super-cell out of 2d tight-binding model tb sc_tb = tb.make_supercell([[2, 1], [-1, 2]]) """ # Can't make super cell for model without periodic directions if self._dim_r==0: raise Exception("\n\nMust have at least one periodic direction to make a super-cell") # convert array to numpy array use_sc_red_lat=np.array(sc_red_lat) # checks on super-lattice array if use_sc_red_lat.shape!=(self._dim_r,self._dim_r): raise Exception("\n\nDimension of sc_red_lat array must be dim_r*dim_r") if use_sc_red_lat.dtype!=int: raise Exception("\n\nsc_red_lat array elements must be integers") for i in range(self._dim_r): for j in range(self._dim_r): if (i==j) and (i not in self._per) and use_sc_red_lat[i,j]!=1: raise Exception("\n\nDiagonal elements of sc_red_lat for non-periodic directions must equal 1.") if (i!=j) and ((i not in self._per) or (j not in self._per)) and use_sc_red_lat[i,j]!=0: raise Exception("\n\nOff-diagonal elements of sc_red_lat for non-periodic directions must equal 0.") if np.abs(np.linalg.det(use_sc_red_lat))<1.0E-6: raise Exception("\n\nSuper-cell lattice vectors length/area/volume too close to zero, or zero.") if np.linalg.det(use_sc_red_lat)<0.0: raise Exception("\n\nSuper-cell lattice vectors need to form right handed system.") # converts reduced vector in original lattice to reduced vector in super-cell lattice def to_red_sc(red_vec_orig): return np.linalg.solve(np.array(use_sc_red_lat.T,dtype=float), np.array(red_vec_orig,dtype=float)) # conservative estimate on range of search for super-cell vectors max_R=np.max(np.abs(use_sc_red_lat))*self._dim_r # candidates for super-cell vectors # this is hard-coded and can be improved! sc_cands=[] if self._dim_r==1: for i in range(-max_R,max_R+1): sc_cands.append(np.array([i])) elif self._dim_r==2: for i in range(-max_R,max_R+1): for j in range(-max_R,max_R+1): sc_cands.append(np.array([i,j])) elif self._dim_r==3: for i in range(-max_R,max_R+1): for j in range(-max_R,max_R+1): for k in range(-max_R,max_R+1): sc_cands.append(np.array([i,j,k])) elif self._dim_r==4: for i in range(-max_R,max_R+1): for j in range(-max_R,max_R+1): for k in range(-max_R,max_R+1): for l in range(-max_R,max_R+1): sc_cands.append(np.array([i,j,k,l])) else: raise Exception("\n\nWrong dimensionality of dim_r!") # find all vectors inside super-cell # store them here sc_vec=[] eps_shift=np.sqrt(2.0)*1.0E-8 # shift of the grid, so to avoid double counting # for vec in sc_cands: # compute reduced coordinates of this candidate vector in the super-cell frame tmp_red=to_red_sc(vec).tolist() # check if in the interior inside=True for t in tmp_red: if t<=-1.0*eps_shift or t>1.0-eps_shift: inside=False if inside==True: sc_vec.append(np.array(vec)) # number of times unit cell is repeated in the super-cell num_sc=len(sc_vec) # check that found enough super-cell vectors if int(round(np.abs(np.linalg.det(use_sc_red_lat))))!=num_sc: raise Exception("\n\nSuper-cell generation failed! Wrong number of super-cell vectors found.") # cartesian vectors of the super lattice sc_cart_lat=np.dot(use_sc_red_lat,self._lat) # orbitals of the super-cell tight-binding model sc_orb=[] for cur_sc_vec in sc_vec: # go over all super-cell vectors for orb in self._orb: # go over all orbitals # shift orbital and compute coordinates in # reduced coordinates of super-cell sc_orb.append(to_red_sc(orb+cur_sc_vec)) # create super-cell tb_model object to be returned sc_tb=tb_model(self._dim_k,self._dim_r,sc_cart_lat,sc_orb,per=self._per,nspin=self._nspin) # remember if came from w90 sc_tb._assume_position_operator_diagonal=self._assume_position_operator_diagonal # repeat onsite energies for i in range(num_sc): for j in range(self._norb): sc_tb.set_onsite(self._site_energies[j],i*self._norb+j) # set hopping terms for c,cur_sc_vec in enumerate(sc_vec): # go over all super-cell vectors for h in range(len(self._hoppings)): # go over all hopping terms of the original model # amplitude of the hop is the same amp=self._hoppings[h][0] # lattice vector of the hopping ind_R=copy.deepcopy(self._hoppings[h][3]) # super-cell component of hopping lattice vector # shift also by current super cell vector sc_part=np.floor(to_red_sc(ind_R+cur_sc_vec)) # round down! sc_part=np.array(sc_part,dtype=int) # find remaining vector in the original reduced coordinates orig_part=ind_R+cur_sc_vec-np.dot(sc_part,use_sc_red_lat) # remaining vector must equal one of the super-cell vectors pair_ind=None for p,pair_sc_vec in enumerate(sc_vec): if False not in (pair_sc_vec==orig_part): if pair_ind!=None: raise Exception("\n\nFound duplicate super cell vector!") pair_ind=p if pair_ind==None: raise Exception("\n\nDid not find super cell vector!") # index of "from" and "to" hopping indices hi=self._hoppings[h][1] + c*self._norb hj=self._hoppings[h][2] + pair_ind*self._norb # add hopping term sc_tb.set_hop(amp,hi,hj,sc_part,mode="add",allow_conjugate_pair=True) # put orbitals to home cell if asked for if to_home==True: sc_tb._shift_to_home() # return new tb model and vectors if needed if return_sc_vectors==False: return sc_tb else: return (sc_tb,sc_vec) def _shift_to_home(self): """Shifts all orbital positions to the home unit cell. After this function is called all reduced coordiantes of orbitals will be between 0 and 1. It may be useful to call this function after using make_supercell.""" # go over all orbitals for i in range(self._norb): cur_orb=self._orb[i] # compute orbital in the home cell round_orb=(np.array(cur_orb)+1.0E-6)%1.0 # find displacement vector needed to bring back to home cell disp_vec=np.array(np.round(cur_orb-round_orb),dtype=int) # check if have at least one non-zero component if True in (disp_vec!=0): # shift orbital self._orb[i]-=np.array(disp_vec,dtype=float) # shift also hoppings if self._dim_k!=0: for h in range(len(self._hoppings)): if self._hoppings[h][1]==i: self._hoppings[h][3]-=disp_vec if self._hoppings[h][2]==i: self._hoppings[h][3]+=disp_vec def k_uniform_mesh(self,mesh_size): r""" Returns a uniform grid of k-points that can be passed to passed to function :func:`pythtb.tb_model.solve_all`. This function is useful for plotting density of states histogram and similar. Returned uniform grid of k-points always contains the origin. :param mesh_size: Number of k-points in the mesh in each periodic direction of the model. :returns: * **k_vec** -- Array of k-vectors on the mesh that can be directly passed to function :func:`pythtb.tb_model.solve_all`. Example usage:: # returns a 10x20x30 mesh of a tight binding model # with three periodic directions k_vec = my_model.k_uniform_mesh([10,20,30]) # solve model on the uniform mesh my_model.solve_all(k_vec) """ # get the mesh size and checks for consistency use_mesh=np.array(list(map(round,mesh_size)),dtype=int) if use_mesh.shape!=(self._dim_k,): print(use_mesh.shape) raise Exception("\n\nIncorrect size of the specified k-mesh!") if np.min(use_mesh)<=0: raise Exception("\n\nMesh must have positive non-zero number of elements.") # construct the mesh if self._dim_k==1: # get a mesh k_vec=np.mgrid[0:use_mesh[0]] # normalize the mesh norm=np.tile(np.array(use_mesh,dtype=float),use_mesh) norm=norm.reshape(use_mesh.tolist()+[1]) norm=norm.transpose([1,0]) k_vec=k_vec/norm # final reshape k_vec=k_vec.transpose([1,0]).reshape([use_mesh[0],1]) elif self._dim_k==2: # get a mesh k_vec=np.mgrid[0:use_mesh[0],0:use_mesh[1]] # normalize the mesh norm=np.tile(np.array(use_mesh,dtype=float),use_mesh) norm=norm.reshape(use_mesh.tolist()+[2]) norm=norm.transpose([2,0,1]) k_vec=k_vec/norm # final reshape k_vec=k_vec.transpose([1,2,0]).reshape([use_mesh[0]*use_mesh[1],2]) elif self._dim_k==3: # get a mesh k_vec=np.mgrid[0:use_mesh[0],0:use_mesh[1],0:use_mesh[2]] # normalize the mesh norm=np.tile(np.array(use_mesh,dtype=float),use_mesh) norm=norm.reshape(use_mesh.tolist()+[3]) norm=norm.transpose([3,0,1,2]) k_vec=k_vec/norm # final reshape k_vec=k_vec.transpose([1,2,3,0]).reshape([use_mesh[0]*use_mesh[1]*use_mesh[2],3]) else: raise Exception("\n\nUnsupported dim_k!") return k_vec def k_path(self,kpts,nk,report=True): r""" Interpolates a path in reciprocal space between specified k-points. In 2D or 3D the k-path can consist of several straight segments connecting high-symmetry points ("nodes"), and the results can be used to plot the bands along this path. The interpolated path that is returned contains as equidistant k-points as possible. :param kpts: Array of k-vectors in reciprocal space between which interpolated path should be constructed. These k-vectors must be given in reduced coordinates. As a special case, in 1D k-space kpts may be a string: * *"full"* -- Implies *[ 0.0, 0.5, 1.0]* (full BZ) * *"fullc"* -- Implies *[-0.5, 0.0, 0.5]* (full BZ, centered) * *"half"* -- Implies *[ 0.0, 0.5]* (half BZ) :param nk: Total number of k-points to be used in making the plot. :param report: Optional parameter specifying whether printout is desired (default is True). :returns: * **k_vec** -- Array of (nearly) equidistant interpolated k-points. The distance between the points is calculated in the Cartesian frame, however coordinates themselves are given in dimensionless reduced coordinates! This is done so that this array can be directly passed to function :func:`pythtb.tb_model.solve_all`. * **k_dist** -- Array giving accumulated k-distance to each k-point in the path. Unlike array *k_vec* this one has dimensions! (Units are defined here so that for an one-dimensional crystal with lattice constant equal to for example *10* the length of the Brillouin zone would equal *1/10=0.1*. In other words factors of :math:`2\pi` are absorbed into *k*.) This array can be used to plot path in the k-space so that the distances between the k-points in the plot are exact. * **k_node** -- Array giving accumulated k-distance to each node on the path in Cartesian coordinates. This array is typically used to plot nodes (typically special points) on the path in k-space. Example usage:: # Construct a path connecting four nodal points in k-space # Path will contain 401 k-points, roughly equally spaced path = [[0.0, 0.0], [0.0, 0.5], [0.5, 0.5], [0.0, 0.0]] (k_vec,k_dist,k_node) = my_model.k_path(path,401) # solve for eigenvalues on that path evals = tb.solve_all(k_vec) # then use evals, k_dist, and k_node to plot bandstructure # (see examples) """ # processing of special cases for kpts if kpts=='full': # full Brillouin zone for 1D case k_list=np.array([[0.],[0.5],[1.]]) elif kpts=='fullc': # centered full Brillouin zone for 1D case k_list=np.array([[-0.5],[0.],[0.5]]) elif kpts=='half': # half Brillouin zone for 1D case k_list=np.array([[0.],[0.5]]) else: k_list=np.array(kpts) # in 1D case if path is specified as a vector, convert it to an (n,1) array if len(k_list.shape)==1 and self._dim_k==1: k_list=np.array([k_list]).T # make sure that k-points in the path have correct dimension if k_list.shape[1]!=self._dim_k: print('input k-space dimension is',k_list.shape[1]) print('k-space dimension taken from model is',self._dim_k) raise Exception("\n\nk-space dimensions do not match") # must have more k-points in the path than number of nodes if nk<k_list.shape[0]: raise Exception("\n\nMust have more points in the path than number of nodes.") # number of nodes n_nodes=k_list.shape[0] # extract the lattice vectors from the TB model lat_per=np.copy(self._lat) # choose only those that correspond to periodic directions lat_per=lat_per[self._per] # compute k_space metric tensor k_metric = np.linalg.inv(np.dot(lat_per,lat_per.T)) # Find distances between nodes and set k_node, which is # accumulated distance since the start of the path # initialize array k_node k_node=np.zeros(n_nodes,dtype=float) for n in range(1,n_nodes): dk = k_list[n]-k_list[n-1] dklen = np.sqrt(np.dot(dk,np.dot(k_metric,dk))) k_node[n]=k_node[n-1]+dklen # Find indices of nodes in interpolated list node_index=[0] for n in range(1,n_nodes-1): frac=k_node[n]/k_node[-1] node_index.append(int(round(frac*(nk-1)))) node_index.append(nk-1) # initialize two arrays temporarily with zeros # array giving accumulated k-distance to each k-point k_dist=np.zeros(nk,dtype=float) # array listing the interpolated k-points k_vec=np.zeros((nk,self._dim_k),dtype=float) # go over all kpoints k_vec[0]=k_list[0] for n in range(1,n_nodes): n_i=node_index[n-1] n_f=node_index[n] kd_i=k_node[n-1] kd_f=k_node[n] k_i=k_list[n-1] k_f=k_list[n] for j in range(n_i,n_f+1): frac=float(j-n_i)/float(n_f-n_i) k_dist[j]=kd_i+frac*(kd_f-kd_i) k_vec[j]=k_i+frac*(k_f-k_i) if report==True: if self._dim_k==1: print(' Path in 1D BZ defined by nodes at '+str(k_list.flatten())) else: print('----- k_path report begin ----------') original=np.get_printoptions() np.set_printoptions(precision=5) print('real-space lattice vectors\n', lat_per) print('k-space metric tensor\n', k_metric) print('internal coordinates of nodes\n', k_list) if (lat_per.shape[0]==lat_per.shape[1]): # lat_per is invertible lat_per_inv=np.linalg.inv(lat_per).T print('reciprocal-space lattice vectors\n', lat_per_inv) # cartesian coordinates of nodes kpts_cart=np.tensordot(k_list,lat_per_inv,axes=1) print('cartesian coordinates of nodes\n',kpts_cart) print('list of segments:') for n in range(1,n_nodes): dk=k_node[n]-k_node[n-1] dk_str=_nice_float(dk,7,5) print(' length = '+dk_str+' from ',k_list[n-1],' to ',k_list[n]) print('node distance list:', k_node) print('node index list: ', np.array(node_index)) np.set_printoptions(precision=original["precision"]) print('----- k_path report end ------------') print() return (k_vec,k_dist,k_node) def ignore_position_operator_offdiagonal(self): """Call to this function enables one to approximately compute Berry-like objects from tight-binding models that were obtained from Wannier90.""" self._assume_position_operator_diagonal=True def position_matrix(self, evec, dir): r""" Returns matrix elements of the position operator along direction *dir* for eigenvectors *evec* at a single k-point. Position operator is defined in reduced coordinates. The returned object :math:`X` is .. math:: X_{m n {\bf k}}^{\alpha} = \langle u_{m {\bf k}} \vert r^{\alpha} \vert u_{n {\bf k}} \rangle Here :math:`r^{\alpha}` is the position operator along direction :math:`\alpha` that is selected by *dir*. :param evec: Eigenvectors for which we are computing matrix elements of the position operator. The shape of this array is evec[band,orbital] if *nspin* equals 1 and evec[band,orbital,spin] if *nspin* equals 2. :param dir: Direction along which we are computing the center. This integer must not be one of the periodic directions since position operator matrix element in that case is not well defined. :returns: * **pos_mat** -- Position operator matrix :math:`X_{m n}` as defined above. This is a square matrix with size determined by number of bands given in *evec* input array. First index of *pos_mat* corresponds to bra vector (*m*) and second index to ket (*n*). Example usage:: # diagonalizes Hamiltonian at some k-points (evals, evecs) = my_model.solve_all(k_vec,eig_vectors=True) # computes position operator matrix elements for 3-rd kpoint # and bottom five bands along first coordinate pos_mat = my_model.position_matrix(evecs[:5,2], 0) See also this example: :ref:`haldane_hwf-example`, """ # make sure specified direction is not periodic! if dir in self._per: raise Exception("Can not compute position matrix elements along periodic direction!") # make sure direction is not out of range if dir<0 or dir>=self._dim_r: raise Exception("Direction out of range!") # check if model came from w90 if self._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() # get coordinates of orbitals along the specified direction pos_tmp=self._orb[:,dir] # reshape arrays in the case of spinfull calculation if self._nspin==2: # tile along spin direction if needed pos_use=np.tile(pos_tmp,(2,1)).transpose().flatten() # also flatten the state along the spin index evec_use=evec.reshape((evec.shape[0],evec.shape[1]*evec.shape[2])) else: pos_use=pos_tmp evec_use=evec # position matrix elements pos_mat=np.zeros((evec_use.shape[0],evec_use.shape[0]),dtype=complex) # go over all bands for i in range(evec_use.shape[0]): for j in range(evec_use.shape[0]): pos_mat[i,j]=np.dot(evec_use[i].conj(),pos_use*evec_use[j]) # make sure matrix is hermitian if np.max(pos_mat-pos_mat.T.conj())>1.0E-9: raise Exception("\n\n Position matrix is not hermitian?!") return pos_mat def position_expectation(self,evec,dir): r""" Returns diagonal matrix elements of the position operator. These elements :math:`X_{n n}` can be interpreted as an average position of n-th Bloch state *evec[n]* along direction *dir*. Generally speaking these centers are *not* hybrid Wannier function centers (which are instead returned by :func:`pythtb.tb_model.position_hwf`). See function :func:`pythtb.tb_model.position_matrix` for definition of matrix :math:`X`. :param evec: Eigenvectors for which we are computing matrix elements of the position operator. The shape of this array is evec[band,orbital] if *nspin* equals 1 and evec[band,orbital,spin] if *nspin* equals 2. :param dir: Direction along which we are computing matrix elements. This integer must not be one of the periodic directions since position operator matrix element in that case is not well defined. :returns: * **pos_exp** -- Diagonal elements of the position operator matrix :math:`X`. Length of this vector is determined by number of bands given in *evec* input array. Example usage:: # diagonalizes Hamiltonian at some k-points (evals, evecs) = my_model.solve_all(k_vec,eig_vectors=True) # computes average position for 3-rd kpoint # and bottom five bands along first coordinate pos_exp = my_model.position_expectation(evecs[:5,2], 0) See also this example: :ref:`haldane_hwf-example`. """ # check if model came from w90 if self._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() pos_exp=self.position_matrix(evec,dir).diagonal() return np.array(np.real(pos_exp),dtype=float) def position_hwf(self,evec,dir,hwf_evec=False,basis="orbital"): r""" Returns eigenvalues and optionally eigenvectors of the position operator matrix :math:`X` in either Bloch or orbital basis. These eigenvectors can be interpreted as linear combinations of Bloch states *evec* that have minimal extent (or spread :math:`\Omega` in the sense of maximally localized Wannier functions) along direction *dir*. The eigenvalues are average positions of these localized states. Note that these eigenvectors are not maximally localized Wannier functions in the usual sense because they are localized only along one direction. They are also not the average positions of the Bloch states *evec*, which are instead computed by :func:`pythtb.tb_model.position_expectation`. See function :func:`pythtb.tb_model.position_matrix` for the definition of the matrix :math:`X`. See also Fig. 3 in Phys. Rev. Lett. 102, 107603 (2009) for a discussion of the hybrid Wannier function centers in the context of a Chern insulator. :param evec: Eigenvectors for which we are computing matrix elements of the position operator. The shape of this array is evec[band,orbital] if *nspin* equals 1 and evec[band,orbital,spin] if *nspin* equals 2. :param dir: Direction along which we are computing matrix elements. This integer must not be one of the periodic directions since position operator matrix element in that case is not well defined. :param hwf_evec: Optional boolean variable. If set to *True* this function will return not only eigenvalues but also eigenvectors of :math:`X`. Default value is *False*. :param basis: Optional parameter. If basis="bloch" then hybrid Wannier function *hwf_evec* is written in the Bloch basis. I.e. hwf[i,j] correspond to the weight of j-th Bloch state from *evec* in the i-th hybrid Wannier function. If basis="orbital" and nspin=1 then hwf[i,orb] correspond to the weight of orb-th orbital in the i-th hybrid Wannier function. If basis="orbital" and nspin=2 then hwf[i,orb,spin] correspond to the weight of orb-th orbital, spin-th spin component in the i-th hybrid Wannier function. Default value is "orbital". :returns: * **hwfc** -- Eigenvalues of the position operator matrix :math:`X` (also called hybrid Wannier function centers). Length of this vector equals number of bands given in *evec* input array. Hybrid Wannier function centers are ordered in ascending order. Note that in general *n*-th hwfc does not correspond to *n*-th electronic state *evec*. * **hwf** -- Eigenvectors of the position operator matrix :math:`X`. (also called hybrid Wannier functions). These are returned only if parameter *hwf_evec* is set to *True*. The shape of this array is [h,x] or [h,x,s] depending on value of *basis* and *nspin*. If *basis* is "bloch" then x refers to indices of Bloch states *evec*. If *basis* is "orbital" then *x* (or *x* and *s*) correspond to orbital index (or orbital and spin index if *nspin* is 2). Example usage:: # diagonalizes Hamiltonian at some k-points (evals, evecs) = my_model.solve_all(k_vec,eig_vectors=True) # computes hybrid Wannier centers (and functions) for 3-rd kpoint # and bottom five bands along first coordinate (hwfc, hwf) = my_model.position_hwf(evecs[:5,2], 0, hwf_evec=True, basis="orbital") See also this example: :ref:`haldane_hwf-example`, """ # check if model came from w90 if self._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() # get position matrix pos_mat=self.position_matrix(evec,dir) # diagonalize if hwf_evec==False: hwfc=np.linalg.eigvalsh(pos_mat) # sort eigenvalues and convert to real numbers hwfc=_nicefy_eig(hwfc) return np.array(hwfc,dtype=float) else: # find eigenvalues and eigenvectors (hwfc,hwf)=np.linalg.eigh(pos_mat) # transpose matrix eig since otherwise it is confusing # now eig[i,:] is eigenvector for eval[i]-th eigenvalue hwf=hwf.T # sort evectors, eigenvalues and convert to real numbers (hwfc,hwf)=_nicefy_eig(hwfc,hwf) # convert to right basis if basis.lower().strip()=="bloch": return (hwfc,hwf) elif basis.lower().strip()=="orbital": if self._nspin==1: ret_hwf=np.zeros((hwf.shape[0],self._norb),dtype=complex) # sum over bloch states to get hwf in orbital basis for i in range(ret_hwf.shape[0]): ret_hwf[i]=np.dot(hwf[i],evec) hwf=ret_hwf else: ret_hwf=np.zeros((hwf.shape[0],self._norb*2),dtype=complex) # get rid of spin indices evec_use=evec.reshape([hwf.shape[0],self._norb*2]) # sum over states for i in range(ret_hwf.shape[0]): ret_hwf[i]=np.dot(hwf[i],evec_use) # restore spin indices hwf=ret_hwf.reshape([hwf.shape[0],self._norb,2]) return (hwfc,hwf) else: raise Exception("\n\nBasis must be either bloch or orbital!") # keeping old name for backwards compatibility # will be removed in future tb_model.set_sites=tb_model.set_onsite tb_model.add_hop=tb_model.set_hop tbmodel=tb_model class wf_array(object): r""" This class is used to solve a tight-binding model :class:`pythtb.tb_model` on a regular or non-regular grid of points in reciprocal space and/or parameter space, and perform on it various calculations. For example it can be used to calculate the Berry phase, Berry curvature, 1st Chern number, etc. *Regular k-space grid*: If the grid is a regular k-mesh (no parametric dimensions), a single call to the function :func:`pythtb.wf_array.solve_on_grid` will both construct a k-mesh that uniformly covers the Brillouin zone, and populate it with wavefunctions (eigenvectors) computed on this grid. The last point in each k-dimension is set so that it represents the same Bloch function as the first one (this involves the insertion of some orbital-position-dependent phase factors). Example :ref:`haldane_bp-example` shows how to use wf_array on a regular grid of points in k-space. Examples :ref:`cone-example` and :ref:`3site_cycle-example` show how to use non-regular grid of points. *Parametric or irregular k-space grid grid*: An irregular grid of points, or a grid that includes also one or more parametric dimensions, can be populated manually with the help of the *[]* operator. For example, to copy eeigenvectors *evec* into coordinate (2,3) in the *wf_array* object *wf* one can simply do:: wf[2,3]=evec The eigenvectors (wavefunctions) *evec* in the example above are expected to be in the format *evec[band,orbital]* (or *evec[band,orbital,spin]* for the spinfull calculation). This is the same format as returned by :func:`pythtb.tb_model.solve_one` or :func:`pythtb.tb_model.solve_all` (in the latter case one needs to restrict it to a single k-point as *evec[:,kpt,:]* if the model has *dim_k>=1*). If wf_array is used for closed paths, either in a reciprocal-space or parametric direction, then one needs to include both the starting and ending eigenfunctions even though they are physically equivalent. If the array dimension in question is a k-vector direction and the path traverses the Brillouin zone in a primitive reciprocal-lattice direction, :func:`pythtb.wf_array.impose_pbc` can be used to associate the starting and ending points with each other; if it is a non-winding loop in k-space or a loop in parameter space, then :func:`pythtb.wf_array.impose_loop` can be used instead. (These may not be necessary if only Berry fluxes are needed.) Example :ref:`3site_cycle-example` shows how one of the directions of *wf_array* object need not be a k-vector direction, but can instead be a Hamiltonian parameter :math:`\lambda` (see also discussion after equation 4.1 in :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>`). :param model: Object of type :class:`pythtb.tb_model` representing tight-binding model associated with this array of eigenvectors. :param mesh_arr: Array giving a dimension of the grid of points in each reciprocal-space or parametric direction. Example usage:: # Construct wf_array capable of storing an 11x21 array of # wavefunctions wf = wf_array(tb, [11, 21]) # populate this wf_array with regular grid of points in # Brillouin zone wf.solve_on_grid([0.0, 0.0]) # Compute set of eigenvectors at one k-point (eval, evec) = tb.solve_one([kx, ky], eig_vectors = True) # Store it manually into a specified location in the array wf[3, 4] = evec # To access the eigenvectors from the same position print wf[3, 4] """ def __init__(self,model,mesh_arr): # number of electronic states for each k-point self._nsta=model._nsta # number of spin components self._nspin=model._nspin # number of orbitals self._norb=model._norb # store orbitals from the model self._orb=np.copy(model._orb) # store entire model as well self._model=copy.deepcopy(model) # store dimension of array of points on which to keep wavefunctions self._mesh_arr=np.array(mesh_arr) self._dim_arr=len(self._mesh_arr) # all dimensions should be 2 or larger, because pbc can be used if True in (self._mesh_arr<=1).tolist(): raise Exception("\n\nDimension of wf_array object in each direction must be 2 or larger.") # generate temporary array used later to generate object ._wfs wfs_dim=np.copy(self._mesh_arr) wfs_dim=np.append(wfs_dim,self._nsta) wfs_dim=np.append(wfs_dim,self._norb) if self._nspin==2: wfs_dim=np.append(wfs_dim,self._nspin) # store wavefunctions here in the form _wfs[kx_index,ky_index, ... ,band,orb,spin] self._wfs=np.zeros(wfs_dim,dtype=complex) def solve_on_grid(self,start_k): r""" Solve a tight-binding model on a regular mesh of k-points covering the entire reciprocal-space unit cell. Both points at the opposite sides of reciprocal-space unit cell are included in the array. This function also automatically imposes periodic boundary conditions on the eigenfunctions. See also the discussion in :func:`pythtb.wf_array.impose_pbc`. :param start_k: Origin of a regular grid of points in the reciprocal space. :returns: * **gaps** -- returns minimal direct bandgap between n-th and n+1-th band on all the k-points in the mesh. Note that in the case of band crossings one may have to use very dense k-meshes to resolve the crossing. Example usage:: # Solve eigenvectors on a regular grid anchored # at a given point wf.solve_on_grid([-0.5, -0.5]) """ # check dimensionality if self._dim_arr!=self._model._dim_k: raise Exception("\n\nIf using solve_on_grid method, dimension of wf_array must equal dim_k of the tight-binding model!") # to return gaps at all k-points if self._norb<=1: all_gaps=None # trivial case since there is only one band else: gap_dim=np.copy(self._mesh_arr)-1 gap_dim=np.append(gap_dim,self._norb*self._nspin-1) all_gaps=np.zeros(gap_dim,dtype=float) # if self._dim_arr==1: # don't need to go over the last point because that will be # computed in the impose_pbc call for i in range(self._mesh_arr[0]-1): # generate a kpoint kpt=[start_k[0]+float(i)/float(self._mesh_arr[0]-1)] # solve at that point (eval,evec)=self._model.solve_one(kpt,eig_vectors=True) # store wavefunctions self[i]=evec # store gaps if all_gaps is not None: all_gaps[i,:]=eval[1:]-eval[:-1] # impose boundary conditions self.impose_pbc(0,self._model._per[0]) elif self._dim_arr==2: for i in range(self._mesh_arr[0]-1): for j in range(self._mesh_arr[1]-1): kpt=[start_k[0]+float(i)/float(self._mesh_arr[0]-1),\ start_k[1]+float(j)/float(self._mesh_arr[1]-1)] (eval,evec)=self._model.solve_one(kpt,eig_vectors=True) self[i,j]=evec if all_gaps is not None: all_gaps[i,j,:]=eval[1:]-eval[:-1] for dir in range(2): self.impose_pbc(dir,self._model._per[dir]) elif self._dim_arr==3: for i in range(self._mesh_arr[0]-1): for j in range(self._mesh_arr[1]-1): for k in range(self._mesh_arr[2]-1): kpt=[start_k[0]+float(i)/float(self._mesh_arr[0]-1),\ start_k[1]+float(j)/float(self._mesh_arr[1]-1),\ start_k[2]+float(k)/float(self._mesh_arr[2]-1)] (eval,evec)=self._model.solve_one(kpt,eig_vectors=True) self[i,j,k]=evec if all_gaps is not None: all_gaps[i,j,k,:]=eval[1:]-eval[:-1] for dir in range(3): self.impose_pbc(dir,self._model._per[dir]) elif self._dim_arr==4: for i in range(self._mesh_arr[0]-1): for j in range(self._mesh_arr[1]-1): for k in range(self._mesh_arr[2]-1): for l in range(self._mesh_arr[3]-1): kpt=[start_k[0]+float(i)/float(self._mesh_arr[0]-1),\ start_k[1]+float(j)/float(self._mesh_arr[1]-1),\ start_k[2]+float(k)/float(self._mesh_arr[2]-1),\ start_k[3]+float(l)/float(self._mesh_arr[3]-1)] (eval,evec)=self._model.solve_one(kpt,eig_vectors=True) self[i,j,k,l]=evec if all_gaps is not None: all_gaps[i,j,k,l,:]=eval[1:]-eval[:-1] for dir in range(4): self.impose_pbc(dir,self._model._per[dir]) else: raise Exception("\n\nWrong dimensionality!") return all_gaps.min(axis=tuple(range(self._dim_arr))) def __check_key(self,key): # do some checks for 1D if self._dim_arr==1: if type(key).__name__!='int': raise TypeError("Key should be an integer!") if key<(-1)*self._mesh_arr[0] or key>=self._mesh_arr[0]: raise IndexError("Key outside the range!") # do checks for higher dimension else: if len(key)!=self._dim_arr: raise TypeError("Wrong dimensionality of key!") for i,k in enumerate(key): if type(k).__name__!='int': raise TypeError("Key should be set of integers!") if k<(-1)*self._mesh_arr[i] or k>=self._mesh_arr[i]: raise IndexError("Key outside the range!") def __getitem__(self,key): # check that key is in the correct range self.__check_key(key) # return wavefunction return self._wfs[key] def __setitem__(self,key,value): # check that key is in the correct range self.__check_key(key) # store wavefunction self._wfs[key]=np.array(value,dtype=complex) def impose_pbc(self,mesh_dir,k_dir): r""" If the *wf_array* object was populated using the :func:`pythtb.wf_array.solve_on_grid` method, this function should not be used since it will be called automatically by the code. The eigenfunctions :math:`\Psi_{n {\bf k}}` are by convention chosen to obey a periodic gauge, i.e., :math:`\Psi_{n,{\bf k+G}}=\Psi_{n {\bf k}}` not only up to a phase, but they are also equal in phase. It follows that the cell-periodic Bloch functions are related by :math:`u_{n,{\bf k+G}}=e^{-i{\bf G}\cdot{\bf r}}\Psi_{n {\bf k}}`. See :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>` section 4.4 and equation 4.18 for more detail. This routine sets the cell-periodic Bloch function at the end of the string in direction :math:`{\bf G}` according to this formula, overwriting the previous value. This function will impose these periodic boundary conditions along one direction of the array. We are assuming that the k-point mesh increases by exactly one reciprocal lattice vector along this direction. This is currently **not** checked by the code; it is the responsibility of the user. Currently *wf_array* does not store the k-vectors on which the model was solved; it only stores the eigenvectors (wavefunctions). :param mesh_dir: Direction of wf_array along which you wish to impose periodic boundary conditions. :param k_dir: Corresponding to the periodic k-vector direction in the Brillouin zone of the underlying *tb_model*. Since version 1.7.0 this parameter is defined so that it is specified between 0 and *dim_r-1*. See example :ref:`3site_cycle-example`, where the periodic boundary condition is applied only along one direction of *wf_array*. Example usage:: # Imposes periodic boundary conditions along the mesh_dir=0 # direction of the wf_array object, assuming that along that # direction the k_dir=1 component of the k-vector is increased # by one reciprocal lattice vector. This could happen, for # example, if the underlying tb_model is two dimensional but # wf_array is a one-dimensional path along k_y direction. wf.impose_pbc(mesh_dir=0,k_dir=1) """ if k_dir not in self._model._per: raise Exception("Periodic boundary condition can be specified only along periodic directions!") # Compute phase factors ffac=np.exp(-2.j*np.pi*self._orb[:,k_dir]) if self._nspin==1: phase=ffac else: # for spinors, same phase multiplies both components phase=np.zeros((self._norb,2),dtype=complex) phase[:,0]=ffac phase[:,1]=ffac # Copy first eigenvector onto last one, multiplying by phase factors # We can use numpy broadcasting since the orbital index is last if mesh_dir==0: self._wfs[-1,...]=self._wfs[0,...]*phase elif mesh_dir==1: self._wfs[:,-1,...]=self._wfs[:,0,...]*phase elif mesh_dir==2: self._wfs[:,:,-1,...]=self._wfs[:,:,0,...]*phase elif mesh_dir==3: self._wfs[:,:,:,-1,...]=self._wfs[:,:,:,0,...]*phase else: raise Exception("\n\nWrong value of mesh_dir.") def impose_loop(self,mesh_dir): r""" If the user knows that the first and last points along the *mesh_dir* direction correspond to the same Hamiltonian (this is **not** checked), then this routine can be used to set the eigenvectors equal (with equal phase), by replacing the last eigenvector with the first one (for each band, and for each other mesh direction, if any). This routine should not be used if the first and last points are related by a reciprocal lattice vector; in that case, :func:`pythtb.wf_array.impose_pbc` should be used instead. :param mesh_dir: Direction of wf_array along which you wish to impose periodic boundary conditions. Example usage:: # Suppose the wf_array object is three-dimensional # corresponding to (kx,ky,lambda) where (kx,ky) are # wavevectors of a 2D insulator and lambda is an # adiabatic parameter that goes around a closed loop. # Then to insure that the states at the ends of the lambda # path are equal (with equal phase) in preparation for # computing Berry phases in lambda for given (kx,ky), # do wf.impose_loop(mesh_dir=2) """ # Copy first eigenvector onto last one if mesh_dir==0: self._wfs[-1,...]=self._wfs[0,...] elif mesh_dir==1: self._wfs[:,-1,...]=self._wfs[:,0,...] elif mesh_dir==2: self._wfs[:,:,-1,...]=self._wfs[:,:,0,...] elif mesh_dir==3: self._wfs[:,:,:,-1,...]=self._wfs[:,:,:,0,...] else: raise Exception("\n\nWrong value of mesh_dir.") def berry_phase(self,occ,dir=None,contin=True,berry_evals=False): r""" Computes the Berry phase along a given array direction and for a given set of occupied states. This assumes that the occupied bands are well separated in energy from unoccupied bands. It is the responsibility of the user to check that this is satisfied. By default, the Berry phase traced over occupied bands is returned, but optionally the individual phases of the eigenvalues of the global unitary rotation matrix (corresponding to "maximally localized Wannier centers" or "Wilson loop eigenvalues") can be requested (see parameter *berry_evals* for more details). For an array of size *N* in direction $dir$, the Berry phase is computed from the *N-1* inner products of neighboring eigenfunctions. This corresponds to an "open-path Berry phase" if the first and last points have no special relation. If they correspond to the same physical Hamiltonian, and have been properly aligned in phase using :func:`pythtb.wf_array.impose_pbc` or :func:`pythtb.wf_array.impose_loop`, then a closed-path Berry phase will be computed. For a one-dimensional wf_array (i.e., a single string), the computed Berry phases are always chosen to be between -pi and pi. For a higher dimensional wf_array, the Berry phase is computed for each one-dimensional string of points, and an array of Berry phases is returned. The Berry phase for the first string (with lowest index) is always constrained to be between -pi and pi. The range of the remaining phases depends on the value of the input parameter *contin*. The discretized formula used to compute Berry phase is described in Sec. 4.5 of :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>`. :param occ: Array of indices of energy bands which are considered to be occupied. :param dir: Index of wf_array direction along which Berry phase is computed. This parameters needs not be specified for a one-dimensional wf_array. :param contin: Optional boolean parameter. If True then the branch choice of the Berry phase (which is indeterminate modulo 2*pi) is made so that neighboring strings (in the direction of increasing index value) have as close as possible phases. The phase of the first string (with lowest index) is always constrained to be between -pi and pi. If False, the Berry phase for every string is constrained to be between -pi and pi. The default value is True. :param berry_evals: Optional boolean parameter. If True then will compute and return the phases of the eigenvalues of the product of overlap matrices. (These numbers correspond also to hybrid Wannier function centers.) These phases are either forced to be between -pi and pi (if *contin* is *False*) or they are made to be continuous (if *contin* is True). :returns: * **pha** -- If *berry_evals* is False (default value) then returns the Berry phase for each string. For a one-dimensional wf_array this is just one number. For a higher-dimensional wf_array *pha* contains one phase for each one-dimensional string in the following format. For example, if *wf_array* contains k-points on mesh with indices [i,j,k] and if direction along which Berry phase is computed is *dir=1* then *pha* will be two dimensional array with indices [i,k], since Berry phase is computed along second direction. If *berry_evals* is True then for each string returns phases of all eigenvalues of the product of overlap matrices. In the convention used for previous example, *pha* in this case would have indices [i,k,n] where *n* refers to index of individual phase of the product matrix eigenvalue. Example usage:: # Computes Berry phases along second direction for three lowest # occupied states. For example, if wf is threedimensional, then # pha[2,3] would correspond to Berry phase of string of states # along wf[2,:,3] pha = wf.berry_phase([0, 1, 2], 1) See also these examples: :ref:`haldane_bp-example`, :ref:`cone-example`, :ref:`3site_cycle-example`, """ # check if model came from w90 if self._model._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() #if dir<0 or dir>self._dim_arr-1: # raise Exception("\n\nDirection key out of range") # # This could be coded more efficiently, but it is hard-coded for now. # # 1D case if self._dim_arr==1: # pick which wavefunctions to use wf_use=self._wfs[:,occ,:] # calculate berry phase ret=_one_berry_loop(wf_use,berry_evals) # 2D case elif self._dim_arr==2: # choice along which direction you wish to calculate berry phase if dir==0: ret=[] for i in range(self._mesh_arr[1]): wf_use=self._wfs[:,i,:,:][:,occ,:] ret.append(_one_berry_loop(wf_use,berry_evals)) elif dir==1: ret=[] for i in range(self._mesh_arr[0]): wf_use=self._wfs[i,:,:,:][:,occ,:] ret.append(_one_berry_loop(wf_use,berry_evals)) else: raise Exception("\n\nWrong direction for Berry phase calculation!") # 3D case elif self._dim_arr==3: # choice along which direction you wish to calculate berry phase if dir==0: ret=[] for i in range(self._mesh_arr[1]): ret_t=[] for j in range(self._mesh_arr[2]): wf_use=self._wfs[:,i,j,:,:][:,occ,:] ret_t.append(_one_berry_loop(wf_use,berry_evals)) ret.append(ret_t) elif dir==1: ret=[] for i in range(self._mesh_arr[0]): ret_t=[] for j in range(self._mesh_arr[2]): wf_use=self._wfs[i,:,j,:,:][:,occ,:] ret_t.append(_one_berry_loop(wf_use,berry_evals)) ret.append(ret_t) elif dir==2: ret=[] for i in range(self._mesh_arr[0]): ret_t=[] for j in range(self._mesh_arr[1]): wf_use=self._wfs[i,j,:,:,:][:,occ,:] ret_t.append(_one_berry_loop(wf_use,berry_evals)) ret.append(ret_t) else: raise Exception("\n\nWrong direction for Berry phase calculation!") else: raise Exception("\n\nWrong dimensionality!") # convert phases to numpy array if self._dim_arr>1 or berry_evals==True: ret=np.array(ret,dtype=float) # make phases of eigenvalues continuous if contin==True: # iron out 2pi jumps, make the gauge choice such that first phase in the # list is fixed, others are then made continuous. if berry_evals==False: # 2D case if self._dim_arr==2: ret=_one_phase_cont(ret,ret[0]) # 3D case elif self._dim_arr==3: for i in range(ret.shape[1]): if i==0: clos=ret[0,0] else: clos=ret[0,i-1] ret[:,i]=_one_phase_cont(ret[:,i],clos) elif self._dim_arr!=1: raise Exception("\n\nWrong dimensionality!") # make eigenvalues continuous. This does not take care of band-character # at band crossing for example it will just connect pairs that are closest # at neighboring points. else: # 2D case if self._dim_arr==2: ret=_array_phases_cont(ret,ret[0,:]) # 3D case elif self._dim_arr==3: for i in range(ret.shape[1]): if i==0: clos=ret[0,0,:] else: clos=ret[0,i-1,:] ret[:,i]=_array_phases_cont(ret[:,i],clos) elif self._dim_arr!=1: raise Exception("\n\nWrong dimensionality!") return ret def position_matrix(self, key, occ, dir): """Similar to :func:`pythtb.tb_model.position_matrix`. Only difference is that states are now specified with key in the mesh *key* and indices of bands *occ*.""" # check if model came from w90 if self._model._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() # evec=self._wfs[tuple(key)][occ] return self._model.position_matrix(evec,dir) def position_expectation(self, key, occ, dir): """Similar to :func:`pythtb.tb_model.position_expectation`. Only difference is that states are now specified with key in the mesh *key* and indices of bands *occ*.""" # check if model came from w90 if self._model._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() # evec=self._wfs[tuple(key)][occ] return self._model.position_expectation(evec,dir) def position_hwf(self, key, occ, dir, hwf_evec=False, basis="bloch"): """Similar to :func:`pythtb.tb_model.position_hwf`. Only difference is that states are now specified with key in the mesh *key* and indices of bands *occ*.""" # check if model came from w90 if self._model._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() # evec=self._wfs[tuple(key)][occ] return self._model.position_hwf(evec,dir,hwf_evec,basis) def berry_flux(self,occ,dirs=None,individual_phases=False): r""" In the case of a 2-dimensional *wf_array* array calculates the integral of Berry curvature over the entire plane. In higher dimensional case (3 or 4) it will compute integrated curvature over all 2-dimensional slices of a higher-dimensional *wf_array*. :param occ: Array of indices of energy bands which are considered to be occupied. :param dirs: Array of indices of two wf_array directions on which the Berry flux is computed. This parameter needs not be specified for a two-dimensional wf_array. By default *dirs* takes first two directions in the array. :param individual_phases: If *True* then returns Berry phase for each plaquette (small square) in the array. Default value is *False*. :returns: * **flux** -- In a 2-dimensional case returns and integral of Berry curvature (if *individual_phases* is *True* then returns integral of Berry phase around each plaquette). In higher dimensional case returns integral of Berry curvature over all slices defined with directions *dirs*. Returned value is an array over the remaining indices of *wf_array*. (If *individual_phases* is *True* then it returns again phases around each plaquette for each slice. First indices define the slice, last two indices index the plaquette.) Example usage:: # Computes integral of Berry curvature of first three bands flux = wf.berry_flux([0, 1, 2]) """ # check if model came from w90 if self._model._assume_position_operator_diagonal==False: _offdiag_approximation_warning_and_stop() # default case is to take first two directions for flux calculation if dirs==None: dirs=[0,1] # consistency checks if dirs[0]==dirs[1]: raise Exception("Need to specify two different directions for Berry flux calculation.") if dirs[0]>=self._dim_arr or dirs[1]>=self._dim_arr or dirs[0]<0 or dirs[1]<0: raise Exception("Direction for Berry flux calculation out of bounds.") # 2D case if self._dim_arr==2: # compute the fluxes through all plaquettes on the entire plane ord=list(range(len(self._wfs.shape))) # select two directions from dirs ord[0]=dirs[0] ord[1]=dirs[1] plane_wfs=self._wfs.transpose(ord) # take bands of choice plane_wfs=plane_wfs[:,:,occ] # compute fluxes all_phases=_one_flux_plane(plane_wfs) # return either total flux or individual phase for each plaquete if individual_phases==False: return all_phases.sum() else: return all_phases # 3D or 4D case elif self._dim_arr in [3,4]: # compute the fluxes through all plaquettes on the entire plane ord=list(range(len(self._wfs.shape))) # select two directions from dirs ord[0]=dirs[0] ord[1]=dirs[1] # find directions over which we wish to loop ld=list(range(self._dim_arr)) ld.remove(dirs[0]) ld.remove(dirs[1]) if len(ld)!=self._dim_arr-2: raise Exception("Hm, this should not happen? Inconsistency with the mesh size.") # add remaining indices if self._dim_arr==3: ord[2]=ld[0] if self._dim_arr==4: ord[2]=ld[0] ord[3]=ld[1] # reorder wavefunctions use_wfs=self._wfs.transpose(ord) # loop over the the remaining direction if self._dim_arr==3: slice_phases=np.zeros((self._mesh_arr[ord[2]],self._mesh_arr[dirs[0]]-1,self._mesh_arr[dirs[1]]-1),dtype=float) for i in range(self._mesh_arr[ord[2]]): # take a 2d slice plane_wfs=use_wfs[:,:,i] # take bands of choice plane_wfs=plane_wfs[:,:,occ] # compute fluxes on the slice slice_phases[i,:,:]=_one_flux_plane(plane_wfs) elif self._dim_arr==4: slice_phases=np.zeros((self._mesh_arr[ord[2]],self._mesh_arr[ord[3]],self._mesh_arr[dirs[0]]-1,self._mesh_arr[dirs[1]]-1),dtype=float) for i in range(self._mesh_arr[ord[2]]): for j in range(self._mesh_arr[ord[3]]): # take a 2d slice plane_wfs=use_wfs[:,:,i,j] # take bands of choice plane_wfs=plane_wfs[:,:,occ] # compute fluxes on the slice slice_phases[i,j,:,:]=_one_flux_plane(plane_wfs) # return either total flux or individual phase for each plaquete if individual_phases==False: return slice_phases.sum(axis=(-2,-1)) else: return slice_phases else: raise Exception("\n\nWrong dimensionality!") def berry_curv(self,occ,individual_phases=False): r""" .. warning:: This function has been renamed as :func:`pythtb.berry_flux` and is provided here only for backwards compatibility with versions of pythtb prior to 1.7.0. Please use related :func:`pythtb.berry_flux` as this function may not exist in future releases. """ print(""" Warning: Usage of function berry_curv is discouraged. It has been renamed as berry_flux, which should be used instead. """) return self.berry_flux(occ,individual_phases) def k_path(kpts,nk,endpoint=True): r""" .. warning:: This function is here only for backwards compatibility with version of pythtb prior to 1.7.0. Please use related :func:`pythtb.tb_model.k_path` function as this function might not exist in the future releases of the code. """ print(""" Warning: Usage of function k_path is discouraged. Instead of the following code: k_vec=k_path(...) please use the following code: (k_vec,k_dist,k_node)=my_model.k_path(...) Note that this k_path function is a member of the tb_model class. """) if kpts=='full': # this means the full Brillouin zone for 1D case if endpoint==True: return np.arange(nk+1,dtype=float)/float(nk) else: return np.arange(nk,dtype=float)/float(nk) elif kpts=='half': # this means the half Brillouin zone for 1D case if endpoint==True: return np.arange(nk+1,dtype=float)/float(2.*nk) else: return np.arange(nk,dtype=float)/float(2.*nk) else: # general case kint=[] k_list=np.array(kpts) # go over all kpoints for i in range(len(k_list)-1): # go over all steps for j in range(nk): cur=k_list[i]+(k_list[i+1]-k_list[i])*float(j)/float(nk) kint.append(cur) # add last point if endpoint==True: kint.append(k_list[-1]) # kint=np.array(kint) return kint def _nicefy_eig(eval,eig=None): "Sort eigenvaules and eigenvectors, if given, and convert to real numbers" # first take only real parts of the eigenvalues eval=np.array(eval.real,dtype=float) # sort energies args=eval.argsort() eval=eval[args] if not (eig is None): eig=eig[args] return (eval,eig) return eval # for nice justified printout def _nice_float(x,just,rnd): return str(round(x,rnd)).rjust(just) def _nice_int(x,just): return str(x).rjust(just) def _nice_complex(x,just,rnd): ret="" ret+=_nice_float(complex(x).real,just,rnd) if complex(x).imag<0.0: ret+=" - " else: ret+=" + " ret+=_nice_float(abs(complex(x).imag),just,rnd) ret+=" i" return ret def _wf_dpr(wf1,wf2): """calculate dot product between two wavefunctions. wf1 and wf2 are of the form [orbital,spin]""" return np.dot(wf1.flatten().conjugate(),wf2.flatten()) def _one_berry_loop(wf,berry_evals=False): """Do one Berry phase calculation (also returns a product of M matrices). Always returns numbers between -pi and pi. wf has format [kpnt,band,orbital,spin] and kpnt has to be one dimensional. Assumes that first and last k-point are the same. Therefore if there are n wavefunctions in total, will calculate phase along n-1 links only! If berry_evals is True then will compute phases for individual states, these corresponds to 1d hybrid Wannier function centers. Otherwise just return one number, Berry phase.""" # number of occupied states nocc=wf.shape[1] # temporary matrices prd=np.identity(nocc,dtype=complex) ovr=np.zeros([nocc,nocc],dtype=complex) # go over all pairs of k-points, assuming that last point is overcounted! for i in range(wf.shape[0]-1): # generate overlap matrix, go over all bands for j in range(nocc): for k in range(nocc): ovr[j,k]=_wf_dpr(wf[i,j,:],wf[i+1,k,:]) # only find Berry phase if berry_evals==False: # multiply overlap matrices prd=np.dot(prd,ovr) # also find phases of individual eigenvalues else: # cleanup matrices with SVD then take product matU,sing,matV=np.linalg.svd(ovr) prd=np.dot(prd,np.dot(matU,matV)) # calculate Berry phase if berry_evals==False: det=np.linalg.det(prd) pha=(-1.0)*np.angle(det) return pha # calculate phases of all eigenvalues else: evals=np.linalg.eigvals(prd) eval_pha=(-1.0)*np.angle(evals) # sort these numbers as well eval_pha=np.sort(eval_pha) return eval_pha def _one_flux_plane(wfs2d): "Compute fluxes on a two-dimensional plane of states." # size of the mesh nk0=wfs2d.shape[0] nk1=wfs2d.shape[1] # number of bands (will compute flux of all bands taken together) nbnd=wfs2d.shape[2] # here store flux through each plaquette of the mesh all_phases=np.zeros((nk0-1,nk1-1),dtype=float) # go over all plaquettes for i in range(nk0-1): for j in range(nk1-1): # generate a small loop made out of four pieces wf_use=[] wf_use.append(wfs2d[i,j]) wf_use.append(wfs2d[i+1,j]) wf_use.append(wfs2d[i+1,j+1]) wf_use.append(wfs2d[i,j+1]) wf_use.append(wfs2d[i,j]) wf_use=np.array(wf_use,dtype=complex) # calculate phase around one plaquette all_phases[i,j]=_one_berry_loop(wf_use) return all_phases def no_2pi(x,clos): "Make x as close to clos by adding or removing 2pi" while abs(clos-x)>np.pi: if clos-x>np.pi: x+=2.0*np.pi elif clos-x<-1.0*np.pi: x-=2.0*np.pi return x def _one_phase_cont(pha,clos): """Reads in 1d array of numbers *pha* and makes sure that they are continuous, i.e., that there are no jumps of 2pi. First number is made as close to *clos* as possible.""" ret=np.copy(pha) # go through entire list and "iron out" 2pi jumps for i in range(len(ret)): # which number to compare to if i==0: cmpr=clos else: cmpr=ret[i-1] # make sure there are no 2pi jumps ret[i]=no_2pi(ret[i],cmpr) return ret def _array_phases_cont(arr_pha,clos): """Reads in 2d array of phases *arr_pha* and makes sure that they are continuous along first index, i.e., that there are no jumps of 2pi. First array of phasese is made as close to *clos* as possible.""" ret=np.zeros_like(arr_pha) # go over all points for i in range(arr_pha.shape[0]): # which phases to compare to if i==0: cmpr=clos else: cmpr=ret[i-1,:] # remember which indices are still available to be matched avail=list(range(arr_pha.shape[1])) # go over all phases in cmpr[:] for j in range(cmpr.shape[0]): # minimal distance between pairs min_dist=1.0E10 # closest index best_k=None # go over each phase in arr_pha[i,:] for k in avail: cur_dist=np.abs(np.exp(1.0j*cmpr[j])-np.exp(1.0j*arr_pha[i,k])) if cur_dist<=min_dist: min_dist=cur_dist best_k=k # remove this index from being possible pair later avail.pop(avail.index(best_k)) # store phase in correct place ret[i,j]=arr_pha[i,best_k] # make sure there are no 2pi jumps ret[i,j]=no_2pi(ret[i,j],cmpr[j]) return ret class w90(object): r""" This class of the PythTB package imports tight-binding model parameters from an output of a `Wannier90 <http://www.wannier.org>`_ code. The `Wannier90 <http://www.wannier.org>`_ code is a post-processing tool that takes as an input electron wavefunctions and energies computed from first-principles using any of the following codes: Quantum-Espresso (PWscf), AbInit, SIESTA, FLEUR, Wien2k, VASP. As an output Wannier90 will create files that contain parameters for a tight-binding model that exactly reproduces the first-principles calculated electron band structure. The interface from Wannier90 to PythTB will use only the following files created by Wannier90: - *prefix*.win - *prefix*\_hr.dat - *prefix*\_centres.xyz - *prefix*\_band.kpt (optional) - *prefix*\_band.dat (optional) The first file (*prefix*.win) is an input file to Wannier90 itself. This file is needed so that PythTB can read in the unit cell vectors. To correctly create the second and the third file (*prefix*\_hr.dat and *prefix*\_centres.dat) one needs to include the following flags in the win file:: hr_plot = True write_xyz = True translate_home_cell = False These lines ensure that *prefix*\_hr.dat and *prefix*\_centres.dat are written and that the centers of the Wannier functions written in the *prefix*\_centres.dat file are not translated to the home cell. The *prefix*\_hr.dat file contains the onsite and hopping terms. The final two files (*prefix*\_band.kpt and *prefix*\_band.dat) are optional. Please see documentation of function :func:`pythtb.w90.w90_bands_consistency` for more detail. So far we tested only Wannier90 version 2.0.1. .. warning:: For the time being PythTB is not optimized to be used with very large tight-binding models. Therefore it is not advisable to use the interface to Wannier90 with large first-principles calculations that contain many k-points and/or electron bands. One way to reduce the computational cost is to wannierize with Wannier90 only the bands of interest (for example, bands near the Fermi level). Units used throught this interface with Wannier90 are electron-volts (eV) and Angstroms. .. warning:: User needs to make sure that the Wannier functions computed using Wannier90 code are well localized. Otherwise the tight-binding model might not interpolate well the band structure. To ensure that the Wannier functions are well localized it is often enough to check that the total spread at the beginning of the minimization procedure (first total spread printed in .wout file) is not more than 20% larger than the total spread at the end of the minimization procedure. If those spreads differ by much more than 20% user needs to specify better initial projection functions. In addition, please note that the interpolation is valid only within the frozen energy window of the disentanglement procedure. .. warning:: So far PythTB assumes that the position operator is diagonal in the tight-binding basis. This is discussed in the :download:`notes on tight-binding formalism <misc/pythtb-formalism.pdf>` in Eq. 2.7., :math:`\langle\phi_{{\bf R} i} \vert {\bf r} \vert \phi_{{\bf R}' j} \rangle = ({\bf R} + {\bf t}_j) \delta_{{\bf R} {\bf R}'} \delta_{ij}`. However, this relation does not hold for Wannier functions! Therefore, if you use tight-binding model derived from this class in computing Berry-like objects that involve position operator such as Berry phase or Berry flux, you would not get the same result as if you computed those objects directly from the first-principles code! Nevertheless, this approximation does not affect other properties such as band structure dispersion. For the testing purposes user can download the following :download:`wannier90 output example <misc/wannier90_example.tar.gz>` and use the following :ref:`script <w90_quick>` to test the functionality of the interface to PythTB. Run the following command in unix terminal to decompress the tarball:: tar -zxf wannier90_example.tar.gz and then run the following :ref:`script <w90_quick>` in the same folder. :param path: Relative path to the folder that contains Wannier90 files. These are *prefix*.win, *prefix*\_hr.dat, *prefix*\_centres.dat and optionally *prefix*\_band.kpt and *prefix*\_band.dat. :param prefix: This is the prefix used by Wannier90 code. Typically the input to the Wannier90 code is name *prefix*.win. Initially this function will read in the entire Wannier90 output. To create :class:`pythtb.tb_model` object user needs to call :func:`pythtb.w90.model`. Example usage:: # reads Wannier90 from folder called *example_a* # it assumes that that folder contains files "silicon.win" and so on silicon=w90("example_a", "silicon") """ def __init__(self,path,prefix): # store path and prefix self.path=path self.prefix=prefix # read in lattice_vectors f=open(self.path+"/"+self.prefix+".win","r") ln=f.readlines() f.close() # get lattice vector self.lat=np.zeros((3,3),dtype=float) found=False for i in range(len(ln)): sp=ln[i].split() if len(sp)>=2: if sp[0].lower()=="begin" and sp[1].lower()=="unit_cell_cart": # get units right if ln[i+1].strip().lower()=="bohr": pref=0.5291772108 skip=1 elif ln[i+1].strip().lower() in ["ang","angstrom"]: pref=1.0 skip=1 else: pref=1.0 skip=0 # now get vectors for j in range(3): sp=ln[i+skip+1+j].split() for k in range(3): self.lat[j,k]=float(sp[k])*pref found=True break if found==False: raise Exception("Unable to find unit_cell_cart block in the .win file.") # read in hamiltonian matrix, in eV f=open(self.path+"/"+self.prefix+"_hr.dat","r") ln=f.readlines() f.close() # # get number of wannier functions self.num_wan=int(ln[1]) # get number of Wigner-Seitz points num_ws=int(ln[2]) # get degenereacies of Wigner-Seitz points deg_ws=[] for j in range(3,len(ln)): sp=ln[j].split() for s in sp: deg_ws.append(int(s)) if len(deg_ws)==num_ws: last_j=j break if len(deg_ws)>num_ws: raise Exception("Too many degeneracies for WS points!") deg_ws=np.array(deg_ws,dtype=int) # now read in matrix elements # Convention used in w90 is to write out: # R1, R2, R3, i, j, ham_r(i,j,R) # where ham_r(i,j,R) corresponds to matrix element < i | H | j+R > self.ham_r={} # format is ham_r[(R1,R2,R3)]["h"][i,j] for < i | H | j+R > ind_R=0 # which R vector in line is this? for j in range(last_j+1,len(ln)): sp=ln[j].split() # get reduced lattice vector components ham_R1=int(sp[0]) ham_R2=int(sp[1]) ham_R3=int(sp[2]) # get Wannier indices ham_i=int(sp[3])-1 ham_j=int(sp[4])-1 # get matrix element ham_val=float(sp[5])+1.0j*float(sp[6]) # store stuff, for each R store hamiltonian and degeneracy ham_key=(ham_R1,ham_R2,ham_R3) if (ham_key in self.ham_r)==False: self.ham_r[ham_key]={ "h":np.zeros((self.num_wan,self.num_wan),dtype=complex), "deg":deg_ws[ind_R] } ind_R+=1 self.ham_r[ham_key]["h"][ham_i,ham_j]=ham_val # check if for every non-zero R there is also -R for R in self.ham_r: if not (R[0]==0 and R[1]==0 and R[2]==0): found_pair=False for P in self.ham_r: if not (R[0]==0 and R[1]==0 and R[2]==0): # check if they are opposite if R[0]==-P[0] and R[1]==-P[1] and R[2]==-P[2]: if found_pair==True: raise Exception("Found duplicate negative R!") found_pair=True if found_pair==False: raise Exception("Did not find negative R for R = "+R+"!") # read in wannier centers f=open(self.path+"/"+self.prefix+"_centres.xyz","r") ln=f.readlines() f.close() # Wannier centers in Cartesian, Angstroms xyz_cen=[] for i in range(2,2+self.num_wan): sp=ln[i].split() if sp[0]=="X": tmp=[] for j in range(3): tmp.append(float(sp[j+1])) xyz_cen.append(tmp) else: raise Exception("Inconsistency in the centres file.") self.xyz_cen=np.array(xyz_cen,dtype=float) # get orbital positions in reduced coordinates self.red_cen=_cart_to_red((self.lat[0],self.lat[1],self.lat[2]),self.xyz_cen) def model(self,zero_energy=0.0,min_hopping_norm=None,max_distance=None,ignorable_imaginary_part=None): """ This function returns :class:`pythtb.tb_model` object that can be used to interpolate the band structure at arbitrary k-point, analyze the wavefunction character, etc. The tight-binding basis orbitals in the returned object are maximally localized Wannier functions as computed by Wannier90. The orbital character of these functions can be inferred either from the *projections* block in the *prefix*.win or from the *prefix*.nnkp file. Please note that the character of the maximally localized Wannier functions is not exactly the same as that specified by the initial projections. One way to ensure that the Wannier functions are as close to the initial projections as possible is to first choose a good set of initial projections (for these initial and final spread should not differ more than 20%) and then perform another Wannier90 run setting *num_iter=0* in the *prefix*.win file. Number of spin components is always set to 1, even if the underlying DFT calculation includes spin. Please refer to the *projections* block or the *prefix*.nnkp file to see which orbitals correspond to which spin. Locations of the orbitals in the returned :class:`pythtb.tb_model` object are equal to the centers of the Wannier functions computed by Wannier90. :param zero_energy: Sets the zero of the energy in the band structure. This value is typically set to the Fermi level computed by the density-functional code (or to the top of the valence band). Units are electron-volts. :param min_hopping_norm: Hopping terms read from Wannier90 with complex norm less than *min_hopping_norm* will not be included in the returned tight-binding model. This parameters is specified in electron-volts. By default all terms regardless of their norm are included. :param max_distance: Hopping terms from site *i* to site *j+R* will be ignored if the distance from orbital *i* to *j+R* is larger than *max_distance*. This parameter is given in Angstroms. By default all terms regardless of the distance are included. :param ignorable_imaginary_part: The hopping term will be assumed to be exactly real if the absolute value of the imaginary part as computed by Wannier90 is less than *ignorable_imaginary_part*. By default imaginary terms are not ignored. Units are again eV. :returns: * **tb** -- The object of type :class:`pythtb.tb_model` that can be used to interpolate Wannier90 band structure to an arbitrary k-point as well as to analyze the character of the wavefunctions. Please note Example usage:: # returns tb_model with all hopping parameters my_model=silicon.model() # simplified model that contains only hopping terms above 0.01 eV my_model_simple=silicon.model(min_hopping_norm=0.01) my_model_simple.display() """ # make the model object tb=tb_model(3,3,self.lat,self.red_cen) # remember that this model was computed from w90 tb._assume_position_operator_diagonal=False # add onsite energies onsite=np.zeros(self.num_wan,dtype=float) for i in range(self.num_wan): tmp_ham=self.ham_r[(0,0,0)]["h"][i,i]/float(self.ham_r[(0,0,0)]["deg"]) onsite[i]=tmp_ham.real if np.abs(tmp_ham.imag)>1.0E-9: raise Exception("Onsite terms should be real!") tb.set_onsite(onsite-zero_energy) # add hopping terms for R in self.ham_r: # avoid double counting use_this_R=True # avoid onsite terms if R[0]==0 and R[1]==0 and R[2]==0: avoid_diagonal=True else: avoid_diagonal=False # avoid taking both R and -R if R[0]!=0: if R[0]<0: use_this_R=False else: if R[1]!=0: if R[1]<0: use_this_R=False else: if R[2]<0: use_this_R=False # get R vector vecR=_red_to_cart((self.lat[0],self.lat[1],self.lat[2]),[R])[0] # scan through unique R if use_this_R==True: for i in range(self.num_wan): vec_i=self.xyz_cen[i] for j in range(self.num_wan): vec_j=self.xyz_cen[j] # get distance between orbitals dist_ijR=np.sqrt(np.dot(-vec_i+vec_j+vecR, -vec_i+vec_j+vecR)) # to prevent double counting if not (avoid_diagonal==True and j<=i): # only if distance between orbitals is small enough if max_distance is not None: if dist_ijR>max_distance: continue # divide the matrix element from w90 with the degeneracy tmp_ham=self.ham_r[R]["h"][i,j]/float(self.ham_r[R]["deg"]) # only if big enough matrix element if min_hopping_norm is not None: if np.abs(tmp_ham)<min_hopping_norm: continue # remove imaginary part if needed if ignorable_imaginary_part is not None: if np.abs(tmp_ham.imag)<ignorable_imaginary_part: tmp_ham=tmp_ham.real+0.0j # set the hopping term tb.set_hop(tmp_ham,i,j,list(R)) return tb def dist_hop(self): """ This is one of the diagnostic tools that can be used to help in determining *min_hopping_norm* and *max_distance* parameter in :func:`pythtb.w90.model` function call. This function returns all hopping terms (from orbital *i* to *j+R*) as well as the distances between the *i* and *j+R* orbitals. For well localized Wannier functions hopping term should decay exponentially with distance. :returns: * **dist** -- Distances between Wannier function centers (*i* and *j+R*) in Angstroms. * **ham** -- Corresponding hopping terms in eV. Example usage:: # get distances and hopping terms (dist,ham)=silicon.dist_hop() # plot logarithm of the hopping term as a function of distance import pylab as plt fig, ax = plt.subplots() ax.scatter(dist,np.log(np.abs(ham))) fig.savefig("localization.pdf") """ ret_ham=[] ret_dist=[] for R in self.ham_r: # treat diagonal terms differently if R[0]==0 and R[1]==0 and R[2]==0: avoid_diagonal=True else: avoid_diagonal=False # get R vector vecR=_red_to_cart((self.lat[0],self.lat[1],self.lat[2]),[R])[0] for i in range(self.num_wan): vec_i=self.xyz_cen[i] for j in range(self.num_wan): vec_j=self.xyz_cen[j] # diagonal terms if not (avoid_diagonal==True and i==j): # divide the matrix element from w90 with the degeneracy ret_ham.append(self.ham_r[R]["h"][i,j]/float(self.ham_r[R]["deg"])) # get distance between orbitals ret_dist.append(np.sqrt(np.dot(-vec_i+vec_j+vecR,-vec_i+vec_j+vecR))) return (np.array(ret_dist),np.array(ret_ham)) def shells(self,num_digits=2): """ This is one of the diagnostic tools that can be used to help in determining *max_distance* parameter in :func:`pythtb.w90.model` function call. :param num_digits: Distances will be rounded up to these many digits. Default value is 2. :returns: * **shells** -- All distances between all Wannier function centers (*i* and *j+R*) in Angstroms. Example usage:: # prints on screen all shells print silicon.shells() """ shells=[] for R in self.ham_r: # get R vector vecR=_red_to_cart((self.lat[0],self.lat[1],self.lat[2]),[R])[0] for i in range(self.num_wan): vec_i=self.xyz_cen[i] for j in range(self.num_wan): vec_j=self.xyz_cen[j] # get distance between orbitals dist_ijR=np.sqrt(np.dot(-vec_i+vec_j+vecR, -vec_i+vec_j+vecR)) # round it up shells.append(round(dist_ijR,num_digits)) # remove duplicates and sort shells=np.sort(list(set(shells))) return shells def w90_bands_consistency(self): """ This function reads in band structure as interpolated by Wannier90. Please note that this is not the same as the band structure calculated by the underlying DFT code. The two will agree only on the coarse set of k-points that were used in Wannier90 generation. The purpose of this function is to compare the interpolation in Wannier90 with that in PythTB. If no terms were ignored in the call to :func:`pythtb.w90.model` then the two should be exactly the same (up to numerical precision). Otherwise one should expect deviations. However, if one carefully chooses the cutoff parameters in :func:`pythtb.w90.model` it is likely that one could reproduce the full band-structure with only few dominant hopping terms. Please note that this tests only the eigenenergies, not eigenvalues (wavefunctions). The code assumes that the following files were generated by Wannier90, - *prefix*\_band.kpt - *prefix*\_band.dat These files will be generated only if the *prefix*.win file contains the *kpoint_path* block. :returns: * **kpts** -- k-points in reduced coordinates used in the interpolation in Wannier90 code. The format of *kpts* is the same as the one used by the input to :func:`pythtb.tb_model.solve_all`. * **ene** -- energies interpolated by Wannier90 in eV. Format is ene[band,kpoint]. Example usage:: # get band structure from wannier90 (w90_kpt,w90_evals)=silicon.w90_bands_consistency() # get simplified model my_model_simple=silicon.model(min_hopping_norm=0.01) # solve simplified model on the same k-path as in wannier90 evals=my_model.solve_all(w90_kpt) # plot comparison of the two import pylab as plt fig, ax = plt.subplots() for i in range(evals.shape[0]): ax.plot(range(evals.shape[1]),evals[i],"r-",zorder=-50) for i in range(w90_evals.shape[0]): ax.plot(range(w90_evals.shape[1]),w90_evals[i],"k-",zorder=-100) fig.savefig("comparison.pdf") """ # read in kpoints in reduced coordinates kpts=np.loadtxt(self.path+"/"+self.prefix+"_band.kpt",skiprows=1) # ignore weights kpts=kpts[:,:3] # read in energies ene=np.loadtxt(self.path+"/"+self.prefix+"_band.dat") # ignore kpath distance ene=ene[:,1] # correct shape ene=ene.reshape((self.num_wan,kpts.shape[0])) return (kpts,ene) def _cart_to_red(tmp,cart): "Convert cartesian vectors cart to reduced coordinates of a1,a2,a3 vectors" (a1,a2,a3)=tmp # matrix with lattice vectors cnv=np.array([a1,a2,a3]) # transpose a matrix cnv=cnv.T # invert a matrix cnv=np.linalg.inv(cnv) # reduced coordinates red=np.zeros_like(cart,dtype=float) for i in range(0,len(cart)): red[i]=np.dot(cnv,cart[i]) return red def _red_to_cart(tmp,red): "Convert reduced to cartesian vectors." (a1,a2,a3)=tmp # cartesian coordinates cart=np.zeros_like(red,dtype=float) for i in range(0,len(cart)): cart[i,:]=a1*red[i][0]+a2*red[i][1]+a3*red[i][2] return cart def _offdiag_approximation_warning_and_stop(): raise Exception(""" ---------------------------------------------------------------------- It looks like you are trying to calculate Berry-like object that involves position operator. However, you are using a tight-binding model that was generated from Wannier90. This procedure introduces approximation as it ignores off-diagonal elements of the position operator in the Wannier basis. This is discussed here in more detail: http://physics.rutgers.edu/pythtb/usage.html#pythtb.w90 If you know what you are doing and wish to continue with the calculation despite this approximation, please call the following function on your tb_model object my_model.ignore_position_operator_offdiagonal() ---------------------------------------------------------------------- """)

lgpl-3.0

JosmanPS/scikit-learn

examples/manifold/plot_compare_methods.py

259

4031

""" ========================================= Comparison of Manifold Learning methods ========================================= An illustration of dimensionality reduction on the S-curve dataset with various manifold learning methods. For a discussion and comparison of these algorithms, see the :ref:`manifold module page <manifold>` For a similar example, where the methods are applied to a sphere dataset, see :ref:`example_manifold_plot_manifold_sphere.py` Note that the purpose of the MDS is to find a low-dimensional representation of the data (here 2D) in which the distances respect well the distances in the original high-dimensional space, unlike other manifold-learning algorithms, it does not seeks an isotropic representation of the data in the low-dimensional space. """ # Author: Jake Vanderplas -- <[email protected]> print(__doc__) from time import time import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib.ticker import NullFormatter from sklearn import manifold, datasets # Next line to silence pyflakes. This import is needed. Axes3D n_points = 1000 X, color = datasets.samples_generator.make_s_curve(n_points, random_state=0) n_neighbors = 10 n_components = 2 fig = plt.figure(figsize=(15, 8)) plt.suptitle("Manifold Learning with %i points, %i neighbors" % (1000, n_neighbors), fontsize=14) try: # compatibility matplotlib < 1.0 ax = fig.add_subplot(251, projection='3d') ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral) ax.view_init(4, -72) except: ax = fig.add_subplot(251, projection='3d') plt.scatter(X[:, 0], X[:, 2], c=color, cmap=plt.cm.Spectral) methods = ['standard', 'ltsa', 'hessian', 'modified'] labels = ['LLE', 'LTSA', 'Hessian LLE', 'Modified LLE'] for i, method in enumerate(methods): t0 = time() Y = manifold.LocallyLinearEmbedding(n_neighbors, n_components, eigen_solver='auto', method=method).fit_transform(X) t1 = time() print("%s: %.2g sec" % (methods[i], t1 - t0)) ax = fig.add_subplot(252 + i) plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral) plt.title("%s (%.2g sec)" % (labels[i], t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') t0 = time() Y = manifold.Isomap(n_neighbors, n_components).fit_transform(X) t1 = time() print("Isomap: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(257) plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral) plt.title("Isomap (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') t0 = time() mds = manifold.MDS(n_components, max_iter=100, n_init=1) Y = mds.fit_transform(X) t1 = time() print("MDS: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(258) plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral) plt.title("MDS (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') t0 = time() se = manifold.SpectralEmbedding(n_components=n_components, n_neighbors=n_neighbors) Y = se.fit_transform(X) t1 = time() print("SpectralEmbedding: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(259) plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral) plt.title("SpectralEmbedding (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') t0 = time() tsne = manifold.TSNE(n_components=n_components, init='pca', random_state=0) Y = tsne.fit_transform(X) t1 = time() print("t-SNE: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(250) plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral) plt.title("t-SNE (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') plt.show()

bsd-3-clause

gdhungana/corrLSS

py/corrLSS/correlation.py

1

9562

import numpy as np import matplotlib.pyplot as plt import treecorr as tc import astropy.table import healpy as hp from corrLSS.util import apply_mask,radec2thetaphi # Cosmology def set_cosmology(): Omega_matter = 0.140247/0.6800232**2 Omega_baryon = 0.022337/0.6800232**2 Omega_curvature = 0 H0 = 68.002320 sigma_8 = 0.811322 n_s = 0.963180 from astropy.cosmology import FlatLambdaCDM cosmo=FlatLambdaCDM(H0=H0,Om0=Omega_matter) return cosmo def arrange_catalog(catfile,rndfile=None,zmin=None,zmax=None,objtype=None,truthfile=None): """ Use treecorr to evaluate two point correlation given a data catalog and a random catalog """ print("Reading data catalog") #datatab=astropy.table.Table.read(catfile) cat=astropy.io.fits.open(catfile) datacat=cat[1].data try: z_data=datacat['Z_COSMO'] print("Using Z_COSMO for z") except: try: z_data=datacat['TRUEZ'] print("Using TRUEZ for z") except: try: z_data=datacat['Z'] print("Using Z for z") except: raise ValueError("None of the specified z-types match. Check fits header") if truthfile is not None: #- required to match targetid for ra,dec tru=astropy.io.fits.open(truthfile) trucat=tru[1].data truid=trucat['TARGETID'] dataid=datacat['TARGETID'] #- map targetid sorted as in dataid tt=np.argsort(truid) ss=np.searchsorted(truid[tt],dataid) srt_idx=tt[ss] np.testing.assert_array_equal(truid[srt_idx],dataid) print("100% targets matched for data catalog") ra_data=trucat['RA'][srt_idx] dec_data=trucat['DEC'][srt_idx] else: ra_data=datacat['ra'] dec_data=datacat['dec'] if objtype is not None: try: kk=np.where(datacat['SOURCETYPE']==objtype)[0] print("Using sourcetype {}".format(objtype)) except: try: kk=np.where(datacat['SPECTYPE']==objtype)[0] print("Using spectype {}".format(objtype)) except: print("Objtype doesn't match header key. Check fits header") print("Total {} in the data: {}".format(objtype,len(kk))) print("Total {} in the data: {}".format(objtype,len(kk))) ra_data=ra_data[kk] dec_data=dec_data[kk] z_data=z_data[kk] cosmo=set_cosmology() if zmin is None: zmin=np.min(z_data) if zmax is None: zmax=np.max(z_data) print("zmin:{} to zmax: {}".format(zmin,zmax)) #TODO make this loop for differnt redshift bins to avoid reading catalogs each time wh=np.logical_and(z_data>zmin,z_data<zmax) ngal=np.count_nonzero(wh) print("Bin contains: {} galaxies".format(np.count_nonzero(wh))) print(cosmo.H0) cmvr_data=cosmo.comoving_distance(z_data[wh])*cosmo.H0.value/100. dmin,dmax=cosmo.comoving_distance([zmin,zmax])*cosmo.H0.value/100. print("Dmin to Dmax: {} to {}".format(dmin,dmax)) print("Organizing data catalog to use") datacat=make_catalog(ra_data[wh],dec_data[wh],cmvr_data) if rndfile is not None: print("Reading random catalog") #rndtab=astropy.table.Table.read(rndfile) rnd=astropy.io.fits.open(rndfile) rndtab=rnd[1].data z_rnd=rndtab['z'] ra_rnd=rndtab['ra'] dec_rnd=rndtab['dec'] whr=np.logical_and(z_rnd>zmin,z_rnd<zmax) nran=np.count_nonzero(whr) print("Bin Contains: {} random objects".format( np.count_nonzero(whr))) cmvr_rnd=cosmo.comoving_distance(z_rnd[whr])*cosmo.H0.value/100. print("Organizing random catalog to use") rndcat=make_catalog(ra_rnd[whr],dec_rnd[whr],cmvr_rnd) return datacat, rndcat else: return datacat def correlate_tc(datacat,rndcat,outfile,cutoff=None): """ datacat and randcat are tc.catalog object """ print("Auto correlating data") dd=tc.NNCorrelation(min_sep=0.1,bin_size=0.025,max_sep=180.) dd.process(datacat) print("Auto correlating random") rr=tc.NNCorrelation(min_sep=0.1,bin_size=0.025,max_sep=180.) rr.process(rndcat) print("Cross Correlating") dr=tc.NNCorrelation(min_sep=0.1,bin_size=0.025,max_sep=180.) dr.process(datacat,rndcat) print("Calculating 2-pt. correlation") xi,xivar=dd.calculateXi(rr,dr) tab=astropy.table.Table([np.exp(dd.logr),xi,xivar],names=('r','xi','xivar')) tab.write(outfile,overwrite=True) def random_data_xyz(datacat,bandwidth=0.2,format='xyz'): """ data cat is treecorr catalog object and should have x, y, and z random is created here in xyz """ from scipy.stats import gaussian_kde if format=='xyz': values=np.vstack([datacat.x,datacat.y,datacat.z]) kde=gaussian_kde(values,bw_method=bandwidth/values.std(ddof=1)) nx,ny,nz=kde.resample(2*len(datacat.z)) randcat=tc.Catalog(x=nx,y=ny,z=nz) elif format=='radecr': values=np.vstack([datacat.ra/datacat.ra_units,datacat.dec/datacat.dec_units,datacat.r]) kde=gaussian_kde(values,bw_method=bandwidth/values.std(ddof=1)) nra,ndec,nr=kde.resample(2*len(datacat.r)) randcat=tc.Catalog(ra=nra,dec=ndec,ra_units='deg',dec_units='deg',r=nr) return randcat def make_catalog(ra,dec,cmvr=None): #- ra, dec in degrees cat=tc.Catalog(ra=ra,dec=dec,r=cmvr,ra_units='deg',dec_units='deg') return cat def two_point(data,data_R,bins,method='landy-szalay',seed=1234,saverandom=False): """ Uses nearest neighbors KDtree to evaluate two point correlation args: data: n samples x m features data array, eg. x,y,z positions bins: 1d bins array return: two - pt correlation correlation give the method. Errors are not returned. A bootstrap sampling can be run N times to evaluate errors. """ from sklearn.neighbors import KDTree data = np.asarray(data) bins = np.asarray(bins) rng = np.random.RandomState(seed) if method not in ['standard', 'landy-szalay']: raise ValueError("method must be 'standard' or 'landy-szalay'") if bins.ndim != 1: raise ValueError("bins must be a 1D array") if data.ndim == 1: data = data[:, np.newaxis] elif data.ndim != 2: raise ValueError("data should be 1D or 2D") n_samples, n_features = data.shape Nbins = len(bins) - 1 # shuffle all but one axis to get background distribution if data_R is None: data_R = data.copy() for i in range(n_features - 1): rng.shuffle(data_R[:, i]) else: data_R = np.asarray(data_R) if (data_R.ndim != 2) or (data_R.shape[-1] != n_features): raise ValueError('data_R must have same n_features as data') factor = len(data_R) * 1. / len(data) KDT_D=KDTree(data) KDT_R=KDTree(data_R) print("Correlating Data, data size: {}".format(len(data))) counts_DD=KDT_D.two_point_correlation(data,bins) print('Correlating Random, random size: {}'.format(len(data_R))) counts_RR=KDT_R.two_point_correlation(data_R,bins) DD=np.diff(counts_DD) RR=np.diff(counts_RR) #- Check for zero in RR RR_zero = (RR == 0) RR[RR_zero]=1 if method == 'standard': corr = factor**2*DD/RR - 1 elif method == 'landy-szalay': print("Cross Correlating") counts_DR=KDT_R.two_point_correlation(data,bins) DR=np.diff(counts_DR) print("Evaluating correlation using {}".format(method)) corr = (factor**2 * DD - 2 * factor * DR + RR)/RR corr[RR_zero] = np.nan return corr def extract_catalog(catalog,zmin=None,zmax=None): print("Reading catalog.") tab = astropy.table.Table.read(catalog) ra = tab['RA'] dec = tab['DEC'] z = tab['Z'] print("Objects in catalog: {}".format(len(z))) if zmin is None: zmin=np.min(z) if zmax is None: zmax=np.max(z) sel=np.where((z >= zmin) & (z < zmax)) ra = ra[sel] dec = dec[sel] z = z[sel] print("Objects in this redshift bin".format(z.shape[0])) #- set cosmology print("Setting Fiducial Cosmology") cosmo = set_cosmology() cmv_r = cosmo.comoving_distance(z)*cosmo.H0.value/100. #- Coordinates: carx,cary,carz = ra_dec_to_xyz(ra,dec) * cmv_r #- set data: data=np.transpose([carx,cary,carz]) return data def make_data_R_catalog(datacat,outfile='random_from_datacat.fits',seed=1234): """ Make random background from shuffling data """ print("Reading data catalog") datatab=astropy.table.Table.read(datacat) ra = datatab['ra'] dec = datatab['dec'] z = datatab['z'] data=np.transpose([ra,dec,z]) #- create random by shuffling all but 1 axis print("Making random catalog from data") data_R = data.copy() n_samples, n_features = data.shape rng = np.random.RandomState(seed) for i in range(n_features - 1): rng.shuffle(data_R[:, i]) randdata=astropy.table.Table([data_R[:,0],data_R[:,1],data_R[:,2]],names=('RA','DEC','Z')) randdata.write(outfile,format='fits') print("Written Random file from data shuffling: {}".format(outfile)) def est_correlation(data,bins,data_R=None,method='landy-szalay'): #- correlation print("Evaluating 2-pt Correlation.") corr=two_point(data,bins,method=method,data_R=data_R) return bins,corr

mit

has2k1/plotnine

plotnine/tests/test_position.py

1

5061

import string import numpy as np import pandas as pd import pytest from plotnine import (ggplot, aes, geom_point, geom_jitter, geom_bar, geom_col, geom_boxplot, geom_text, geom_rect, after_stat, position_dodge, position_dodge2, position_jitter, position_jitterdodge, position_nudge, position_stack, theme) from plotnine.positions.position import position from plotnine.exceptions import PlotnineError n = 6 m = 10 random_state = np.random.RandomState(1234567890) df1 = pd.DataFrame({'x': [1, 2, 1, 2], 'y': [1, 1, 2, 2]}) df2 = pd.DataFrame({'x': np.repeat(range(n+1), range(n+1)), 'z': np.repeat(range(n//2), range(3, n*2, 4))}) df3 = pd.DataFrame({ 'x': random_state.choice(['A', 'B'], n*m), 'y': random_state.randint(0, 20, n*m), 'c': random_state.choice([False, False, True, False], n*m) }) random_state.seed(1234567890) _theme = theme(subplots_adjust={'right': 0.85}) def test_jitter(): df1 = pd.DataFrame({'x': [1, 2, 1, 2], 'y': [1, 1, 2, 2]}) p = (ggplot(df1, aes('x', 'y')) + geom_point(size=10) + geom_jitter(size=10, color='red', random_state=random_state) + geom_jitter(size=10, color='blue', width=0.1, height=0.1, random_state=random_state)) assert p + _theme == 'jitter' with pytest.raises(PlotnineError): geom_jitter(position=position_jitter(), width=0.1) def test_nudge(): p = (ggplot(df1, aes('x', 'y')) + geom_point(size=10) + geom_point(size=10, color='red', position=position_nudge(.25, .25))) assert p + _theme == 'nudge' def test_stack(): p = (ggplot(df2, aes('factor(z)')) + geom_bar(aes(fill='factor(x)'), position='stack')) assert p + _theme == 'stack' def test_stack_negative(): df = df1.copy() _loc = df.columns.get_loc df.iloc[0, _loc('y')] *= -1 df.iloc[len(df)-1, _loc('y')] *= -1 p = (ggplot(df) + geom_col(aes('factor(x)', 'y', fill='factor(y)'), position='stack') + geom_text(aes('factor(x)', 'y', label='y'), position=position_stack(vjust=0.5)) ) assert p + _theme == 'stack-negative' def test_fill(): p = (ggplot(df2, aes('factor(z)')) + geom_bar(aes(fill='factor(x)'), position='fill')) assert p + _theme == 'fill' def test_dodge(): p = (ggplot(df2, aes('factor(z)')) + geom_bar(aes(fill='factor(x)'), position='dodge')) assert p + _theme == 'dodge' def test_dodge_preserve_single(): df1 = pd.DataFrame({'x': ['a', 'b', 'b'], 'y': ['a', 'a', 'b']}) p = (ggplot(df1, aes('x', fill='y')) + geom_bar(position=position_dodge(preserve='single'))) assert p + _theme == 'dodge_preserve_single' def test_dodge_preserve_single_text(): df1 = pd.DataFrame({'x': ['a', 'b', 'b', 'b'], 'y': ['a', 'a', 'b', 'b']}) d = position_dodge(preserve='single', width=0.9) p = (ggplot(df1, aes('x', fill='y')) + geom_bar(position=d) + geom_text( aes(y=after_stat('count'), label=after_stat('count')), stat='count', position=d, va='bottom') ) assert p + _theme == 'dodge_preserve_single_text' def test_dodge2(): p = (ggplot(df3, aes('x', 'y', color='c')) + geom_boxplot(position='dodge2', size=2)) assert p + _theme == 'dodge2' def test_dodge2_varwidth(): p = (ggplot(df3, aes('x', 'y', color='c')) + geom_boxplot( position=position_dodge2(preserve='single'), varwidth=True, size=2) ) assert p + _theme == 'dodge2_varwidth' def test_jitterdodge(): df = pd.DataFrame({ 'x': np.ones(n*2), 'y': np.repeat(np.arange(n), 2), 'letters': np.repeat(list(string.ascii_lowercase[:n]), 2)}) position = position_jitterdodge(random_state=random_state) p = (ggplot(df, aes('x', 'y', fill='letters')) + geom_point(size=10, fill='black') + geom_point(size=10, position=position)) assert p + _theme == 'jitterdodge' def test_position_from_geom(): geom = geom_point(position='jitter') assert isinstance(position.from_geom(geom), position_jitter) geom = geom_point(position='position_jitter') assert isinstance(position.from_geom(geom), position_jitter) geom = geom_point(position=position_jitter()) assert isinstance(position.from_geom(geom), position_jitter) geom = geom_point(position=position_jitter) assert isinstance(position.from_geom(geom), position_jitter) def test_dodge_empty_data(): empty_df = pd.DataFrame({'x': [], 'y': []}) p = (ggplot(df1, aes('x', 'y')) + geom_point() + geom_rect( empty_df, aes(xmin='x', xmax='x+1', ymin='y', ymax='y+1'), position='dodge') ) p.draw_test()

gpl-2.0

poryfly/scikit-learn

examples/semi_supervised/plot_label_propagation_digits.py

268

2723

""" =================================================== Label Propagation digits: Demonstrating performance =================================================== This example demonstrates the power of semisupervised learning by training a Label Spreading model to classify handwritten digits with sets of very few labels. The handwritten digit dataset has 1797 total points. The model will be trained using all points, but only 30 will be labeled. Results in the form of a confusion matrix and a series of metrics over each class will be very good. At the end, the top 10 most uncertain predictions will be shown. """ print(__doc__) # Authors: Clay Woolam <[email protected]> # Licence: BSD import numpy as np import matplotlib.pyplot as plt from scipy import stats from sklearn import datasets from sklearn.semi_supervised import label_propagation from sklearn.metrics import confusion_matrix, classification_report digits = datasets.load_digits() rng = np.random.RandomState(0) indices = np.arange(len(digits.data)) rng.shuffle(indices) X = digits.data[indices[:330]] y = digits.target[indices[:330]] images = digits.images[indices[:330]] n_total_samples = len(y) n_labeled_points = 30 indices = np.arange(n_total_samples) unlabeled_set = indices[n_labeled_points:] # shuffle everything around y_train = np.copy(y) y_train[unlabeled_set] = -1 ############################################################################### # Learn with LabelSpreading lp_model = label_propagation.LabelSpreading(gamma=0.25, max_iter=5) lp_model.fit(X, y_train) predicted_labels = lp_model.transduction_[unlabeled_set] true_labels = y[unlabeled_set] cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_) print("Label Spreading model: %d labeled & %d unlabeled points (%d total)" % (n_labeled_points, n_total_samples - n_labeled_points, n_total_samples)) print(classification_report(true_labels, predicted_labels)) print("Confusion matrix") print(cm) # calculate uncertainty values for each transduced distribution pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T) # pick the top 10 most uncertain labels uncertainty_index = np.argsort(pred_entropies)[-10:] ############################################################################### # plot f = plt.figure(figsize=(7, 5)) for index, image_index in enumerate(uncertainty_index): image = images[image_index] sub = f.add_subplot(2, 5, index + 1) sub.imshow(image, cmap=plt.cm.gray_r) plt.xticks([]) plt.yticks([]) sub.set_title('predict: %i\ntrue: %i' % ( lp_model.transduction_[image_index], y[image_index])) f.suptitle('Learning with small amount of labeled data') plt.show()

bsd-3-clause

JoseGuzman/myIPythonNotebooks

pub/nerst.py

1

2413

""" nerst.py Jose Guzman, [email protected] Created: Fri Apr 29 08:04:57 CEST 2016 Solves the Nerst equation for different chloride conditions. """ from math import log from terminaltables import AsciiTable import numpy as np def nerst(t, oution, inion, z): """ Solves the following equation: .. math:: E = \frac{R T}{z F} \ln\frac{out}{in}\\ Arguments --------- oution : float Extracellular ionic concentration intion : float Intracellular ionic concentration z : int Valence of the ion t : temp Temperature in celsius Returns ------- voltage : float the potential (in mV) at which the net flux of current is zero. Examples -------- >>> nerst( t = 30, oution = 126.5, inion = 10, z=-1) >>> -66.294937 """ K = 273.15 + t # transform in kelvin volt = ((8.31451*K)/(z*96485.3))*log(oution/inion) return(volt*1000) # in mV if __name__ == '__main__': data = [['Reference', 'E_rev (Cl-) mV']] # data from Pavlidis % Madison 1999 ECl = nerst(t = 30, oution=126.5, inion=10, z=-1) data.append(['Pavlidis & Madison, 1999', ECl]) # data from Sasaki et al ECl = nerst(t = 30, oution=133.3, inion=4, z=-1) data.append(['Sasaki et al., 2012', ECl]) # data from Mitra et al, 2011 ECl = nerst(t = 20, oution=129, inion=9, z=-1) data.append(['Mitra et al., 2011', ECl]) # data from Kraushaar and Jonas, 2000 ECl = nerst(t = 20, oution=126.5, inion=149, z=-1) data.append(['Krausharr and Jonas, 2000',ECl]) # data from Espinoza ECl = nerst(t = 20, oution=133.5, inion=44, z=-1) data.append(['Espinoza et al., **', ECl]) # data for brain organoids (for Cl) ECl = nerst(t = 20, oution=133.5, inion=28, z=-1) data.append(['Guzman et al., **', ECl]) table = AsciiTable(data) print table.table import matplotlib.pyplot as plt x = np.arange(0.1, 50.0, 0.01) k = lambda x:58*np.log10(x/100.) # K-nerst equation y = k(x) plt.semilogx(x,y, color='royalblue') plt.vlines(2.5, -120, k(2.5), linestyle=':', color='brown') plt.hlines(k(2.5), 0.01, 2.5, linestyle=':', color='brown') plt.ylim(ymin=-120) plt.xlim(xmin=0.1) plt.ylabel('Resting membrane potential (mV)') plt.xlabel('Log [K$^+$]') plt.show()

gpl-2.0

josephcslater/scipy

scipy/signal/filter_design.py

14

135076

"""Filter design. """ from __future__ import division, print_function, absolute_import import warnings import math import numpy import numpy as np from numpy import (atleast_1d, poly, polyval, roots, real, asarray, resize, pi, absolute, logspace, r_, sqrt, tan, log10, arctan, arcsinh, sin, exp, cosh, arccosh, ceil, conjugate, zeros, sinh, append, concatenate, prod, ones, array, mintypecode) from numpy.polynomial.polynomial import polyval as npp_polyval from scipy import special, optimize from scipy.special import comb, factorial from scipy._lib._numpy_compat import polyvalfromroots __all__ = ['findfreqs', 'freqs', 'freqz', 'tf2zpk', 'zpk2tf', 'normalize', 'lp2lp', 'lp2hp', 'lp2bp', 'lp2bs', 'bilinear', 'iirdesign', 'iirfilter', 'butter', 'cheby1', 'cheby2', 'ellip', 'bessel', 'band_stop_obj', 'buttord', 'cheb1ord', 'cheb2ord', 'ellipord', 'buttap', 'cheb1ap', 'cheb2ap', 'ellipap', 'besselap', 'BadCoefficients', 'freqs_zpk', 'freqz_zpk', 'tf2sos', 'sos2tf', 'zpk2sos', 'sos2zpk', 'group_delay', 'sosfreqz', 'iirnotch', 'iirpeak'] class BadCoefficients(UserWarning): """Warning about badly conditioned filter coefficients""" pass abs = absolute def findfreqs(num, den, N, kind='ba'): """ Find array of frequencies for computing the response of an analog filter. Parameters ---------- num, den : array_like, 1-D The polynomial coefficients of the numerator and denominator of the transfer function of the filter or LTI system, where the coefficients are ordered from highest to lowest degree. Or, the roots of the transfer function numerator and denominator (i.e. zeroes and poles). N : int The length of the array to be computed. kind : str {'ba', 'zp'}, optional Specifies whether the numerator and denominator are specified by their polynomial coefficients ('ba'), or their roots ('zp'). Returns ------- w : (N,) ndarray A 1-D array of frequencies, logarithmically spaced. Examples -------- Find a set of nine frequencies that span the "interesting part" of the frequency response for the filter with the transfer function H(s) = s / (s^2 + 8s + 25) >>> from scipy import signal >>> signal.findfreqs([1, 0], [1, 8, 25], N=9) array([ 1.00000000e-02, 3.16227766e-02, 1.00000000e-01, 3.16227766e-01, 1.00000000e+00, 3.16227766e+00, 1.00000000e+01, 3.16227766e+01, 1.00000000e+02]) """ if kind == 'ba': ep = atleast_1d(roots(den)) + 0j tz = atleast_1d(roots(num)) + 0j elif kind == 'zp': ep = atleast_1d(den) + 0j tz = atleast_1d(num) + 0j else: raise ValueError("input must be one of {'ba', 'zp'}") if len(ep) == 0: ep = atleast_1d(-1000) + 0j ez = r_['-1', numpy.compress(ep.imag >= 0, ep, axis=-1), numpy.compress((abs(tz) < 1e5) & (tz.imag >= 0), tz, axis=-1)] integ = abs(ez) < 1e-10 hfreq = numpy.around(numpy.log10(numpy.max(3 * abs(ez.real + integ) + 1.5 * ez.imag)) + 0.5) lfreq = numpy.around(numpy.log10(0.1 * numpy.min(abs(real(ez + integ)) + 2 * ez.imag)) - 0.5) w = logspace(lfreq, hfreq, N) return w def freqs(b, a, worN=None, plot=None): """ Compute frequency response of analog filter. Given the M-order numerator `b` and N-order denominator `a` of an analog filter, compute its frequency response:: b[0]*(jw)**M + b[1]*(jw)**(M-1) + ... + b[M] H(w) = ---------------------------------------------- a[0]*(jw)**N + a[1]*(jw)**(N-1) + ... + a[N] Parameters ---------- b : array_like Numerator of a linear filter. a : array_like Denominator of a linear filter. worN : {None, int, array_like}, optional If None, then compute at 200 frequencies around the interesting parts of the response curve (determined by pole-zero locations). If a single integer, then compute at that many frequencies. Otherwise, compute the response at the angular frequencies (e.g. rad/s) given in `worN`. plot : callable, optional A callable that takes two arguments. If given, the return parameters `w` and `h` are passed to plot. Useful for plotting the frequency response inside `freqs`. Returns ------- w : ndarray The angular frequencies at which `h` was computed. h : ndarray The frequency response. See Also -------- freqz : Compute the frequency response of a digital filter. Notes ----- Using Matplotlib's "plot" function as the callable for `plot` produces unexpected results, this plots the real part of the complex transfer function, not the magnitude. Try ``lambda w, h: plot(w, abs(h))``. Examples -------- >>> from scipy.signal import freqs, iirfilter >>> b, a = iirfilter(4, [1, 10], 1, 60, analog=True, ftype='cheby1') >>> w, h = freqs(b, a, worN=np.logspace(-1, 2, 1000)) >>> import matplotlib.pyplot as plt >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.xlabel('Frequency') >>> plt.ylabel('Amplitude response [dB]') >>> plt.grid() >>> plt.show() """ if worN is None: w = findfreqs(b, a, 200) elif isinstance(worN, int): N = worN w = findfreqs(b, a, N) else: w = worN w = atleast_1d(w) s = 1j * w h = polyval(b, s) / polyval(a, s) if plot is not None: plot(w, h) return w, h def freqs_zpk(z, p, k, worN=None): """ Compute frequency response of analog filter. Given the zeros `z`, poles `p`, and gain `k` of a filter, compute its frequency response:: (jw-z[0]) * (jw-z[1]) * ... * (jw-z[-1]) H(w) = k * ---------------------------------------- (jw-p[0]) * (jw-p[1]) * ... * (jw-p[-1]) Parameters ---------- z : array_like Zeroes of a linear filter p : array_like Poles of a linear filter k : scalar Gain of a linear filter worN : {None, int, array_like}, optional If None, then compute at 200 frequencies around the interesting parts of the response curve (determined by pole-zero locations). If a single integer, then compute at that many frequencies. Otherwise, compute the response at the angular frequencies (e.g. rad/s) given in `worN`. Returns ------- w : ndarray The angular frequencies at which `h` was computed. h : ndarray The frequency response. See Also -------- freqs : Compute the frequency response of an analog filter in TF form freqz : Compute the frequency response of a digital filter in TF form freqz_zpk : Compute the frequency response of a digital filter in ZPK form Notes ----- .. versionadded: 0.19.0 Examples -------- >>> from scipy.signal import freqs_zpk, iirfilter >>> z, p, k = iirfilter(4, [1, 10], 1, 60, analog=True, ftype='cheby1', ... output='zpk') >>> w, h = freqs_zpk(z, p, k, worN=np.logspace(-1, 2, 1000)) >>> import matplotlib.pyplot as plt >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.xlabel('Frequency') >>> plt.ylabel('Amplitude response [dB]') >>> plt.grid() >>> plt.show() """ k = np.asarray(k) if k.size > 1: raise ValueError('k must be a single scalar gain') if worN is None: w = findfreqs(z, p, 200, kind='zp') elif isinstance(worN, int): N = worN w = findfreqs(z, p, N, kind='zp') else: w = worN w = atleast_1d(w) s = 1j * w num = polyvalfromroots(s, z) den = polyvalfromroots(s, p) h = k * num/den return w, h def freqz(b, a=1, worN=None, whole=False, plot=None): """ Compute the frequency response of a digital filter. Given the M-order numerator `b` and N-order denominator `a` of a digital filter, compute its frequency response:: jw -jw -jwM jw B(e ) b[0] + b[1]e + .... + b[M]e H(e ) = ---- = ----------------------------------- jw -jw -jwN A(e ) a[0] + a[1]e + .... + a[N]e Parameters ---------- b : array_like numerator of a linear filter a : array_like denominator of a linear filter worN : {None, int, array_like}, optional If None (default), then compute at 512 frequencies equally spaced around the unit circle. If a single integer, then compute at that many frequencies. If an array_like, compute the response at the frequencies given (in radians/sample). whole : bool, optional Normally, frequencies are computed from 0 to the Nyquist frequency, pi radians/sample (upper-half of unit-circle). If `whole` is True, compute frequencies from 0 to 2*pi radians/sample. plot : callable A callable that takes two arguments. If given, the return parameters `w` and `h` are passed to plot. Useful for plotting the frequency response inside `freqz`. Returns ------- w : ndarray The normalized frequencies at which `h` was computed, in radians/sample. h : ndarray The frequency response, as complex numbers. See Also -------- sosfreqz Notes ----- Using Matplotlib's "plot" function as the callable for `plot` produces unexpected results, this plots the real part of the complex transfer function, not the magnitude. Try ``lambda w, h: plot(w, abs(h))``. Examples -------- >>> from scipy import signal >>> b = signal.firwin(80, 0.5, window=('kaiser', 8)) >>> w, h = signal.freqz(b) >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> plt.title('Digital filter frequency response') >>> ax1 = fig.add_subplot(111) >>> plt.plot(w, 20 * np.log10(abs(h)), 'b') >>> plt.ylabel('Amplitude [dB]', color='b') >>> plt.xlabel('Frequency [rad/sample]') >>> ax2 = ax1.twinx() >>> angles = np.unwrap(np.angle(h)) >>> plt.plot(w, angles, 'g') >>> plt.ylabel('Angle (radians)', color='g') >>> plt.grid() >>> plt.axis('tight') >>> plt.show() """ b, a = map(atleast_1d, (b, a)) if whole: lastpoint = 2 * pi else: lastpoint = pi if worN is None: N = 512 w = numpy.linspace(0, lastpoint, N, endpoint=False) elif isinstance(worN, int): N = worN w = numpy.linspace(0, lastpoint, N, endpoint=False) else: w = worN w = atleast_1d(w) zm1 = exp(-1j * w) h = polyval(b[::-1], zm1) / polyval(a[::-1], zm1) if plot is not None: plot(w, h) return w, h def freqz_zpk(z, p, k, worN=None, whole=False): """ Compute the frequency response of a digital filter in ZPK form. Given the Zeros, Poles and Gain of a digital filter, compute its frequency response:: :math:`H(z)=k \prod_i (z - Z[i]) / \prod_j (z - P[j])` where :math:`k` is the `gain`, :math:`Z` are the `zeros` and :math:`P` are the `poles`. Parameters ---------- z : array_like Zeroes of a linear filter p : array_like Poles of a linear filter k : scalar Gain of a linear filter worN : {None, int, array_like}, optional If None (default), then compute at 512 frequencies equally spaced around the unit circle. If a single integer, then compute at that many frequencies. If an array_like, compute the response at the frequencies given (in radians/sample). whole : bool, optional Normally, frequencies are computed from 0 to the Nyquist frequency, pi radians/sample (upper-half of unit-circle). If `whole` is True, compute frequencies from 0 to 2*pi radians/sample. Returns ------- w : ndarray The normalized frequencies at which `h` was computed, in radians/sample. h : ndarray The frequency response. See Also -------- freqs : Compute the frequency response of an analog filter in TF form freqs_zpk : Compute the frequency response of an analog filter in ZPK form freqz : Compute the frequency response of a digital filter in TF form Notes ----- .. versionadded: 0.19.0 Examples -------- >>> from scipy import signal >>> z, p, k = signal.butter(4, 0.2, output='zpk') >>> w, h = signal.freqz_zpk(z, p, k) >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> plt.title('Digital filter frequency response') >>> ax1 = fig.add_subplot(111) >>> plt.plot(w, 20 * np.log10(abs(h)), 'b') >>> plt.ylabel('Amplitude [dB]', color='b') >>> plt.xlabel('Frequency [rad/sample]') >>> ax2 = ax1.twinx() >>> angles = np.unwrap(np.angle(h)) >>> plt.plot(w, angles, 'g') >>> plt.ylabel('Angle (radians)', color='g') >>> plt.grid() >>> plt.axis('tight') >>> plt.show() """ z, p = map(atleast_1d, (z, p)) if whole: lastpoint = 2 * pi else: lastpoint = pi if worN is None: N = 512 w = numpy.linspace(0, lastpoint, N, endpoint=False) elif isinstance(worN, int): N = worN w = numpy.linspace(0, lastpoint, N, endpoint=False) else: w = worN w = atleast_1d(w) zm1 = exp(1j * w) h = k * polyvalfromroots(zm1, z) / polyvalfromroots(zm1, p) return w, h def group_delay(system, w=None, whole=False): r"""Compute the group delay of a digital filter. The group delay measures by how many samples amplitude envelopes of various spectral components of a signal are delayed by a filter. It is formally defined as the derivative of continuous (unwrapped) phase:: d jw D(w) = - -- arg H(e) dw Parameters ---------- system : tuple of array_like (b, a) Numerator and denominator coefficients of a filter transfer function. w : {None, int, array-like}, optional If None (default), then compute at 512 frequencies equally spaced around the unit circle. If a single integer, then compute at that many frequencies. If array, compute the delay at the frequencies given (in radians/sample). whole : bool, optional Normally, frequencies are computed from 0 to the Nyquist frequency, pi radians/sample (upper-half of unit-circle). If `whole` is True, compute frequencies from 0 to ``2*pi`` radians/sample. Returns ------- w : ndarray The normalized frequencies at which the group delay was computed, in radians/sample. gd : ndarray The group delay. Notes ----- The similar function in MATLAB is called `grpdelay`. If the transfer function :math:`H(z)` has zeros or poles on the unit circle, the group delay at corresponding frequencies is undefined. When such a case arises the warning is raised and the group delay is set to 0 at those frequencies. For the details of numerical computation of the group delay refer to [1]_. .. versionadded: 0.16.0 See Also -------- freqz : Frequency response of a digital filter References ---------- .. [1] Richard G. Lyons, "Understanding Digital Signal Processing, 3rd edition", p. 830. Examples -------- >>> from scipy import signal >>> b, a = signal.iirdesign(0.1, 0.3, 5, 50, ftype='cheby1') >>> w, gd = signal.group_delay((b, a)) >>> import matplotlib.pyplot as plt >>> plt.title('Digital filter group delay') >>> plt.plot(w, gd) >>> plt.ylabel('Group delay [samples]') >>> plt.xlabel('Frequency [rad/sample]') >>> plt.show() """ if w is None: w = 512 if isinstance(w, int): if whole: w = np.linspace(0, 2 * pi, w, endpoint=False) else: w = np.linspace(0, pi, w, endpoint=False) w = np.atleast_1d(w) b, a = map(np.atleast_1d, system) c = np.convolve(b, a[::-1]) cr = c * np.arange(c.size) z = np.exp(-1j * w) num = np.polyval(cr[::-1], z) den = np.polyval(c[::-1], z) singular = np.absolute(den) < 10 * EPSILON if np.any(singular): warnings.warn( "The group delay is singular at frequencies [{0}], setting to 0". format(", ".join("{0:.3f}".format(ws) for ws in w[singular])) ) gd = np.zeros_like(w) gd[~singular] = np.real(num[~singular] / den[~singular]) - a.size + 1 return w, gd def _validate_sos(sos): """Helper to validate a SOS input""" sos = np.atleast_2d(sos) if sos.ndim != 2: raise ValueError('sos array must be 2D') n_sections, m = sos.shape if m != 6: raise ValueError('sos array must be shape (n_sections, 6)') if not (sos[:, 3] == 1).all(): raise ValueError('sos[:, 3] should be all ones') return sos, n_sections def sosfreqz(sos, worN=None, whole=False): """ Compute the frequency response of a digital filter in SOS format. Given `sos`, an array with shape (n, 6) of second order sections of a digital filter, compute the frequency response of the system function:: B0(z) B1(z) B{n-1}(z) H(z) = ----- * ----- * ... * --------- A0(z) A1(z) A{n-1}(z) for z = exp(omega*1j), where B{k}(z) and A{k}(z) are numerator and denominator of the transfer function of the k-th second order section. Parameters ---------- sos : array_like Array of second-order filter coefficients, must have shape ``(n_sections, 6)``. Each row corresponds to a second-order section, with the first three columns providing the numerator coefficients and the last three providing the denominator coefficients. worN : {None, int, array_like}, optional If None (default), then compute at 512 frequencies equally spaced around the unit circle. If a single integer, then compute at that many frequencies. If an array_like, compute the response at the frequencies given (in radians/sample). whole : bool, optional Normally, frequencies are computed from 0 to the Nyquist frequency, pi radians/sample (upper-half of unit-circle). If `whole` is True, compute frequencies from 0 to 2*pi radians/sample. Returns ------- w : ndarray The normalized frequencies at which `h` was computed, in radians/sample. h : ndarray The frequency response, as complex numbers. See Also -------- freqz, sosfilt Notes ----- .. versionadded:: 0.19.0 Examples -------- Design a 15th-order bandpass filter in SOS format. >>> from scipy import signal >>> sos = signal.ellip(15, 0.5, 60, (0.2, 0.4), btype='bandpass', ... output='sos') Compute the frequency response at 1500 points from DC to Nyquist. >>> w, h = signal.sosfreqz(sos, worN=1500) Plot the response. >>> import matplotlib.pyplot as plt >>> plt.subplot(2, 1, 1) >>> db = 20*np.log10(np.abs(h)) >>> plt.plot(w/np.pi, db) >>> plt.ylim(-75, 5) >>> plt.grid(True) >>> plt.yticks([0, -20, -40, -60]) >>> plt.ylabel('Gain [dB]') >>> plt.title('Frequency Response') >>> plt.subplot(2, 1, 2) >>> plt.plot(w/np.pi, np.angle(h)) >>> plt.grid(True) >>> plt.yticks([-np.pi, -0.5*np.pi, 0, 0.5*np.pi, np.pi], ... [r'$-\\pi$', r'$-\\pi/2$', '0', r'$\\pi/2$', r'$\\pi$']) >>> plt.ylabel('Phase [rad]') >>> plt.xlabel('Normalized frequency (1.0 = Nyquist)') >>> plt.show() If the same filter is implemented as a single transfer function, numerical error corrupts the frequency response: >>> b, a = signal.ellip(15, 0.5, 60, (0.2, 0.4), btype='bandpass', ... output='ba') >>> w, h = signal.freqz(b, a, worN=1500) >>> plt.subplot(2, 1, 1) >>> db = 20*np.log10(np.abs(h)) >>> plt.plot(w/np.pi, db) >>> plt.subplot(2, 1, 2) >>> plt.plot(w/np.pi, np.angle(h)) >>> plt.show() """ sos, n_sections = _validate_sos(sos) if n_sections == 0: raise ValueError('Cannot compute frequencies with no sections') h = 1. for row in sos: w, rowh = freqz(row[:3], row[3:], worN=worN, whole=whole) h *= rowh return w, h def _cplxreal(z, tol=None): """ Split into complex and real parts, combining conjugate pairs. The 1D input vector `z` is split up into its complex (`zc`) and real (`zr`) elements. Every complex element must be part of a complex-conjugate pair, which are combined into a single number (with positive imaginary part) in the output. Two complex numbers are considered a conjugate pair if their real and imaginary parts differ in magnitude by less than ``tol * abs(z)``. Parameters ---------- z : array_like Vector of complex numbers to be sorted and split tol : float, optional Relative tolerance for testing realness and conjugate equality. Default is ``100 * spacing(1)`` of `z`'s data type (i.e. 2e-14 for float64) Returns ------- zc : ndarray Complex elements of `z`, with each pair represented by a single value having positive imaginary part, sorted first by real part, and then by magnitude of imaginary part. The pairs are averaged when combined to reduce error. zr : ndarray Real elements of `z` (those having imaginary part less than `tol` times their magnitude), sorted by value. Raises ------ ValueError If there are any complex numbers in `z` for which a conjugate cannot be found. See Also -------- _cplxpair Examples -------- >>> a = [4, 3, 1, 2-2j, 2+2j, 2-1j, 2+1j, 2-1j, 2+1j, 1+1j, 1-1j] >>> zc, zr = _cplxreal(a) >>> print zc [ 1.+1.j 2.+1.j 2.+1.j 2.+2.j] >>> print zr [ 1. 3. 4.] """ z = atleast_1d(z) if z.size == 0: return z, z elif z.ndim != 1: raise ValueError('_cplxreal only accepts 1D input') if tol is None: # Get tolerance from dtype of input tol = 100 * np.finfo((1.0 * z).dtype).eps # Sort by real part, magnitude of imaginary part (speed up further sorting) z = z[np.lexsort((abs(z.imag), z.real))] # Split reals from conjugate pairs real_indices = abs(z.imag) <= tol * abs(z) zr = z[real_indices].real if len(zr) == len(z): # Input is entirely real return array([]), zr # Split positive and negative halves of conjugates z = z[~real_indices] zp = z[z.imag > 0] zn = z[z.imag < 0] if len(zp) != len(zn): raise ValueError('Array contains complex value with no matching ' 'conjugate.') # Find runs of (approximately) the same real part same_real = np.diff(zp.real) <= tol * abs(zp[:-1]) diffs = numpy.diff(concatenate(([0], same_real, [0]))) run_starts = numpy.where(diffs > 0)[0] run_stops = numpy.where(diffs < 0)[0] # Sort each run by their imaginary parts for i in range(len(run_starts)): start = run_starts[i] stop = run_stops[i] + 1 for chunk in (zp[start:stop], zn[start:stop]): chunk[...] = chunk[np.lexsort([abs(chunk.imag)])] # Check that negatives match positives if any(abs(zp - zn.conj()) > tol * abs(zn)): raise ValueError('Array contains complex value with no matching ' 'conjugate.') # Average out numerical inaccuracy in real vs imag parts of pairs zc = (zp + zn.conj()) / 2 return zc, zr def _cplxpair(z, tol=None): """ Sort into pairs of complex conjugates. Complex conjugates in `z` are sorted by increasing real part. In each pair, the number with negative imaginary part appears first. If pairs have identical real parts, they are sorted by increasing imaginary magnitude. Two complex numbers are considered a conjugate pair if their real and imaginary parts differ in magnitude by less than ``tol * abs(z)``. The pairs are forced to be exact complex conjugates by averaging the positive and negative values. Purely real numbers are also sorted, but placed after the complex conjugate pairs. A number is considered real if its imaginary part is smaller than `tol` times the magnitude of the number. Parameters ---------- z : array_like 1-dimensional input array to be sorted. tol : float, optional Relative tolerance for testing realness and conjugate equality. Default is ``100 * spacing(1)`` of `z`'s data type (i.e. 2e-14 for float64) Returns ------- y : ndarray Complex conjugate pairs followed by real numbers. Raises ------ ValueError If there are any complex numbers in `z` for which a conjugate cannot be found. See Also -------- _cplxreal Examples -------- >>> a = [4, 3, 1, 2-2j, 2+2j, 2-1j, 2+1j, 2-1j, 2+1j, 1+1j, 1-1j] >>> z = _cplxpair(a) >>> print(z) [ 1.-1.j 1.+1.j 2.-1.j 2.+1.j 2.-1.j 2.+1.j 2.-2.j 2.+2.j 1.+0.j 3.+0.j 4.+0.j] """ z = atleast_1d(z) if z.size == 0 or np.isrealobj(z): return np.sort(z) if z.ndim != 1: raise ValueError('z must be 1-dimensional') zc, zr = _cplxreal(z, tol) # Interleave complex values and their conjugates, with negative imaginary # parts first in each pair zc = np.dstack((zc.conj(), zc)).flatten() z = np.append(zc, zr) return z def tf2zpk(b, a): r"""Return zero, pole, gain (z, p, k) representation from a numerator, denominator representation of a linear filter. Parameters ---------- b : array_like Numerator polynomial coefficients. a : array_like Denominator polynomial coefficients. Returns ------- z : ndarray Zeros of the transfer function. p : ndarray Poles of the transfer function. k : float System gain. Notes ----- If some values of `b` are too close to 0, they are removed. In that case, a BadCoefficients warning is emitted. The `b` and `a` arrays are interpreted as coefficients for positive, descending powers of the transfer function variable. So the inputs :math:`b = [b_0, b_1, ..., b_M]` and :math:`a =[a_0, a_1, ..., a_N]` can represent an analog filter of the form: .. math:: H(s) = \frac {b_0 s^M + b_1 s^{(M-1)} + \cdots + b_M} {a_0 s^N + a_1 s^{(N-1)} + \cdots + a_N} or a discrete-time filter of the form: .. math:: H(z) = \frac {b_0 z^M + b_1 z^{(M-1)} + \cdots + b_M} {a_0 z^N + a_1 z^{(N-1)} + \cdots + a_N} This "positive powers" form is found more commonly in controls engineering. If `M` and `N` are equal (which is true for all filters generated by the bilinear transform), then this happens to be equivalent to the "negative powers" discrete-time form preferred in DSP: .. math:: H(z) = \frac {b_0 + b_1 z^{-1} + \cdots + b_M z^{-M}} {a_0 + a_1 z^{-1} + \cdots + a_N z^{-N}} Although this is true for common filters, remember that this is not true in the general case. If `M` and `N` are not equal, the discrete-time transfer function coefficients must first be converted to the "positive powers" form before finding the poles and zeros. """ b, a = normalize(b, a) b = (b + 0.0) / a[0] a = (a + 0.0) / a[0] k = b[0] b /= b[0] z = roots(b) p = roots(a) return z, p, k def zpk2tf(z, p, k): """ Return polynomial transfer function representation from zeros and poles Parameters ---------- z : array_like Zeros of the transfer function. p : array_like Poles of the transfer function. k : float System gain. Returns ------- b : ndarray Numerator polynomial coefficients. a : ndarray Denominator polynomial coefficients. """ z = atleast_1d(z) k = atleast_1d(k) if len(z.shape) > 1: temp = poly(z[0]) b = zeros((z.shape[0], z.shape[1] + 1), temp.dtype.char) if len(k) == 1: k = [k[0]] * z.shape[0] for i in range(z.shape[0]): b[i] = k[i] * poly(z[i]) else: b = k * poly(z) a = atleast_1d(poly(p)) # Use real output if possible. Copied from numpy.poly, since # we can't depend on a specific version of numpy. if issubclass(b.dtype.type, numpy.complexfloating): # if complex roots are all complex conjugates, the roots are real. roots = numpy.asarray(z, complex) pos_roots = numpy.compress(roots.imag > 0, roots) neg_roots = numpy.conjugate(numpy.compress(roots.imag < 0, roots)) if len(pos_roots) == len(neg_roots): if numpy.all(numpy.sort_complex(neg_roots) == numpy.sort_complex(pos_roots)): b = b.real.copy() if issubclass(a.dtype.type, numpy.complexfloating): # if complex roots are all complex conjugates, the roots are real. roots = numpy.asarray(p, complex) pos_roots = numpy.compress(roots.imag > 0, roots) neg_roots = numpy.conjugate(numpy.compress(roots.imag < 0, roots)) if len(pos_roots) == len(neg_roots): if numpy.all(numpy.sort_complex(neg_roots) == numpy.sort_complex(pos_roots)): a = a.real.copy() return b, a def tf2sos(b, a, pairing='nearest'): """ Return second-order sections from transfer function representation Parameters ---------- b : array_like Numerator polynomial coefficients. a : array_like Denominator polynomial coefficients. pairing : {'nearest', 'keep_odd'}, optional The method to use to combine pairs of poles and zeros into sections. See `zpk2sos`. Returns ------- sos : ndarray Array of second-order filter coefficients, with shape ``(n_sections, 6)``. See `sosfilt` for the SOS filter format specification. See Also -------- zpk2sos, sosfilt Notes ----- It is generally discouraged to convert from TF to SOS format, since doing so usually will not improve numerical precision errors. Instead, consider designing filters in ZPK format and converting directly to SOS. TF is converted to SOS by first converting to ZPK format, then converting ZPK to SOS. .. versionadded:: 0.16.0 """ return zpk2sos(*tf2zpk(b, a), pairing=pairing) def sos2tf(sos): """ Return a single transfer function from a series of second-order sections Parameters ---------- sos : array_like Array of second-order filter coefficients, must have shape ``(n_sections, 6)``. See `sosfilt` for the SOS filter format specification. Returns ------- b : ndarray Numerator polynomial coefficients. a : ndarray Denominator polynomial coefficients. Notes ----- .. versionadded:: 0.16.0 """ sos = np.asarray(sos) b = [1.] a = [1.] n_sections = sos.shape[0] for section in range(n_sections): b = np.polymul(b, sos[section, :3]) a = np.polymul(a, sos[section, 3:]) return b, a def sos2zpk(sos): """ Return zeros, poles, and gain of a series of second-order sections Parameters ---------- sos : array_like Array of second-order filter coefficients, must have shape ``(n_sections, 6)``. See `sosfilt` for the SOS filter format specification. Returns ------- z : ndarray Zeros of the transfer function. p : ndarray Poles of the transfer function. k : float System gain. Notes ----- .. versionadded:: 0.16.0 """ sos = np.asarray(sos) n_sections = sos.shape[0] z = np.empty(n_sections*2, np.complex128) p = np.empty(n_sections*2, np.complex128) k = 1. for section in range(n_sections): zpk = tf2zpk(sos[section, :3], sos[section, 3:]) z[2*section:2*(section+1)] = zpk[0] p[2*section:2*(section+1)] = zpk[1] k *= zpk[2] return z, p, k def _nearest_real_complex_idx(fro, to, which): """Get the next closest real or complex element based on distance""" assert which in ('real', 'complex') order = np.argsort(np.abs(fro - to)) mask = np.isreal(fro[order]) if which == 'complex': mask = ~mask return order[np.where(mask)[0][0]] def zpk2sos(z, p, k, pairing='nearest'): """ Return second-order sections from zeros, poles, and gain of a system Parameters ---------- z : array_like Zeros of the transfer function. p : array_like Poles of the transfer function. k : float System gain. pairing : {'nearest', 'keep_odd'}, optional The method to use to combine pairs of poles and zeros into sections. See Notes below. Returns ------- sos : ndarray Array of second-order filter coefficients, with shape ``(n_sections, 6)``. See `sosfilt` for the SOS filter format specification. See Also -------- sosfilt Notes ----- The algorithm used to convert ZPK to SOS format is designed to minimize errors due to numerical precision issues. The pairing algorithm attempts to minimize the peak gain of each biquadratic section. This is done by pairing poles with the nearest zeros, starting with the poles closest to the unit circle. *Algorithms* The current algorithms are designed specifically for use with digital filters. (The output coefficents are not correct for analog filters.) The steps in the ``pairing='nearest'`` and ``pairing='keep_odd'`` algorithms are mostly shared. The ``nearest`` algorithm attempts to minimize the peak gain, while ``'keep_odd'`` minimizes peak gain under the constraint that odd-order systems should retain one section as first order. The algorithm steps and are as follows: As a pre-processing step, add poles or zeros to the origin as necessary to obtain the same number of poles and zeros for pairing. If ``pairing == 'nearest'`` and there are an odd number of poles, add an additional pole and a zero at the origin. The following steps are then iterated over until no more poles or zeros remain: 1. Take the (next remaining) pole (complex or real) closest to the unit circle to begin a new filter section. 2. If the pole is real and there are no other remaining real poles [#]_, add the closest real zero to the section and leave it as a first order section. Note that after this step we are guaranteed to be left with an even number of real poles, complex poles, real zeros, and complex zeros for subsequent pairing iterations. 3. Else: 1. If the pole is complex and the zero is the only remaining real zero*, then pair the pole with the *next* closest zero (guaranteed to be complex). This is necessary to ensure that there will be a real zero remaining to eventually create a first-order section (thus keeping the odd order). 2. Else pair the pole with the closest remaining zero (complex or real). 3. Proceed to complete the second-order section by adding another pole and zero to the current pole and zero in the section: 1. If the current pole and zero are both complex, add their conjugates. 2. Else if the pole is complex and the zero is real, add the conjugate pole and the next closest real zero. 3. Else if the pole is real and the zero is complex, add the conjugate zero and the real pole closest to those zeros. 4. Else (we must have a real pole and real zero) add the next real pole closest to the unit circle, and then add the real zero closest to that pole. .. [#] This conditional can only be met for specific odd-order inputs with the ``pairing == 'keep_odd'`` method. .. versionadded:: 0.16.0 Examples -------- Design a 6th order low-pass elliptic digital filter for a system with a sampling rate of 8000 Hz that has a pass-band corner frequency of 1000 Hz. The ripple in the pass-band should not exceed 0.087 dB, and the attenuation in the stop-band should be at least 90 dB. In the following call to `signal.ellip`, we could use ``output='sos'``, but for this example, we'll use ``output='zpk'``, and then convert to SOS format with `zpk2sos`: >>> from scipy import signal >>> z, p, k = signal.ellip(6, 0.087, 90, 1000/(0.5*8000), output='zpk') Now convert to SOS format. >>> sos = signal.zpk2sos(z, p, k) The coefficients of the numerators of the sections: >>> sos[:, :3] array([[ 0.0014154 , 0.00248707, 0.0014154 ], [ 1. , 0.72965193, 1. ], [ 1. , 0.17594966, 1. ]]) The symmetry in the coefficients occurs because all the zeros are on the unit circle. The coefficients of the denominators of the sections: >>> sos[:, 3:] array([[ 1. , -1.32543251, 0.46989499], [ 1. , -1.26117915, 0.6262586 ], [ 1. , -1.25707217, 0.86199667]]) The next example shows the effect of the `pairing` option. We have a system with three poles and three zeros, so the SOS array will have shape (2, 6). The means there is, in effect, an extra pole and an extra zero at the origin in the SOS representation. >>> z1 = np.array([-1, -0.5-0.5j, -0.5+0.5j]) >>> p1 = np.array([0.75, 0.8+0.1j, 0.8-0.1j]) With ``pairing='nearest'`` (the default), we obtain >>> signal.zpk2sos(z1, p1, 1) array([[ 1. , 1. , 0.5 , 1. , -0.75, 0. ], [ 1. , 1. , 0. , 1. , -1.6 , 0.65]]) The first section has the zeros {-0.5-0.05j, -0.5+0.5j} and the poles {0, 0.75}, and the second section has the zeros {-1, 0} and poles {0.8+0.1j, 0.8-0.1j}. Note that the extra pole and zero at the origin have been assigned to different sections. With ``pairing='keep_odd'``, we obtain: >>> signal.zpk2sos(z1, p1, 1, pairing='keep_odd') array([[ 1. , 1. , 0. , 1. , -0.75, 0. ], [ 1. , 1. , 0.5 , 1. , -1.6 , 0.65]]) The extra pole and zero at the origin are in the same section. The first section is, in effect, a first-order section. """ # TODO in the near future: # 1. Add SOS capability to `filtfilt`, `freqz`, etc. somehow (#3259). # 2. Make `decimate` use `sosfilt` instead of `lfilter`. # 3. Make sosfilt automatically simplify sections to first order # when possible. Note this might make `sosfiltfilt` a bit harder (ICs). # 4. Further optimizations of the section ordering / pole-zero pairing. # See the wiki for other potential issues. valid_pairings = ['nearest', 'keep_odd'] if pairing not in valid_pairings: raise ValueError('pairing must be one of %s, not %s' % (valid_pairings, pairing)) if len(z) == len(p) == 0: return array([[k, 0., 0., 1., 0., 0.]]) # ensure we have the same number of poles and zeros, and make copies p = np.concatenate((p, np.zeros(max(len(z) - len(p), 0)))) z = np.concatenate((z, np.zeros(max(len(p) - len(z), 0)))) n_sections = (max(len(p), len(z)) + 1) // 2 sos = zeros((n_sections, 6)) if len(p) % 2 == 1 and pairing == 'nearest': p = np.concatenate((p, [0.])) z = np.concatenate((z, [0.])) assert len(p) == len(z) # Ensure we have complex conjugate pairs # (note that _cplxreal only gives us one element of each complex pair): z = np.concatenate(_cplxreal(z)) p = np.concatenate(_cplxreal(p)) p_sos = np.zeros((n_sections, 2), np.complex128) z_sos = np.zeros_like(p_sos) for si in range(n_sections): # Select the next "worst" pole p1_idx = np.argmin(np.abs(1 - np.abs(p))) p1 = p[p1_idx] p = np.delete(p, p1_idx) # Pair that pole with a zero if np.isreal(p1) and np.isreal(p).sum() == 0: # Special case to set a first-order section z1_idx = _nearest_real_complex_idx(z, p1, 'real') z1 = z[z1_idx] z = np.delete(z, z1_idx) p2 = z2 = 0 else: if not np.isreal(p1) and np.isreal(z).sum() == 1: # Special case to ensure we choose a complex zero to pair # with so later (setting up a first-order section) z1_idx = _nearest_real_complex_idx(z, p1, 'complex') assert not np.isreal(z[z1_idx]) else: # Pair the pole with the closest zero (real or complex) z1_idx = np.argmin(np.abs(p1 - z)) z1 = z[z1_idx] z = np.delete(z, z1_idx) # Now that we have p1 and z1, figure out what p2 and z2 need to be if not np.isreal(p1): if not np.isreal(z1): # complex pole, complex zero p2 = p1.conj() z2 = z1.conj() else: # complex pole, real zero p2 = p1.conj() z2_idx = _nearest_real_complex_idx(z, p1, 'real') z2 = z[z2_idx] assert np.isreal(z2) z = np.delete(z, z2_idx) else: if not np.isreal(z1): # real pole, complex zero z2 = z1.conj() p2_idx = _nearest_real_complex_idx(p, z1, 'real') p2 = p[p2_idx] assert np.isreal(p2) else: # real pole, real zero # pick the next "worst" pole to use idx = np.where(np.isreal(p))[0] assert len(idx) > 0 p2_idx = idx[np.argmin(np.abs(np.abs(p[idx]) - 1))] p2 = p[p2_idx] # find a real zero to match the added pole assert np.isreal(p2) z2_idx = _nearest_real_complex_idx(z, p2, 'real') z2 = z[z2_idx] assert np.isreal(z2) z = np.delete(z, z2_idx) p = np.delete(p, p2_idx) p_sos[si] = [p1, p2] z_sos[si] = [z1, z2] assert len(p) == len(z) == 0 # we've consumed all poles and zeros del p, z # Construct the system, reversing order so the "worst" are last p_sos = np.reshape(p_sos[::-1], (n_sections, 2)) z_sos = np.reshape(z_sos[::-1], (n_sections, 2)) gains = np.ones(n_sections) gains[0] = k for si in range(n_sections): x = zpk2tf(z_sos[si], p_sos[si], gains[si]) sos[si] = np.concatenate(x) return sos def _align_nums(nums): """Aligns the shapes of multiple numerators. Given an array of numerator coefficient arrays [[a_1, a_2,..., a_n],..., [b_1, b_2,..., b_m]], this function pads shorter numerator arrays with zero's so that all numerators have the same length. Such alignment is necessary for functions like 'tf2ss', which needs the alignment when dealing with SIMO transfer functions. Parameters ---------- nums: array_like Numerator or list of numerators. Not necessarily with same length. Returns ------- nums: array The numerator. If `nums` input was a list of numerators then a 2d array with padded zeros for shorter numerators is returned. Otherwise returns ``np.asarray(nums)``. """ try: # The statement can throw a ValueError if one # of the numerators is a single digit and another # is array-like e.g. if nums = [5, [1, 2, 3]] nums = asarray(nums) if not np.issubdtype(nums.dtype, np.number): raise ValueError("dtype of numerator is non-numeric") return nums except ValueError: nums = [np.atleast_1d(num) for num in nums] max_width = max(num.size for num in nums) # pre-allocate aligned_nums = np.zeros((len(nums), max_width)) # Create numerators with padded zeros for index, num in enumerate(nums): aligned_nums[index, -num.size:] = num return aligned_nums def normalize(b, a): """Normalize numerator/denominator of a continuous-time transfer function. If values of `b` are too close to 0, they are removed. In that case, a BadCoefficients warning is emitted. Parameters ---------- b: array_like Numerator of the transfer function. Can be a 2d array to normalize multiple transfer functions. a: array_like Denominator of the transfer function. At most 1d. Returns ------- num: array The numerator of the normalized transfer function. At least a 1d array. A 2d-array if the input `num` is a 2d array. den: 1d-array The denominator of the normalized transfer function. Notes ----- Coefficients for both the numerator and denominator should be specified in descending exponent order (e.g., ``s^2 + 3s + 5`` would be represented as ``[1, 3, 5]``). """ num, den = b, a den = np.atleast_1d(den) num = np.atleast_2d(_align_nums(num)) if den.ndim != 1: raise ValueError("Denominator polynomial must be rank-1 array.") if num.ndim > 2: raise ValueError("Numerator polynomial must be rank-1 or" " rank-2 array.") if np.all(den == 0): raise ValueError("Denominator must have at least on nonzero element.") # Trim leading zeros in denominator, leave at least one. den = np.trim_zeros(den, 'f') # Normalize transfer function num, den = num / den[0], den / den[0] # Count numerator columns that are all zero leading_zeros = 0 for col in num.T: if np.allclose(col, 0, atol=1e-14): leading_zeros += 1 else: break # Trim leading zeros of numerator if leading_zeros > 0: warnings.warn("Badly conditioned filter coefficients (numerator): the " "results may be meaningless", BadCoefficients) # Make sure at least one column remains if leading_zeros == num.shape[1]: leading_zeros -= 1 num = num[:, leading_zeros:] # Squeeze first dimension if singular if num.shape[0] == 1: num = num[0, :] return num, den def lp2lp(b, a, wo=1.0): """ Transform a lowpass filter prototype to a different frequency. Return an analog low-pass filter with cutoff frequency `wo` from an analog low-pass filter prototype with unity cutoff frequency, in transfer function ('ba') representation. """ a, b = map(atleast_1d, (a, b)) try: wo = float(wo) except TypeError: wo = float(wo[0]) d = len(a) n = len(b) M = max((d, n)) pwo = pow(wo, numpy.arange(M - 1, -1, -1)) start1 = max((n - d, 0)) start2 = max((d - n, 0)) b = b * pwo[start1] / pwo[start2:] a = a * pwo[start1] / pwo[start1:] return normalize(b, a) def lp2hp(b, a, wo=1.0): """ Transform a lowpass filter prototype to a highpass filter. Return an analog high-pass filter with cutoff frequency `wo` from an analog low-pass filter prototype with unity cutoff frequency, in transfer function ('ba') representation. """ a, b = map(atleast_1d, (a, b)) try: wo = float(wo) except TypeError: wo = float(wo[0]) d = len(a) n = len(b) if wo != 1: pwo = pow(wo, numpy.arange(max((d, n)))) else: pwo = numpy.ones(max((d, n)), b.dtype.char) if d >= n: outa = a[::-1] * pwo outb = resize(b, (d,)) outb[n:] = 0.0 outb[:n] = b[::-1] * pwo[:n] else: outb = b[::-1] * pwo outa = resize(a, (n,)) outa[d:] = 0.0 outa[:d] = a[::-1] * pwo[:d] return normalize(outb, outa) def lp2bp(b, a, wo=1.0, bw=1.0): """ Transform a lowpass filter prototype to a bandpass filter. Return an analog band-pass filter with center frequency `wo` and bandwidth `bw` from an analog low-pass filter prototype with unity cutoff frequency, in transfer function ('ba') representation. """ a, b = map(atleast_1d, (a, b)) D = len(a) - 1 N = len(b) - 1 artype = mintypecode((a, b)) ma = max([N, D]) Np = N + ma Dp = D + ma bprime = numpy.zeros(Np + 1, artype) aprime = numpy.zeros(Dp + 1, artype) wosq = wo * wo for j in range(Np + 1): val = 0.0 for i in range(0, N + 1): for k in range(0, i + 1): if ma - i + 2 * k == j: val += comb(i, k) * b[N - i] * (wosq) ** (i - k) / bw ** i bprime[Np - j] = val for j in range(Dp + 1): val = 0.0 for i in range(0, D + 1): for k in range(0, i + 1): if ma - i + 2 * k == j: val += comb(i, k) * a[D - i] * (wosq) ** (i - k) / bw ** i aprime[Dp - j] = val return normalize(bprime, aprime) def lp2bs(b, a, wo=1.0, bw=1.0): """ Transform a lowpass filter prototype to a bandstop filter. Return an analog band-stop filter with center frequency `wo` and bandwidth `bw` from an analog low-pass filter prototype with unity cutoff frequency, in transfer function ('ba') representation. """ a, b = map(atleast_1d, (a, b)) D = len(a) - 1 N = len(b) - 1 artype = mintypecode((a, b)) M = max([N, D]) Np = M + M Dp = M + M bprime = numpy.zeros(Np + 1, artype) aprime = numpy.zeros(Dp + 1, artype) wosq = wo * wo for j in range(Np + 1): val = 0.0 for i in range(0, N + 1): for k in range(0, M - i + 1): if i + 2 * k == j: val += (comb(M - i, k) * b[N - i] * (wosq) ** (M - i - k) * bw ** i) bprime[Np - j] = val for j in range(Dp + 1): val = 0.0 for i in range(0, D + 1): for k in range(0, M - i + 1): if i + 2 * k == j: val += (comb(M - i, k) * a[D - i] * (wosq) ** (M - i - k) * bw ** i) aprime[Dp - j] = val return normalize(bprime, aprime) def bilinear(b, a, fs=1.0): """Return a digital filter from an analog one using a bilinear transform. The bilinear transform substitutes ``(z-1) / (z+1)`` for ``s``. """ fs = float(fs) a, b = map(atleast_1d, (a, b)) D = len(a) - 1 N = len(b) - 1 artype = float M = max([N, D]) Np = M Dp = M bprime = numpy.zeros(Np + 1, artype) aprime = numpy.zeros(Dp + 1, artype) for j in range(Np + 1): val = 0.0 for i in range(N + 1): for k in range(i + 1): for l in range(M - i + 1): if k + l == j: val += (comb(i, k) * comb(M - i, l) * b[N - i] * pow(2 * fs, i) * (-1) ** k) bprime[j] = real(val) for j in range(Dp + 1): val = 0.0 for i in range(D + 1): for k in range(i + 1): for l in range(M - i + 1): if k + l == j: val += (comb(i, k) * comb(M - i, l) * a[D - i] * pow(2 * fs, i) * (-1) ** k) aprime[j] = real(val) return normalize(bprime, aprime) def iirdesign(wp, ws, gpass, gstop, analog=False, ftype='ellip', output='ba'): """Complete IIR digital and analog filter design. Given passband and stopband frequencies and gains, construct an analog or digital IIR filter of minimum order for a given basic type. Return the output in numerator, denominator ('ba'), pole-zero ('zpk') or second order sections ('sos') form. Parameters ---------- wp, ws : float Passband and stopband edge frequencies. For digital filters, these are normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`wp` and `ws` are thus in half-cycles / sample.) For example: - Lowpass: wp = 0.2, ws = 0.3 - Highpass: wp = 0.3, ws = 0.2 - Bandpass: wp = [0.2, 0.5], ws = [0.1, 0.6] - Bandstop: wp = [0.1, 0.6], ws = [0.2, 0.5] For analog filters, `wp` and `ws` are angular frequencies (e.g. rad/s). gpass : float The maximum loss in the passband (dB). gstop : float The minimum attenuation in the stopband (dB). analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. ftype : str, optional The type of IIR filter to design: - Butterworth : 'butter' - Chebyshev I : 'cheby1' - Chebyshev II : 'cheby2' - Cauer/elliptic: 'ellip' - Bessel/Thomson: 'bessel' output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. See Also -------- butter : Filter design using order and critical points cheby1, cheby2, ellip, bessel buttord : Find order and critical points from passband and stopband spec cheb1ord, cheb2ord, ellipord iirfilter : General filter design using order and critical frequencies Notes ----- The ``'sos'`` output parameter was added in 0.16.0. """ try: ordfunc = filter_dict[ftype][1] except KeyError: raise ValueError("Invalid IIR filter type: %s" % ftype) except IndexError: raise ValueError(("%s does not have order selection. Use " "iirfilter function.") % ftype) wp = atleast_1d(wp) ws = atleast_1d(ws) band_type = 2 * (len(wp) - 1) band_type += 1 if wp[0] >= ws[0]: band_type += 1 btype = {1: 'lowpass', 2: 'highpass', 3: 'bandstop', 4: 'bandpass'}[band_type] N, Wn = ordfunc(wp, ws, gpass, gstop, analog=analog) return iirfilter(N, Wn, rp=gpass, rs=gstop, analog=analog, btype=btype, ftype=ftype, output=output) def iirfilter(N, Wn, rp=None, rs=None, btype='band', analog=False, ftype='butter', output='ba'): """ IIR digital and analog filter design given order and critical points. Design an Nth-order digital or analog filter and return the filter coefficients. Parameters ---------- N : int The order of the filter. Wn : array_like A scalar or length-2 sequence giving the critical frequencies. For digital filters, `Wn` is normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`Wn` is thus in half-cycles / sample.) For analog filters, `Wn` is an angular frequency (e.g. rad/s). rp : float, optional For Chebyshev and elliptic filters, provides the maximum ripple in the passband. (dB) rs : float, optional For Chebyshev and elliptic filters, provides the minimum attenuation in the stop band. (dB) btype : {'bandpass', 'lowpass', 'highpass', 'bandstop'}, optional The type of filter. Default is 'bandpass'. analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. ftype : str, optional The type of IIR filter to design: - Butterworth : 'butter' - Chebyshev I : 'cheby1' - Chebyshev II : 'cheby2' - Cauer/elliptic: 'ellip' - Bessel/Thomson: 'bessel' output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. See Also -------- butter : Filter design using order and critical points cheby1, cheby2, ellip, bessel buttord : Find order and critical points from passband and stopband spec cheb1ord, cheb2ord, ellipord iirdesign : General filter design using passband and stopband spec Notes ----- The ``'sos'`` output parameter was added in 0.16.0. Examples -------- Generate a 17th-order Chebyshev II bandpass filter and plot the frequency response: >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> b, a = signal.iirfilter(17, [50, 200], rs=60, btype='band', ... analog=True, ftype='cheby2') >>> w, h = signal.freqs(b, a, 1000) >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> ax.semilogx(w, 20 * np.log10(abs(h))) >>> ax.set_title('Chebyshev Type II bandpass frequency response') >>> ax.set_xlabel('Frequency [radians / second]') >>> ax.set_ylabel('Amplitude [dB]') >>> ax.axis((10, 1000, -100, 10)) >>> ax.grid(which='both', axis='both') >>> plt.show() """ ftype, btype, output = [x.lower() for x in (ftype, btype, output)] Wn = asarray(Wn) try: btype = band_dict[btype] except KeyError: raise ValueError("'%s' is an invalid bandtype for filter." % btype) try: typefunc = filter_dict[ftype][0] except KeyError: raise ValueError("'%s' is not a valid basic IIR filter." % ftype) if output not in ['ba', 'zpk', 'sos']: raise ValueError("'%s' is not a valid output form." % output) if rp is not None and rp < 0: raise ValueError("passband ripple (rp) must be positive") if rs is not None and rs < 0: raise ValueError("stopband attenuation (rs) must be positive") # Get analog lowpass prototype if typefunc == buttap: z, p, k = typefunc(N) elif typefunc == besselap: z, p, k = typefunc(N, norm=bessel_norms[ftype]) elif typefunc == cheb1ap: if rp is None: raise ValueError("passband ripple (rp) must be provided to " "design a Chebyshev I filter.") z, p, k = typefunc(N, rp) elif typefunc == cheb2ap: if rs is None: raise ValueError("stopband attenuation (rs) must be provided to " "design an Chebyshev II filter.") z, p, k = typefunc(N, rs) elif typefunc == ellipap: if rs is None or rp is None: raise ValueError("Both rp and rs must be provided to design an " "elliptic filter.") z, p, k = typefunc(N, rp, rs) else: raise NotImplementedError("'%s' not implemented in iirfilter." % ftype) # Pre-warp frequencies for digital filter design if not analog: if numpy.any(Wn < 0) or numpy.any(Wn > 1): raise ValueError("Digital filter critical frequencies " "must be 0 <= Wn <= 1") fs = 2.0 warped = 2 * fs * tan(pi * Wn / fs) else: warped = Wn # transform to lowpass, bandpass, highpass, or bandstop if btype in ('lowpass', 'highpass'): if numpy.size(Wn) != 1: raise ValueError('Must specify a single critical frequency Wn') if btype == 'lowpass': z, p, k = _zpklp2lp(z, p, k, wo=warped) elif btype == 'highpass': z, p, k = _zpklp2hp(z, p, k, wo=warped) elif btype in ('bandpass', 'bandstop'): try: bw = warped[1] - warped[0] wo = sqrt(warped[0] * warped[1]) except IndexError: raise ValueError('Wn must specify start and stop frequencies') if btype == 'bandpass': z, p, k = _zpklp2bp(z, p, k, wo=wo, bw=bw) elif btype == 'bandstop': z, p, k = _zpklp2bs(z, p, k, wo=wo, bw=bw) else: raise NotImplementedError("'%s' not implemented in iirfilter." % btype) # Find discrete equivalent if necessary if not analog: z, p, k = _zpkbilinear(z, p, k, fs=fs) # Transform to proper out type (pole-zero, state-space, numer-denom) if output == 'zpk': return z, p, k elif output == 'ba': return zpk2tf(z, p, k) elif output == 'sos': return zpk2sos(z, p, k) def _relative_degree(z, p): """ Return relative degree of transfer function from zeros and poles """ degree = len(p) - len(z) if degree < 0: raise ValueError("Improper transfer function. " "Must have at least as many poles as zeros.") else: return degree # TODO: merge these into existing functions or make public versions def _zpkbilinear(z, p, k, fs): """ Return a digital filter from an analog one using a bilinear transform. Transform a set of poles and zeros from the analog s-plane to the digital z-plane using Tustin's method, which substitutes ``(z-1) / (z+1)`` for ``s``, maintaining the shape of the frequency response. Parameters ---------- z : array_like Zeros of the analog IIR filter transfer function. p : array_like Poles of the analog IIR filter transfer function. k : float System gain of the analog IIR filter transfer function. fs : float Sample rate, as ordinary frequency (e.g. hertz). No prewarping is done in this function. Returns ------- z : ndarray Zeros of the transformed digital filter transfer function. p : ndarray Poles of the transformed digital filter transfer function. k : float System gain of the transformed digital filter. """ z = atleast_1d(z) p = atleast_1d(p) degree = _relative_degree(z, p) fs2 = 2*fs # Bilinear transform the poles and zeros z_z = (fs2 + z) / (fs2 - z) p_z = (fs2 + p) / (fs2 - p) # Any zeros that were at infinity get moved to the Nyquist frequency z_z = append(z_z, -ones(degree)) # Compensate for gain change k_z = k * real(prod(fs2 - z) / prod(fs2 - p)) return z_z, p_z, k_z def _zpklp2lp(z, p, k, wo=1.0): r""" Transform a lowpass filter prototype to a different frequency. Return an analog low-pass filter with cutoff frequency `wo` from an analog low-pass filter prototype with unity cutoff frequency, using zeros, poles, and gain ('zpk') representation. Parameters ---------- z : array_like Zeros of the analog IIR filter transfer function. p : array_like Poles of the analog IIR filter transfer function. k : float System gain of the analog IIR filter transfer function. wo : float Desired cutoff, as angular frequency (e.g. rad/s). Defaults to no change. Returns ------- z : ndarray Zeros of the transformed low-pass filter transfer function. p : ndarray Poles of the transformed low-pass filter transfer function. k : float System gain of the transformed low-pass filter. Notes ----- This is derived from the s-plane substitution .. math:: s \rightarrow \frac{s}{\omega_0} """ z = atleast_1d(z) p = atleast_1d(p) wo = float(wo) # Avoid int wraparound degree = _relative_degree(z, p) # Scale all points radially from origin to shift cutoff frequency z_lp = wo * z p_lp = wo * p # Each shifted pole decreases gain by wo, each shifted zero increases it. # Cancel out the net change to keep overall gain the same k_lp = k * wo**degree return z_lp, p_lp, k_lp def _zpklp2hp(z, p, k, wo=1.0): r""" Transform a lowpass filter prototype to a highpass filter. Return an analog high-pass filter with cutoff frequency `wo` from an analog low-pass filter prototype with unity cutoff frequency, using zeros, poles, and gain ('zpk') representation. Parameters ---------- z : array_like Zeros of the analog IIR filter transfer function. p : array_like Poles of the analog IIR filter transfer function. k : float System gain of the analog IIR filter transfer function. wo : float Desired cutoff, as angular frequency (e.g. rad/s). Defaults to no change. Returns ------- z : ndarray Zeros of the transformed high-pass filter transfer function. p : ndarray Poles of the transformed high-pass filter transfer function. k : float System gain of the transformed high-pass filter. Notes ----- This is derived from the s-plane substitution .. math:: s \rightarrow \frac{\omega_0}{s} This maintains symmetry of the lowpass and highpass responses on a logarithmic scale. """ z = atleast_1d(z) p = atleast_1d(p) wo = float(wo) degree = _relative_degree(z, p) # Invert positions radially about unit circle to convert LPF to HPF # Scale all points radially from origin to shift cutoff frequency z_hp = wo / z p_hp = wo / p # If lowpass had zeros at infinity, inverting moves them to origin. z_hp = append(z_hp, zeros(degree)) # Cancel out gain change caused by inversion k_hp = k * real(prod(-z) / prod(-p)) return z_hp, p_hp, k_hp def _zpklp2bp(z, p, k, wo=1.0, bw=1.0): r""" Transform a lowpass filter prototype to a bandpass filter. Return an analog band-pass filter with center frequency `wo` and bandwidth `bw` from an analog low-pass filter prototype with unity cutoff frequency, using zeros, poles, and gain ('zpk') representation. Parameters ---------- z : array_like Zeros of the analog IIR filter transfer function. p : array_like Poles of the analog IIR filter transfer function. k : float System gain of the analog IIR filter transfer function. wo : float Desired passband center, as angular frequency (e.g. rad/s). Defaults to no change. bw : float Desired passband width, as angular frequency (e.g. rad/s). Defaults to 1. Returns ------- z : ndarray Zeros of the transformed band-pass filter transfer function. p : ndarray Poles of the transformed band-pass filter transfer function. k : float System gain of the transformed band-pass filter. Notes ----- This is derived from the s-plane substitution .. math:: s \rightarrow \frac{s^2 + {\omega_0}^2}{s \cdot \mathrm{BW}} This is the "wideband" transformation, producing a passband with geometric (log frequency) symmetry about `wo`. """ z = atleast_1d(z) p = atleast_1d(p) wo = float(wo) bw = float(bw) degree = _relative_degree(z, p) # Scale poles and zeros to desired bandwidth z_lp = z * bw/2 p_lp = p * bw/2 # Square root needs to produce complex result, not NaN z_lp = z_lp.astype(complex) p_lp = p_lp.astype(complex) # Duplicate poles and zeros and shift from baseband to +wo and -wo z_bp = concatenate((z_lp + sqrt(z_lp**2 - wo**2), z_lp - sqrt(z_lp**2 - wo**2))) p_bp = concatenate((p_lp + sqrt(p_lp**2 - wo**2), p_lp - sqrt(p_lp**2 - wo**2))) # Move degree zeros to origin, leaving degree zeros at infinity for BPF z_bp = append(z_bp, zeros(degree)) # Cancel out gain change from frequency scaling k_bp = k * bw**degree return z_bp, p_bp, k_bp def _zpklp2bs(z, p, k, wo=1.0, bw=1.0): r""" Transform a lowpass filter prototype to a bandstop filter. Return an analog band-stop filter with center frequency `wo` and stopband width `bw` from an analog low-pass filter prototype with unity cutoff frequency, using zeros, poles, and gain ('zpk') representation. Parameters ---------- z : array_like Zeros of the analog IIR filter transfer function. p : array_like Poles of the analog IIR filter transfer function. k : float System gain of the analog IIR filter transfer function. wo : float Desired stopband center, as angular frequency (e.g. rad/s). Defaults to no change. bw : float Desired stopband width, as angular frequency (e.g. rad/s). Defaults to 1. Returns ------- z : ndarray Zeros of the transformed band-stop filter transfer function. p : ndarray Poles of the transformed band-stop filter transfer function. k : float System gain of the transformed band-stop filter. Notes ----- This is derived from the s-plane substitution .. math:: s \rightarrow \frac{s \cdot \mathrm{BW}}{s^2 + {\omega_0}^2} This is the "wideband" transformation, producing a stopband with geometric (log frequency) symmetry about `wo`. """ z = atleast_1d(z) p = atleast_1d(p) wo = float(wo) bw = float(bw) degree = _relative_degree(z, p) # Invert to a highpass filter with desired bandwidth z_hp = (bw/2) / z p_hp = (bw/2) / p # Square root needs to produce complex result, not NaN z_hp = z_hp.astype(complex) p_hp = p_hp.astype(complex) # Duplicate poles and zeros and shift from baseband to +wo and -wo z_bs = concatenate((z_hp + sqrt(z_hp**2 - wo**2), z_hp - sqrt(z_hp**2 - wo**2))) p_bs = concatenate((p_hp + sqrt(p_hp**2 - wo**2), p_hp - sqrt(p_hp**2 - wo**2))) # Move any zeros that were at infinity to the center of the stopband z_bs = append(z_bs, +1j*wo * ones(degree)) z_bs = append(z_bs, -1j*wo * ones(degree)) # Cancel out gain change caused by inversion k_bs = k * real(prod(-z) / prod(-p)) return z_bs, p_bs, k_bs def butter(N, Wn, btype='low', analog=False, output='ba'): """ Butterworth digital and analog filter design. Design an Nth-order digital or analog Butterworth filter and return the filter coefficients. Parameters ---------- N : int The order of the filter. Wn : array_like A scalar or length-2 sequence giving the critical frequencies. For a Butterworth filter, this is the point at which the gain drops to 1/sqrt(2) that of the passband (the "-3 dB point"). For digital filters, `Wn` is normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`Wn` is thus in half-cycles / sample.) For analog filters, `Wn` is an angular frequency (e.g. rad/s). btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional The type of filter. Default is 'lowpass'. analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. See Also -------- buttord, buttap Notes ----- The Butterworth filter has maximally flat frequency response in the passband. The ``'sos'`` output parameter was added in 0.16.0. Examples -------- Plot the filter's frequency response, showing the critical points: >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> b, a = signal.butter(4, 100, 'low', analog=True) >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.title('Butterworth filter frequency response') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.axvline(100, color='green') # cutoff frequency >>> plt.show() """ return iirfilter(N, Wn, btype=btype, analog=analog, output=output, ftype='butter') def cheby1(N, rp, Wn, btype='low', analog=False, output='ba'): """ Chebyshev type I digital and analog filter design. Design an Nth-order digital or analog Chebyshev type I filter and return the filter coefficients. Parameters ---------- N : int The order of the filter. rp : float The maximum ripple allowed below unity gain in the passband. Specified in decibels, as a positive number. Wn : array_like A scalar or length-2 sequence giving the critical frequencies. For Type I filters, this is the point in the transition band at which the gain first drops below -`rp`. For digital filters, `Wn` is normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`Wn` is thus in half-cycles / sample.) For analog filters, `Wn` is an angular frequency (e.g. rad/s). btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional The type of filter. Default is 'lowpass'. analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. See Also -------- cheb1ord, cheb1ap Notes ----- The Chebyshev type I filter maximizes the rate of cutoff between the frequency response's passband and stopband, at the expense of ripple in the passband and increased ringing in the step response. Type I filters roll off faster than Type II (`cheby2`), but Type II filters do not have any ripple in the passband. The equiripple passband has N maxima or minima (for example, a 5th-order filter has 3 maxima and 2 minima). Consequently, the DC gain is unity for odd-order filters, or -rp dB for even-order filters. The ``'sos'`` output parameter was added in 0.16.0. Examples -------- Plot the filter's frequency response, showing the critical points: >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> b, a = signal.cheby1(4, 5, 100, 'low', analog=True) >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.title('Chebyshev Type I frequency response (rp=5)') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.axvline(100, color='green') # cutoff frequency >>> plt.axhline(-5, color='green') # rp >>> plt.show() """ return iirfilter(N, Wn, rp=rp, btype=btype, analog=analog, output=output, ftype='cheby1') def cheby2(N, rs, Wn, btype='low', analog=False, output='ba'): """ Chebyshev type II digital and analog filter design. Design an Nth-order digital or analog Chebyshev type II filter and return the filter coefficients. Parameters ---------- N : int The order of the filter. rs : float The minimum attenuation required in the stop band. Specified in decibels, as a positive number. Wn : array_like A scalar or length-2 sequence giving the critical frequencies. For Type II filters, this is the point in the transition band at which the gain first reaches -`rs`. For digital filters, `Wn` is normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`Wn` is thus in half-cycles / sample.) For analog filters, `Wn` is an angular frequency (e.g. rad/s). btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional The type of filter. Default is 'lowpass'. analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. See Also -------- cheb2ord, cheb2ap Notes ----- The Chebyshev type II filter maximizes the rate of cutoff between the frequency response's passband and stopband, at the expense of ripple in the stopband and increased ringing in the step response. Type II filters do not roll off as fast as Type I (`cheby1`). The ``'sos'`` output parameter was added in 0.16.0. Examples -------- Plot the filter's frequency response, showing the critical points: >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> b, a = signal.cheby2(4, 40, 100, 'low', analog=True) >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.title('Chebyshev Type II frequency response (rs=40)') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.axvline(100, color='green') # cutoff frequency >>> plt.axhline(-40, color='green') # rs >>> plt.show() """ return iirfilter(N, Wn, rs=rs, btype=btype, analog=analog, output=output, ftype='cheby2') def ellip(N, rp, rs, Wn, btype='low', analog=False, output='ba'): """ Elliptic (Cauer) digital and analog filter design. Design an Nth-order digital or analog elliptic filter and return the filter coefficients. Parameters ---------- N : int The order of the filter. rp : float The maximum ripple allowed below unity gain in the passband. Specified in decibels, as a positive number. rs : float The minimum attenuation required in the stop band. Specified in decibels, as a positive number. Wn : array_like A scalar or length-2 sequence giving the critical frequencies. For elliptic filters, this is the point in the transition band at which the gain first drops below -`rp`. For digital filters, `Wn` is normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`Wn` is thus in half-cycles / sample.) For analog filters, `Wn` is an angular frequency (e.g. rad/s). btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional The type of filter. Default is 'lowpass'. analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. See Also -------- ellipord, ellipap Notes ----- Also known as Cauer or Zolotarev filters, the elliptical filter maximizes the rate of transition between the frequency response's passband and stopband, at the expense of ripple in both, and increased ringing in the step response. As `rp` approaches 0, the elliptical filter becomes a Chebyshev type II filter (`cheby2`). As `rs` approaches 0, it becomes a Chebyshev type I filter (`cheby1`). As both approach 0, it becomes a Butterworth filter (`butter`). The equiripple passband has N maxima or minima (for example, a 5th-order filter has 3 maxima and 2 minima). Consequently, the DC gain is unity for odd-order filters, or -rp dB for even-order filters. The ``'sos'`` output parameter was added in 0.16.0. Examples -------- Plot the filter's frequency response, showing the critical points: >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> b, a = signal.ellip(4, 5, 40, 100, 'low', analog=True) >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.title('Elliptic filter frequency response (rp=5, rs=40)') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.axvline(100, color='green') # cutoff frequency >>> plt.axhline(-40, color='green') # rs >>> plt.axhline(-5, color='green') # rp >>> plt.show() """ return iirfilter(N, Wn, rs=rs, rp=rp, btype=btype, analog=analog, output=output, ftype='elliptic') def bessel(N, Wn, btype='low', analog=False, output='ba', norm='phase'): """ Bessel/Thomson digital and analog filter design. Design an Nth-order digital or analog Bessel filter and return the filter coefficients. Parameters ---------- N : int The order of the filter. Wn : array_like A scalar or length-2 sequence giving the critical frequencies (defined by the `norm` parameter). For analog filters, `Wn` is an angular frequency (e.g. rad/s). For digital filters, `Wn` is normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`Wn` is thus in half-cycles / sample.) btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional The type of filter. Default is 'lowpass'. analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. (See Notes.) output : {'ba', 'zpk', 'sos'}, optional Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or second-order sections ('sos'). Default is 'ba'. norm : {'phase', 'delay', 'mag'}, optional Critical frequency normalization: ``phase`` The filter is normalized such that the phase response reaches its midpoint at angular (e.g. rad/s) frequency `Wn`. This happens for both low-pass and high-pass filters, so this is the "phase-matched" case. The magnitude response asymptotes are the same as a Butterworth filter of the same order with a cutoff of `Wn`. This is the default, and matches MATLAB's implementation. ``delay`` The filter is normalized such that the group delay in the passband is 1/`Wn` (e.g. seconds). This is the "natural" type obtained by solving Bessel polynomials. ``mag`` The filter is normalized such that the gain magnitude is -3 dB at angular frequency `Wn`. .. versionadded:: 0.18.0 Returns ------- b, a : ndarray, ndarray Numerator (`b`) and denominator (`a`) polynomials of the IIR filter. Only returned if ``output='ba'``. z, p, k : ndarray, ndarray, float Zeros, poles, and system gain of the IIR filter transfer function. Only returned if ``output='zpk'``. sos : ndarray Second-order sections representation of the IIR filter. Only returned if ``output=='sos'``. Notes ----- Also known as a Thomson filter, the analog Bessel filter has maximally flat group delay and maximally linear phase response, with very little ringing in the step response. [1]_ The Bessel is inherently an analog filter. This function generates digital Bessel filters using the bilinear transform, which does not preserve the phase response of the analog filter. As such, it is only approximately correct at frequencies below about fs/4. To get maximally-flat group delay at higher frequencies, the analog Bessel filter must be transformed using phase-preserving techniques. See `besselap` for implementation details and references. The ``'sos'`` output parameter was added in 0.16.0. Examples -------- Plot the phase-normalized frequency response, showing the relationship to the Butterworth's cutoff frequency (green): >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> b, a = signal.butter(4, 100, 'low', analog=True) >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(np.abs(h)), color='silver', ls='dashed') >>> b, a = signal.bessel(4, 100, 'low', analog=True, norm='phase') >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(np.abs(h))) >>> plt.title('Bessel filter magnitude response (with Butterworth)') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.axvline(100, color='green') # cutoff frequency >>> plt.show() and the phase midpoint: >>> plt.figure() >>> plt.semilogx(w, np.unwrap(np.angle(h))) >>> plt.axvline(100, color='green') # cutoff frequency >>> plt.axhline(-np.pi, color='red') # phase midpoint >>> plt.title('Bessel filter phase response') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Phase [radians]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.show() Plot the magnitude-normalized frequency response, showing the -3 dB cutoff: >>> b, a = signal.bessel(3, 10, 'low', analog=True, norm='mag') >>> w, h = signal.freqs(b, a) >>> plt.semilogx(w, 20 * np.log10(np.abs(h))) >>> plt.axhline(-3, color='red') # -3 dB magnitude >>> plt.axvline(10, color='green') # cutoff frequency >>> plt.title('Magnitude-normalized Bessel filter frequency response') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.show() Plot the delay-normalized filter, showing the maximally-flat group delay at 0.1 seconds: >>> b, a = signal.bessel(5, 1/0.1, 'low', analog=True, norm='delay') >>> w, h = signal.freqs(b, a) >>> plt.figure() >>> plt.semilogx(w[1:], -np.diff(np.unwrap(np.angle(h)))/np.diff(w)) >>> plt.axhline(0.1, color='red') # 0.1 seconds group delay >>> plt.title('Bessel filter group delay') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Group delay [seconds]') >>> plt.margins(0, 0.1) >>> plt.grid(which='both', axis='both') >>> plt.show() References ---------- .. [1] Thomson, W.E., "Delay Networks having Maximally Flat Frequency Characteristics", Proceedings of the Institution of Electrical Engineers, Part III, November 1949, Vol. 96, No. 44, pp. 487-490. """ return iirfilter(N, Wn, btype=btype, analog=analog, output=output, ftype='bessel_'+norm) def maxflat(): pass def yulewalk(): pass def band_stop_obj(wp, ind, passb, stopb, gpass, gstop, type): """ Band Stop Objective Function for order minimization. Returns the non-integer order for an analog band stop filter. Parameters ---------- wp : scalar Edge of passband `passb`. ind : int, {0, 1} Index specifying which `passb` edge to vary (0 or 1). passb : ndarray Two element sequence of fixed passband edges. stopb : ndarray Two element sequence of fixed stopband edges. gstop : float Amount of attenuation in stopband in dB. gpass : float Amount of ripple in the passband in dB. type : {'butter', 'cheby', 'ellip'} Type of filter. Returns ------- n : scalar Filter order (possibly non-integer). """ passbC = passb.copy() passbC[ind] = wp nat = (stopb * (passbC[0] - passbC[1]) / (stopb ** 2 - passbC[0] * passbC[1])) nat = min(abs(nat)) if type == 'butter': GSTOP = 10 ** (0.1 * abs(gstop)) GPASS = 10 ** (0.1 * abs(gpass)) n = (log10((GSTOP - 1.0) / (GPASS - 1.0)) / (2 * log10(nat))) elif type == 'cheby': GSTOP = 10 ** (0.1 * abs(gstop)) GPASS = 10 ** (0.1 * abs(gpass)) n = arccosh(sqrt((GSTOP - 1.0) / (GPASS - 1.0))) / arccosh(nat) elif type == 'ellip': GSTOP = 10 ** (0.1 * gstop) GPASS = 10 ** (0.1 * gpass) arg1 = sqrt((GPASS - 1.0) / (GSTOP - 1.0)) arg0 = 1.0 / nat d0 = special.ellipk([arg0 ** 2, 1 - arg0 ** 2]) d1 = special.ellipk([arg1 ** 2, 1 - arg1 ** 2]) n = (d0[0] * d1[1] / (d0[1] * d1[0])) else: raise ValueError("Incorrect type: %s" % type) return n def buttord(wp, ws, gpass, gstop, analog=False): """Butterworth filter order selection. Return the order of the lowest order digital or analog Butterworth filter that loses no more than `gpass` dB in the passband and has at least `gstop` dB attenuation in the stopband. Parameters ---------- wp, ws : float Passband and stopband edge frequencies. For digital filters, these are normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`wp` and `ws` are thus in half-cycles / sample.) For example: - Lowpass: wp = 0.2, ws = 0.3 - Highpass: wp = 0.3, ws = 0.2 - Bandpass: wp = [0.2, 0.5], ws = [0.1, 0.6] - Bandstop: wp = [0.1, 0.6], ws = [0.2, 0.5] For analog filters, `wp` and `ws` are angular frequencies (e.g. rad/s). gpass : float The maximum loss in the passband (dB). gstop : float The minimum attenuation in the stopband (dB). analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. Returns ------- ord : int The lowest order for a Butterworth filter which meets specs. wn : ndarray or float The Butterworth natural frequency (i.e. the "3dB frequency"). Should be used with `butter` to give filter results. See Also -------- butter : Filter design using order and critical points cheb1ord : Find order and critical points from passband and stopband spec cheb2ord, ellipord iirfilter : General filter design using order and critical frequencies iirdesign : General filter design using passband and stopband spec Examples -------- Design an analog bandpass filter with passband within 3 dB from 20 to 50 rad/s, while rejecting at least -40 dB below 14 and above 60 rad/s. Plot its frequency response, showing the passband and stopband constraints in gray. >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> N, Wn = signal.buttord([20, 50], [14, 60], 3, 40, True) >>> b, a = signal.butter(N, Wn, 'band', True) >>> w, h = signal.freqs(b, a, np.logspace(1, 2, 500)) >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.title('Butterworth bandpass filter fit to constraints') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.grid(which='both', axis='both') >>> plt.fill([1, 14, 14, 1], [-40, -40, 99, 99], '0.9', lw=0) # stop >>> plt.fill([20, 20, 50, 50], [-99, -3, -3, -99], '0.9', lw=0) # pass >>> plt.fill([60, 60, 1e9, 1e9], [99, -40, -40, 99], '0.9', lw=0) # stop >>> plt.axis([10, 100, -60, 3]) >>> plt.show() """ wp = atleast_1d(wp) ws = atleast_1d(ws) filter_type = 2 * (len(wp) - 1) filter_type += 1 if wp[0] >= ws[0]: filter_type += 1 # Pre-warp frequencies for digital filter design if not analog: passb = tan(pi * wp / 2.0) stopb = tan(pi * ws / 2.0) else: passb = wp * 1.0 stopb = ws * 1.0 if filter_type == 1: # low nat = stopb / passb elif filter_type == 2: # high nat = passb / stopb elif filter_type == 3: # stop wp0 = optimize.fminbound(band_stop_obj, passb[0], stopb[0] - 1e-12, args=(0, passb, stopb, gpass, gstop, 'butter'), disp=0) passb[0] = wp0 wp1 = optimize.fminbound(band_stop_obj, stopb[1] + 1e-12, passb[1], args=(1, passb, stopb, gpass, gstop, 'butter'), disp=0) passb[1] = wp1 nat = ((stopb * (passb[0] - passb[1])) / (stopb ** 2 - passb[0] * passb[1])) elif filter_type == 4: # pass nat = ((stopb ** 2 - passb[0] * passb[1]) / (stopb * (passb[0] - passb[1]))) nat = min(abs(nat)) GSTOP = 10 ** (0.1 * abs(gstop)) GPASS = 10 ** (0.1 * abs(gpass)) ord = int(ceil(log10((GSTOP - 1.0) / (GPASS - 1.0)) / (2 * log10(nat)))) # Find the Butterworth natural frequency WN (or the "3dB" frequency") # to give exactly gpass at passb. try: W0 = (GPASS - 1.0) ** (-1.0 / (2.0 * ord)) except ZeroDivisionError: W0 = 1.0 print("Warning, order is zero...check input parameters.") # now convert this frequency back from lowpass prototype # to the original analog filter if filter_type == 1: # low WN = W0 * passb elif filter_type == 2: # high WN = passb / W0 elif filter_type == 3: # stop WN = numpy.zeros(2, float) discr = sqrt((passb[1] - passb[0]) ** 2 + 4 * W0 ** 2 * passb[0] * passb[1]) WN[0] = ((passb[1] - passb[0]) + discr) / (2 * W0) WN[1] = ((passb[1] - passb[0]) - discr) / (2 * W0) WN = numpy.sort(abs(WN)) elif filter_type == 4: # pass W0 = numpy.array([-W0, W0], float) WN = (-W0 * (passb[1] - passb[0]) / 2.0 + sqrt(W0 ** 2 / 4.0 * (passb[1] - passb[0]) ** 2 + passb[0] * passb[1])) WN = numpy.sort(abs(WN)) else: raise ValueError("Bad type: %s" % filter_type) if not analog: wn = (2.0 / pi) * arctan(WN) else: wn = WN if len(wn) == 1: wn = wn[0] return ord, wn def cheb1ord(wp, ws, gpass, gstop, analog=False): """Chebyshev type I filter order selection. Return the order of the lowest order digital or analog Chebyshev Type I filter that loses no more than `gpass` dB in the passband and has at least `gstop` dB attenuation in the stopband. Parameters ---------- wp, ws : float Passband and stopband edge frequencies. For digital filters, these are normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`wp` and `ws` are thus in half-cycles / sample.) For example: - Lowpass: wp = 0.2, ws = 0.3 - Highpass: wp = 0.3, ws = 0.2 - Bandpass: wp = [0.2, 0.5], ws = [0.1, 0.6] - Bandstop: wp = [0.1, 0.6], ws = [0.2, 0.5] For analog filters, `wp` and `ws` are angular frequencies (e.g. rad/s). gpass : float The maximum loss in the passband (dB). gstop : float The minimum attenuation in the stopband (dB). analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. Returns ------- ord : int The lowest order for a Chebyshev type I filter that meets specs. wn : ndarray or float The Chebyshev natural frequency (the "3dB frequency") for use with `cheby1` to give filter results. See Also -------- cheby1 : Filter design using order and critical points buttord : Find order and critical points from passband and stopband spec cheb2ord, ellipord iirfilter : General filter design using order and critical frequencies iirdesign : General filter design using passband and stopband spec Examples -------- Design a digital lowpass filter such that the passband is within 3 dB up to 0.2*(fs/2), while rejecting at least -40 dB above 0.3*(fs/2). Plot its frequency response, showing the passband and stopband constraints in gray. >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> N, Wn = signal.cheb1ord(0.2, 0.3, 3, 40) >>> b, a = signal.cheby1(N, 3, Wn, 'low') >>> w, h = signal.freqz(b, a) >>> plt.semilogx(w / np.pi, 20 * np.log10(abs(h))) >>> plt.title('Chebyshev I lowpass filter fit to constraints') >>> plt.xlabel('Normalized frequency') >>> plt.ylabel('Amplitude [dB]') >>> plt.grid(which='both', axis='both') >>> plt.fill([.01, 0.2, 0.2, .01], [-3, -3, -99, -99], '0.9', lw=0) # stop >>> plt.fill([0.3, 0.3, 2, 2], [ 9, -40, -40, 9], '0.9', lw=0) # pass >>> plt.axis([0.08, 1, -60, 3]) >>> plt.show() """ wp = atleast_1d(wp) ws = atleast_1d(ws) filter_type = 2 * (len(wp) - 1) if wp[0] < ws[0]: filter_type += 1 else: filter_type += 2 # Pre-warp frequencies for digital filter design if not analog: passb = tan(pi * wp / 2.0) stopb = tan(pi * ws / 2.0) else: passb = wp * 1.0 stopb = ws * 1.0 if filter_type == 1: # low nat = stopb / passb elif filter_type == 2: # high nat = passb / stopb elif filter_type == 3: # stop wp0 = optimize.fminbound(band_stop_obj, passb[0], stopb[0] - 1e-12, args=(0, passb, stopb, gpass, gstop, 'cheby'), disp=0) passb[0] = wp0 wp1 = optimize.fminbound(band_stop_obj, stopb[1] + 1e-12, passb[1], args=(1, passb, stopb, gpass, gstop, 'cheby'), disp=0) passb[1] = wp1 nat = ((stopb * (passb[0] - passb[1])) / (stopb ** 2 - passb[0] * passb[1])) elif filter_type == 4: # pass nat = ((stopb ** 2 - passb[0] * passb[1]) / (stopb * (passb[0] - passb[1]))) nat = min(abs(nat)) GSTOP = 10 ** (0.1 * abs(gstop)) GPASS = 10 ** (0.1 * abs(gpass)) ord = int(ceil(arccosh(sqrt((GSTOP - 1.0) / (GPASS - 1.0))) / arccosh(nat))) # Natural frequencies are just the passband edges if not analog: wn = (2.0 / pi) * arctan(passb) else: wn = passb if len(wn) == 1: wn = wn[0] return ord, wn def cheb2ord(wp, ws, gpass, gstop, analog=False): """Chebyshev type II filter order selection. Return the order of the lowest order digital or analog Chebyshev Type II filter that loses no more than `gpass` dB in the passband and has at least `gstop` dB attenuation in the stopband. Parameters ---------- wp, ws : float Passband and stopband edge frequencies. For digital filters, these are normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`wp` and `ws` are thus in half-cycles / sample.) For example: - Lowpass: wp = 0.2, ws = 0.3 - Highpass: wp = 0.3, ws = 0.2 - Bandpass: wp = [0.2, 0.5], ws = [0.1, 0.6] - Bandstop: wp = [0.1, 0.6], ws = [0.2, 0.5] For analog filters, `wp` and `ws` are angular frequencies (e.g. rad/s). gpass : float The maximum loss in the passband (dB). gstop : float The minimum attenuation in the stopband (dB). analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. Returns ------- ord : int The lowest order for a Chebyshev type II filter that meets specs. wn : ndarray or float The Chebyshev natural frequency (the "3dB frequency") for use with `cheby2` to give filter results. See Also -------- cheby2 : Filter design using order and critical points buttord : Find order and critical points from passband and stopband spec cheb1ord, ellipord iirfilter : General filter design using order and critical frequencies iirdesign : General filter design using passband and stopband spec Examples -------- Design a digital bandstop filter which rejects -60 dB from 0.2*(fs/2) to 0.5*(fs/2), while staying within 3 dB below 0.1*(fs/2) or above 0.6*(fs/2). Plot its frequency response, showing the passband and stopband constraints in gray. >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> N, Wn = signal.cheb2ord([0.1, 0.6], [0.2, 0.5], 3, 60) >>> b, a = signal.cheby2(N, 60, Wn, 'stop') >>> w, h = signal.freqz(b, a) >>> plt.semilogx(w / np.pi, 20 * np.log10(abs(h))) >>> plt.title('Chebyshev II bandstop filter fit to constraints') >>> plt.xlabel('Normalized frequency') >>> plt.ylabel('Amplitude [dB]') >>> plt.grid(which='both', axis='both') >>> plt.fill([.01, .1, .1, .01], [-3, -3, -99, -99], '0.9', lw=0) # stop >>> plt.fill([.2, .2, .5, .5], [ 9, -60, -60, 9], '0.9', lw=0) # pass >>> plt.fill([.6, .6, 2, 2], [-99, -3, -3, -99], '0.9', lw=0) # stop >>> plt.axis([0.06, 1, -80, 3]) >>> plt.show() """ wp = atleast_1d(wp) ws = atleast_1d(ws) filter_type = 2 * (len(wp) - 1) if wp[0] < ws[0]: filter_type += 1 else: filter_type += 2 # Pre-warp frequencies for digital filter design if not analog: passb = tan(pi * wp / 2.0) stopb = tan(pi * ws / 2.0) else: passb = wp * 1.0 stopb = ws * 1.0 if filter_type == 1: # low nat = stopb / passb elif filter_type == 2: # high nat = passb / stopb elif filter_type == 3: # stop wp0 = optimize.fminbound(band_stop_obj, passb[0], stopb[0] - 1e-12, args=(0, passb, stopb, gpass, gstop, 'cheby'), disp=0) passb[0] = wp0 wp1 = optimize.fminbound(band_stop_obj, stopb[1] + 1e-12, passb[1], args=(1, passb, stopb, gpass, gstop, 'cheby'), disp=0) passb[1] = wp1 nat = ((stopb * (passb[0] - passb[1])) / (stopb ** 2 - passb[0] * passb[1])) elif filter_type == 4: # pass nat = ((stopb ** 2 - passb[0] * passb[1]) / (stopb * (passb[0] - passb[1]))) nat = min(abs(nat)) GSTOP = 10 ** (0.1 * abs(gstop)) GPASS = 10 ** (0.1 * abs(gpass)) ord = int(ceil(arccosh(sqrt((GSTOP - 1.0) / (GPASS - 1.0))) / arccosh(nat))) # Find frequency where analog response is -gpass dB. # Then convert back from low-pass prototype to the original filter. new_freq = cosh(1.0 / ord * arccosh(sqrt((GSTOP - 1.0) / (GPASS - 1.0)))) new_freq = 1.0 / new_freq if filter_type == 1: nat = passb / new_freq elif filter_type == 2: nat = passb * new_freq elif filter_type == 3: nat = numpy.zeros(2, float) nat[0] = (new_freq / 2.0 * (passb[0] - passb[1]) + sqrt(new_freq ** 2 * (passb[1] - passb[0]) ** 2 / 4.0 + passb[1] * passb[0])) nat[1] = passb[1] * passb[0] / nat[0] elif filter_type == 4: nat = numpy.zeros(2, float) nat[0] = (1.0 / (2.0 * new_freq) * (passb[0] - passb[1]) + sqrt((passb[1] - passb[0]) ** 2 / (4.0 * new_freq ** 2) + passb[1] * passb[0])) nat[1] = passb[0] * passb[1] / nat[0] if not analog: wn = (2.0 / pi) * arctan(nat) else: wn = nat if len(wn) == 1: wn = wn[0] return ord, wn def ellipord(wp, ws, gpass, gstop, analog=False): """Elliptic (Cauer) filter order selection. Return the order of the lowest order digital or analog elliptic filter that loses no more than `gpass` dB in the passband and has at least `gstop` dB attenuation in the stopband. Parameters ---------- wp, ws : float Passband and stopband edge frequencies. For digital filters, these are normalized from 0 to 1, where 1 is the Nyquist frequency, pi radians/sample. (`wp` and `ws` are thus in half-cycles / sample.) For example: - Lowpass: wp = 0.2, ws = 0.3 - Highpass: wp = 0.3, ws = 0.2 - Bandpass: wp = [0.2, 0.5], ws = [0.1, 0.6] - Bandstop: wp = [0.1, 0.6], ws = [0.2, 0.5] For analog filters, `wp` and `ws` are angular frequencies (e.g. rad/s). gpass : float The maximum loss in the passband (dB). gstop : float The minimum attenuation in the stopband (dB). analog : bool, optional When True, return an analog filter, otherwise a digital filter is returned. Returns ------- ord : int The lowest order for an Elliptic (Cauer) filter that meets specs. wn : ndarray or float The Chebyshev natural frequency (the "3dB frequency") for use with `ellip` to give filter results. See Also -------- ellip : Filter design using order and critical points buttord : Find order and critical points from passband and stopband spec cheb1ord, cheb2ord iirfilter : General filter design using order and critical frequencies iirdesign : General filter design using passband and stopband spec Examples -------- Design an analog highpass filter such that the passband is within 3 dB above 30 rad/s, while rejecting -60 dB at 10 rad/s. Plot its frequency response, showing the passband and stopband constraints in gray. >>> from scipy import signal >>> import matplotlib.pyplot as plt >>> N, Wn = signal.ellipord(30, 10, 3, 60, True) >>> b, a = signal.ellip(N, 3, 60, Wn, 'high', True) >>> w, h = signal.freqs(b, a, np.logspace(0, 3, 500)) >>> plt.semilogx(w, 20 * np.log10(abs(h))) >>> plt.title('Elliptical highpass filter fit to constraints') >>> plt.xlabel('Frequency [radians / second]') >>> plt.ylabel('Amplitude [dB]') >>> plt.grid(which='both', axis='both') >>> plt.fill([.1, 10, 10, .1], [1e4, 1e4, -60, -60], '0.9', lw=0) # stop >>> plt.fill([30, 30, 1e9, 1e9], [-99, -3, -3, -99], '0.9', lw=0) # pass >>> plt.axis([1, 300, -80, 3]) >>> plt.show() """ wp = atleast_1d(wp) ws = atleast_1d(ws) filter_type = 2 * (len(wp) - 1) filter_type += 1 if wp[0] >= ws[0]: filter_type += 1 # Pre-warp frequencies for digital filter design if not analog: passb = tan(pi * wp / 2.0) stopb = tan(pi * ws / 2.0) else: passb = wp * 1.0 stopb = ws * 1.0 if filter_type == 1: # low nat = stopb / passb elif filter_type == 2: # high nat = passb / stopb elif filter_type == 3: # stop wp0 = optimize.fminbound(band_stop_obj, passb[0], stopb[0] - 1e-12, args=(0, passb, stopb, gpass, gstop, 'ellip'), disp=0) passb[0] = wp0 wp1 = optimize.fminbound(band_stop_obj, stopb[1] + 1e-12, passb[1], args=(1, passb, stopb, gpass, gstop, 'ellip'), disp=0) passb[1] = wp1 nat = ((stopb * (passb[0] - passb[1])) / (stopb ** 2 - passb[0] * passb[1])) elif filter_type == 4: # pass nat = ((stopb ** 2 - passb[0] * passb[1]) / (stopb * (passb[0] - passb[1]))) nat = min(abs(nat)) GSTOP = 10 ** (0.1 * gstop) GPASS = 10 ** (0.1 * gpass) arg1 = sqrt((GPASS - 1.0) / (GSTOP - 1.0)) arg0 = 1.0 / nat d0 = special.ellipk([arg0 ** 2, 1 - arg0 ** 2]) d1 = special.ellipk([arg1 ** 2, 1 - arg1 ** 2]) ord = int(ceil(d0[0] * d1[1] / (d0[1] * d1[0]))) if not analog: wn = arctan(passb) * 2.0 / pi else: wn = passb if len(wn) == 1: wn = wn[0] return ord, wn def buttap(N): """Return (z,p,k) for analog prototype of Nth-order Butterworth filter. The filter will have an angular (e.g. rad/s) cutoff frequency of 1. See Also -------- butter : Filter design function using this prototype """ if abs(int(N)) != N: raise ValueError("Filter order must be a nonnegative integer") z = numpy.array([]) m = numpy.arange(-N+1, N, 2) # Middle value is 0 to ensure an exactly real pole p = -numpy.exp(1j * pi * m / (2 * N)) k = 1 return z, p, k def cheb1ap(N, rp): """ Return (z,p,k) for Nth-order Chebyshev type I analog lowpass filter. The returned filter prototype has `rp` decibels of ripple in the passband. The filter's angular (e.g. rad/s) cutoff frequency is normalized to 1, defined as the point at which the gain first drops below ``-rp``. See Also -------- cheby1 : Filter design function using this prototype """ if abs(int(N)) != N: raise ValueError("Filter order must be a nonnegative integer") elif N == 0: # Avoid divide-by-zero error # Even order filters have DC gain of -rp dB return numpy.array([]), numpy.array([]), 10**(-rp/20) z = numpy.array([]) # Ripple factor (epsilon) eps = numpy.sqrt(10 ** (0.1 * rp) - 1.0) mu = 1.0 / N * arcsinh(1 / eps) # Arrange poles in an ellipse on the left half of the S-plane m = numpy.arange(-N+1, N, 2) theta = pi * m / (2*N) p = -sinh(mu + 1j*theta) k = numpy.prod(-p, axis=0).real if N % 2 == 0: k = k / sqrt((1 + eps * eps)) return z, p, k def cheb2ap(N, rs): """ Return (z,p,k) for Nth-order Chebyshev type I analog lowpass filter. The returned filter prototype has `rs` decibels of ripple in the stopband. The filter's angular (e.g. rad/s) cutoff frequency is normalized to 1, defined as the point at which the gain first reaches ``-rs``. See Also -------- cheby2 : Filter design function using this prototype """ if abs(int(N)) != N: raise ValueError("Filter order must be a nonnegative integer") elif N == 0: # Avoid divide-by-zero warning return numpy.array([]), numpy.array([]), 1 # Ripple factor (epsilon) de = 1.0 / sqrt(10 ** (0.1 * rs) - 1) mu = arcsinh(1.0 / de) / N if N % 2: m = numpy.concatenate((numpy.arange(-N+1, 0, 2), numpy.arange(2, N, 2))) else: m = numpy.arange(-N+1, N, 2) z = -conjugate(1j / sin(m * pi / (2.0 * N))) # Poles around the unit circle like Butterworth p = -exp(1j * pi * numpy.arange(-N+1, N, 2) / (2 * N)) # Warp into Chebyshev II p = sinh(mu) * p.real + 1j * cosh(mu) * p.imag p = 1.0 / p k = (numpy.prod(-p, axis=0) / numpy.prod(-z, axis=0)).real return z, p, k EPSILON = 2e-16 def _vratio(u, ineps, mp): [s, c, d, phi] = special.ellipj(u, mp) ret = abs(ineps - s / c) return ret def _kratio(m, k_ratio): m = float(m) if m < 0: m = 0.0 if m > 1: m = 1.0 if abs(m) > EPSILON and (abs(m) + EPSILON) < 1: k = special.ellipk([m, 1 - m]) r = k[0] / k[1] - k_ratio elif abs(m) > EPSILON: r = -k_ratio else: r = 1e20 return abs(r) def ellipap(N, rp, rs): """Return (z,p,k) of Nth-order elliptic analog lowpass filter. The filter is a normalized prototype that has `rp` decibels of ripple in the passband and a stopband `rs` decibels down. The filter's angular (e.g. rad/s) cutoff frequency is normalized to 1, defined as the point at which the gain first drops below ``-rp``. See Also -------- ellip : Filter design function using this prototype References ---------- .. [1] Lutova, Tosic, and Evans, "Filter Design for Signal Processing", Chapters 5 and 12. """ if abs(int(N)) != N: raise ValueError("Filter order must be a nonnegative integer") elif N == 0: # Avoid divide-by-zero warning # Even order filters have DC gain of -rp dB return numpy.array([]), numpy.array([]), 10**(-rp/20) elif N == 1: p = -sqrt(1.0 / (10 ** (0.1 * rp) - 1.0)) k = -p z = [] return asarray(z), asarray(p), k eps = numpy.sqrt(10 ** (0.1 * rp) - 1) ck1 = eps / numpy.sqrt(10 ** (0.1 * rs) - 1) ck1p = numpy.sqrt(1 - ck1 * ck1) if ck1p == 1: raise ValueError("Cannot design a filter with given rp and rs" " specifications.") val = special.ellipk([ck1 * ck1, ck1p * ck1p]) if abs(1 - ck1p * ck1p) < EPSILON: krat = 0 else: krat = N * val[0] / val[1] m = optimize.fmin(_kratio, [0.5], args=(krat,), maxfun=250, maxiter=250, disp=0) if m < 0 or m > 1: m = optimize.fminbound(_kratio, 0, 1, args=(krat,), maxfun=250, maxiter=250, disp=0) capk = special.ellipk(m) j = numpy.arange(1 - N % 2, N, 2) jj = len(j) [s, c, d, phi] = special.ellipj(j * capk / N, m * numpy.ones(jj)) snew = numpy.compress(abs(s) > EPSILON, s, axis=-1) z = 1.0 / (sqrt(m) * snew) z = 1j * z z = numpy.concatenate((z, conjugate(z))) r = optimize.fmin(_vratio, special.ellipk(m), args=(1. / eps, ck1p * ck1p), maxfun=250, maxiter=250, disp=0) v0 = capk * r / (N * val[0]) [sv, cv, dv, phi] = special.ellipj(v0, 1 - m) p = -(c * d * sv * cv + 1j * s * dv) / (1 - (d * sv) ** 2.0) if N % 2: newp = numpy.compress(abs(p.imag) > EPSILON * numpy.sqrt(numpy.sum(p * numpy.conjugate(p), axis=0).real), p, axis=-1) p = numpy.concatenate((p, conjugate(newp))) else: p = numpy.concatenate((p, conjugate(p))) k = (numpy.prod(-p, axis=0) / numpy.prod(-z, axis=0)).real if N % 2 == 0: k = k / numpy.sqrt((1 + eps * eps)) return z, p, k # TODO: Make this a real public function scipy.misc.ff def _falling_factorial(x, n): r""" Return the factorial of `x` to the `n` falling. This is defined as: .. math:: x^\underline n = (x)_n = x (x-1) \cdots (x-n+1) This can more efficiently calculate ratios of factorials, since: n!/m! == falling_factorial(n, n-m) where n >= m skipping the factors that cancel out the usual factorial n! == ff(n, n) """ val = 1 for k in range(x - n + 1, x + 1): val *= k return val def _bessel_poly(n, reverse=False): """ Return the coefficients of Bessel polynomial of degree `n` If `reverse` is true, a reverse Bessel polynomial is output. Output is a list of coefficients: [1] = 1 [1, 1] = 1*s + 1 [1, 3, 3] = 1*s^2 + 3*s + 3 [1, 6, 15, 15] = 1*s^3 + 6*s^2 + 15*s + 15 [1, 10, 45, 105, 105] = 1*s^4 + 10*s^3 + 45*s^2 + 105*s + 105 etc. Output is a Python list of arbitrary precision long ints, so n is only limited by your hardware's memory. Sequence is http://oeis.org/A001498 , and output can be confirmed to match http://oeis.org/A001498/b001498.txt : >>> i = 0 >>> for n in range(51): ... for x in _bessel_poly(n, reverse=True): ... print(i, x) ... i += 1 """ if abs(int(n)) != n: raise ValueError("Polynomial order must be a nonnegative integer") else: n = int(n) # np.int32 doesn't work, for instance out = [] for k in range(n + 1): num = _falling_factorial(2*n - k, n) den = 2**(n - k) * factorial(k, exact=True) out.append(num // den) if reverse: return out[::-1] else: return out def _campos_zeros(n): """ Return approximate zero locations of Bessel polynomials y_n(x) for order `n` using polynomial fit (Campos-Calderon 2011) """ if n == 1: return asarray([-1+0j]) s = npp_polyval(n, [0, 0, 2, 0, -3, 1]) b3 = npp_polyval(n, [16, -8]) / s b2 = npp_polyval(n, [-24, -12, 12]) / s b1 = npp_polyval(n, [8, 24, -12, -2]) / s b0 = npp_polyval(n, [0, -6, 0, 5, -1]) / s r = npp_polyval(n, [0, 0, 2, 1]) a1 = npp_polyval(n, [-6, -6]) / r a2 = 6 / r k = np.arange(1, n+1) x = npp_polyval(k, [0, a1, a2]) y = npp_polyval(k, [b0, b1, b2, b3]) return x + 1j*y def _aberth(f, fp, x0, tol=1e-15, maxiter=50): """ Given a function `f`, its first derivative `fp`, and a set of initial guesses `x0`, simultaneously find the roots of the polynomial using the Aberth-Ehrlich method. ``len(x0)`` should equal the number of roots of `f`. (This is not a complete implementation of Bini's algorithm.) """ N = len(x0) x = array(x0, complex) beta = np.empty_like(x0) for iteration in range(maxiter): alpha = -f(x) / fp(x) # Newton's method # Model "repulsion" between zeros for k in range(N): beta[k] = np.sum(1/(x[k] - x[k+1:])) beta[k] += np.sum(1/(x[k] - x[:k])) x += alpha / (1 + alpha * beta) if not all(np.isfinite(x)): raise RuntimeError('Root-finding calculation failed') # Mekwi: The iterative process can be stopped when |hn| has become # less than the largest error one is willing to permit in the root. if all(abs(alpha) <= tol): break else: raise Exception('Zeros failed to converge') return x def _bessel_zeros(N): """ Find zeros of ordinary Bessel polynomial of order `N`, by root-finding of modified Bessel function of the second kind """ if N == 0: return asarray([]) # Generate starting points x0 = _campos_zeros(N) # Zeros are the same for exp(1/x)*K_{N+0.5}(1/x) and Nth-order ordinary # Bessel polynomial y_N(x) def f(x): return special.kve(N+0.5, 1/x) # First derivative of above def fp(x): return (special.kve(N-0.5, 1/x)/(2*x**2) - special.kve(N+0.5, 1/x)/(x**2) + special.kve(N+1.5, 1/x)/(2*x**2)) # Starting points converge to true zeros x = _aberth(f, fp, x0) # Improve precision using Newton's method on each for i in range(len(x)): x[i] = optimize.newton(f, x[i], fp, tol=1e-15) # Average complex conjugates to make them exactly symmetrical x = np.mean((x, x[::-1].conj()), 0) # Zeros should sum to -1 if abs(np.sum(x) + 1) > 1e-15: raise RuntimeError('Generated zeros are inaccurate') return x def _norm_factor(p, k): """ Numerically find frequency shift to apply to delay-normalized filter such that -3 dB point is at 1 rad/sec. `p` is an array_like of polynomial poles `k` is a float gain First 10 values are listed in "Bessel Scale Factors" table, "Bessel Filters Polynomials, Poles and Circuit Elements 2003, C. Bond." """ p = asarray(p, dtype=complex) def G(w): """ Gain of filter """ return abs(k / prod(1j*w - p)) def cutoff(w): """ When gain = -3 dB, return 0 """ return G(w) - 1/np.sqrt(2) return optimize.newton(cutoff, 1.5) def besselap(N, norm='phase'): """ Return (z,p,k) for analog prototype of an Nth-order Bessel filter. Parameters ---------- N : int The order of the filter. norm : {'phase', 'delay', 'mag'}, optional Frequency normalization: ``phase`` The filter is normalized such that the phase response reaches its midpoint at an angular (e.g. rad/s) cutoff frequency of 1. This happens for both low-pass and high-pass filters, so this is the "phase-matched" case. [6]_ The magnitude response asymptotes are the same as a Butterworth filter of the same order with a cutoff of `Wn`. This is the default, and matches MATLAB's implementation. ``delay`` The filter is normalized such that the group delay in the passband is 1 (e.g. 1 second). This is the "natural" type obtained by solving Bessel polynomials ``mag`` The filter is normalized such that the gain magnitude is -3 dB at angular frequency 1. This is called "frequency normalization" by Bond. [1]_ .. versionadded:: 0.18.0 Returns ------- z : ndarray Zeros of the transfer function. Is always an empty array. p : ndarray Poles of the transfer function. k : scalar Gain of the transfer function. For phase-normalized, this is always 1. See Also -------- bessel : Filter design function using this prototype Notes ----- To find the pole locations, approximate starting points are generated [2]_ for the zeros of the ordinary Bessel polynomial [3]_, then the Aberth-Ehrlich method [4]_ [5]_ is used on the Kv(x) Bessel function to calculate more accurate zeros, and these locations are then inverted about the unit circle. References ---------- .. [1] C.R. Bond, "Bessel Filter Constants", http://www.crbond.com/papers/bsf.pdf .. [2] Campos and Calderon, "Approximate closed-form formulas for the zeros of the Bessel Polynomials", :arXiv:`1105.0957`. .. [3] Thomson, W.E., "Delay Networks having Maximally Flat Frequency Characteristics", Proceedings of the Institution of Electrical Engineers, Part III, November 1949, Vol. 96, No. 44, pp. 487-490. .. [4] Aberth, "Iteration Methods for Finding all Zeros of a Polynomial Simultaneously", Mathematics of Computation, Vol. 27, No. 122, April 1973 .. [5] Ehrlich, "A modified Newton method for polynomials", Communications of the ACM, Vol. 10, Issue 2, pp. 107-108, Feb. 1967, :DOI:`10.1145/363067.363115` .. [6] Miller and Bohn, "A Bessel Filter Crossover, and Its Relation to Others", RaneNote 147, 1998, http://www.rane.com/note147.html """ if abs(int(N)) != N: raise ValueError("Filter order must be a nonnegative integer") if N == 0: p = [] k = 1 else: # Find roots of reverse Bessel polynomial p = 1/_bessel_zeros(N) a_last = _falling_factorial(2*N, N) // 2**N # Shift them to a different normalization if required if norm in ('delay', 'mag'): # Normalized for group delay of 1 k = a_last if norm == 'mag': # -3 dB magnitude point is at 1 rad/sec norm_factor = _norm_factor(p, k) p /= norm_factor k = norm_factor**-N * a_last elif norm == 'phase': # Phase-matched (1/2 max phase shift at 1 rad/sec) # Asymptotes are same as Butterworth filter p *= 10**(-math.log10(a_last)/N) k = 1 else: raise ValueError('normalization not understood') return asarray([]), asarray(p, dtype=complex), float(k) def iirnotch(w0, Q): """ Design second-order IIR notch digital filter. A notch filter is a band-stop filter with a narrow bandwidth (high quality factor). It rejects a narrow frequency band and leaves the rest of the spectrum little changed. Parameters ---------- w0 : float Normalized frequency to remove from a signal. It is a scalar that must satisfy ``0 < w0 < 1``, with ``w0 = 1`` corresponding to half of the sampling frequency. Q : float Quality factor. Dimensionless parameter that characterizes notch filter -3 dB bandwidth ``bw`` relative to its center frequency, ``Q = w0/bw``. Returns ------- b, a : ndarray, ndarray Numerator (``b``) and denominator (``a``) polynomials of the IIR filter. See Also -------- iirpeak Notes ----- .. versionadded: 0.19.0 References ---------- .. [1] Sophocles J. Orfanidis, "Introduction To Signal Processing", Prentice-Hall, 1996 Examples -------- Design and plot filter to remove the 60Hz component from a signal sampled at 200Hz, using a quality factor Q = 30 >>> from scipy import signal >>> import numpy as np >>> import matplotlib.pyplot as plt >>> fs = 200.0 # Sample frequency (Hz) >>> f0 = 60.0 # Frequency to be removed from signal (Hz) >>> Q = 30.0 # Quality factor >>> w0 = f0/(fs/2) # Normalized Frequency >>> # Design notch filter >>> b, a = signal.iirnotch(w0, Q) >>> # Frequency response >>> w, h = signal.freqz(b, a) >>> # Generate frequency axis >>> freq = w*fs/(2*np.pi) >>> # Plot >>> fig, ax = plt.subplots(2, 1, figsize=(8, 6)) >>> ax[0].plot(freq, 20*np.log10(abs(h)), color='blue') >>> ax[0].set_title("Frequency Response") >>> ax[0].set_ylabel("Amplitude (dB)", color='blue') >>> ax[0].set_xlim([0, 100]) >>> ax[0].set_ylim([-25, 10]) >>> ax[0].grid() >>> ax[1].plot(freq, np.unwrap(np.angle(h))*180/np.pi, color='green') >>> ax[1].set_ylabel("Angle (degrees)", color='green') >>> ax[1].set_xlabel("Frequency (Hz)") >>> ax[1].set_xlim([0, 100]) >>> ax[1].set_yticks([-90, -60, -30, 0, 30, 60, 90]) >>> ax[1].set_ylim([-90, 90]) >>> ax[1].grid() >>> plt.show() """ return _design_notch_peak_filter(w0, Q, "notch") def iirpeak(w0, Q): """ Design second-order IIR peak (resonant) digital filter. A peak filter is a band-pass filter with a narrow bandwidth (high quality factor). It rejects components outside a narrow frequency band. Parameters ---------- w0 : float Normalized frequency to be retained in a signal. It is a scalar that must satisfy ``0 < w0 < 1``, with ``w0 = 1`` corresponding to half of the sampling frequency. Q : float Quality factor. Dimensionless parameter that characterizes peak filter -3 dB bandwidth ``bw`` relative to its center frequency, ``Q = w0/bw``. Returns ------- b, a : ndarray, ndarray Numerator (``b``) and denominator (``a``) polynomials of the IIR filter. See Also -------- iirnotch Notes ----- .. versionadded: 0.19.0 References ---------- .. [1] Sophocles J. Orfanidis, "Introduction To Signal Processing", Prentice-Hall, 1996 Examples -------- Design and plot filter to remove the frequencies other than the 300Hz component from a signal sampled at 1000Hz, using a quality factor Q = 30 >>> from scipy import signal >>> import numpy as np >>> import matplotlib.pyplot as plt >>> fs = 1000.0 # Sample frequency (Hz) >>> f0 = 300.0 # Frequency to be retained (Hz) >>> Q = 30.0 # Quality factor >>> w0 = f0/(fs/2) # Normalized Frequency >>> # Design peak filter >>> b, a = signal.iirpeak(w0, Q) >>> # Frequency response >>> w, h = signal.freqz(b, a) >>> # Generate frequency axis >>> freq = w*fs/(2*np.pi) >>> # Plot >>> fig, ax = plt.subplots(2, 1, figsize=(8, 6)) >>> ax[0].plot(freq, 20*np.log10(abs(h)), color='blue') >>> ax[0].set_title("Frequency Response") >>> ax[0].set_ylabel("Amplitude (dB)", color='blue') >>> ax[0].set_xlim([0, 500]) >>> ax[0].set_ylim([-50, 10]) >>> ax[0].grid() >>> ax[1].plot(freq, np.unwrap(np.angle(h))*180/np.pi, color='green') >>> ax[1].set_ylabel("Angle (degrees)", color='green') >>> ax[1].set_xlabel("Frequency (Hz)") >>> ax[1].set_xlim([0, 500]) >>> ax[1].set_yticks([-90, -60, -30, 0, 30, 60, 90]) >>> ax[1].set_ylim([-90, 90]) >>> ax[1].grid() >>> plt.show() """ return _design_notch_peak_filter(w0, Q, "peak") def _design_notch_peak_filter(w0, Q, ftype): """ Design notch or peak digital filter. Parameters ---------- w0 : float Normalized frequency to remove from a signal. It is a scalar that must satisfy ``0 < w0 < 1``, with ``w0 = 1`` corresponding to half of the sampling frequency. Q : float Quality factor. Dimensionless parameter that characterizes notch filter -3 dB bandwidth ``bw`` relative to its center frequency, ``Q = w0/bw``. ftype : str The type of IIR filter to design: - notch filter : ``notch`` - peak filter : ``peak`` Returns ------- b, a : ndarray, ndarray Numerator (``b``) and denominator (``a``) polynomials of the IIR filter. """ # Guarantee that the inputs are floats w0 = float(w0) Q = float(Q) # Checks if w0 is within the range if w0 > 1.0 or w0 < 0.0: raise ValueError("w0 should be such that 0 < w0 < 1") # Get bandwidth bw = w0/Q # Normalize inputs bw = bw*np.pi w0 = w0*np.pi # Compute -3dB atenuation gb = 1/np.sqrt(2) if ftype == "notch": # Compute beta: formula 11.3.4 (p.575) from reference [1] beta = (np.sqrt(1.0-gb**2.0)/gb)*np.tan(bw/2.0) elif ftype == "peak": # Compute beta: formula 11.3.19 (p.579) from reference [1] beta = (gb/np.sqrt(1.0-gb**2.0))*np.tan(bw/2.0) else: raise ValueError("Unknown ftype.") # Compute gain: formula 11.3.6 (p.575) from reference [1] gain = 1.0/(1.0+beta) # Compute numerator b and denominator a # formulas 11.3.7 (p.575) and 11.3.21 (p.579) # from reference [1] if ftype == "notch": b = gain*np.array([1.0, -2.0*np.cos(w0), 1.0]) else: b = (1.0-gain)*np.array([1.0, 0.0, -1.0]) a = np.array([1.0, -2.0*gain*np.cos(w0), (2.0*gain-1.0)]) return b, a filter_dict = {'butter': [buttap, buttord], 'butterworth': [buttap, buttord], 'cauer': [ellipap, ellipord], 'elliptic': [ellipap, ellipord], 'ellip': [ellipap, ellipord], 'bessel': [besselap], 'bessel_phase': [besselap], 'bessel_delay': [besselap], 'bessel_mag': [besselap], 'cheby1': [cheb1ap, cheb1ord], 'chebyshev1': [cheb1ap, cheb1ord], 'chebyshevi': [cheb1ap, cheb1ord], 'cheby2': [cheb2ap, cheb2ord], 'chebyshev2': [cheb2ap, cheb2ord], 'chebyshevii': [cheb2ap, cheb2ord], } band_dict = {'band': 'bandpass', 'bandpass': 'bandpass', 'pass': 'bandpass', 'bp': 'bandpass', 'bs': 'bandstop', 'bandstop': 'bandstop', 'bands': 'bandstop', 'stop': 'bandstop', 'l': 'lowpass', 'low': 'lowpass', 'lowpass': 'lowpass', 'lp': 'lowpass', 'high': 'highpass', 'highpass': 'highpass', 'h': 'highpass', 'hp': 'highpass', } bessel_norms = {'bessel': 'phase', 'bessel_phase': 'phase', 'bessel_delay': 'delay', 'bessel_mag': 'mag'}

bsd-3-clause

QudevETH/PycQED_py3

pycqed/tests/test_unit_conversions.py

1

1978

import unittest import numpy as np import matplotlib.pyplot as plt from pycqed.analysis.tools.plotting import SI_prefix_and_scale_factor from pycqed.analysis.tools.plotting import set_xlabel, set_ylabel from pycqed.analysis.tools.plotting import SI_val_to_msg_str class Test_SI_prefix_scale_factor(unittest.TestCase): def test_non_SI(self): unit = 'arb.unit.' scale_factor, post_unit = SI_prefix_and_scale_factor(val=5, unit=unit) self.assertEqual(scale_factor, 1) self.assertEqual(unit, post_unit) def test_SI_scale_factors(self): unit = 'V' scale_factor, post_unit = SI_prefix_and_scale_factor(val=5, unit=unit) self.assertEqual(scale_factor, 1) self.assertEqual(' '+unit, post_unit) scale_factor, post_unit = SI_prefix_and_scale_factor(val=5000, unit=unit) self.assertEqual(scale_factor, 1/1000) self.assertEqual('k'+unit, post_unit) scale_factor, post_unit = SI_prefix_and_scale_factor(val=0.05, unit=unit) self.assertEqual(scale_factor, 1000) self.assertEqual('m'+unit, post_unit) class test_SI_unit_aware_labels(unittest.TestCase): def test_label_scaling(self): """ This test creates a dummy plot and checks if the tick labels are rescaled correctly """ f, ax = plt.subplots() x = np.linspace(-6, 6, 101) y = np.cos(x) ax.plot(x*1000, y/1e5) set_xlabel(ax, 'Distance', 'm') set_ylabel(ax, 'Amplitude', 'V') xlab = ax.get_xlabel() ylab = ax.get_ylabel() self.assertEqual(xlab, 'Distance (km)') self.assertEqual(ylab, 'Amplitude (μV)') def test_SI_val_to_msg_str(self): val, unit = SI_val_to_msg_str(1030, 'm') self.assertEqual(val, str(1.03)) self.assertEqual(unit, 'km')

mit

ishay2b/tensorflow

tensorflow/examples/learn/wide_n_deep_tutorial.py

18

8111

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Example code for TensorFlow Wide & Deep Tutorial using TF.Learn API.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import shutil import sys import tempfile import pandas as pd from six.moves import urllib import tensorflow as tf CSV_COLUMNS = [ "age", "workclass", "fnlwgt", "education", "education_num", "marital_status", "occupation", "relationship", "race", "gender", "capital_gain", "capital_loss", "hours_per_week", "native_country", "income_bracket" ] gender = tf.feature_column.categorical_column_with_vocabulary_list( "gender", ["Female", "Male"]) education = tf.feature_column.categorical_column_with_vocabulary_list( "education", [ "Bachelors", "HS-grad", "11th", "Masters", "9th", "Some-college", "Assoc-acdm", "Assoc-voc", "7th-8th", "Doctorate", "Prof-school", "5th-6th", "10th", "1st-4th", "Preschool", "12th" ]) marital_status = tf.feature_column.categorical_column_with_vocabulary_list( "marital_status", [ "Married-civ-spouse", "Divorced", "Married-spouse-absent", "Never-married", "Separated", "Married-AF-spouse", "Widowed" ]) relationship = tf.feature_column.categorical_column_with_vocabulary_list( "relationship", [ "Husband", "Not-in-family", "Wife", "Own-child", "Unmarried", "Other-relative" ]) workclass = tf.feature_column.categorical_column_with_vocabulary_list( "workclass", [ "Self-emp-not-inc", "Private", "State-gov", "Federal-gov", "Local-gov", "?", "Self-emp-inc", "Without-pay", "Never-worked" ]) # To show an example of hashing: occupation = tf.feature_column.categorical_column_with_hash_bucket( "occupation", hash_bucket_size=1000) native_country = tf.feature_column.categorical_column_with_hash_bucket( "native_country", hash_bucket_size=1000) # Continuous base columns. age = tf.feature_column.numeric_column("age") education_num = tf.feature_column.numeric_column("education_num") capital_gain = tf.feature_column.numeric_column("capital_gain") capital_loss = tf.feature_column.numeric_column("capital_loss") hours_per_week = tf.feature_column.numeric_column("hours_per_week") # Transformations. age_buckets = tf.feature_column.bucketized_column( age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65]) # Wide columns and deep columns. base_columns = [ gender, education, marital_status, relationship, workclass, occupation, native_country, age_buckets, ] crossed_columns = [ tf.feature_column.crossed_column( ["education", "occupation"], hash_bucket_size=1000), tf.feature_column.crossed_column( [age_buckets, "education", "occupation"], hash_bucket_size=1000), tf.feature_column.crossed_column( ["native_country", "occupation"], hash_bucket_size=1000) ] deep_columns = [ tf.feature_column.indicator_column(workclass), tf.feature_column.indicator_column(education), tf.feature_column.indicator_column(gender), tf.feature_column.indicator_column(relationship), # To show an example of embedding tf.feature_column.embedding_column(native_country, dimension=8), tf.feature_column.embedding_column(occupation, dimension=8), age, education_num, capital_gain, capital_loss, hours_per_week, ] def maybe_download(train_data, test_data): """Maybe downloads training data and returns train and test file names.""" if train_data: train_file_name = train_data else: train_file = tempfile.NamedTemporaryFile(delete=False) urllib.request.urlretrieve( "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", train_file.name) # pylint: disable=line-too-long train_file_name = train_file.name train_file.close() print("Training data is downloaded to %s" % train_file_name) if test_data: test_file_name = test_data else: test_file = tempfile.NamedTemporaryFile(delete=False) urllib.request.urlretrieve( "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test", test_file.name) # pylint: disable=line-too-long test_file_name = test_file.name test_file.close() print("Test data is downloaded to %s"% test_file_name) return train_file_name, test_file_name def build_estimator(model_dir, model_type): """Build an estimator.""" if model_type == "wide": m = tf.estimator.LinearClassifier( model_dir=model_dir, feature_columns=base_columns + crossed_columns) elif model_type == "deep": m = tf.estimator.DNNClassifier( model_dir=model_dir, feature_columns=deep_columns, hidden_units=[100, 50]) else: m = tf.estimator.DNNLinearCombinedClassifier( model_dir=model_dir, linear_feature_columns=crossed_columns, dnn_feature_columns=deep_columns, dnn_hidden_units=[100, 50]) return m def input_fn(data_file, num_epochs, shuffle): """Input builder function.""" df_data = pd.read_csv( tf.gfile.Open(data_file), names=CSV_COLUMNS, skipinitialspace=True, engine="python", skiprows=1) # remove NaN elements df_data = df_data.dropna(how="any", axis=0) labels = df_data["income_bracket"].apply(lambda x: ">50K" in x).astype(int) return tf.estimator.inputs.pandas_input_fn( x=df_data, y=labels, batch_size=100, num_epochs=num_epochs, shuffle=shuffle, num_threads=5) def train_and_eval(model_dir, model_type, train_steps, train_data, test_data): """Train and evaluate the model.""" train_file_name, test_file_name = maybe_download(train_data, test_data) # Specify file path below if want to find the output easily model_dir = tempfile.mkdtemp() if not model_dir else model_dir m = build_estimator(model_dir, model_type) # set num_epochs to None to get infinite stream of data. m.train( input_fn=input_fn(train_file_name, num_epochs=None, shuffle=True), steps=train_steps) # set steps to None to run evaluation until all data consumed. results = m.evaluate( input_fn=input_fn(test_file_name, num_epochs=1, shuffle=False), steps=None) print("model directory = %s" % model_dir) for key in sorted(results): print("%s: %s" % (key, results[key])) # Manual cleanup shutil.rmtree(model_dir) FLAGS = None def main(_): train_and_eval(FLAGS.model_dir, FLAGS.model_type, FLAGS.train_steps, FLAGS.train_data, FLAGS.test_data) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.register("type", "bool", lambda v: v.lower() == "true") parser.add_argument( "--model_dir", type=str, default="", help="Base directory for output models." ) parser.add_argument( "--model_type", type=str, default="wide_n_deep", help="Valid model types: {'wide', 'deep', 'wide_n_deep'}." ) parser.add_argument( "--train_steps", type=int, default=2000, help="Number of training steps." ) parser.add_argument( "--train_data", type=str, default="", help="Path to the training data." ) parser.add_argument( "--test_data", type=str, default="", help="Path to the test data." ) FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

apache-2.0

mjgrav2001/scikit-learn

examples/model_selection/plot_confusion_matrix.py

244

2496

""" ================ Confusion matrix ================ Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. The diagonal elements represent the number of points for which the predicted label is equal to the true label, while off-diagonal elements are those that are mislabeled by the classifier. The higher the diagonal values of the confusion matrix the better, indicating many correct predictions. The figures show the confusion matrix with and without normalization by class support size (number of elements in each class). This kind of normalization can be interesting in case of class imbalance to have a more visual interpretation of which class is being misclassified. Here the results are not as good as they could be as our choice for the regularization parameter C was not the best. In real life applications this parameter is usually chosen using :ref:`grid_search`. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets from sklearn.cross_validation import train_test_split from sklearn.metrics import confusion_matrix # import some data to play with iris = datasets.load_iris() X = iris.data y = iris.target # Split the data into a training set and a test set X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) # Run classifier, using a model that is too regularized (C too low) to see # the impact on the results classifier = svm.SVC(kernel='linear', C=0.01) y_pred = classifier.fit(X_train, y_train).predict(X_test) def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues): plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(iris.target_names)) plt.xticks(tick_marks, iris.target_names, rotation=45) plt.yticks(tick_marks, iris.target_names) plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') # Compute confusion matrix cm = confusion_matrix(y_test, y_pred) np.set_printoptions(precision=2) print('Confusion matrix, without normalization') print(cm) plt.figure() plot_confusion_matrix(cm) # Normalize the confusion matrix by row (i.e by the number of samples # in each class) cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print('Normalized confusion matrix') print(cm_normalized) plt.figure() plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix') plt.show()

bsd-3-clause

valexandersaulys/prudential_insurance_kaggle

venv/lib/python2.7/site-packages/sklearn/mixture/tests/test_gmm.py

48

17414

import unittest import copy import sys from nose.tools import assert_true import numpy as np from numpy.testing import (assert_array_equal, assert_array_almost_equal, assert_raises) from scipy import stats from sklearn import mixture from sklearn.datasets.samples_generator import make_spd_matrix from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_raise_message from sklearn.metrics.cluster import adjusted_rand_score from sklearn.externals.six.moves import cStringIO as StringIO rng = np.random.RandomState(0) def test_sample_gaussian(): # Test sample generation from mixture.sample_gaussian where covariance # is diagonal, spherical and full n_features, n_samples = 2, 300 axis = 1 mu = rng.randint(10) * rng.rand(n_features) cv = (rng.rand(n_features) + 1.0) ** 2 samples = mixture.sample_gaussian( mu, cv, covariance_type='diag', n_samples=n_samples) assert_true(np.allclose(samples.mean(axis), mu, atol=1.3)) assert_true(np.allclose(samples.var(axis), cv, atol=1.5)) # the same for spherical covariances cv = (rng.rand() + 1.0) ** 2 samples = mixture.sample_gaussian( mu, cv, covariance_type='spherical', n_samples=n_samples) assert_true(np.allclose(samples.mean(axis), mu, atol=1.5)) assert_true(np.allclose( samples.var(axis), np.repeat(cv, n_features), atol=1.5)) # and for full covariances A = rng.randn(n_features, n_features) cv = np.dot(A.T, A) + np.eye(n_features) samples = mixture.sample_gaussian( mu, cv, covariance_type='full', n_samples=n_samples) assert_true(np.allclose(samples.mean(axis), mu, atol=1.3)) assert_true(np.allclose(np.cov(samples), cv, atol=2.5)) # Numerical stability check: in SciPy 0.12.0 at least, eigh may return # tiny negative values in its second return value. from sklearn.mixture import sample_gaussian x = sample_gaussian([0, 0], [[4, 3], [1, .1]], covariance_type='full', random_state=42) assert_true(np.isfinite(x).all()) def _naive_lmvnpdf_diag(X, mu, cv): # slow and naive implementation of lmvnpdf ref = np.empty((len(X), len(mu))) stds = np.sqrt(cv) for i, (m, std) in enumerate(zip(mu, stds)): ref[:, i] = np.log(stats.norm.pdf(X, m, std)).sum(axis=1) return ref def test_lmvnpdf_diag(): # test a slow and naive implementation of lmvnpdf and # compare it to the vectorized version (mixture.lmvnpdf) to test # for correctness n_features, n_components, n_samples = 2, 3, 10 mu = rng.randint(10) * rng.rand(n_components, n_features) cv = (rng.rand(n_components, n_features) + 1.0) ** 2 X = rng.randint(10) * rng.rand(n_samples, n_features) ref = _naive_lmvnpdf_diag(X, mu, cv) lpr = mixture.log_multivariate_normal_density(X, mu, cv, 'diag') assert_array_almost_equal(lpr, ref) def test_lmvnpdf_spherical(): n_features, n_components, n_samples = 2, 3, 10 mu = rng.randint(10) * rng.rand(n_components, n_features) spherecv = rng.rand(n_components, 1) ** 2 + 1 X = rng.randint(10) * rng.rand(n_samples, n_features) cv = np.tile(spherecv, (n_features, 1)) reference = _naive_lmvnpdf_diag(X, mu, cv) lpr = mixture.log_multivariate_normal_density(X, mu, spherecv, 'spherical') assert_array_almost_equal(lpr, reference) def test_lmvnpdf_full(): n_features, n_components, n_samples = 2, 3, 10 mu = rng.randint(10) * rng.rand(n_components, n_features) cv = (rng.rand(n_components, n_features) + 1.0) ** 2 X = rng.randint(10) * rng.rand(n_samples, n_features) fullcv = np.array([np.diag(x) for x in cv]) reference = _naive_lmvnpdf_diag(X, mu, cv) lpr = mixture.log_multivariate_normal_density(X, mu, fullcv, 'full') assert_array_almost_equal(lpr, reference) def test_lvmpdf_full_cv_non_positive_definite(): n_features, n_samples = 2, 10 rng = np.random.RandomState(0) X = rng.randint(10) * rng.rand(n_samples, n_features) mu = np.mean(X, 0) cv = np.array([[[-1, 0], [0, 1]]]) expected_message = "'covars' must be symmetric, positive-definite" assert_raise_message(ValueError, expected_message, mixture.log_multivariate_normal_density, X, mu, cv, 'full') def test_GMM_attributes(): n_components, n_features = 10, 4 covariance_type = 'diag' g = mixture.GMM(n_components, covariance_type, random_state=rng) weights = rng.rand(n_components) weights = weights / weights.sum() means = rng.randint(-20, 20, (n_components, n_features)) assert_true(g.n_components == n_components) assert_true(g.covariance_type == covariance_type) g.weights_ = weights assert_array_almost_equal(g.weights_, weights) g.means_ = means assert_array_almost_equal(g.means_, means) covars = (0.1 + 2 * rng.rand(n_components, n_features)) ** 2 g.covars_ = covars assert_array_almost_equal(g.covars_, covars) assert_raises(ValueError, g._set_covars, []) assert_raises(ValueError, g._set_covars, np.zeros((n_components - 2, n_features))) assert_raises(ValueError, mixture.GMM, n_components=20, covariance_type='badcovariance_type') class GMMTester(): do_test_eval = True def _setUp(self): self.n_components = 10 self.n_features = 4 self.weights = rng.rand(self.n_components) self.weights = self.weights / self.weights.sum() self.means = rng.randint(-20, 20, (self.n_components, self.n_features)) self.threshold = -0.5 self.I = np.eye(self.n_features) self.covars = { 'spherical': (0.1 + 2 * rng.rand(self.n_components, self.n_features)) ** 2, 'tied': (make_spd_matrix(self.n_features, random_state=0) + 5 * self.I), 'diag': (0.1 + 2 * rng.rand(self.n_components, self.n_features)) ** 2, 'full': np.array([make_spd_matrix(self.n_features, random_state=0) + 5 * self.I for x in range(self.n_components)])} def test_eval(self): if not self.do_test_eval: return # DPGMM does not support setting the means and # covariances before fitting There is no way of fixing this # due to the variational parameters being more expressive than # covariance matrices g = self.model(n_components=self.n_components, covariance_type=self.covariance_type, random_state=rng) # Make sure the means are far apart so responsibilities.argmax() # picks the actual component used to generate the observations. g.means_ = 20 * self.means g.covars_ = self.covars[self.covariance_type] g.weights_ = self.weights gaussidx = np.repeat(np.arange(self.n_components), 5) n_samples = len(gaussidx) X = rng.randn(n_samples, self.n_features) + g.means_[gaussidx] ll, responsibilities = g.score_samples(X) self.assertEqual(len(ll), n_samples) self.assertEqual(responsibilities.shape, (n_samples, self.n_components)) assert_array_almost_equal(responsibilities.sum(axis=1), np.ones(n_samples)) assert_array_equal(responsibilities.argmax(axis=1), gaussidx) def test_sample(self, n=100): g = self.model(n_components=self.n_components, covariance_type=self.covariance_type, random_state=rng) # Make sure the means are far apart so responsibilities.argmax() # picks the actual component used to generate the observations. g.means_ = 20 * self.means g.covars_ = np.maximum(self.covars[self.covariance_type], 0.1) g.weights_ = self.weights samples = g.sample(n) self.assertEqual(samples.shape, (n, self.n_features)) def test_train(self, params='wmc'): g = mixture.GMM(n_components=self.n_components, covariance_type=self.covariance_type) g.weights_ = self.weights g.means_ = self.means g.covars_ = 20 * self.covars[self.covariance_type] # Create a training set by sampling from the predefined distribution. X = g.sample(n_samples=100) g = self.model(n_components=self.n_components, covariance_type=self.covariance_type, random_state=rng, min_covar=1e-1, n_iter=1, init_params=params) g.fit(X) # Do one training iteration at a time so we can keep track of # the log likelihood to make sure that it increases after each # iteration. trainll = [] for _ in range(5): g.params = params g.init_params = '' g.fit(X) trainll.append(self.score(g, X)) g.n_iter = 10 g.init_params = '' g.params = params g.fit(X) # finish fitting # Note that the log likelihood will sometimes decrease by a # very small amount after it has more or less converged due to # the addition of min_covar to the covariance (to prevent # underflow). This is why the threshold is set to -0.5 # instead of 0. delta_min = np.diff(trainll).min() self.assertTrue( delta_min > self.threshold, "The min nll increase is %f which is lower than the admissible" " threshold of %f, for model %s. The likelihoods are %s." % (delta_min, self.threshold, self.covariance_type, trainll)) def test_train_degenerate(self, params='wmc'): # Train on degenerate data with 0 in some dimensions # Create a training set by sampling from the predefined distribution. X = rng.randn(100, self.n_features) X.T[1:] = 0 g = self.model(n_components=2, covariance_type=self.covariance_type, random_state=rng, min_covar=1e-3, n_iter=5, init_params=params) g.fit(X) trainll = g.score(X) self.assertTrue(np.sum(np.abs(trainll / 100 / X.shape[1])) < 5) def test_train_1d(self, params='wmc'): # Train on 1-D data # Create a training set by sampling from the predefined distribution. X = rng.randn(100, 1) # X.T[1:] = 0 g = self.model(n_components=2, covariance_type=self.covariance_type, random_state=rng, min_covar=1e-7, n_iter=5, init_params=params) g.fit(X) trainll = g.score(X) if isinstance(g, mixture.DPGMM): self.assertTrue(np.sum(np.abs(trainll / 100)) < 5) else: self.assertTrue(np.sum(np.abs(trainll / 100)) < 2) def score(self, g, X): return g.score(X).sum() class TestGMMWithSphericalCovars(unittest.TestCase, GMMTester): covariance_type = 'spherical' model = mixture.GMM setUp = GMMTester._setUp class TestGMMWithDiagonalCovars(unittest.TestCase, GMMTester): covariance_type = 'diag' model = mixture.GMM setUp = GMMTester._setUp class TestGMMWithTiedCovars(unittest.TestCase, GMMTester): covariance_type = 'tied' model = mixture.GMM setUp = GMMTester._setUp class TestGMMWithFullCovars(unittest.TestCase, GMMTester): covariance_type = 'full' model = mixture.GMM setUp = GMMTester._setUp def test_multiple_init(): # Test that multiple inits does not much worse than a single one X = rng.randn(30, 5) X[:10] += 2 g = mixture.GMM(n_components=2, covariance_type='spherical', random_state=rng, min_covar=1e-7, n_iter=5) train1 = g.fit(X).score(X).sum() g.n_init = 5 train2 = g.fit(X).score(X).sum() assert_true(train2 >= train1 - 1.e-2) def test_n_parameters(): # Test that the right number of parameters is estimated n_samples, n_dim, n_components = 7, 5, 2 X = rng.randn(n_samples, n_dim) n_params = {'spherical': 13, 'diag': 21, 'tied': 26, 'full': 41} for cv_type in ['full', 'tied', 'diag', 'spherical']: g = mixture.GMM(n_components=n_components, covariance_type=cv_type, random_state=rng, min_covar=1e-7, n_iter=1) g.fit(X) assert_true(g._n_parameters() == n_params[cv_type]) def test_1d_1component(): # Test all of the covariance_types return the same BIC score for # 1-dimensional, 1 component fits. n_samples, n_dim, n_components = 100, 1, 1 X = rng.randn(n_samples, n_dim) g_full = mixture.GMM(n_components=n_components, covariance_type='full', random_state=rng, min_covar=1e-7, n_iter=1) g_full.fit(X) g_full_bic = g_full.bic(X) for cv_type in ['tied', 'diag', 'spherical']: g = mixture.GMM(n_components=n_components, covariance_type=cv_type, random_state=rng, min_covar=1e-7, n_iter=1) g.fit(X) assert_array_almost_equal(g.bic(X), g_full_bic) def assert_fit_predict_correct(model, X): model2 = copy.deepcopy(model) predictions_1 = model.fit(X).predict(X) predictions_2 = model2.fit_predict(X) assert adjusted_rand_score(predictions_1, predictions_2) == 1.0 def test_fit_predict(): """ test that gmm.fit_predict is equivalent to gmm.fit + gmm.predict """ lrng = np.random.RandomState(101) n_samples, n_dim, n_comps = 100, 2, 2 mu = np.array([[8, 8]]) component_0 = lrng.randn(n_samples, n_dim) component_1 = lrng.randn(n_samples, n_dim) + mu X = np.vstack((component_0, component_1)) for m_constructor in (mixture.GMM, mixture.VBGMM, mixture.DPGMM): model = m_constructor(n_components=n_comps, covariance_type='full', min_covar=1e-7, n_iter=5, random_state=np.random.RandomState(0)) assert_fit_predict_correct(model, X) model = mixture.GMM(n_components=n_comps, n_iter=0) z = model.fit_predict(X) assert np.all(z == 0), "Quick Initialization Failed!" def test_aic(): # Test the aic and bic criteria n_samples, n_dim, n_components = 50, 3, 2 X = rng.randn(n_samples, n_dim) SGH = 0.5 * (X.var() + np.log(2 * np.pi)) # standard gaussian entropy for cv_type in ['full', 'tied', 'diag', 'spherical']: g = mixture.GMM(n_components=n_components, covariance_type=cv_type, random_state=rng, min_covar=1e-7) g.fit(X) aic = 2 * n_samples * SGH * n_dim + 2 * g._n_parameters() bic = (2 * n_samples * SGH * n_dim + np.log(n_samples) * g._n_parameters()) bound = n_dim * 3. / np.sqrt(n_samples) assert_true(np.abs(g.aic(X) - aic) / n_samples < bound) assert_true(np.abs(g.bic(X) - bic) / n_samples < bound) def check_positive_definite_covars(covariance_type): r"""Test that covariance matrices do not become non positive definite Due to the accumulation of round-off errors, the computation of the covariance matrices during the learning phase could lead to non-positive definite covariance matrices. Namely the use of the formula: .. math:: C = (\sum_i w_i x_i x_i^T) - \mu \mu^T instead of: .. math:: C = \sum_i w_i (x_i - \mu)(x_i - \mu)^T while mathematically equivalent, was observed a ``LinAlgError`` exception, when computing a ``GMM`` with full covariance matrices and fixed mean. This function ensures that some later optimization will not introduce the problem again. """ rng = np.random.RandomState(1) # we build a dataset with 2 2d component. The components are unbalanced # (respective weights 0.9 and 0.1) X = rng.randn(100, 2) X[-10:] += (3, 3) # Shift the 10 last points gmm = mixture.GMM(2, params="wc", covariance_type=covariance_type, min_covar=1e-3) # This is a non-regression test for issue #2640. The following call used # to trigger: # numpy.linalg.linalg.LinAlgError: 2-th leading minor not positive definite gmm.fit(X) if covariance_type == "diag" or covariance_type == "spherical": assert_greater(gmm.covars_.min(), 0) else: if covariance_type == "tied": covs = [gmm.covars_] else: covs = gmm.covars_ for c in covs: assert_greater(np.linalg.det(c), 0) def test_positive_definite_covars(): # Check positive definiteness for all covariance types for covariance_type in ["full", "tied", "diag", "spherical"]: yield check_positive_definite_covars, covariance_type def test_verbose_first_level(): # Create sample data X = rng.randn(30, 5) X[:10] += 2 g = mixture.GMM(n_components=2, n_init=2, verbose=1) old_stdout = sys.stdout sys.stdout = StringIO() try: g.fit(X) finally: sys.stdout = old_stdout def test_verbose_second_level(): # Create sample data X = rng.randn(30, 5) X[:10] += 2 g = mixture.GMM(n_components=2, n_init=2, verbose=2) old_stdout = sys.stdout sys.stdout = StringIO() try: g.fit(X) finally: sys.stdout = old_stdout

gpl-2.0

unnati-xyz/droidcon-twitter-analytics

geeksrus/utils/dbconn.py

1

2020

from pymongo import MongoClient import pandas as pd def connect_mongo(db, host='localhost', port=27017, username=None, password=None): """ A util for making a connection to mongo """ if username and password: mongo_uri = 'mongodb://%s:%s@%s:%s/%s' % (username, password, host, port, db) conn = MongoClient(mongo_uri) else: conn = MongoClient(host, port) return conn[db] def read_mongo(db_conn, collection, query={}, no_id=True): """ Read from Mongo and Store into DataFrame """ # Make a query to the specific DB and Collection print(query) cursor = db_conn[collection].find(query) # Expand the cursor and construct the DataFrame df = pd.DataFrame(list(cursor)) # Delete the _id if no_id: del df['_id'] return df def read_mongo_projection(db_conn, collection, query={}, no_id=True): """ Read from Mongo and Store into DataFrame """ # Make a query to the specific DB and Collection cursor = db_conn[collection].find(projection=query) # Expand the cursor and construct the DataFrame df = pd.DataFrame(list(cursor)) # Delete the _id if no_id: del df['_id'] return df def write_mongo(db_conn, collection, document): result = db_conn[collection].insert(document) def find_and_sort_desc(db_conn, collection, field, no_id = True): # Make a query to the specific DB and Collection cursor = db_conn[collection].find().sort(field, -1) # Expand the cursor and construct the DataFrame df = pd.DataFrame(list(cursor)) # Delete the _id if no_id: del df['_id'] return df def find_with_project_and_sort(db_conn, collection, field,query={}, no_id = True): # Make a query to the specific DB and Collection cursor = db_conn[collection].find(projection=query).sort(field, -1) # Expand the cursor and construct the DataFrame df = pd.DataFrame(list(cursor)) # Delete the _id if no_id: del df['_id'] return df

mit

IndraVikas/scikit-learn

examples/calibration/plot_calibration_curve.py

225

5903

""" ============================== Probability Calibration curves ============================== When performing classification one often wants to predict not only the class label, but also the associated probability. This probability gives some kind of confidence on the prediction. This example demonstrates how to display how well calibrated the predicted probabilities are and how to calibrate an uncalibrated classifier. The experiment is performed on an artificial dataset for binary classification with 100.000 samples (1.000 of them are used for model fitting) with 20 features. Of the 20 features, only 2 are informative and 10 are redundant. The first figure shows the estimated probabilities obtained with logistic regression, Gaussian naive Bayes, and Gaussian naive Bayes with both isotonic calibration and sigmoid calibration. The calibration performance is evaluated with Brier score, reported in the legend (the smaller the better). One can observe here that logistic regression is well calibrated while raw Gaussian naive Bayes performs very badly. This is because of the redundant features which violate the assumption of feature-independence and result in an overly confident classifier, which is indicated by the typical transposed-sigmoid curve. Calibration of the probabilities of Gaussian naive Bayes with isotonic regression can fix this issue as can be seen from the nearly diagonal calibration curve. Sigmoid calibration also improves the brier score slightly, albeit not as strongly as the non-parametric isotonic regression. This can be attributed to the fact that we have plenty of calibration data such that the greater flexibility of the non-parametric model can be exploited. The second figure shows the calibration curve of a linear support-vector classifier (LinearSVC). LinearSVC shows the opposite behavior as Gaussian naive Bayes: the calibration curve has a sigmoid curve, which is typical for an under-confident classifier. In the case of LinearSVC, this is caused by the margin property of the hinge loss, which lets the model focus on hard samples that are close to the decision boundary (the support vectors). Both kinds of calibration can fix this issue and yield nearly identical results. This shows that sigmoid calibration can deal with situations where the calibration curve of the base classifier is sigmoid (e.g., for LinearSVC) but not where it is transposed-sigmoid (e.g., Gaussian naive Bayes). """ print(__doc__) # Author: Alexandre Gramfort <[email protected]> # Jan Hendrik Metzen <[email protected]> # License: BSD Style. import matplotlib.pyplot as plt from sklearn import datasets from sklearn.naive_bayes import GaussianNB from sklearn.svm import LinearSVC from sklearn.linear_model import LogisticRegression from sklearn.metrics import (brier_score_loss, precision_score, recall_score, f1_score) from sklearn.calibration import CalibratedClassifierCV, calibration_curve from sklearn.cross_validation import train_test_split # Create dataset of classification task with many redundant and few # informative features X, y = datasets.make_classification(n_samples=100000, n_features=20, n_informative=2, n_redundant=10, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.99, random_state=42) def plot_calibration_curve(est, name, fig_index): """Plot calibration curve for est w/o and with calibration. """ # Calibrated with isotonic calibration isotonic = CalibratedClassifierCV(est, cv=2, method='isotonic') # Calibrated with sigmoid calibration sigmoid = CalibratedClassifierCV(est, cv=2, method='sigmoid') # Logistic regression with no calibration as baseline lr = LogisticRegression(C=1., solver='lbfgs') fig = plt.figure(fig_index, figsize=(10, 10)) ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2) ax2 = plt.subplot2grid((3, 1), (2, 0)) ax1.plot([0, 1], [0, 1], "k:", label="Perfectly calibrated") for clf, name in [(lr, 'Logistic'), (est, name), (isotonic, name + ' + Isotonic'), (sigmoid, name + ' + Sigmoid')]: clf.fit(X_train, y_train) y_pred = clf.predict(X_test) if hasattr(clf, "predict_proba"): prob_pos = clf.predict_proba(X_test)[:, 1] else: # use decision function prob_pos = clf.decision_function(X_test) prob_pos = \ (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min()) clf_score = brier_score_loss(y_test, prob_pos, pos_label=y.max()) print("%s:" % name) print("\tBrier: %1.3f" % (clf_score)) print("\tPrecision: %1.3f" % precision_score(y_test, y_pred)) print("\tRecall: %1.3f" % recall_score(y_test, y_pred)) print("\tF1: %1.3f\n" % f1_score(y_test, y_pred)) fraction_of_positives, mean_predicted_value = \ calibration_curve(y_test, prob_pos, n_bins=10) ax1.plot(mean_predicted_value, fraction_of_positives, "s-", label="%s (%1.3f)" % (name, clf_score)) ax2.hist(prob_pos, range=(0, 1), bins=10, label=name, histtype="step", lw=2) ax1.set_ylabel("Fraction of positives") ax1.set_ylim([-0.05, 1.05]) ax1.legend(loc="lower right") ax1.set_title('Calibration plots (reliability curve)') ax2.set_xlabel("Mean predicted value") ax2.set_ylabel("Count") ax2.legend(loc="upper center", ncol=2) plt.tight_layout() # Plot calibration cuve for Gaussian Naive Bayes plot_calibration_curve(GaussianNB(), "Naive Bayes", 1) # Plot calibration cuve for Linear SVC plot_calibration_curve(LinearSVC(), "SVC", 2) plt.show()

bsd-3-clause

CalebBell/thermo

tests/test_interaction_parameters.py

1

3433

# -*- coding: utf-8 -*- '''Chemical Engineering Design Library (ChEDL). Utilities for process modeling. Copyright (C) 2017 Caleb Bell <[email protected]> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.''' from math import exp, log import pytest import numpy as np import pandas as pd from fluids.constants import calorie, R from thermo.interaction_parameters import IPDB from fluids.numerics import assert_close, assert_close1d, assert_close2d def run_validate_db(): from thermo.interaction_parameters import ip_files for name in ip_files.keys(): IPDB.validate_table(name) def test_basic_chemsep_PR(): kij = IPDB.get_ip_specific('ChemSep PR', ['124-38-9', '67-56-1'], 'kij') assert_close(kij, 0.0583) kij_auto = IPDB.get_ip_automatic(['124-38-9', '67-56-1'], 'PR kij', 'kij') assert_close(kij, kij_auto) kij_missing = IPDB.get_ip_specific('ChemSep PR', ['1249-38-9', '67-56-1'], 'kij') assert kij_missing == 0 assert False == IPDB.has_ip_specific('ChemSep PR', ['1249-38-9', '67-56-1'], 'kij') assert True == IPDB.has_ip_specific('ChemSep PR', ['124-38-9', '67-56-1'], 'kij') assert IPDB.get_tables_with_type('PR kij') == ['ChemSep PR'] # interaction parameter matrix kij_C1C4 = IPDB.get_ip_symmetric_matrix('ChemSep PR', ['74-82-8', '74-84-0', '74-98-6', '106-97-8'], 'kij') kij_C1C4_known = [[0.0, -0.0059, 0.0119, 0.0185], [-0.0059, 0.0, 0.0011, 0.0089], [0.0119, 0.0011, 0.0, 0.0033], [0.0185, 0.0089, 0.0033, 0.0]] assert_close2d(kij_C1C4, kij_C1C4_known) # Test for asymetric works the same since the model is asymmetric kij_C1C4 = IPDB.get_ip_symmetric_matrix('ChemSep PR', ['74-82-8', '74-84-0', '74-98-6', '106-97-8'], 'kij') assert_close2d(kij_C1C4, kij_C1C4_known) def test_basic_chemsep_NRTL(): # ethanol water, converted to metric, simple T dependence bijs = IPDB.get_ip_asymmetric_matrix('ChemSep NRTL', ['64-17-5', '7732-18-5'], 'bij') alphas_known = [[0.0, 0.2937, 0.3009], [0.2937, 0.0, 0.2999], [0.3009, 0.2999, 0.0]] # Test is works both symmetric and asymmetric alphas = IPDB.get_ip_asymmetric_matrix('ChemSep NRTL', ['64-17-5', '7732-18-5', '67-56-1'], 'alphaij') assert_close2d(alphas, alphas_known) alphas = IPDB.get_ip_symmetric_matrix('ChemSep NRTL', ['64-17-5', '7732-18-5', '67-56-1'], 'alphaij') assert_close2d(alphas, alphas_known)

mit

antoinecarme/pyaf

tests/model_control/test_ozone_custom_models_enabled.py

1

1923

import pandas as pd import numpy as np import pyaf.ForecastEngine as autof import pyaf.Bench.TS_datasets as tsds #get_ipython().magic('matplotlib inline') def pickleModel(iModel): import pickle output = pickle.dumps(iModel) lReloadedObject = pickle.loads(output) output2 = pickle.dumps(lReloadedObject) assert(iModel.to_json() == lReloadedObject.to_json()) return lReloadedObject; def build_model(transformations, trends, periodics, autoregs): b1 = tsds.load_ozone_exogenous() df = b1.mPastData lEngine = autof.cForecastEngine() lEngine H = b1.mHorizon; # lEngine.mOptions.enable_slow_mode(); # lEngine.mOptions.mDebugPerformance = True; lEngine.mOptions.set_active_transformations(transformations); lEngine.mOptions.set_active_trends(trends); lEngine.mOptions.set_active_periodics(periodics); lEngine.mOptions.set_active_autoregressions(autoregs); lExogenousData = (b1.mExogenousDataFrame , b1.mExogenousVariables) lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H); lEngine.getModelInfo(); print(lEngine.mSignalDecomposition.mTrPerfDetails.head()); lEngine2 = pickleModel(lEngine) lEngine2.mSignalDecomposition.mBestModel.mTimeInfo.mResolution lEngine2.standardPlots("outputs/my_ozone"); dfapp_in = df.copy(); dfapp_in.tail() #H = 12 dfapp_out = lEngine2.forecast(dfapp_in, H); #dfapp_out.to_csv("outputs/ozone_apply_out.csv") dfapp_out.tail(2 * H) print("Forecast Columns " , dfapp_out.columns); Forecast_DF = dfapp_out[[b1.mTimeVar , b1.mSignalVar, b1.mSignalVar + '_Forecast']] print(Forecast_DF.info()) print("Forecasts\n" , Forecast_DF.tail(H)); print("\n\n<ModelInfo>") print(lEngine2.to_json()); print("</ModelInfo>\n\n") print("\n\n<Forecast>") print(Forecast_DF.tail(2*H).to_json(date_format='iso')) print("</Forecast>\n\n")

bsd-3-clause

hurricane42/data

pew-religions/Religion-Leah.py

37

3271

#!/usr/bin/env python import numpy as np import pandas as pd religions = ['Buddhist', 'Catholic', 'Evangel Prot', 'Hindu', 'Hist Black Prot', 'Jehovahs Witness', 'Jewish', 'Mainline Prot', 'Mormon', 'Muslim', 'Orthodox Christian', 'Unaffiliated'] csv = open("current.csv", 'w') csv.truncate() def write_row(matrix): arr = np.asarray(matrix[0])[0] row = ','.join([str(a) for a in arr]) + '\n' csv.write(row) # Intitial distribution of religions in US first = np.matrix([.007, .208, .254, .007, .065, .008, .019, .147, .016, .009, .005, .228]) # Normed to sum to 100% current = first / np.sum(first) t0 = current write_row(current) # Transition matrix trans = np.matrix(((0.390296314, 0.027141947, 0.06791021, 0.001857564, 0, 0, 0.011166082, 0.059762879, 0, 0, 0, 0.396569533), (0.005370791, 0.593173325, 0.103151608, 0.000649759, 0.010486747, 0.005563864, 0.002041424, 0.053825329, 0.004760476, 0.001130529, 0.000884429, 0.199488989), (0.00371836, 0.023900817, 0.650773331, 0.000250102, 0.016774503, 0.003098214, 0.001865491, 0.122807467, 0.004203107, 0.000186572, 0.002123778, 0.151866648), (0, 0, 0.0033732, 0.804072618, 0, 0.001511151, 0, 0.01234639, 0, 0.00209748, 0, 0.17659916), (0.002051357, 0.016851659, 0.09549708, 0, 0.699214315, 0.010620473, 0.000338804, 0.024372871, 0.000637016, 0.009406884, 0.000116843, 0.129892558), (0, 0.023278276, 0.109573979, 0, 0.077957568, 0.336280578, 0, 0.074844833, 0.007624035, 0, 0, 0.35110361), (0.006783201, 0.004082693, 0.014329604, 0, 0, 0.000610585, 0.745731278, 0.009587587, 0, 0, 0.002512334, 0.184058682), (0.005770357, 0.038017215, 0.187857555, 0.000467601, 0.008144075, 0.004763516, 0.003601208, 0.451798506, 0.005753587, 0.000965543, 0.00109818, 0.25750798), (0.007263135, 0.01684885, 0.06319935, 0.000248467, 0.0059394, 0, 0.001649896, 0.03464334, 0.642777489, 0.002606278, 0, 0.208904711), (0, 0.005890381, 0.023573308, 0, 0.011510643, 0, 0.005518343, 0.014032084, 0, 0.772783807, 0, 0.15424369), (0.004580353, 0.042045841, 0.089264134 , 0, 0.00527346, 0, 0, 0.061471387, 0.005979218, 0.009113978, 0.526728084, 0.243246723), (0.006438308, 0.044866331, 0.1928814, 0.002035375, 0.04295005, 0.010833621, 0.011541439, 0.09457963, 0.01365141, 0.005884336, 0.002892072, 0.525359211))) # Fertility array fert = np.matrix(((2.1, 2.3, 2.3, 2.1, 2.5, 2.1, 2, 1.9, 3.4, 2.8, 2.1, 1.7))) # Create data frame for printing later religionDataFrame = pd.DataFrame() for x in range(0,100): ### beginning of conversion step # apply transition matrix to current distribution current = current * trans ### beginning of fertility step # divide by two for couple number current = current/2 # adjust by fertility current = np.multiply(fert, current) # normalize to 100% current = current / np.sum(current) write_row(current) # add to data frame religionDataFrame = religionDataFrame.append(pd.DataFrame(current), ignore_index=True) csv.close() religionDataFrame.columns = religions religionDataFrame.to_csv("current_pandas_save.csv")

mit

oesteban/seaborn

seaborn/rcmod.py

4

16004

"""Functions that alter the matplotlib rc dictionary on the fly.""" from distutils.version import LooseVersion import functools import numpy as np import matplotlib as mpl from . import palettes mpl_ge_150 = LooseVersion(mpl.__version__) >= '1.5.0' _style_keys = ( "axes.facecolor", "axes.edgecolor", "axes.grid", "axes.axisbelow", "axes.linewidth", "axes.labelcolor", "figure.facecolor", "grid.color", "grid.linestyle", "text.color", "xtick.color", "ytick.color", "xtick.direction", "ytick.direction", "xtick.major.size", "ytick.major.size", "xtick.minor.size", "ytick.minor.size", "legend.frameon", "legend.numpoints", "legend.scatterpoints", "lines.solid_capstyle", "image.cmap", "font.family", "font.sans-serif", ) _context_keys = ( "figure.figsize", "font.size", "axes.labelsize", "axes.titlesize", "xtick.labelsize", "ytick.labelsize", "legend.fontsize", "grid.linewidth", "lines.linewidth", "patch.linewidth", "lines.markersize", "lines.markeredgewidth", "xtick.major.width", "ytick.major.width", "xtick.minor.width", "ytick.minor.width", "xtick.major.pad", "ytick.major.pad" ) def set(context="notebook", style="darkgrid", palette="deep", font="sans-serif", font_scale=1, color_codes=False, rc=None): """Set aesthetic parameters in one step. Each set of parameters can be set directly or temporarily, see the referenced functions below for more information. Parameters ---------- context : string or dict Plotting context parameters, see :func:`plotting_context` style : string or dict Axes style parameters, see :func:`axes_style` palette : string or sequence Color palette, see :func:`color_palette` font : string Font family, see matplotlib font manager. font_scale : float, optional Separate scaling factor to independently scale the size of the font elements. color_codes : bool If ``True`` and ``palette`` is a seaborn palette, remap the shorthand color codes (e.g. "b", "g", "r", etc.) to the colors from this palette. rc : dict or None Dictionary of rc parameter mappings to override the above. """ set_context(context, font_scale) set_style(style, rc={"font.family": font}) set_palette(palette, color_codes=color_codes) if rc is not None: mpl.rcParams.update(rc) def reset_defaults(): """Restore all RC params to default settings.""" mpl.rcParams.update(mpl.rcParamsDefault) def reset_orig(): """Restore all RC params to original settings (respects custom rc).""" mpl.rcParams.update(mpl.rcParamsOrig) def axes_style(style=None, rc=None): """Return a parameter dict for the aesthetic style of the plots. This affects things like the color of the axes, whether a grid is enabled by default, and other aesthetic elements. This function returns an object that can be used in a ``with`` statement to temporarily change the style parameters. Parameters ---------- style : dict, None, or one of {darkgrid, whitegrid, dark, white, ticks} A dictionary of parameters or the name of a preconfigured set. rc : dict, optional Parameter mappings to override the values in the preset seaborn style dictionaries. This only updates parameters that are considered part of the style definition. Examples -------- >>> st = axes_style("whitegrid") >>> set_style("ticks", {"xtick.major.size": 8, "ytick.major.size": 8}) >>> import matplotlib.pyplot as plt >>> with axes_style("white"): ... f, ax = plt.subplots() ... ax.plot(x, y) # doctest: +SKIP See Also -------- set_style : set the matplotlib parameters for a seaborn theme plotting_context : return a parameter dict to to scale plot elements color_palette : define the color palette for a plot """ if style is None: style_dict = {k: mpl.rcParams[k] for k in _style_keys} elif isinstance(style, dict): style_dict = style else: styles = ["white", "dark", "whitegrid", "darkgrid", "ticks"] if style not in styles: raise ValueError("style must be one of %s" % ", ".join(styles)) # Define colors here dark_gray = ".15" light_gray = ".8" # Common parameters style_dict = { "figure.facecolor": "white", "text.color": dark_gray, "axes.labelcolor": dark_gray, "legend.frameon": False, "legend.numpoints": 1, "legend.scatterpoints": 1, "xtick.direction": "out", "ytick.direction": "out", "xtick.color": dark_gray, "ytick.color": dark_gray, "axes.axisbelow": True, "image.cmap": "Greys", "font.family": ["sans-serif"], "font.sans-serif": ["Arial", "Liberation Sans", "Bitstream Vera Sans", "sans-serif"], "grid.linestyle": "-", "lines.solid_capstyle": "round", } # Set grid on or off if "grid" in style: style_dict.update({ "axes.grid": True, }) else: style_dict.update({ "axes.grid": False, }) # Set the color of the background, spines, and grids if style.startswith("dark"): style_dict.update({ "axes.facecolor": "#EAEAF2", "axes.edgecolor": "white", "axes.linewidth": 0, "grid.color": "white", }) elif style == "whitegrid": style_dict.update({ "axes.facecolor": "white", "axes.edgecolor": light_gray, "axes.linewidth": 1, "grid.color": light_gray, }) elif style in ["white", "ticks"]: style_dict.update({ "axes.facecolor": "white", "axes.edgecolor": dark_gray, "axes.linewidth": 1.25, "grid.color": light_gray, }) # Show or hide the axes ticks if style == "ticks": style_dict.update({ "xtick.major.size": 6, "ytick.major.size": 6, "xtick.minor.size": 3, "ytick.minor.size": 3, }) else: style_dict.update({ "xtick.major.size": 0, "ytick.major.size": 0, "xtick.minor.size": 0, "ytick.minor.size": 0, }) # Override these settings with the provided rc dictionary if rc is not None: rc = {k: v for k, v in rc.items() if k in _style_keys} style_dict.update(rc) # Wrap in an _AxesStyle object so this can be used in a with statement style_object = _AxesStyle(style_dict) return style_object def set_style(style=None, rc=None): """Set the aesthetic style of the plots. This affects things like the color of the axes, whether a grid is enabled by default, and other aesthetic elements. Parameters ---------- style : dict, None, or one of {darkgrid, whitegrid, dark, white, ticks} A dictionary of parameters or the name of a preconfigured set. rc : dict, optional Parameter mappings to override the values in the preset seaborn style dictionaries. This only updates parameters that are considered part of the style definition. Examples -------- >>> set_style("whitegrid") >>> set_style("ticks", {"xtick.major.size": 8, "ytick.major.size": 8}) See Also -------- axes_style : return a dict of parameters or use in a ``with`` statement to temporarily set the style. set_context : set parameters to scale plot elements set_palette : set the default color palette for figures """ style_object = axes_style(style, rc) mpl.rcParams.update(style_object) def plotting_context(context=None, font_scale=1, rc=None): """Return a parameter dict to scale elements of the figure. This affects things like the size of the labels, lines, and other elements of the plot, but not the overall style. The base context is "notebook", and the other contexts are "paper", "talk", and "poster", which are version of the notebook parameters scaled by .8, 1.3, and 1.6, respectively. This function returns an object that can be used in a ``with`` statement to temporarily change the context parameters. Parameters ---------- context : dict, None, or one of {paper, notebook, talk, poster} A dictionary of parameters or the name of a preconfigured set. font_scale : float, optional Separate scaling factor to independently scale the size of the font elements. rc : dict, optional Parameter mappings to override the values in the preset seaborn context dictionaries. This only updates parameters that are considered part of the context definition. Examples -------- >>> c = plotting_context("poster") >>> c = plotting_context("notebook", font_scale=1.5) >>> c = plotting_context("talk", rc={"lines.linewidth": 2}) >>> import matplotlib.pyplot as plt >>> with plotting_context("paper"): ... f, ax = plt.subplots() ... ax.plot(x, y) # doctest: +SKIP See Also -------- set_context : set the matplotlib parameters to scale plot elements axes_style : return a dict of parameters defining a figure style color_palette : define the color palette for a plot """ if context is None: context_dict = {k: mpl.rcParams[k] for k in _context_keys} elif isinstance(context, dict): context_dict = context else: contexts = ["paper", "notebook", "talk", "poster"] if context not in contexts: raise ValueError("context must be in %s" % ", ".join(contexts)) # Set up dictionary of default parameters base_context = { "figure.figsize": np.array([8, 5.5]), "font.size": 12, "axes.labelsize": 11, "axes.titlesize": 12, "xtick.labelsize": 10, "ytick.labelsize": 10, "legend.fontsize": 10, "grid.linewidth": 1, "lines.linewidth": 1.75, "patch.linewidth": .3, "lines.markersize": 7, "lines.markeredgewidth": 0, "xtick.major.width": 1, "ytick.major.width": 1, "xtick.minor.width": .5, "ytick.minor.width": .5, "xtick.major.pad": 7, "ytick.major.pad": 7, } # Scale all the parameters by the same factor depending on the context scaling = dict(paper=.8, notebook=1, talk=1.3, poster=1.6)[context] context_dict = {k: v * scaling for k, v in base_context.items()} # Now independently scale the fonts font_keys = ["axes.labelsize", "axes.titlesize", "legend.fontsize", "xtick.labelsize", "ytick.labelsize", "font.size"] font_dict = {k: context_dict[k] * font_scale for k in font_keys} context_dict.update(font_dict) # Implement hack workaround for matplotlib bug # See https://github.com/mwaskom/seaborn/issues/344 # There is a bug in matplotlib 1.4.2 that makes points invisible when # they don't have an edgewidth. It will supposedly be fixed in 1.4.3. if mpl.__version__ == "1.4.2": context_dict["lines.markeredgewidth"] = 0.01 # Override these settings with the provided rc dictionary if rc is not None: rc = {k: v for k, v in rc.items() if k in _context_keys} context_dict.update(rc) # Wrap in a _PlottingContext object so this can be used in a with statement context_object = _PlottingContext(context_dict) return context_object def set_context(context=None, font_scale=1, rc=None): """Set the plotting context parameters. This affects things like the size of the labels, lines, and other elements of the plot, but not the overall style. The base context is "notebook", and the other contexts are "paper", "talk", and "poster", which are version of the notebook parameters scaled by .8, 1.3, and 1.6, respectively. Parameters ---------- context : dict, None, or one of {paper, notebook, talk, poster} A dictionary of parameters or the name of a preconfigured set. font_scale : float, optional Separate scaling factor to independently scale the size of the font elements. rc : dict, optional Parameter mappings to override the values in the preset seaborn context dictionaries. This only updates parameters that are considered part of the context definition. Examples -------- >>> set_context("paper") >>> set_context("talk", font_scale=1.4) >>> set_context("talk", rc={"lines.linewidth": 2}) See Also -------- plotting_context : return a dictionary of rc parameters, or use in a ``with`` statement to temporarily set the context. set_style : set the default parameters for figure style set_palette : set the default color palette for figures """ context_object = plotting_context(context, font_scale, rc) mpl.rcParams.update(context_object) class _RCAesthetics(dict): def __enter__(self): rc = mpl.rcParams self._orig = {k: rc[k] for k in self._keys} self._set(self) def __exit__(self, exc_type, exc_value, exc_tb): self._set(self._orig) def __call__(self, func): @functools.wraps(func) def wrapper(*args, **kwargs): with self: return func(*args, **kwargs) return wrapper class _AxesStyle(_RCAesthetics): """Light wrapper on a dict to set style temporarily.""" _keys = _style_keys _set = staticmethod(set_style) class _PlottingContext(_RCAesthetics): """Light wrapper on a dict to set context temporarily.""" _keys = _context_keys _set = staticmethod(set_context) def set_palette(palette, n_colors=None, desat=None, color_codes=False): """Set the matplotlib color cycle using a seaborn palette. Parameters ---------- palette : hls | husl | matplotlib colormap | seaborn color palette Palette definition. Should be something that :func:`color_palette` can process. n_colors : int Number of colors in the cycle. The default number of colors will depend on the format of ``palette``, see the :func:`color_palette` documentation for more information. desat : float Proportion to desaturate each color by. color_codes : bool If ``True`` and ``palette`` is a seaborn palette, remap the shorthand color codes (e.g. "b", "g", "r", etc.) to the colors from this palette. Examples -------- >>> set_palette("Reds") >>> set_palette("Set1", 8, .75) See Also -------- color_palette : build a color palette or set the color cycle temporarily in a ``with`` statement. set_context : set parameters to scale plot elements set_style : set the default parameters for figure style """ colors = palettes.color_palette(palette, n_colors, desat) if mpl_ge_150: from cycler import cycler cyl = cycler('color', colors) mpl.rcParams['axes.prop_cycle'] = cyl else: mpl.rcParams["axes.color_cycle"] = list(colors) mpl.rcParams["patch.facecolor"] = colors[0] if color_codes: palettes.set_color_codes(palette)

bsd-3-clause

tody411/NPR-SFS

npr_sfs/results/compare.py

1

1859

# -*- coding: utf-8 -*- ## @package npr_sfs.results.compare # # npr_sfs.results.compare utility package. # @author tody # @date 2015/09/01 import os import matplotlib.pyplot as plt from npr_sfs.results.results import batchResults, resultFile, resultDir from npr_sfs.datasets.loader import loadData from npr_sfs.io_util.image import loadRGBA from npr_sfs.plot.window import showMaximize batch_name="Compare" _root_dir = os.path.dirname(__file__) def methodNames(): dirs = os.listdir(_root_dir) method_dirs = [metohd_dir for metohd_dir in dirs if os.path.isdir(metohd_dir) and metohd_dir != batch_name] return method_dirs def methodDir(method_name): return os.path.join(_root_dir, method_name) def methodFile(method_name, data_name): return os.path.join(methodDir(method_name), data_name + ".png") def batch_func(data_name): method_names = methodNames() NO_32F = loadData(data_name, loader_func=loadRGBA) fig = plt.figure(figsize=(10, 4)) fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.9, wspace=0.05, hspace=0.1) font_size = 15 fig.suptitle("NPR-SFS", fontsize=font_size) num_cols = len(method_names) + 1 fig.add_subplot(1, num_cols, 1) plt.title("Ground truth", fontsize=font_size) plt.imshow(NO_32F) plt.axis('off') col_id = 2 for method_name in method_names: method_file = methodFile(method_name, data_name) N_32F = loadRGBA(method_file) fig.add_subplot(1, num_cols, col_id) plt.title(method_name, fontsize=font_size) plt.imshow(N_32F) plt.axis('off') col_id += 1 result_dir = resultDir(batch_name) result_file = resultFile(result_dir, data_name) plt.savefig(result_file) if __name__ == '__main__': print methodNames() batchResults(batch_func, batch_name)

mit

dialounke/pylayers

pylayers/mobility/agent.py

1

11549

""" .. automodule:: :members: Agent Class ================== .. autoclass:: Agent :members: """ from SimPy.SimulationRT import Simulation #from simpy.simulation import * from pylayers.mobility.transit.Person import Person from pylayers.mobility.transit.World import world from pylayers.mobility.transit.SteeringBehavior import Seek, Separation, Containment, InterpenetrationConstraint, queue_steering_mind import numpy as np import networkx as nx import time import ConfigParser import pandas as pd import pylayers.util.pyutil as pyu from pylayers.network.network import Node, Network from pylayers.network.communication import Gcom, TX, RX from pylayers.location.localization import Localization, PLocalization from pylayers.gis.layout import Layout from pylayers.util.utilnet import * #from pylayers.util.pymysqldb import Database import pdb """" .. currentmodule:: pylayers.mobility.agent .. autosummary:: :toctree: generated/ """ class Agent(object): """ Class Agent Members ------- args ID name typ net epwr gcom sim wstd sens dcond meca : transit.Person net : pylayers.network.Network sim : PN : rxt rxr """ def __init__(self, **args): """ Mobile Agent Init Parameters ---------- 'ID': string agent ID 'name': string Agent name 'typ': string agent typ . 'ag' for moving agent, 'ap' for static acces point 'pos' : np.array([]) numpy array containing the initial position of the agent 'roomId': int Room number where the agent is initialized (Layout.Gr) 'meca_updt': float update time interval for the mechanical process 'loc': bool enable/disable localization process of the agent 'loc_updt': float update time interval for localization process 'L': pylayers.gis.Layout() 'net':pylayers.network.Network(), 'wstd': list of string list of used radio access techology of the agent 'world': transit.world() Soon deprecated 'save': list of string list of save method ( soon deprecated) 'sim':Simpy.SimulationRT.Simulation(), 'epwr': dictionnary dictionnary of emmited power of transsmitter{'wstd#':epwr value} 'sens': dictionnary dictionnary of sensitivity of reveicer {'wstd#':sens value} 'dcond': dictionnary Not used yet 'gcom':pylayers.communication.Gcom() Communication graph 'comm_mod': string Communication between nodes mode: 'autonomous': all TOAs are refreshed regulary 'synchro' : only visilbe TOAs are refreshed """ defaults = {'ID': '0', 'name': 'johndoe', 'typ': 'ag', 'color': 'k', 'pdshow': False, 'pos': np.array([]), 'roomId': -1, 'froom': [], 'wait': [], 'seed': 0, 'cdest': 'random', 'meca_updt': 0.1, 'loc': False, 'loc_updt': 0.5, 'loc_method': ['geo'], 'L': Layout(), 'network': True, 'net': Network(), 'wstd': ['rat1'], 'world': world(), 'save': [], 'sim': Simulation(), 'epwr': {}, 'sens': {}, 'dcond': {}, 'gcom': Gcom(), 'comm_mode': 'autonomous'} for key, value in defaults.items(): if key not in args: args[key] = value self.args = args self.ID = args['ID'] self.name = args['name'] self.typ = args['typ'] # Create Network self.net = args['net'] self.epwr = args['epwr'] self.gcom = args['gcom'] self.sim = args['sim'] self.wstd = args['wstd'] if args['epwr'] == {}: self.epwr = {x: 0 for x in self.wstd} else: self.epwr = args['epwr'] if args['sens'] == {}: self.sens = {x: -180 for x in self.wstd} else: self.sens = args['sens'] try: self.dcond = args['dcond'] except: pass # check if node id already given if self.ID in self.net.nodes(): raise NameError( 'another agent has the ID: ' + self.ID + ' .Please use an other ID') if self.typ == 'ag': # mechanical init self.meca = Person(ID=self.ID, color=args['color'], pdshow=args['pdshow'], roomId=args['roomId'], L=args['L'], net=self.net, interval=args['meca_updt'], wld=args['world'], sim=args['sim'], seed=args['seed'], moving=True, froom=args['froom'], wait=args['wait'], cdest=args['cdest'], save=args['save'] ) self.meca.behaviors = [Seek(), Containment(), Separation(), InterpenetrationConstraint()] self.meca.steering_mind = queue_steering_mind # Network init self.node = Node(ID=self.ID,name=self.name, p=conv_vecarr(self.meca.position), t=self.sim.now(), wstd=args['wstd'], epwr=self.epwr, sens=self.sens, typ=self.typ) self.net.add_nodes_from(self.node.nodes(data=True)) self.sim.activate(self.meca, self.meca.move(), 0.0) self.PN = self.net.node[self.ID]['PN'] # Communication init if args['comm_mode'] == 'synchro' and args['network']: # The TOA requests are made every refreshTOA time ( can be modified in agent.ini) # This Mode will be deprecated in future version self.rxr = RX(net=self.net, ID=self.ID, dcond=self.dcond, gcom=self.gcom, sim=self.sim) self.rxt = RX(net=self.net, ID=self.ID, dcond=self.dcond, gcom=self.gcom, sim=self.sim) self.sim.activate(self.rxr, self.rxr.refresh_RSS(), 0.0) self.sim.activate(self.rxt, self.rxt.refresh_TOA(), 0.0) elif args['comm_mode'] == 'autonomous' and args['network']: # The requests are made by node only when they are in # visibility of pairs. # self.rxr only manage a refresh RSS process self.rxr = RX(net=self.net, ID=self.ID, gcom=self.gcom, sim=self.sim) # self.tx manage all requests to other nodes self.tx = TX(net=self.net, ID=self.ID, gcom=self.gcom, sim=self.sim) # self.tx replies to requests from self.tx self.rx = RX(net=self.net, ID=self.ID, gcom=self.gcom, sim=self.sim) self.sim.activate(self.rxr, self.rxr.refresh_RSS(), 0.0) self.sim.activate(self.tx, self.tx.request(), 0.0) self.sim.activate(self.rx, self.rx.wait_request(), 0.0) elif self.typ == 'ap': if args['roomId'] == -1: self.node = Node(ID=self.ID, p=self.args['pos'], t=self.sim.now(), wstd=args['wstd'], epwr=self.epwr, sens=self.sens, typ=self.typ) else: pp = np.array(args['L'].Gr.pos[self.args['roomId']]) self.node = Node( ID=self.ID, p=pp, t=self.sim.now(), wstd=args['wstd'], epwr=self.epwr, sens=self.sens, typ=self.typ) self.net.add_nodes_from(self.node.nodes(data=True)) self.sim = args['sim'] self.PN = self.net.node[self.ID]['PN'] self.PN.node[self.ID]['pe'] = self.net.node[self.ID]['p'] if args['comm_mode'] == 'autonomous' and args['network']: self.rx = RX(net=self.net, ID=self.ID, gcom=self.gcom, sim=self.sim) self.sim.activate(self.rx, self.rx.wait_request(), 0.0) p = self.args['pos'] self.posdf = pd.DataFrame( {'t': pd.Timestamp(0), 'x': p[0], 'y': p[1], 'z': p[2], 'vx': np.array([0.0]), 'vy': np.array([0.0]), 'ax': np.array([0.0]), 'ay': np.array([0.0]), }, columns=['t', 'x', 'y', 'z', 'vx', 'vy', 'ax', 'ay'], index=np.array([0])) else: raise NameError( 'wrong agent typ, it must be either agent (ag) or acces point (ap) ') if self.typ == 'ap': self.MoA = 1 else: self.MoA = 0 if 'mysql' in args['save']: config = ConfigParser.ConfigParser() config.read(pyu.getlong('simulnet.ini', 'ini')) sql_opt = dict(config.items('Mysql')) db = Database(sql_opt['host'], sql_opt['user'], sql_opt['passwd'], sql_opt['dbname']) db.writenode(self.ID, self.name, self.MoA) if 'txt' in args['save']: pyu.writenode(self) if self.typ != 'ap' and args['loc']: self.loc = Localization(net=self.net, ID=self.ID, method=args['loc_method']) self.Ploc = PLocalization(loc=self.loc, loc_updt_time=args['loc_updt'], tx=self.tx, sim=args['sim']) self.sim.activate(self.Ploc, self.Ploc.run(), 1.5) def __repr__(self): s = 'General Agent info \n********************\n' s = s + 'name : ' + self.name + '\n' s = s + 'ID: ' + self.ID + '\n' s = s + 'typ: ' + self.typ s = s + '\n\n More Agent information about:' s = s + '\n+ Mecanichal => self.meca' s = s + '\n+ Network => self.net' s = s + '\n+ Personnal Network => self.PN' s = s + '\n+ Localization => self.loc\n\n' try: s = s + self.PN.__repr__() + '\n\n' except: s = s + 'No network simulated' if self.typ != 'ap': s = s + self.meca.__repr__() + '\n\n' try: s = s + self.loc.__repr__() + '\n\n' except: s = s + 'no localization simulated' return s

mit

cleverhans-lab/cleverhans

cleverhans_v3.1.0/scripts/plot_success_fail_curve.py

1

1459

#!/usr/bin/env python3 """ Plots a success-fail curve ( https://openreview.net/forum?id=H1g0piA9tQ ) Usage: plot_success_fail_curve.py model.joblib plot_success_fail_curve.py model1.joblib model2.joblib This script is mostly intended to rapidly visualize success-fail curves during model development and testing. To make nicely labeled plots formatted to fit the page / column of a publication, you should probably write your own script that calls some of the same plotting commands. """ from matplotlib import pyplot import tensorflow as tf from cleverhans.utils_tf import silence silence() # silence call must precede this imports. pylint doesn't like that # pylint: disable=C0413 from cleverhans.compat import flags from cleverhans.plot.success_fail import DEFAULT_FAIL_NAMES from cleverhans.plot.success_fail import plot_report_from_path FLAGS = flags.FLAGS def main(argv=None): """Takes the path to a directory with reports and renders success fail plots.""" report_paths = argv[1:] fail_names = FLAGS.fail_names.split(",") for report_path in report_paths: plot_report_from_path(report_path, label=report_path, fail_names=fail_names) pyplot.legend() pyplot.xlim(-0.01, 1.0) pyplot.ylim(0.0, 1.0) pyplot.show() if __name__ == "__main__": flags.DEFINE_string( "fail_names", ",".join(DEFAULT_FAIL_NAMES), "Names of adversarial datasets for failure rate", ) tf.app.run()

mit

d-meiser/lindblad

examples/scanEIT.py

1

3959

#!/usr/bin/python import sys import subprocess import numpy as np try: from matplotlib import rc rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']}) rc('text', usetex=True) import matplotlib.pyplot as plt have_matplotlib = True except ImportError: print "matplotlib not available" have_matplotlib = False try: import joblib from multiprocessing import cpu_count cpuNum = cpu_count() # use maximum number of cores available if cpuNum > 1: n_jobs = cpuNum print "Using joblib with", n_jobs, "processes." else: n_jobs = 1 print "Using joblib with 1 process." have_joblib = True except ImportError: print "joblib not available." have_joblib = False if (len(sys.argv) == 2): # one argument has been provided for output plot name outputFilename = str(sys.argv[1]) elif (len(sys.argv) == 1): # no filename provided, use default outputFilename = 'test.png' else: # improper usage, use default and inform print("Usage: python scanEIT-SmallRange.py filename") outputFilename = 'test.png' def get_steady_state(arguments = None): if arguments: output = subprocess.check_output(["./SteadyStateEIT"] + [str(a) for a in arguments]) else: output = subprocess.check_output(["./SteadyStateEIT"]) density_matrix = np.matrix(output) density_matrix = density_matrix.reshape(4, 8) density_matrix = density_matrix[:, 0:8:2] + 1.0j * density_matrix[:,1:8:2] return density_matrix def get_polarization(arguments = None): density_matrix = get_steady_state(arguments) return [density_matrix[0, 3] , density_matrix[2, 3]] def absorption(polarizationArray): # post-process density matrix elements # to get absorption from atomic polarization return polarizationArray[:,0].imag - polarizationArray[:,1].imag def rotation(polarizationArray): # post-process density matrix elements # to get light polarization rotation from atomic polarization return polarizationArray[:,0].real + polarizationArray[:,1].real def compute_polarization(OmegaR, OmegaBs, Delta, gamma, Gamma, deltaB): if have_joblib: polarizations = joblib.Parallel(n_jobs = n_jobs)( joblib.delayed(get_polarization)( [OmegaR, ob, Delta, gamma, Gamma, deltaB]) for ob in OmegaBs) else: polarizations = [get_polarization([OmegaR, ob, Delta, gamma, Gamma, deltaB]) for ob in OmegaBs] polarizations = np.array(polarizations) return polarizations def main(argv): Bunits = 700e3 * 2.0 * np.pi OmegaR = 1.25e6 * 2.0 * np.pi gamma = 1.0e3 * 2.0 * np.pi Gamma = 6.0e6 * 2.0 * np.pi Delta = 0.0 * Gamma; OmegaB = np.arange(-0.1, 0.1, 0.0005) * Bunits deltaB = Bunits * 0.01 NonZeroPolarization = compute_polarization(OmegaR, OmegaB, Delta, gamma, Gamma, deltaB) absorptionNonZeroField = absorption(NonZeroPolarization) rotationNonZeroField = rotation(NonZeroPolarization) deltaB = Bunits * 0.0 ZeroPolarization = compute_polarization(OmegaR, OmegaB, Delta, gamma, Gamma, deltaB) absorptionZeroField = absorption(ZeroPolarization) rotationZeroField = rotation(ZeroPolarization) if not have_matplotlib: return plt.subplot(2,1,1) plt.plot(OmegaB / (Bunits), 1.0e3 * absorptionZeroField) plt.plot(OmegaB / (Bunits), 1.0e3 * absorptionNonZeroField,'--r') plt.ylabel(r'${\rm Absorption\; [arb. units]}$') plt.subplot(2,1,2) plt.plot(OmegaB / (Bunits), 1.0e3 * rotationZeroField) plt.plot(OmegaB / (Bunits), 1.0e3 * rotationNonZeroField,'--r') plt.xlabel(r'$B_z({\rm G})$') plt.ylabel(r'${\rm Faraday Rotation\; [arb. units]}$') plt.gcf().set_size_inches(4, 6) plt.gcf().subplots_adjust(bottom = 0.1, left = 0.2, top = 0.97, right = 0.95) plt.savefig(outputFilename,format='png') if __name__== "__main__": main(sys.argv)

gpl-3.0

NunoEdgarGub1/scikit-learn

examples/svm/plot_svm_anova.py

250

2000

""" ================================================= SVM-Anova: SVM with univariate feature selection ================================================= This example shows how to perform univariate feature before running a SVC (support vector classifier) to improve the classification scores. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets, feature_selection, cross_validation from sklearn.pipeline import Pipeline ############################################################################### # Import some data to play with digits = datasets.load_digits() y = digits.target # Throw away data, to be in the curse of dimension settings y = y[:200] X = digits.data[:200] n_samples = len(y) X = X.reshape((n_samples, -1)) # add 200 non-informative features X = np.hstack((X, 2 * np.random.random((n_samples, 200)))) ############################################################################### # Create a feature-selection transform and an instance of SVM that we # combine together to have an full-blown estimator transform = feature_selection.SelectPercentile(feature_selection.f_classif) clf = Pipeline([('anova', transform), ('svc', svm.SVC(C=1.0))]) ############################################################################### # Plot the cross-validation score as a function of percentile of features score_means = list() score_stds = list() percentiles = (1, 3, 6, 10, 15, 20, 30, 40, 60, 80, 100) for percentile in percentiles: clf.set_params(anova__percentile=percentile) # Compute cross-validation score using all CPUs this_scores = cross_validation.cross_val_score(clf, X, y, n_jobs=1) score_means.append(this_scores.mean()) score_stds.append(this_scores.std()) plt.errorbar(percentiles, score_means, np.array(score_stds)) plt.title( 'Performance of the SVM-Anova varying the percentile of features selected') plt.xlabel('Percentile') plt.ylabel('Prediction rate') plt.axis('tight') plt.show()

bsd-3-clause

OshynSong/scikit-learn

examples/svm/plot_custom_kernel.py

171

1546

""" ====================== SVM with custom kernel ====================== Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface and the support vectors. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets # import some data to play with iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. We could # avoid this ugly slicing by using a two-dim dataset Y = iris.target def my_kernel(X, Y): """ We create a custom kernel: (2 0) k(X, Y) = X ( ) Y.T (0 1) """ M = np.array([[2, 0], [0, 1.0]]) return np.dot(np.dot(X, M), Y.T) h = .02 # step size in the mesh # we create an instance of SVM and fit out data. clf = svm.SVC(kernel=my_kernel) clf.fit(X, Y) # Plot the decision boundary. For that, we will assign a color to each # point in the mesh [x_min, m_max]x[y_min, y_max]. x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired) # Plot also the training points plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired) plt.title('3-Class classification using Support Vector Machine with custom' ' kernel') plt.axis('tight') plt.show()

bsd-3-clause

anna-effeindzourou/trunk

examples/anna_scripts/triax/triaxial_uniformrm.py

1

11635

# -*- coding: utf-8 from yade import ymport, utils,pack,export import gts,os from yade import geom #import matplotlib from yade import plot #from pylab import * #import os.path, locale #### set False when running in batch mode #defaultTable = True defaultTable = False ####------------------------------------- ####------------------------------------- utils.readParamsFromTable( rm = 0.33, noTableOk = True ) from yade.params.table import * print 'rm=',rm O.tags['description']='triaxial_rm_'+str(rm) ################################# ##### FUNCTIONS #### ################################# def hMax(n): idHMax=0 hMax=-1000000.0 for i in O.bodies: h=i.state.pos[n] if (h>hMax): hMax=h idHMax=i.id hMax=hMax+O.bodies[idHMax].shape.radius return (hMax) def hMin(n): idHMin=0 hMin=100000.0 for i in O.bodies: h=i.state.pos[n] if (h<hMin): hMin=h idHMin=i.id hMin=hMin-O.bodies[idHMin].shape.radius return (hMin) #Function in order to calculate rmin (minimum radius) and rmax (maximum radius) def MinMax(): rmax=0 rmin=10 r=0 for i in O.bodies: if(type(i.shape)==Sphere): r=i.shape.radius if(r>rmax): rmax=r if(r<rmin): rmin=r l=[rmin,rmax] return (l) def sup(): for i in O.bodies: if (type(i.shape)==Sphere) and (i.state.pos[2]>0.098): O.bodies.erase(i.id) def scalar(u,v): ps=u[0]*v[0]+u[1]*v[1]+u[2]*v[2] return ps def cross(u,v): ps=Vector3(u[1]*v[2]-u[2]*v[1], u[2]*v[0]-u[0]*v[2] ,u[0]*v[1]-u[1]*v[0]) return ps def limitfinder(): for b in O.bodies: if(b.state.pos[2]>=L-2*radius): if isinstance(b.shape,GridNode): top_boundary.append(b.id) b.shape.color=(1,0,0) b.state.blockedDOFs='z' if(b.state.pos[2]<0.1*radius ): if isinstance(b.shape,GridNode): bottom_boundary.append(b.id) b.state.blockedDOFs='z' b.shape.color=(1,0,0) ############################## ##### SCRIPT #### ############################## try: os.mkdir('data') except: pass try: os.mkdir('paraview') except: pass isBatch = runningInBatch() #################### ### ENGINES ### #################### O.engines=[ ForceResetter(), InsertionSortCollider([ Bo1_Sphere_Aabb(), Bo1_Wall_Aabb(), Bo1_PFacet_Aabb(), Bo1_Facet_Aabb(), ]), InteractionLoop([ Ig2_GridNode_GridNode_GridNodeGeom6D(), Ig2_GridConnection_GridConnection_GridCoGridCoGeom(), Ig2_Sphere_PFacet_ScGridCoGeom(), Ig2_Sphere_Sphere_ScGeom(), Ig2_Facet_Sphere_ScGeom(), Ig2_Wall_Sphere_ScGeom() ], [Ip2_CohFrictMat_CohFrictMat_CohFrictPhys(setCohesionNow=True,setCohesionOnNewContacts=True), Ip2_FrictMat_FrictMat_FrictPhys()], [Law2_ScGeom6D_CohFrictPhys_CohesionMoment(), Law2_ScGeom_FrictPhys_CundallStrack(), Law2_ScGridCoGeom_FrictPhys_CundallStrack(), Law2_GridCoGridCoGeom_FrictPhys_CundallStrack() ] ), ] ###################### ### PROPERTIES ### ###################### radius=0.0025*rm sigma=-3e6 #### Parameters of a rectangular grid ### L=0.205 #length [m] l=0.101/2. #half width (radius) [m] nbL=36#number of nodes for the length [#] doit etre paire nbl=44 #number of nodes for the perimeter [#] ABSOLUMENT MULTIPLE de 4 !!! #nbL=1 #number of nodes for the length [#] doit etre paire #nbl=4 #number of nodes for the perimeter [#] ABSOLUMENT MULTIPLE de 4 !!! r=radius color=[155./255.,155./255.,100./255.] oriBody = Quaternion(Vector3(0,0,1),(pi/2)) nodesIds=[] nodesIds1=[] cylIds=[] pfIds=[] top_boundary=[] bottom_boundary=[] #################### ### MATERIAL ### #################### poisson=0.28 E=2*7.9e10*(1+poisson) ##1e11 density=7.8e10 Et=0 Emem=E/1e3 frictionAngle=0.096 frictionAngleW=0.228 O.materials.append(CohFrictMat(young=Emem,poisson=poisson,density=density,frictionAngle=0,normalCohesion=1e19,shearCohesion=1e19,momentRotationLaw=False,alphaKr=0,label='NodeMat')) O.materials.append(FrictMat(young=Emem,poisson=poisson,density=density,frictionAngle=0,label='memMat')) O.materials.append(FrictMat(young=E,poisson=poisson,density=density,frictionAngle=frictionAngleW,label='Wallmat')) O.materials.append(FrictMat(young=E,poisson=poisson,density=density,frictionAngle=frictionAngle,label='Smat')) ############################## ### SAMPLE GENERATION ### ############################## kw={'color':[0.6,0.6,0.6],'wire':False,'dynamic':True,'material':3} #pile=ymport.text('spheres.txt',**kw) #pile2=O.bodies.append(pile) #sup() #print hMin(2), hMax(2) #zmin=hMin(2) #zmax=hMax(2) ##L=hMax(2) ################################## ##### MEMBRANE GENERATION ### ################################## #mesh=2 #Create all nodes first : for i in range(0,nbL+1): for j in range(0,nbl): z=i*L/float(nbL) y=l*sin(2*pi*j/float(nbl)) x=l*cos(2*pi*j/float(nbl)) nodesIds.append( O.bodies.append(gridNode([x,y,z],r,wire=False,fixed=False,material='NodeMat',color=color)) ) ##Create connection between the nodes for i in range(0,nbL+1): for j in range(0,nbl-1): O.bodies.append( gridConnection(nodesIds[i*nbl+j],nodesIds[i*nbl+j+1],r,color=color,mask=5,material='memMat') ) for i in range(0,nbL,1): for j in range(0,nbl): O.bodies.append( gridConnection(nodesIds[i*nbl+j],nodesIds[(i+1)*nbl+j],r,color=color,mask=5,material='memMat') ) for i in range(-1,nbL): j=nbl O.bodies.append( gridConnection(nodesIds[i*nbl+j],nodesIds[(i+1)*nbl+j-1],r,color=color,mask=5,material='memMat') ) for i in range(0,nbL): for j in range(0,nbl-1): if (j%2==0): O.bodies.append( gridConnection(nodesIds[i*nbl+j],nodesIds[(i+1)*nbl+j+1],r,color=color,mask=5,material='memMat') ) else: O.bodies.append( gridConnection(nodesIds[(i+1)*nbl+j],nodesIds[i*nbl+j+1],r,color=color,mask=5,material='memMat') ) for i in range(0,nbL): j=nbl #O.bodies[nodesIds[(i-1)*nbl+j]].shape.color=Vector3(155./255.,155./255.,1.) #O.bodies[nodesIds[(i)*nbl+j-1]].shape.color=Vector3(1,0,0) O.bodies.append( gridConnection(nodesIds[(i-1)*nbl+j],nodesIds[(i+1)*nbl+j-1],r,color=color,mask=5,material='memMat') ) ###Create PFacets ##wire=True for i in range(0,nbL): for j in range(0,nbl-1): if (j%2==0): pfIds.append(O.bodies.append(pfacet(nodesIds[i*nbl+j],nodesIds[(i+1)*nbl+j],nodesIds[(i+1)*nbl+j+1],color=color,mask=5,material='memMat'))) pfIds.append(O.bodies.append(pfacet(nodesIds[i*nbl+j],nodesIds[(i+1)*nbl+j+1],nodesIds[(i)*nbl+j+1],color=color,mask=5,material='memMat'))) else: pfIds.append(O.bodies.append(pfacet(nodesIds[i*nbl+j],nodesIds[(i+1)*nbl+j],nodesIds[(i)*nbl+j+1],color=color,mask=5,material='memMat'))) pfIds.append(O.bodies.append(pfacet(nodesIds[i*nbl+j+1],nodesIds[(i+1)*nbl+j],nodesIds[(i+1)*nbl+j+1],color=color,mask=5,material='memMat'))) for i in range(0,nbL,1): j=nbl pfIds.append(O.bodies.append(pfacet( nodesIds[i*nbl+j],nodesIds[(i-1)*nbl+j],nodesIds[(i+1)*nbl+j-1],color=color,material='memMat' ))) pfIds.append(O.bodies.append(pfacet( nodesIds[(i)*nbl+j-1],nodesIds[(i+1)*nbl+j-1],nodesIds[(i-1)*nbl+j],color=color,material='memMat' ))) limitfinder() ######################### ##### WALL GENERATION ## ######################### O.materials.append(FrictMat(young=E,poisson=poisson,density=density,frictionAngle=frictionAngleW,label='Wallmat')) topPlate=utils.wall(position=hMax(2)+radius,sense=0, axis=2,color=Vector3(1,0,0),material='Wallmat') O.bodies.append(topPlate) bottomPlate=utils.wall(position=-hMin(2)-radius,sense=0, axis=2,color=Vector3(1,0,0),material='Wallmat') O.bodies.append(bottomPlate) ################### #### APPLY LOAD ## ################### normalVEL=0 loading=True S0=pi*l**2 normalSTRESS=sigma shearing=False sigmaN=0 #### APPLY CONFINING PRESSURE def Apply_load(): global sigmaN, Fn, top, load,shearing,loading,u Fn=abs(O.forces.f(topPlate.id)[2]) sigmaN=Fn/S0 if abs((normalSTRESS-sigmaN)/normalSTRESS)<0.001: topPlate.state.vel[2]=0 def Apply_confiningpressure(): #print 'Apply_confiningpressure' for i in pfIds: e0 =O.bodies[i].shape.node3.state.pos - O.bodies[i].shape.node1.state.pos e1 =O.bodies[i].shape.node2.state.pos - O.bodies[i].shape.node1.state.pos e2 =O.bodies[i].shape.node2.state.pos - O.bodies[i].shape.node3.state.pos P=(O.bodies[i].shape.node1.state.pos+O.bodies[i].shape.node2.state.pos+O.bodies[i].shape.node3.state.pos)/3 #print e0,e1,e2 #nodesIds.append( O.bodies.append(gridNode([P[0],P[1],P[2]],r,wire=False,fixed=True,material='NodeMat',color=color)) ) #print 'P=',P v0 = e0 v1 = e1 v2 = P - O.bodies[i].shape.node1.state.pos ##// Compute dot products dot00 = scalar(v0,v0) dot01 = scalar(v0,v1) dot02 = scalar(v0,v2) dot11 = scalar(v1,v1) dot12 = scalar(v1,v2) ##// Compute the barycentric coordinates of the projection P invDenom = 1 / (dot00 * dot11 - dot01 * dot01) p1 = (dot11 * dot02 - dot01 * dot12) * invDenom p2 = (dot00 * dot12 - dot01 * dot02) * invDenom p3 = 1-p1-p2 a = sqrt(scalar(e0,e0)) b = sqrt(scalar(e1,e1)) c = sqrt(scalar(e2,e2)) s=0.5*(a+b+c) area= sqrt(s*(s-a)*(s-b)*(s-c)) Fapplied=area*sigma normal = cross(e0,e1) normal=normal/normal.norm() F=Fapplied p1normal=F*p1*normal p2normal=F*p2*normal p3normal=F*p3*normal O.forces.addF(O.bodies[i].shape.node1.id,p1normal,permanent=False) O.forces.addF(O.bodies[i].shape.node2.id,p2normal,permanent=False) O.forces.addF(O.bodies[i].shape.node3.id,p3normal,permanent=False) #Apply_confiningpressure() sigma3=0 def check_confiningpressure(): global sigma3 sigma3=0 for i in pfIds: e0 =O.bodies[i].shape.node3.state.pos - O.bodies[i].shape.node1.state.pos e1 =O.bodies[i].shape.node2.state.pos - O.bodies[i].shape.node1.state.pos e2 =O.bodies[i].shape.node2.state.pos - O.bodies[i].shape.node3.state.pos a = sqrt(scalar(e0,e0)) b = sqrt(scalar(e1,e1)) c = sqrt(scalar(e2,e2)) s=0.5*(a+b+c) area= sqrt(s*(s-a)*(s-b)*(s-c)) F=(O.forces.f(O.bodies[i].shape.node1.id) + O.forces.f(O.bodies[i].shape.node2.id)+O.forces.f(O.bodies[i].shape.node3.id)).norm() sigma3=sigma3+F/area #print sigma3 return sigma3 pos=topPlate.state.pos[2] def dataCollector(): global pos,p,q,sigma1 #if(pos<0.16): #O.wait() #saveData() #O.exitNoBacktrace() S=pi*l**2 Fnt=O.forces.f(topPlate.id)[2] Fnb=O.forces.f(bottomPlate.id)[2] sigma1=Fnt/S sigma3=check_confiningpressure() pos=topPlate.state.pos[2] q=(sigma1-3e6) p=(sigma1+2*3e6)/2 plot.addData(t=O.time,pos=pos,Fnt=Fnt,Fnb=Fnb,sigma1=sigma1,sigma3=sigma3,unbF=unbalancedForce(),p=p,q=q) def saveData(): plot.saveDataTxt('data/'+O.tags['description']+'.dat',vars=('t','pos','Fnt','Fnb','sigma1','sigma3','unbF')) plot.plots={'p':('q')} #### MOVE TOP AND BOTTOM WALL #v=1.7e-03 v=1.7e-05 def moveWall(v): topPlate.state.vel=(0,0,-v) #bottomPlate.state.vel=(0,0,v) #g=-9.81 g=0 #moveWall(v) #limitfinder() ########################### ##### ENGINE DEFINITION ## ########################### O.dt=0.5*PWaveTimeStep() O.engines=O.engines+[ #PyRunner(iterPeriod=1,initRun=True,command='Apply_load()'), PyRunner(iterPeriod=1,dead=False,command='Apply_confiningpressure()'), #PyRunner(iterPeriod=1,initRun=True,command='Apply_load()'), NewtonIntegrator(damping=0.7,gravity=(0,0,g),label='Newton'), #PyRunner(initRun=True,iterPeriod=1,command='dataCollector()'), #VTKRecorder(iterPeriod=500,initRun=True,fileName='paraview/'+O.tags['description']+'_',recorders=['spheres','velocity']), ] if not isBatch: # VISUALIZATION from yade import qt qt.Controller() #qtv = qt.View() #qtr = qt.Renderer() plot.plot(noShow=False, subPlots=True) #O.run(5000) #moveWall(v) else: O.run(1,True) moveWall(v) O.wait() saveData()

gpl-2.0

gotomypc/scikit-learn

sklearn/linear_model/tests/test_omp.py

272

7752

# Author: Vlad Niculae # Licence: BSD 3 clause import numpy as np from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_warns from sklearn.utils.testing import ignore_warnings from sklearn.linear_model import (orthogonal_mp, orthogonal_mp_gram, OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV, LinearRegression) from sklearn.utils import check_random_state from sklearn.datasets import make_sparse_coded_signal n_samples, n_features, n_nonzero_coefs, n_targets = 20, 30, 5, 3 y, X, gamma = make_sparse_coded_signal(n_targets, n_features, n_samples, n_nonzero_coefs, random_state=0) G, Xy = np.dot(X.T, X), np.dot(X.T, y) # this makes X (n_samples, n_features) # and y (n_samples, 3) def test_correct_shapes(): assert_equal(orthogonal_mp(X, y[:, 0], n_nonzero_coefs=5).shape, (n_features,)) assert_equal(orthogonal_mp(X, y, n_nonzero_coefs=5).shape, (n_features, 3)) def test_correct_shapes_gram(): assert_equal(orthogonal_mp_gram(G, Xy[:, 0], n_nonzero_coefs=5).shape, (n_features,)) assert_equal(orthogonal_mp_gram(G, Xy, n_nonzero_coefs=5).shape, (n_features, 3)) def test_n_nonzero_coefs(): assert_true(np.count_nonzero(orthogonal_mp(X, y[:, 0], n_nonzero_coefs=5)) <= 5) assert_true(np.count_nonzero(orthogonal_mp(X, y[:, 0], n_nonzero_coefs=5, precompute=True)) <= 5) def test_tol(): tol = 0.5 gamma = orthogonal_mp(X, y[:, 0], tol=tol) gamma_gram = orthogonal_mp(X, y[:, 0], tol=tol, precompute=True) assert_true(np.sum((y[:, 0] - np.dot(X, gamma)) ** 2) <= tol) assert_true(np.sum((y[:, 0] - np.dot(X, gamma_gram)) ** 2) <= tol) def test_with_without_gram(): assert_array_almost_equal( orthogonal_mp(X, y, n_nonzero_coefs=5), orthogonal_mp(X, y, n_nonzero_coefs=5, precompute=True)) def test_with_without_gram_tol(): assert_array_almost_equal( orthogonal_mp(X, y, tol=1.), orthogonal_mp(X, y, tol=1., precompute=True)) def test_unreachable_accuracy(): assert_array_almost_equal( orthogonal_mp(X, y, tol=0), orthogonal_mp(X, y, n_nonzero_coefs=n_features)) assert_array_almost_equal( assert_warns(RuntimeWarning, orthogonal_mp, X, y, tol=0, precompute=True), orthogonal_mp(X, y, precompute=True, n_nonzero_coefs=n_features)) def test_bad_input(): assert_raises(ValueError, orthogonal_mp, X, y, tol=-1) assert_raises(ValueError, orthogonal_mp, X, y, n_nonzero_coefs=-1) assert_raises(ValueError, orthogonal_mp, X, y, n_nonzero_coefs=n_features + 1) assert_raises(ValueError, orthogonal_mp_gram, G, Xy, tol=-1) assert_raises(ValueError, orthogonal_mp_gram, G, Xy, n_nonzero_coefs=-1) assert_raises(ValueError, orthogonal_mp_gram, G, Xy, n_nonzero_coefs=n_features + 1) def test_perfect_signal_recovery(): idx, = gamma[:, 0].nonzero() gamma_rec = orthogonal_mp(X, y[:, 0], 5) gamma_gram = orthogonal_mp_gram(G, Xy[:, 0], 5) assert_array_equal(idx, np.flatnonzero(gamma_rec)) assert_array_equal(idx, np.flatnonzero(gamma_gram)) assert_array_almost_equal(gamma[:, 0], gamma_rec, decimal=2) assert_array_almost_equal(gamma[:, 0], gamma_gram, decimal=2) def test_estimator(): omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs) omp.fit(X, y[:, 0]) assert_equal(omp.coef_.shape, (n_features,)) assert_equal(omp.intercept_.shape, ()) assert_true(np.count_nonzero(omp.coef_) <= n_nonzero_coefs) omp.fit(X, y) assert_equal(omp.coef_.shape, (n_targets, n_features)) assert_equal(omp.intercept_.shape, (n_targets,)) assert_true(np.count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs) omp.set_params(fit_intercept=False, normalize=False) omp.fit(X, y[:, 0]) assert_equal(omp.coef_.shape, (n_features,)) assert_equal(omp.intercept_, 0) assert_true(np.count_nonzero(omp.coef_) <= n_nonzero_coefs) omp.fit(X, y) assert_equal(omp.coef_.shape, (n_targets, n_features)) assert_equal(omp.intercept_, 0) assert_true(np.count_nonzero(omp.coef_) <= n_targets * n_nonzero_coefs) def test_identical_regressors(): newX = X.copy() newX[:, 1] = newX[:, 0] gamma = np.zeros(n_features) gamma[0] = gamma[1] = 1. newy = np.dot(newX, gamma) assert_warns(RuntimeWarning, orthogonal_mp, newX, newy, 2) def test_swapped_regressors(): gamma = np.zeros(n_features) # X[:, 21] should be selected first, then X[:, 0] selected second, # which will take X[:, 21]'s place in case the algorithm does # column swapping for optimization (which is the case at the moment) gamma[21] = 1.0 gamma[0] = 0.5 new_y = np.dot(X, gamma) new_Xy = np.dot(X.T, new_y) gamma_hat = orthogonal_mp(X, new_y, 2) gamma_hat_gram = orthogonal_mp_gram(G, new_Xy, 2) assert_array_equal(np.flatnonzero(gamma_hat), [0, 21]) assert_array_equal(np.flatnonzero(gamma_hat_gram), [0, 21]) def test_no_atoms(): y_empty = np.zeros_like(y) Xy_empty = np.dot(X.T, y_empty) gamma_empty = ignore_warnings(orthogonal_mp)(X, y_empty, 1) gamma_empty_gram = ignore_warnings(orthogonal_mp)(G, Xy_empty, 1) assert_equal(np.all(gamma_empty == 0), True) assert_equal(np.all(gamma_empty_gram == 0), True) def test_omp_path(): path = orthogonal_mp(X, y, n_nonzero_coefs=5, return_path=True) last = orthogonal_mp(X, y, n_nonzero_coefs=5, return_path=False) assert_equal(path.shape, (n_features, n_targets, 5)) assert_array_almost_equal(path[:, :, -1], last) path = orthogonal_mp_gram(G, Xy, n_nonzero_coefs=5, return_path=True) last = orthogonal_mp_gram(G, Xy, n_nonzero_coefs=5, return_path=False) assert_equal(path.shape, (n_features, n_targets, 5)) assert_array_almost_equal(path[:, :, -1], last) def test_omp_return_path_prop_with_gram(): path = orthogonal_mp(X, y, n_nonzero_coefs=5, return_path=True, precompute=True) last = orthogonal_mp(X, y, n_nonzero_coefs=5, return_path=False, precompute=True) assert_equal(path.shape, (n_features, n_targets, 5)) assert_array_almost_equal(path[:, :, -1], last) def test_omp_cv(): y_ = y[:, 0] gamma_ = gamma[:, 0] ompcv = OrthogonalMatchingPursuitCV(normalize=True, fit_intercept=False, max_iter=10, cv=5) ompcv.fit(X, y_) assert_equal(ompcv.n_nonzero_coefs_, n_nonzero_coefs) assert_array_almost_equal(ompcv.coef_, gamma_) omp = OrthogonalMatchingPursuit(normalize=True, fit_intercept=False, n_nonzero_coefs=ompcv.n_nonzero_coefs_) omp.fit(X, y_) assert_array_almost_equal(ompcv.coef_, omp.coef_) def test_omp_reaches_least_squares(): # Use small simple data; it's a sanity check but OMP can stop early rng = check_random_state(0) n_samples, n_features = (10, 8) n_targets = 3 X = rng.randn(n_samples, n_features) Y = rng.randn(n_samples, n_targets) omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_features) lstsq = LinearRegression() omp.fit(X, Y) lstsq.fit(X, Y) assert_array_almost_equal(omp.coef_, lstsq.coef_)

bsd-3-clause

dkriegner/xrayutilities

lib/xrayutilities/materials/material.py

1

66506

# This file is part of xrayutilities. # # xrayutilities is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, see <http://www.gnu.org/licenses/>. # # Copyright (C) 2009 Eugen Wintersberger <[email protected]> # Copyright (C) 2009-2020 Dominik Kriegner <[email protected]> # Copyright (C) 2012 Tanja Etzelstorfer <[email protected]> """ Classes decribing materials. Materials are devided with respect to their crystalline state in either Amorphous or Crystal types. While for most materials their crystalline state is defined few materials are also included as amorphous which can be useful for calculation of their optical properties. """ import abc import copy import numbers import operator import re import warnings from math import ceil, copysign, isclose import numpy import scipy.optimize from .. import config, math, utilities from ..exception import InputError from ..math import VecCross, VecDot, VecNorm from . import cif, elements from .atom import Atom from .spacegrouplattice import WyckoffBase numpy.seterr(divide='ignore', invalid='ignore') map_ijkl2ij = {"00": 0, "11": 1, "22": 2, "12": 3, "20": 4, "01": 5, "21": 6, "02": 7, "10": 8} map_ij2ijkl = {"0": [0, 0], "1": [1, 1], "2": [2, 2], "3": [1, 2], "4": [2, 0], "5": [0, 1], "6": [2, 1], "7": [0, 2], "8": [1, 0]} def index_map_ijkl2ij(i, j): return map_ijkl2ij["%i%i" % (i, j)] def index_map_ij2ijkl(ij): return map_ij2ijkl["%i" % ij] def Cij2Cijkl(cij): """ Converts the elastic constants matrix (tensor of rank 2) to the full rank 4 cijkl tensor. Parameters ---------- cij : array-like (6, 6) cij matrix Returns ------- cijkl ndarray (3, 3, 3, 3) cijkl tensor as numpy array """ # first have to build a 9x9 matrix from the 6x6 one m = numpy.zeros((9, 9), dtype=numpy.double) m[0:6, 0:6] = cij[:, :] m[6:9, 0:6] = cij[3:6, :] m[0:6, 6:9] = cij[:, 3:6] m[6:9, 6:9] = cij[3:6, 3:6] # now create the full tensor cijkl = numpy.empty((3, 3, 3, 3), dtype=numpy.double) for i in range(0, 3): for j in range(0, 3): for k in range(0, 3): for n in range(0, 3): mi = index_map_ijkl2ij(i, j) mj = index_map_ijkl2ij(k, n) cijkl[i, j, k, n] = m[mi, mj] return cijkl def Cijkl2Cij(cijkl): """ Converts the full rank 4 tensor of the elastic constants to the (6, 6) matrix of elastic constants. Parameters ---------- cijkl ndarray (3, 3, 3, 3) cijkl tensor as numpy array Returns ------- cij : array-like (6, 6) cij matrix """ cij = numpy.empty((6, 6), dtype=numpy.double) for i in range(6): for j in range(6): ij = index_map_ij2ijkl(i) kl = index_map_ij2ijkl(j) cij[i, j] = cijkl[ij[0], ij[1], kl[0], kl[1]] return cij class Material(utilities.ABC): """ base class for all Materials. common properties of amorphous and crystalline materials are described by this class from which Amorphous and Crystal are derived from. """ def __init__(self, name, cij=None): if cij is None: self.cij = numpy.zeros((6, 6), dtype=numpy.double) self.cijkl = numpy.zeros((3, 3, 3, 3), dtype=numpy.double) elif isinstance(cij, (tuple, list, numpy.ndarray)): self.cij = numpy.asarray(cij, dtype=numpy.double) self.cijkl = Cij2Cijkl(self.cij) else: raise TypeError("Elastic constants must be a list or numpy array!") self.name = name self.transform = None self._density = None def __getattr__(self, name): if name.startswith("c"): index = name[1:] if len(index) > 2: raise AttributeError("Cij indices must be between 1 and 6") i = int(index[0]) j = int(index[1]) if i > 6 or i < 1 or j > 6 or j < 1: raise AttributeError("Cij indices must be between 1 and 6") if callable(self.transform): cij = Cijkl2Cij(self.transform(Cij2Cijkl(self.cij))) else: cij = self.cij return cij[i - 1, j - 1] else: object.__getattribute__(self, name) def _getmu(self): return self.cij[3, 3] def _getlam(self): return self.cij[0, 1] def _getnu(self): return self.lam / 2. / (self.mu + self.lam) def _getdensity(self): return self._density density = property(_getdensity) mu = property(_getmu) lam = property(_getlam) nu = property(_getnu) @abc.abstractmethod def delta(self, en='config'): """ abstract method which every implementation of a Material has to override """ pass @abc.abstractmethod def ibeta(self, en='config'): """ abstract method which every implementation of a Material has to override """ pass def chi0(self, en='config'): """ calculates the complex chi_0 values often needed in simulations. They are closely related to delta and beta (n = 1 + chi_r0/2 + i*chi_i0/2 vs. n = 1 - delta + i*beta) """ return (-2 * self.delta(en) + 2j * self.ibeta(en)) def idx_refraction(self, en="config"): """ function to calculate the complex index of refraction of a material in the x-ray range Parameters ---------- en : energy of the x-rays, if omitted the value from the xrayutilities configuration is used Returns ------- n (complex) """ n = 1. - self.delta(en) + 1.j * self.ibeta(en) return n def critical_angle(self, en='config', deg=True): """ calculate critical angle for total external reflection Parameters ---------- en : float or str, optional energy of the x-rays in eV, if omitted the value from the xrayutilities configuration is used deg : bool, optional return angle in degree if True otherwise radians (default:True) Returns ------- float Angle of total external reflection """ rn = 1. - self.delta(en) alphac = numpy.arccos(rn) if deg: alphac = numpy.degrees(alphac) return alphac def absorption_length(self, en='config'): """ wavelength dependent x-ray absorption length defined as mu = lambda/(2*pi*2*beta) with lambda and beta as the x-ray wavelength and complex part of the refractive index respectively. Parameters ---------- en : float or str, optional energy of the x-rays in eV Returns ------- float the absorption length in um """ if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) return utilities.en2lam(en) / (2 * numpy.pi * self.ibeta(en) * 2) / 1e4 def __str__(self): ostr = "%s: %s\n" % (self.__class__.__name__, self.name) if numpy.any(self.cij): ostr += "Elastic tensor (6x6):\n" d = numpy.get_printoptions() numpy.set_printoptions(precision=2, linewidth=78, suppress=False) ostr += str(self.cij) + '\n' numpy.set_printoptions(**d) return ostr class Amorphous(Material): """ amorphous materials are described by this class """ def __init__(self, name, density, atoms=None, cij=None): """ constructor of an amorphous material. The amorphous material is described by its density and atom composition. Parameters ---------- name : str name of the material. To allow automatic parsing of the chemical elements use the abbreviation of the chemical element from the periodic table. To specify alloys, use e.g. 'Ir0.2Mn0.8' or 'H2O'. density : float mass density in kg/m^3 atoms : list, optional list of atoms together with their fractional content. When the name is a simply chemical formula then this can be None. To specify more complicated materials use [('Ir', 0.2), ('Mn', 0.8), ...]. Instead of the elements as string you can also use an Atom object. If the contents to not add up to 1 they will be normalized without notice. cij : array-like, optional elasticity matrix """ super().__init__(name, cij) self._density = density self.base = list() if atoms is None: comp = Amorphous.parseChemForm(name) if config.VERBOSITY >= config.DEBUG: print("XU.materials.Amorphous: using '%s' as chemical formula" % ''.join(['%s%.2f ' % (e.name, c) for e, c in comp])) for (e, c) in comp: self.base.append((e, c)) else: frsum = numpy.sum([at[1] for at in atoms]) for at, fr in atoms: if not isinstance(at, Atom): a = getattr(elements, at) else: a = at self.base.append((a, fr/frsum)) @staticmethod def parseChemForm(cstring): """ Parse a string containing a simple chemical formula and transform it to a list of elements together with their relative atomic fraction. e.g. 'H2O' -> [(H, 2/3), (O, 1/3)], where H and O are the Element objects of Hydrogen and Oxygen. Note that every chemical element needs to start with a capital letter! Complicated formulas containing bracket are not supported! Parameters ---------- cstring : str string containing the chemical fomula Returns ------- list of tuples chemical element and atomic fraction """ if re.findall(r'[]', cstring): raise ValueError('unsupported chemical formula (%s) given.' % cstring) elems = re.findall('[A-Z][^A-Z]*', cstring) r = re.compile(r"([a-zA-Z]+)([0-9\.]+)") ret = [] csum = 0 for e in elems: if r.match(e): elstr, cont = r.match(e).groups() cont = float(cont) else: elstr, cont = (e, 1.0) ret.append((elstr, cont)) csum += cont for i, r in enumerate(ret): ret[i] = (getattr(elements, r[0]), r[1]/csum) return ret def _get_f(self, q, en): """ optimized method to calculate the atomic scattering factor for all atoms in the unit cell by calling the database only as much as needed. Parameters ---------- q : float or array-like momentum transfer for which the atomic scattering factor should be calculated en : float or str x-ray energy (eV) Returns ------- list atomic scattering factors for every atom in the unit cell """ f = {} for at, occ in self.base: if at.num not in f: f[at.num] = at.f(q, en) return [f[a.num] for a, o in self.base] def delta(self, en='config'): """ function to calculate the real part of the deviation of the refractive index from 1 (n=1-delta+i*beta) Parameters ---------- en : float, array-like or str, optional energy of the x-rays in eV Returns ------- float or array-like """ re = scipy.constants.physical_constants['classical electron radius'][0] re *= 1e10 if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) lam = utilities.en2lam(en) delta = 0. m = 0. f = self._get_f(0., en) for (at, occ), fa in zip(self.base, f): delta += numpy.real(fa) * occ m += at.weight * occ delta *= re / (2 * numpy.pi) * lam ** 2 / (m / self.density) * 1e-30 return delta def ibeta(self, en='config'): """ function to calculate the imaginary part of the deviation of the refractive index from 1 (n=1-delta+i*beta) Parameters ---------- en : float, array-like or str, optional energy of the x-rays in eV Returns ------- float or array-like """ re = scipy.constants.physical_constants['classical electron radius'][0] re *= 1e10 if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) lam = utilities.en2lam(en) beta = 0. m = 0. f = self._get_f(0., en) for (at, occ), fa in zip(self.base, f): beta += numpy.imag(fa) * occ m += at.weight * occ beta *= re / (2 * numpy.pi) * lam ** 2 / (m / self.density) * 1e-30 return beta def chi0(self, en='config'): """ calculates the complex chi_0 values often needed in simulations. They are closely related to delta and beta (n = 1 + chi_r0/2 + i*chi_i0/2 vs. n = 1 - delta + i*beta) """ re = scipy.constants.physical_constants['classical electron radius'][0] re *= 1e10 if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) lam = utilities.en2lam(en) beta = 0. delta = 0. m = 0. f = self._get_f(0., en) for (at, occ), f0 in zip(self.base, f): beta += numpy.imag(f0) * occ delta += numpy.real(f0) * occ m += at.weight * occ beta *= re / (2 * numpy.pi) * lam ** 2 / (m / self.density) * 1e-30 delta *= re / (2 * numpy.pi) * lam ** 2 / (m / self.density) * 1e-30 return (-2 * delta + 2j * beta) def __str__(self): ostr = super().__str__() ostr += "density: %.2f\n" % self.density if self.base: ostr += "atoms: " for at, o in self.base: ostr += "(%s, %.3f) " % (at.name, o) ostr += "\n" return ostr class Crystal(Material): """ Crystalline materials are described by this class """ def __init__(self, name, lat, cij=None, thetaDebye=None): super().__init__(name, cij) self.lattice = lat if isinstance(thetaDebye, numbers.Number): self.thetaDebye = float(thetaDebye) else: self.thetaDebye = thetaDebye @classmethod def fromCIF(cls, ciffilestr, **kwargs): """ Create a Crystal from a CIF file. The default data-set from the cif file will be used to create the Crystal. Parameters ---------- ciffilestr : str, bytes filename of the CIF file or string representation of the CIF file kwargs : dict keyword arguments are passed to the init-method of CIFFile Returns ------- Crystal """ cf = cif.CIFFile(ciffilestr, **kwargs) lat = cf.SGLattice() return cls(cf.data[cf._default_dataset].name, lat) def loadLatticefromCIF(self, ciffilestr): """ load the unit cell data (lattice) from the CIF file. Other material properties stay unchanged. Parameters ---------- ciffilestr : str, bytes filename of the CIF file or string representation of the CIF file """ cf = cif.CIFFile(ciffilestr) self.lattice = cf.SGLattice() def toCIF(self, ciffilename): """ Export the Crystal to a CIF file. Parameters ---------- ciffilename : str filename of the CIF file """ cif.cifexport(ciffilename, self) @property def a(self): return self.lattice.a @property def b(self): return self.lattice.b @property def c(self): return self.lattice.c @property def alpha(self): return self.lattice.alpha @property def beta(self): return self.lattice.beta @property def gamma(self): return self.lattice.gamma @property def a1(self): return self.lattice._ai[0, :] @property def a2(self): return self.lattice._ai[1, :] @property def a3(self): return self.lattice._ai[2, :] @property def B(self): return self.lattice._qtransform.matrix def __eq__(self, other): """ compare if another Crystal instance is equal to the current one. Currently this considers only the lattice to be equal. Additional parameters like thetaDebye and the eleastic parameters are ignored. Parameters ---------- other: Crystal another instance of Crystal to compare """ return self.lattice == other.lattice def Q(self, *hkl): """ Return the Q-space position for a certain material. Parameters ---------- hkl : list or array-like Miller indices (or Q(h, k, l) is also possible) """ return self.lattice.GetQ(*hkl) def HKL(self, *q): """ Return the HKL-coordinates for a certain Q-space position. Parameters ---------- q : list or array-like Q-position. its also possible to use HKL(qx, qy, qz). """ return self.lattice.GetHKL(*q) def chemical_composition(self, natoms=None, with_spaces=False, ndigits=2): """ determine chemical composition from occupancy of atomic positions. Parameters ---------- mat : Crystal instance of Crystal natoms : int, optional number of atoms to normalize the formula, if None some automatic normalization is attempted using the greatest common divisor of the number of atoms per unit cell. If the number of atoms of any element is fractional natoms=1 is used. with_spaces : bool, optional add spaces between the different entries in the output string for CIF combatibility ndigits : int, optional number of digits to which floating point numbers are rounded to Returns ------- str representation of the chemical composition """ elem = {} for a in self.lattice.base(): e = a[0].name occ = a[2] if e in elem: elem[e] += occ else: elem[e] = occ natom = sum([elem[e] for e in elem]) isint = True for e in elem: if not float(elem[e]).is_integer(): isint = False # determine number of atoms if not natoms: if isint: gcd = math.gcd([int(elem[e]) for e in elem]) natoms = natom/gcd else: natoms = 1 # generate output strig cstr = '' fmtstr = '%d' if isint else '%%.%df' % ndigits for e in elem: n = elem[e] / float(natom) * natoms cstr += e if n != 1: cstr += fmtstr % n cstr += ' ' if with_spaces else '' return cstr.strip() def environment(self, *pos, **kwargs): """ Returns a list of neighboring atoms for a given position within the unit cell. If the material does not contain any atoms a dummy atom will be placed on the unit cell corners. Parameters ---------- pos : list or array-like fractional coordinate in the unit cell maxdist : float maximum distance wanted in the list of neighbors (default: 7) Returns ------- list of tuples (distance, atomType, multiplicity) giving distance sorted list of atoms """ valid_kwargs = {'maxdist': 'maximum distance needed in the output'} utilities.check_kwargs(kwargs, valid_kwargs, 'Crystal.environment') maxdist = kwargs.get('maxdist', 7) if len(pos) < 3: pos = pos[0] if len(pos) < 3: raise InputError("need 3 coordinates of the " "reference position") refpos = self.lattice._ai.T @ pos lst = [] # determine lattice base if self.lattice.nsites > 0: base = list(self.lattice.base()) else: base = [(elements.Dummy, (0, 0, 0), 1, 0)] # find maximally needed super cell na = int(ceil(maxdist / math.VecNorm(self.a1))) nb = int(ceil(maxdist / math.VecNorm(self.a2))) nc = int(ceil(maxdist / math.VecNorm(self.a3))) nab = int(ceil(maxdist / math.VecNorm(self.a1 + self.a2))) nac = int(ceil(maxdist / math.VecNorm(self.a1 + self.a3))) nbc = int(ceil(maxdist / math.VecNorm(self.a2 + self.a3))) nabc = int(ceil(maxdist / math.VecNorm(self.a1 + self.a2 + self.a3))) Na = max(na, nab, nac, nabc) Nb = max(nb, nab, nbc, nabc) Nc = max(nc, nac, nbc, nabc) # determine distance of all atoms w.r.t. the refpos ucidx = numpy.mgrid[-Na:Na+1, -Nb:Nb+1, -Nc:Nc+1].reshape(3, -1) for a, p, o, b in base: ucpos = self.lattice._ai.T @ p pos = ucpos + numpy.einsum('ji, ...i', self.lattice._ai.T, ucidx.T) distance = math.VecNorm(pos - refpos) lst += [(d, a, o) for d in distance] # sort and merge return list lst.sort(key=operator.itemgetter(0, 1)) rl = [] if len(lst) < 1 or lst[0][0] > maxdist: return rl mult = lst[0][2] for i in range(1, len(lst)): if (isclose(lst[i - 1][0] - lst[i][0], 0, abs_tol=1e-8) and lst[i - 1][1] == lst[i][1]): mult += lst[i - 1][2] # add occupancy else: rl.append((lst[i - 1][0], lst[i - 1][1], mult)) mult = lst[i][2] if lst[i][0] > maxdist: break return rl def planeDistance(self, *hkl): """ determines the lattice plane spacing for the planes specified by (hkl) Parameters ---------- h, k, l : list, tuple or floats Miller indices of the lattice planes given either as list, tuple or seperate arguments Returns ------- float the lattice plane spacing Examples -------- >>> xu.materials.Si.planeDistance(0, 0, 4) 1.3577600000000001 or >>> xu.materials.Si.planeDistance((1, 1, 1)) 3.1356124059796255 """ if len(hkl) < 3: hkl = hkl[0] if len(hkl) < 3: raise InputError("need 3 indices for the lattice point") return 2 * numpy.pi / math.VecNorm(self.Q(hkl)) def _getdensity(self): """ calculates the mass density of an material from the mass of the atoms in the unit cell. Returns ------- float mass density in kg/m^3 """ m = 0. for at, pos, occ, b in self.lattice.base(): m += at.weight * occ return m / self.lattice.UnitCellVolume() * 1e30 density = property(_getdensity) def _get_f(self, q, en): """ optimized method to calculate the atomic scattering factor for all atoms in the unit cell by calling the database only as much as needed. Parameters ---------- q : float or array-like momentum transfer for which the atomic scattering factor should be calculated en : float or str x-ray energy (eV) Returns ------- list atomic scattering factors for every atom in the unit cell """ f = {} if self.lattice.nsites > 0: for at, pos, occ, b in self.lattice.base(): if at.num not in f: f[at.num] = at.f(q, en) return [f[a.num] for a, p, o, b in self.lattice.base()] else: return None def _get_lamen(self, en): if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) lam = utilities.en2lam(en) return lam, en def delta(self, en='config'): """ function to calculate the real part of the deviation of the refractive index from 1 (n=1-delta+i*beta) Parameters ---------- en : float or str, optional x-ray energy eV, if omitted the value from the xrayutilities configuration is used Returns ------- float """ re = scipy.constants.physical_constants['classical electron radius'][0] re *= 1e10 lam, en = self._get_lamen(en) delta = 0. f = self._get_f(0, en) for (at, pos, occ, b), fa in zip(self.lattice.base(), f): delta += numpy.real(fa) * occ delta *= re / (2 * numpy.pi) * lam ** 2 / \ self.lattice.UnitCellVolume() return delta def ibeta(self, en='config'): """ function to calculate the imaginary part of the deviation of the refractive index from 1 (n=1-delta+i*beta) Parameters ---------- en : float or str, optional x-ray energy eV, if omitted the value from the xrayutilities configuration is used Returns ------- float """ re = scipy.constants.physical_constants['classical electron radius'][0] re *= 1e10 lam, en = self._get_lamen(en) beta = 0. f = self._get_f(0, en) for (at, pos, occ, b), fa in zip(self.lattice.base(), f): beta += numpy.imag(fa) * occ beta *= re / (2 * numpy.pi) * lam ** 2 / self.lattice.UnitCellVolume() return beta def chi0(self, en='config'): """ calculates the complex chi_0 values often needed in simulations. They are closely related to delta and beta (n = 1 + chi_r0/2 + i*chi_i0/2 vs. n = 1 - delta + i*beta) """ re = scipy.constants.physical_constants['classical electron radius'][0] re *= 1e10 lam, en = self._get_lamen(en) beta = 0. delta = 0. if self.lattice.nsites > 0: f = self._get_f(0, en) for (at, pos, occ, b), f0 in zip(self.lattice.base(), f): beta += numpy.imag(f0) * occ delta += numpy.real(f0) * occ v = self.lattice.UnitCellVolume() beta *= re / (2 * numpy.pi) * lam ** 2 / v delta *= re / (2 * numpy.pi) * lam ** 2 / v return (-2 * delta + 2j * beta) def _debyewallerfactor(self, temp, qnorm): """ Calculate the Debye Waller temperature factor according to the Debye temperature Parameters ---------- temp : float actual temperature (K) qnorm : float or array-like norm of the q-vector(s) for which the factor should be calculated Returns ------- float or array-like the Debye Waller factor(s) with the same shape as qnorm """ if temp != 0 and self.thetaDebye: # W(q) = 3/2* hbar^2*q^2/(m*kB*tD) * (D1(tD/T)/(tD/T) + 1/4) # DWF = exp(-W(q)) consistent with Vaclav H. and several books hbar = scipy.constants.hbar kb = scipy.constants.Boltzmann x = self.thetaDebye / float(temp) m = 0. im = 0 for a, p, o, b in self.lattice.base(): m += a.weight im += 1 m = m / float(im) exponentf = 3 / 2. * hbar ** 2 * 1.0e20 / \ (m * kb * self.thetaDebye) * (math.Debye1(x) / x + 0.25) if config.VERBOSITY >= config.DEBUG: print("XU.materials.Crystal: DWF = exp(-W*q**2) W= %g" % exponentf) dwf = numpy.exp(-exponentf * qnorm ** 2) else: dwf = 1.0 return dwf def chih(self, q, en='config', temp=0, polarization='S'): """ calculates the complex polarizability of a material for a certain momentum transfer and energy Parameters ---------- q : list, tuple or array-like momentum transfer vector in (1/A) en : float or str, optional x-ray energy eV, if omitted the value from the xrayutilities configuration is used temp : float, optional temperature used for Debye-Waller-factor calculation polarization : {'S', 'P'}, optional sigma or pi polarization Returns ------- tuple (abs(chih_real), abs(chih_imag)) complex polarizability """ if isinstance(q, (list, tuple)): q = numpy.array(q, dtype=numpy.double) elif isinstance(q, numpy.ndarray): pass else: raise TypeError("q must be a list or numpy array!") qnorm = math.VecNorm(q) if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) if polarization not in ('S', 'P'): raise ValueError("polarization must be 'S':sigma or 'P': pi!") if self.lattice.nsites == 0: return (0, 0) dwf = self._debyewallerfactor(temp, qnorm) sr = 0. + 0.j si = 0. + 0.j # a: atom, p: position, o: occupancy, b: temperature-factor f = self._get_f(qnorm, en) for (a, p, o, b), F in zip(self.lattice.base(), f): r = self.lattice.GetPoint(p) if temp == 0: dwf = numpy.exp(-b * qnorm ** 2 / (4 * numpy.pi) ** 2) fr = numpy.real(F) * o fi = numpy.imag(F) * o sr += fr * numpy.exp(-1.j * math.VecDot(q, r)) * dwf si += fi * numpy.exp(-1.j * math.VecDot(q, r)) * dwf # classical electron radius c = scipy.constants r_e = 1 / (4 * numpy.pi * c.epsilon_0) * c.e ** 2 / \ (c.electron_mass * c.speed_of_light ** 2) * 1e10 lam = utilities.en2lam(en) fact = -lam ** 2 * r_e / (numpy.pi * self.lattice.UnitCellVolume()) rchi = numpy.abs(fact * sr) ichi = numpy.abs(fact * si) if polarization == 'P': theta = numpy.arcsin(qnorm * utilities.en2lam(en) / (4*numpy.pi)) rchi *= numpy.cos(2 * theta) ichi *= numpy.cos(2 * theta) return rchi, ichi def dTheta(self, Q, en='config'): """ function to calculate the refractive peak shift Parameters ---------- Q : list, tuple or array-like momentum transfer vector (1/A) en : float or str, optional x-ray energy eV, if omitted the value from the xrayutilities configuration is used Returns ------- float peak shift in degree """ if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) lam = utilities.en2lam(en) dth = numpy.degrees( 2 * self.delta(en) / numpy.sin(2 * numpy.arcsin( lam * VecNorm(Q) / (4 * numpy.pi)))) return dth def __str__(self): ostr = super().__str__() ostr += "Lattice:\n" ostr += str(self.lattice) return ostr def StructureFactor(self, q, en='config', temp=0): """ calculates the structure factor of a material for a certain momentum transfer and energy at a certain temperature of the material Parameters ---------- q : list, tuple or array-like vectorial momentum transfer en : float or str, optional x-ray energy eV, if omitted the value from the xrayutilities configuration is used temp : float temperature used for Debye-Waller-factor calculation Returns ------- complex the complex structure factor """ if isinstance(q, (list, tuple)): q = numpy.array(q, dtype=numpy.double) elif isinstance(q, numpy.ndarray): pass else: raise TypeError("q must be a list or numpy array!") if isinstance(en, str) and en == 'config': en = utilities.energy(config.ENERGY) if self.lattice.nsites == 0: return 1. qnorm = math.VecNorm(q) dwf = self._debyewallerfactor(temp, qnorm) s = 0. + 0.j f = self._get_f(qnorm, en) # a: atom, p: position, o: occupancy, b: temperature-factor for (a, p, o, b), fq in zip(self.lattice.base(), f): r = self.lattice.GetPoint(p) if temp == 0: dwf = numpy.exp(-b * qnorm ** 2 / (4 * numpy.pi) ** 2) s += fq * o * numpy.exp(-1.j * math.VecDot(q, r)) * dwf return s def StructureFactorForEnergy(self, q0, en, temp=0): """ calculates the structure factor of a material for a certain momentum transfer and a bunch of energies Parameters ---------- q0 : list, tuple or array-like vectorial momentum transfer en : list, tuple or array-like energy values in eV temp : float temperature used for Debye-Waller-factor calculation Returns ------- array-like complex valued structure factor array """ if isinstance(q0, (list, tuple)): q = numpy.array(q0, dtype=numpy.double) elif isinstance(q0, numpy.ndarray): q = q0 else: raise TypeError("q must be a list or numpy array!") qnorm = math.VecNorm(q) if isinstance(en, (list, tuple)): en = numpy.array(en, dtype=numpy.double) elif isinstance(en, numpy.ndarray): pass else: raise TypeError("Energy data must be provided as a list " "or numpy array!") if self.lattice.nsites == 0: return numpy.ones(len(en)) dwf = self._debyewallerfactor(temp, qnorm) s = 0. + 0.j f = self._get_f(qnorm, en) # a: atom, p: position, o: occupancy, b: temperature-factor for (a, p, o, b), fq in zip(self.lattice.base(), f): if temp == 0: dwf = numpy.exp(-b * qnorm ** 2 / (4 * numpy.pi) ** 2) r = self.lattice.GetPoint(p) s += fq * o * dwf * numpy.exp(-1.j * math.VecDot(q, r)) return s def StructureFactorForQ(self, q, en0='config', temp=0): """ calculates the structure factor of a material for a bunch of momentum transfers and a certain energy Parameters ---------- q : list of vectors or array-like vectorial momentum transfers; list of vectores (list, tuple or array) of length 3 e.g.: (Si.Q(0, 0, 4), Si.Q(0, 0, 4.1),...) or numpy.array([Si.Q(0, 0, 4), Si.Q(0, 0, 4.1)]) en0 : float or str, optional x-ray energy eV, if omitted the value from the xrayutilities configuration is used temp : float temperature used for Debye-Waller-factor calculation Returns ------- array-like complex valued structure factor array """ if isinstance(q, (list, tuple, numpy.ndarray)): q = numpy.asarray(q, dtype=numpy.double) else: raise TypeError("q must be a list or numpy array!") if len(q.shape) != 2: raise ValueError("q does not have the correct shape (shape = %s)" % str(q.shape)) qnorm = numpy.linalg.norm(q, axis=1) if isinstance(en0, str) and en0 == 'config': en0 = utilities.energy(config.ENERGY) if self.lattice.nsites == 0: return numpy.ones(len(q)) dwf = self._debyewallerfactor(temp, qnorm) s = 0. + 0.j f = self._get_f(qnorm, en0) # a: atom, p: position, o: occupancy, b: temperature-factor for (a, p, o, b), fq in zip(self.lattice.base(), f): if temp == 0: dwf = numpy.exp(-b * qnorm ** 2 / (4 * numpy.pi) ** 2) r = self.lattice.GetPoint(p) s += fq * o * numpy.exp(-1.j * numpy.dot(q, r)) * dwf return s def ApplyStrain(self, strain): """ Applies a certain strain on the lattice of the material. The result is a change in the base vectors of the real space as well as reciprocal space lattice. The full strain matrix (3x3) needs to be given. Note: NO elastic response of the material will be considered! """ # let strain act on the unit cell vectors self.lattice.ApplyStrain(strain) def GetMismatch(self, mat): """ Calculate the mismatch strain between the material and a second material """ raise NotImplementedError("XU.material.GetMismatch: " "not implemented yet") def distances(self): """ function to obtain distances of atoms in the crystal up to the unit cell size (largest value of a, b, c is the cut-off) returns a list of tuples with distance d and number of occurence n [(d1, n1), (d2, n2),...] Note: if the base of the material is empty the list will be empty """ if self.lattice.nsites == 0: return [] cutoff = numpy.max((self.lattice.a, self.lattice.b, self.lattice.c)) tmp_data = [] for at1 in self.lattice.base(): for at2 in self.lattice.base(): dis = math.VecNorm(self.lattice.GetPoint(at1[1] - at2[1])) dis2 = math.VecNorm(self.lattice.GetPoint( at1[1] - at2[1] + numpy.array((1, 0, 0)))) dis3 = math.VecNorm(self.lattice.GetPoint( at1[1] - at2[1] + numpy.array((0, 1, 0)))) dis4 = math.VecNorm(self.lattice.GetPoint( at1[1] - at2[1] + numpy.array((0, 0, 1)))) dis5 = math.VecNorm(self.lattice.GetPoint( at1[1] - at2[1] + numpy.array((-1, 0, 0)))) dis6 = math.VecNorm(self.lattice.GetPoint( at1[1] - at2[1] + numpy.array((0, -1, 0)))) dis7 = math.VecNorm(self.lattice.GetPoint( at1[1] - at2[1] + numpy.array((0, 0, -1)))) distances = sorted([dis, dis2, dis3, dis4, dis5, dis6, dis7]) for dis in distances: if dis < cutoff: tmp_data.append(dis) # sort the list and compress equal entries tmp_data.sort() self._distances = [0] self._dis_hist = [0] for dis in tmp_data: if numpy.round(dis - self._distances[-1], config.DIGITS) == 0: self._dis_hist[-1] += 1 else: self._distances.append(dis) self._dis_hist.append(1) # create return value ret = [] for i in range(len(self._distances)): ret.append((self._distances[i], self._dis_hist[i])) return ret def show_unitcell(self, fig=None, subplot=111, scale=0.6, complexity=11, linewidth=1.5, mode='matplotlib'): """ visualization of the unit cell using either matplotlibs basic 3D functionality (expect rendering inaccuracies!) or the mayavi mlab package (accurate rendering -> recommended!) Note: For more flexible visualization consider using the CIF-export feature and use a proper crystal structure viewer. Parameters ---------- fig : matplotlib Figure, Mayavi Scene, or None, optional subplot : int or list, optional subplot to use for the visualization when using matplotlib. This argument of fowarded to the first argument of matplotlibs `add_subplot` function scale : float, optional scale the size of the atoms by this additional factor. By default the size of the atoms corresponds to 60% of their atomic radius. complexity : int, optional number of steps to approximate the atoms as spheres. Higher values make spheres more accurate, but cause slower plotting. linewidth : float, optional line thickness of the unit cell outline mode : str, optional defines the plot backend used, can be 'matplotlib' (default) or 'mayavi'. Returns ------- figure object of either matplotlib or Mayavi """ if mode == 'matplotlib': plot, plt = utilities.import_matplotlib_pyplot('XU.materials') try: import mpl_toolkits.mplot3d except ImportError: plot = False else: plot, mlab = utilities.import_mayavi_mlab('XU.materials') try: import mayavi from matplotlib.colors import to_rgb except ImportError: plot = False if not plot: print('matplotlib and/or mayavi.mlab needed for show_unitcell()') return def plot_sphere(fig, vecpos, r, alpha, complexity, color): """ Visualize a sphere using either matplotlib or Mayavi """ if mode == 'matplotlib': ax = fig.gca() phi, theta = numpy.mgrid[0:numpy.pi:1j*complexity, 0:2*numpy.pi:1j*complexity] x = r*numpy.sin(phi)*numpy.cos(theta) + vecpos[0] y = r*numpy.sin(phi)*numpy.sin(theta) + vecpos[1] z = r*numpy.cos(phi) + vecpos[2] ax.plot_surface(x, y, z, rstride=1, cstride=1, color=color, alpha=alpha, linewidth=0) else: mlab.points3d(vecpos[0], vecpos[1], vecpos[2], r, opacity=alpha, transparent=False, color=to_rgb(color), resolution=complexity, scale_factor=2, figure=fig) def plot_line(fig, start, end, color, linewidth): """ Draw a line between two 3D points, either using matplotlib or Mayavi. """ if mode == 'matplotlib': ax = fig.gca() ax.plot((start[0], end[0]), (start[1], end[1]), (start[2], end[2]), color=color, lw=linewidth) else: mlab.plot3d((start[0], end[0]), (start[1], end[1]), (start[2], end[2]), color=to_rgb(color), tube_radius=linewidth/20, figure=fig) if mode == 'matplotlib': if fig is None: fig = plt.figure() elif not isinstance(fig, plt.Figure): raise TypeError("'fig' argument must be a matplotlib figure!") ax = fig.add_subplot(subplot, projection='3d') else: if fig is None: fig = mlab.figure(bgcolor=(1, 1, 1)) elif not isinstance(fig, mayavi.core.scene.Scene): raise TypeError("'fig' argument must be a Mayavi Scene!") for a, pos, occ, b in self.lattice.base(): r = a.radius * scale for i in range(-1, 2): for j in range(-1, 2): for k in range(-1, 2): atpos = (pos + [i, j, k]) if all(a > -config.EPSILON and a < 1+config.EPSILON for a in atpos): vecpos = atpos[0]*self.a1 + atpos[1]*self.a2 +\ atpos[2]*self.a3 plot_sphere(fig, vecpos, r, occ, complexity, a.color) # plot unit cell outlines plot_line(fig, (0, 0, 0), self.a1, 'k', linewidth) plot_line(fig, (0, 0, 0), self.a2, 'k', linewidth) plot_line(fig, (0, 0, 0), self.a3, 'k', linewidth) plot_line(fig, self.a1, self.a1+self.a2, 'k', linewidth) plot_line(fig, self.a1, self.a1+self.a3, 'k', linewidth) plot_line(fig, self.a2, self.a1+self.a2, 'k', linewidth) plot_line(fig, self.a2, self.a2+self.a3, 'k', linewidth) plot_line(fig, self.a3, self.a1+self.a3, 'k', linewidth) plot_line(fig, self.a3, self.a2+self.a3, 'k', linewidth) plot_line(fig, self.a1+self.a2, self.a1+self.a2+self.a3, 'k', linewidth) plot_line(fig, self.a1+self.a3, self.a1+self.a2+self.a3, 'k', linewidth) plot_line(fig, self.a2+self.a3, self.a1+self.a2+self.a3, 'k', linewidth) if mode == 'matplotib': if config.VERBOSITY >= config.INFO_LOW: warnings.warn("show_unitcell: 3D projection might appear " "distorted (limited 3D capabilities of " "matplotlib!). Use mayavi mode or CIF " "export and other viewers for better " "visualization.") plt.tight_layout() return fig def CubicElasticTensor(c11, c12, c44): """ Assemble the 6x6 matrix of elastic constants for a cubic material from the three independent components of a cubic crystal Parameters ---------- c11, c12, c44 : float independent components of the elastic tensor of cubic materials Returns ------- cij : ndarray 6x6 matrix with elastic constants """ m = numpy.zeros((6, 6), dtype=numpy.double) m[0, 0] = c11 m[1, 1] = c11 m[2, 2] = c11 m[3, 3] = c44 m[4, 4] = c44 m[5, 5] = c44 m[0, 1] = m[0, 2] = c12 m[1, 0] = m[1, 2] = c12 m[2, 0] = m[2, 1] = c12 return m def HexagonalElasticTensor(c11, c12, c13, c33, c44): """ Assemble the 6x6 matrix of elastic constants for a hexagonal material from the five independent components of a hexagonal crystal Parameters ---------- c11, c12, c13, c33, c44 : float independent components of the elastic tensor of a hexagonal material Returns ------- cij : ndarray 6x6 matrix with elastic constants """ m = numpy.zeros((6, 6), dtype=numpy.double) m[0, 0] = m[1, 1] = c11 m[2, 2] = c33 m[3, 3] = m[4, 4] = c44 m[5, 5] = 0.5 * (c11 - c12) m[0, 1] = m[1, 0] = c12 m[0, 2] = m[1, 2] = m[2, 0] = m[2, 1] = c13 return m def WZTensorFromCub(c11ZB, c12ZB, c44ZB): """ Determines the hexagonal elastic tensor from the values of the cubic elastic tensor under the assumptions presented in Phys. Rev. B 6, 4546 (1972), which are valid for the WZ <-> ZB polymorphs. Parameters ---------- c11, c12, c44 : float independent components of the elastic tensor of cubic materials Returns ------- cij : ndarray 6x6 matrix with elastic constants Implementation according to a patch submitted by Julian Stangl """ # matrix conversions: cubic (111) to hexagonal (001) direction P = (1 / 6.) * numpy.array([[3, 3, 6], [2, 4, 8], [1, 5, -2], [2, 4, -4], [2, -2, 2], [1, -1, 4]]) Q = (1 / (3 * numpy.sqrt(2))) * numpy.array([1, -1, -2]) cZBvec = numpy.array([c11ZB, c12ZB, c44ZB]) cWZvec_BAR = numpy.dot(P, cZBvec) delta = numpy.dot(Q, cZBvec) D = numpy.array([delta**2 / cWZvec_BAR[2], 0, -delta**2 / cWZvec_BAR[2], 0, delta**2 / cWZvec_BAR[0], delta**2 / cWZvec_BAR[2]]) cWZvec = cWZvec_BAR - D.T return HexagonalElasticTensor(cWZvec[0], cWZvec[2], cWZvec[3], cWZvec[1], cWZvec[4]) class Alloy(Crystal): """ alloys two materials from the same crystal system. If the materials have the same space group the Wyckoff positions within the unit cell will also reflect the alloying. """ def __init__(self, matA, matB, x): self.check_compatibility(matA, matB) lat = copy.deepcopy(matA.lattice) super().__init__("None", lat, matA.cij) self.matA = matA self.matB = matB self._setxb(x) @staticmethod def check_compatibility(matA, matB): csA = matA.lattice.crystal_system.split(':')[0] csB = matB.lattice.crystal_system.split(':')[0] if csA != csB: raise InputError("Crystal systems of the two materials are " "incompatible!") @staticmethod def lattice_const_AB(latA, latB, x, name=''): """ method to calculated the interpolation of lattice parameters and unit cell angles of the Alloy. By default linear interpolation between the value of material A and B is performed. Parameters ---------- latA, latB : float or vector property (lattice parameter/angle) of material A and B. A property can be a scalar or vector. x : float fraction of material B in the alloy. name : str, optional label of the property which is interpolated. Can be 'a', 'b', 'c', 'alpha', 'beta', or 'gamma'. """ return (latB - latA) * x + latA def _getxb(self): return self._xb def _setxb(self, x): self._xb = x self.name = ("%s(%2.2f)%s(%2.2f)" % (self.matA.name, 1-x, self.matB.name, x)) # modify the free parameters of the lattice for k in self.lattice.free_parameters: setattr(self.lattice, k, self.lattice_const_AB(getattr(self.matA, k), getattr(self.matB, k), x, name=k)) # set elastic constants self.cij = (self.matB.cij - self.matA.cij) * x + self.matA.cij self.cijkl = (self.matB.cijkl - self.matA.cijkl) * x + self.matA.cijkl # alloying in unit cell if self.matA.lattice.space_group == self.matB.lattice.space_group: self.lattice._wbase = WyckoffBase() for a, wp, o, b in self.matA.lattice._wbase: self.lattice._wbase.append(a, wp, occ=o*(1-x), b=b) for a, wp, o, b in self.matB.lattice._wbase: if (a, wp, o, b) in self.lattice._wbase: idx = self.lattice._wbase.index((a, wp, o, b)) occ = self.lattice._wbase[idx][2] self.lattice._wbase[idx] = (a, wp, occ+o*x, b) else: self.lattice._wbase.append(a, wp, occ=o*x, b=b) x = property(_getxb, _setxb) def _checkfinitenumber(self, arg, name=""): if isinstance(arg, numbers.Number) and numpy.isfinite(arg): return float(arg) else: raise TypeError("argument (%s) must be a scalar!" % name) def _checkarray(self, arg, name=""): if isinstance(arg, (list, tuple, numpy.ndarray)): return numpy.asarray(arg, dtype=numpy.double) else: raise TypeError("argument (%s) must be of type " "list, tuple or numpy.ndarray" % name) def _definehelpers(self, hkl, cijA, cijB): """ define helper functions for solving the content from reciprocal space positions """ def a1(x): return self.lattice_const_AB(self.matA.a1, self.matB.a1, x, name='a') def a2(x): return self.lattice_const_AB(self.matA.a2, self.matB.a2, x, name='b') def a3(x): return self.lattice_const_AB(self.matA.a3, self.matB.a3, x, name='c') def V(x): return numpy.dot(a3(x), numpy.cross(a1(x), a2(x))) def b1(x): return 2 * numpy.pi / V(x) * numpy.cross(a2(x), a3(x)) def b2(x): return 2 * numpy.pi / V(x) * numpy.cross(a3(x), a1(x)) def b3(x): return 2 * numpy.pi / V(x) * numpy.cross(a1(x), a2(x)) def qhklx(x): return hkl[0] * b1(x) + hkl[1] * b2(x) + hkl[2] * b3(x) def frac(x): return ((cijB[0, 2] + cijB[1, 2] - (cijA[0, 2] + cijA[1, 2])) * x + (cijA[0, 2] + cijA[1, 2])) / \ ((cijB[2, 2] - cijA[2, 2]) * x + cijA[2, 2]) return a1, a2, a3, V, b1, b2, b3, qhklx, frac def RelaxationTriangle(self, hkl, sub, exp): """ function which returns the relaxation triangle for a Alloy of given composition. Reciprocal space coordinates are calculated using the user-supplied experimental class Parameters ---------- hkl : list or array-like Miller Indices sub : Crystal, or float substrate material or lattice constant exp : Experiment object from which the Transformation object and ndir are needed Returns ------- qy, qz : float reciprocal space coordinates of the corners of the relaxation triangle """ hkl = self._checkarray(hkl, "hkl") trans = exp._transform ndir = exp.ndir / VecNorm(exp.ndir) if isinstance(sub, Crystal): asub = sub.lattice.a elif isinstance(sub, float): asub = sub else: raise TypeError("Second argument (sub) must be of type float or " "an instance of xrayutilities.materials.Crystal") # test if inplane direction of hkl is the same as the one for the # experiment otherwise warn the user hklinplane = VecCross(VecCross(exp.ndir, hkl), exp.ndir) if not numpy.isclose(VecNorm(VecCross(hklinplane, exp.idir)), 0): warnings.warn("Alloy: given hkl differs from the geometry of the " "Experiment instance in the azimuthal direction") # transform elastic constants to correct coordinate frame cijA = Cijkl2Cij(trans(self.matA.cijkl, rank=4)) cijB = Cijkl2Cij(trans(self.matB.cijkl, rank=4)) a1, a2, a3, V, b1, b2, b3, qhklx, frac = self._definehelpers(hkl, cijA, cijB) qr_i = trans(qhklx(self.x))[1] qr_p = trans(qhklx(self.x))[2] qs_i = copysign(2*numpy.pi/asub * VecNorm(VecCross(ndir, hkl)), qr_i) qs_p = 2*numpy.pi/asub * abs(VecDot(ndir, hkl)) # calculate pseudomorphic points for A and B def abulk(x): return math.VecNorm(a1(x)) def aperp(x): return abulk(self.x) * (1 + frac(x) * (1 - asub / abulk(self.x))) qp_i = copysign(2*numpy.pi/asub * VecNorm(VecCross(ndir, hkl)), qr_i) qp_p = 2*numpy.pi/aperp(self.x) * abs(VecDot(ndir, hkl)) # assembly return values qy = numpy.array([qr_i, qp_i, qs_i, qr_i], dtype=numpy.double) qz = numpy.array([qr_p, qp_p, qs_p, qr_p], dtype=numpy.double) return qy, qz class CubicAlloy(Alloy): def __init__(self, matA, matB, x): # here one could check if material is really cubic!! Alloy.__init__(self, matA, matB, x) def ContentBsym(self, q_perp, hkl, inpr, asub, relax): """ function that determines the content of B in the alloy from the reciprocal space position of a symetric peak. As an additional input the substrates lattice parameter and the degree of relaxation must be given Parameters ---------- q_perp : float perpendicular peak position of the reflection hkl of the alloy in reciprocal space hkl : list Miller indices of the measured symmetric reflection (also defines the surface normal inpr : list Miller indices of a Bragg peak defining the inplane reference direction asub : float substrate lattice parameter relax : float degree of relaxation (needed to obtain the content from symmetric reciprocal space position) Returns ------- content : float the content of B in the alloy determined from the input variables """ # check input parameters q_perp = self._checkfinitenumber(q_perp, "q_perp") hkl = self._checkarray(hkl, "hkl") inpr = self._checkarray(inpr, "inpr") asub = self._checkfinitenumber(asub, "asub") relax = self._checkfinitenumber(relax, "relax") # calculate lattice constants from reciprocal space positions n = self.Q(hkl) / VecNorm(self.Q(hkl)) # the following line is not generally true! only cubic materials aperp = 2 * numpy.pi / q_perp * abs(VecDot(n, hkl)) # transform the elastic tensors to a coordinate frame attached to the # surface normal inp1 = VecCross(n, inpr) / VecNorm(VecCross(n, inpr)) inp2 = VecCross(n, inp1) trans = math.CoordinateTransform(inp1, inp2, n) if config.VERBOSITY >= config.DEBUG: print("XU.materials.Alloy.ContentB: inp1/inp2: ", inp1, inp2) cijA = Cijkl2Cij(trans(self.matA.cijkl, rank=4)) cijB = Cijkl2Cij(trans(self.matB.cijkl, rank=4)) a1, a2, a3, V, b1, b2, b3, qhklx, frac = self._definehelpers(hkl, cijA, cijB) # the following line is not generally true! only cubic materials def abulk_perp(x): return abs(2 * numpy.pi / numpy.inner(qhklx(x), n) * numpy.inner(n, hkl)) # can we use abulk_perp here? for cubic materials this should work?! def ainp(x): return asub + relax * (abulk_perp(x) - asub) if config.VERBOSITY >= config.DEBUG: print("XU.materials.Alloy.ContentB: abulk_perp: %8.5g" % (abulk_perp(0.))) def equation(x): return ((aperp - abulk_perp(x)) + (ainp(x) - abulk_perp(x)) * frac(x)) x = scipy.optimize.brentq(equation, -0.1, 1.1) return x def ContentBasym(self, q_inp, q_perp, hkl, sur): """ function that determines the content of B in the alloy from the reciprocal space position of an asymmetric peak. Parameters ---------- q_inp : float inplane peak position of reflection hkl of the alloy in reciprocal space q_perp : float perpendicular peak position of the reflection hkl of the alloy in reciprocal space hkl : list Miller indices of the measured asymmetric reflection sur : list Miller indices of the surface (determines the perpendicular direction) Returns ------- content : float content of B in the alloy determined from the input variables list [a_inplane a_perp, a_bulk_perp(x), eps_inplane, eps_perp]; lattice parameters calculated from the reciprocal space positions as well as the strain (eps) of the layer """ # check input parameters q_inp = self._checkfinitenumber(q_inp, "q_inp") q_perp = self._checkfinitenumber(q_perp, "q_perp") hkl = self._checkarray(hkl, "hkl") sur = self._checkarray(sur, "sur") # check if reflection is asymmetric if math.VecNorm(math.VecCross(self.Q(hkl), self.Q(sur))) < 1.e-8: raise InputError("Miller indices of a symmetric reflection were" "given where an asymmetric reflection is needed") # calculate lattice constants from reciprocal space positions n = self.Q(sur) / VecNorm(self.Q(sur)) q_hkl = self.Q(hkl) # the following two lines are not generally true! only cubic materials ainp = 2 * numpy.pi / abs(q_inp) * VecNorm(VecCross(n, hkl)) aperp = 2 * numpy.pi / abs(q_perp) * abs(VecDot(n, hkl)) # transform the elastic tensors to a coordinate frame attached to the # surface normal inp1 = VecCross(n, q_hkl) / VecNorm(VecCross(n, q_hkl)) inp2 = VecCross(n, inp1) trans = math.CoordinateTransform(inp1, inp2, n) cijA = Cijkl2Cij(trans(self.matA.cijkl, rank=4)) cijB = Cijkl2Cij(trans(self.matB.cijkl, rank=4)) a1, a2, a3, V, b1, b2, b3, qhklx, frac = self._definehelpers(hkl, cijA, cijB) # the following two lines are not generally true! only cubic materials def abulk_inp(x): return abs(2 * numpy.pi / numpy.inner(qhklx(x), inp2) * VecNorm(VecCross(n, hkl))) def abulk_perp(x): return abs(2 * numpy.pi / numpy.inner(qhklx(x), n) * numpy.inner(n, hkl)) if config.VERBOSITY >= config.DEBUG: print("XU.materials.Alloy.ContentB: abulk_inp/perp: %8.5g %8.5g" % (abulk_inp(0.), abulk_perp(0.))) def equation(x): return ((aperp - abulk_perp(x)) + (ainp - abulk_inp(x)) * frac(x)) x = scipy.optimize.brentq(equation, -0.1, 1.1) eps_inplane = (ainp - abulk_perp(x)) / abulk_perp(x) eps_perp = (aperp - abulk_perp(x)) / abulk_perp(x) return x, [ainp, aperp, abulk_perp(x), eps_inplane, eps_perp] def PseudomorphicMaterial(sub, layer, relaxation=0, trans=None): """ This function returns a material whos lattice is pseudomorphic on a particular substrate material. The two materials must have similar unit cell definitions for the algorithm to work correctly, i.e. it does not work for combiniations of materials with different lattice symmetry. It is also crucial that the layer object includes values for the elastic tensor. Parameters ---------- sub : Crystal substrate material layer : Crystal bulk material of the layer, including its elasticity tensor relaxation : float, optional degree of relaxation 0: pseudomorphic, 1: relaxed (default: 0) trans : Tranform Transformation which transforms lattice directions into a surface orientated coordinate frame (x, y inplane, z out of plane). If None a (001) surface geometry of a cubic material is assumed. Returns ------- An instance of Crystal holding the new pseudomorphically strained material. Raises ------ InputError If the layer material has no elastic parameters """ def get_inplane(lat): """determine inplane lattice parameter""" return (math.VecNorm(lat.GetPoint(trans.inverse((1, 0, 0)))) + math.VecNorm(lat.GetPoint(trans.inverse((0, 1, 0))))) / 2. if not trans: trans = math.Transform(numpy.identity(3)) if numpy.all(layer.cijkl == 0): raise InputError("'layer' argument needs elastic parameters") # calculate the strain asub = get_inplane(sub.lattice) abulk = get_inplane(layer.lattice) apar = asub + (abulk - asub) * relaxation epar = (apar - abulk) / abulk cT = trans(layer.cijkl, rank=4) eperp = -epar * (cT[1, 1, 2, 2] + cT[2, 2, 0, 0]) / (cT[2, 2, 2, 2]) eps = trans.inverse(numpy.diag((epar, epar, eperp)), rank=2) if config.VERBOSITY >= config.INFO_ALL: print("XU.materials.PseudomorphicMaterial: applying strain (inplane, " "perpendicular): %.4g %.4g" % (epar, eperp)) # create the pseudomorphic material pmlatt = copy.deepcopy(layer.lattice) pmat = Crystal(layer.name, pmlatt, layer.cij) pmat.ApplyStrain(eps) return pmat

gpl-2.0

hsiaoyi0504/scikit-learn

sklearn/linear_model/logistic.py

105

56686

""" Logistic Regression """ # Author: Gael Varoquaux <[email protected]> # Fabian Pedregosa <[email protected]> # Alexandre Gramfort <[email protected]> # Manoj Kumar <[email protected]> # Lars Buitinck # Simon Wu <[email protected]> import numbers import warnings import numpy as np from scipy import optimize, sparse from .base import LinearClassifierMixin, SparseCoefMixin, BaseEstimator from ..feature_selection.from_model import _LearntSelectorMixin from ..preprocessing import LabelEncoder, LabelBinarizer from ..svm.base import _fit_liblinear from ..utils import check_array, check_consistent_length, compute_class_weight from ..utils import check_random_state from ..utils.extmath import (logsumexp, log_logistic, safe_sparse_dot, squared_norm) from ..utils.optimize import newton_cg from ..utils.validation import (as_float_array, DataConversionWarning, check_X_y) from ..utils.fixes import expit from ..externals.joblib import Parallel, delayed from ..cross_validation import check_cv from ..externals import six from ..metrics import SCORERS # .. some helper functions for logistic_regression_path .. def _intercept_dot(w, X, y): """Computes y * np.dot(X, w). It takes into consideration if the intercept should be fit or not. Parameters ---------- w : ndarray, shape (n_features,) or (n_features + 1,) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. y : ndarray, shape (n_samples,) Array of labels. """ c = 0. if w.size == X.shape[1] + 1: c = w[-1] w = w[:-1] z = safe_sparse_dot(X, w) + c return w, c, y * z def _logistic_loss_and_grad(w, X, y, alpha, sample_weight=None): """Computes the logistic loss and gradient. Parameters ---------- w : ndarray, shape (n_features,) or (n_features + 1,) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. y : ndarray, shape (n_samples,) Array of labels. alpha : float Regularization parameter. alpha is equal to 1 / C. sample_weight : ndarray, shape (n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. Returns ------- out : float Logistic loss. grad : ndarray, shape (n_features,) or (n_features + 1,) Logistic gradient. """ _, n_features = X.shape grad = np.empty_like(w) w, c, yz = _intercept_dot(w, X, y) if sample_weight is None: sample_weight = np.ones(y.shape[0]) # Logistic loss is the negative of the log of the logistic function. out = -np.sum(sample_weight * log_logistic(yz)) + .5 * alpha * np.dot(w, w) z = expit(yz) z0 = sample_weight * (z - 1) * y grad[:n_features] = safe_sparse_dot(X.T, z0) + alpha * w # Case where we fit the intercept. if grad.shape[0] > n_features: grad[-1] = z0.sum() return out, grad def _logistic_loss(w, X, y, alpha, sample_weight=None): """Computes the logistic loss. Parameters ---------- w : ndarray, shape (n_features,) or (n_features + 1,) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. y : ndarray, shape (n_samples,) Array of labels. alpha : float Regularization parameter. alpha is equal to 1 / C. sample_weight : ndarray, shape (n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. Returns ------- out : float Logistic loss. """ w, c, yz = _intercept_dot(w, X, y) if sample_weight is None: sample_weight = np.ones(y.shape[0]) # Logistic loss is the negative of the log of the logistic function. out = -np.sum(sample_weight * log_logistic(yz)) + .5 * alpha * np.dot(w, w) return out def _logistic_grad_hess(w, X, y, alpha, sample_weight=None): """Computes the gradient and the Hessian, in the case of a logistic loss. Parameters ---------- w : ndarray, shape (n_features,) or (n_features + 1,) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. y : ndarray, shape (n_samples,) Array of labels. alpha : float Regularization parameter. alpha is equal to 1 / C. sample_weight : ndarray, shape (n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. Returns ------- grad : ndarray, shape (n_features,) or (n_features + 1,) Logistic gradient. Hs : callable Function that takes the gradient as a parameter and returns the matrix product of the Hessian and gradient. """ n_samples, n_features = X.shape grad = np.empty_like(w) fit_intercept = grad.shape[0] > n_features w, c, yz = _intercept_dot(w, X, y) if sample_weight is None: sample_weight = np.ones(y.shape[0]) z = expit(yz) z0 = sample_weight * (z - 1) * y grad[:n_features] = safe_sparse_dot(X.T, z0) + alpha * w # Case where we fit the intercept. if fit_intercept: grad[-1] = z0.sum() # The mat-vec product of the Hessian d = sample_weight * z * (1 - z) if sparse.issparse(X): dX = safe_sparse_dot(sparse.dia_matrix((d, 0), shape=(n_samples, n_samples)), X) else: # Precompute as much as possible dX = d[:, np.newaxis] * X if fit_intercept: # Calculate the double derivative with respect to intercept # In the case of sparse matrices this returns a matrix object. dd_intercept = np.squeeze(np.array(dX.sum(axis=0))) def Hs(s): ret = np.empty_like(s) ret[:n_features] = X.T.dot(dX.dot(s[:n_features])) ret[:n_features] += alpha * s[:n_features] # For the fit intercept case. if fit_intercept: ret[:n_features] += s[-1] * dd_intercept ret[-1] = dd_intercept.dot(s[:n_features]) ret[-1] += d.sum() * s[-1] return ret return grad, Hs def _multinomial_loss(w, X, Y, alpha, sample_weight): """Computes multinomial loss and class probabilities. Parameters ---------- w : ndarray, shape (n_classes * n_features,) or (n_classes * (n_features + 1),) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. Y : ndarray, shape (n_samples, n_classes) Transformed labels according to the output of LabelBinarizer. alpha : float Regularization parameter. alpha is equal to 1 / C. sample_weight : ndarray, shape (n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. Returns ------- loss : float Multinomial loss. p : ndarray, shape (n_samples, n_classes) Estimated class probabilities. w : ndarray, shape (n_classes, n_features) Reshaped param vector excluding intercept terms. """ n_classes = Y.shape[1] n_features = X.shape[1] fit_intercept = w.size == (n_classes * (n_features + 1)) w = w.reshape(n_classes, -1) sample_weight = sample_weight[:, np.newaxis] if fit_intercept: intercept = w[:, -1] w = w[:, :-1] else: intercept = 0 p = safe_sparse_dot(X, w.T) p += intercept p -= logsumexp(p, axis=1)[:, np.newaxis] loss = -(sample_weight * Y * p).sum() loss += 0.5 * alpha * squared_norm(w) p = np.exp(p, p) return loss, p, w def _multinomial_loss_grad(w, X, Y, alpha, sample_weight): """Computes the multinomial loss, gradient and class probabilities. Parameters ---------- w : ndarray, shape (n_classes * n_features,) or (n_classes * (n_features + 1),) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. Y : ndarray, shape (n_samples, n_classes) Transformed labels according to the output of LabelBinarizer. alpha : float Regularization parameter. alpha is equal to 1 / C. sample_weight : ndarray, shape (n_samples,) optional Array of weights that are assigned to individual samples. Returns ------- loss : float Multinomial loss. grad : ndarray, shape (n_classes * n_features,) or (n_classes * (n_features + 1),) Ravelled gradient of the multinomial loss. p : ndarray, shape (n_samples, n_classes) Estimated class probabilities """ n_classes = Y.shape[1] n_features = X.shape[1] fit_intercept = (w.size == n_classes * (n_features + 1)) grad = np.zeros((n_classes, n_features + bool(fit_intercept))) loss, p, w = _multinomial_loss(w, X, Y, alpha, sample_weight) sample_weight = sample_weight[:, np.newaxis] diff = sample_weight * (p - Y) grad[:, :n_features] = safe_sparse_dot(diff.T, X) grad[:, :n_features] += alpha * w if fit_intercept: grad[:, -1] = diff.sum(axis=0) return loss, grad.ravel(), p def _multinomial_grad_hess(w, X, Y, alpha, sample_weight): """ Computes the gradient and the Hessian, in the case of a multinomial loss. Parameters ---------- w : ndarray, shape (n_classes * n_features,) or (n_classes * (n_features + 1),) Coefficient vector. X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. Y : ndarray, shape (n_samples, n_classes) Transformed labels according to the output of LabelBinarizer. alpha : float Regularization parameter. alpha is equal to 1 / C. sample_weight : ndarray, shape (n_samples,) optional Array of weights that are assigned to individual samples. Returns ------- grad : array, shape (n_classes * n_features,) or (n_classes * (n_features + 1),) Ravelled gradient of the multinomial loss. hessp : callable Function that takes in a vector input of shape (n_classes * n_features) or (n_classes * (n_features + 1)) and returns matrix-vector product with hessian. References ---------- Barak A. Pearlmutter (1993). Fast Exact Multiplication by the Hessian. http://www.bcl.hamilton.ie/~barak/papers/nc-hessian.pdf """ n_features = X.shape[1] n_classes = Y.shape[1] fit_intercept = w.size == (n_classes * (n_features + 1)) # `loss` is unused. Refactoring to avoid computing it does not # significantly speed up the computation and decreases readability loss, grad, p = _multinomial_loss_grad(w, X, Y, alpha, sample_weight) sample_weight = sample_weight[:, np.newaxis] # Hessian-vector product derived by applying the R-operator on the gradient # of the multinomial loss function. def hessp(v): v = v.reshape(n_classes, -1) if fit_intercept: inter_terms = v[:, -1] v = v[:, :-1] else: inter_terms = 0 # r_yhat holds the result of applying the R-operator on the multinomial # estimator. r_yhat = safe_sparse_dot(X, v.T) r_yhat += inter_terms r_yhat += (-p * r_yhat).sum(axis=1)[:, np.newaxis] r_yhat *= p r_yhat *= sample_weight hessProd = np.zeros((n_classes, n_features + bool(fit_intercept))) hessProd[:, :n_features] = safe_sparse_dot(r_yhat.T, X) hessProd[:, :n_features] += v * alpha if fit_intercept: hessProd[:, -1] = r_yhat.sum(axis=0) return hessProd.ravel() return grad, hessp def _check_solver_option(solver, multi_class, penalty, dual): if solver not in ['liblinear', 'newton-cg', 'lbfgs']: raise ValueError("Logistic Regression supports only liblinear," " newton-cg and lbfgs solvers, got %s" % solver) if multi_class not in ['multinomial', 'ovr']: raise ValueError("multi_class should be either multinomial or " "ovr, got %s" % multi_class) if multi_class == 'multinomial' and solver == 'liblinear': raise ValueError("Solver %s does not support " "a multinomial backend." % solver) if solver != 'liblinear': if penalty != 'l2': raise ValueError("Solver %s supports only l2 penalties, " "got %s penalty." % (solver, penalty)) if dual: raise ValueError("Solver %s supports only " "dual=False, got dual=%s" % (solver, dual)) def logistic_regression_path(X, y, pos_class=None, Cs=10, fit_intercept=True, max_iter=100, tol=1e-4, verbose=0, solver='lbfgs', coef=None, copy=True, class_weight=None, dual=False, penalty='l2', intercept_scaling=1., multi_class='ovr', random_state=None): """Compute a Logistic Regression model for a list of regularization parameters. This is an implementation that uses the result of the previous model to speed up computations along the set of solutions, making it faster than sequentially calling LogisticRegression for the different parameters. Read more in the :ref:`User Guide <logistic_regression>`. Parameters ---------- X : array-like or sparse matrix, shape (n_samples, n_features) Input data. y : array-like, shape (n_samples,) Input data, target values. Cs : int | array-like, shape (n_cs,) List of values for the regularization parameter or integer specifying the number of regularization parameters that should be used. In this case, the parameters will be chosen in a logarithmic scale between 1e-4 and 1e4. pos_class : int, None The class with respect to which we perform a one-vs-all fit. If None, then it is assumed that the given problem is binary. fit_intercept : bool Whether to fit an intercept for the model. In this case the shape of the returned array is (n_cs, n_features + 1). max_iter : int Maximum number of iterations for the solver. tol : float Stopping criterion. For the newton-cg and lbfgs solvers, the iteration will stop when ``max{|g_i | i = 1, ..., n} <= tol`` where ``g_i`` is the i-th component of the gradient. verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. solver : {'lbfgs', 'newton-cg', 'liblinear'} Numerical solver to use. coef : array-like, shape (n_features,), default None Initialization value for coefficients of logistic regression. copy : bool, default True Whether or not to produce a copy of the data. Setting this to True will be useful in cases, when logistic_regression_path is called repeatedly with the same data, as y is modified along the path. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` dual : bool Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features. penalty : str, 'l1' or 'l2' Used to specify the norm used in the penalization. The newton-cg and lbfgs solvers support only l2 penalties. intercept_scaling : float, default 1. This parameter is useful only when the solver 'liblinear' is used and self.fit_intercept is set to True. In this case, x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. multi_class : str, {'ovr', 'multinomial'} Multiclass option can be either 'ovr' or 'multinomial'. If the option chosen is 'ovr', then a binary problem is fit for each label. Else the loss minimised is the multinomial loss fit across the entire probability distribution. Works only for the 'lbfgs' and 'newton-cg' solvers. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use when shuffling the data. Returns ------- coefs : ndarray, shape (n_cs, n_features) or (n_cs, n_features + 1) List of coefficients for the Logistic Regression model. If fit_intercept is set to True then the second dimension will be n_features + 1, where the last item represents the intercept. Cs : ndarray Grid of Cs used for cross-validation. Notes ----- You might get slighly different results with the solver liblinear than with the others since this uses LIBLINEAR which penalizes the intercept. """ if isinstance(Cs, numbers.Integral): Cs = np.logspace(-4, 4, Cs) _check_solver_option(solver, multi_class, penalty, dual) # Preprocessing. X = check_array(X, accept_sparse='csr', dtype=np.float64) y = check_array(y, ensure_2d=False, copy=copy, dtype=None) _, n_features = X.shape check_consistent_length(X, y) classes = np.unique(y) random_state = check_random_state(random_state) if pos_class is None and multi_class != 'multinomial': if (classes.size > 2): raise ValueError('To fit OvR, use the pos_class argument') # np.unique(y) gives labels in sorted order. pos_class = classes[1] # If class_weights is a dict (provided by the user), the weights # are assigned to the original labels. If it is "auto", then # the class_weights are assigned after masking the labels with a OvR. sample_weight = np.ones(X.shape[0]) le = LabelEncoder() if isinstance(class_weight, dict): if solver == "liblinear": if classes.size == 2: # Reconstruct the weights with keys 1 and -1 temp = {1: class_weight[pos_class], -1: class_weight[classes[0]]} class_weight = temp.copy() else: raise ValueError("In LogisticRegressionCV the liblinear " "solver cannot handle multiclass with " "class_weight of type dict. Use the lbfgs, " "newton-cg solvers or set " "class_weight='auto'") else: class_weight_ = compute_class_weight(class_weight, classes, y) sample_weight = class_weight_[le.fit_transform(y)] # For doing a ovr, we need to mask the labels first. for the # multinomial case this is not necessary. if multi_class == 'ovr': w0 = np.zeros(n_features + int(fit_intercept)) mask_classes = [-1, 1] mask = (y == pos_class) y[mask] = 1 y[~mask] = -1 # To take care of object dtypes, i.e 1 and -1 are in the form of # strings. y = as_float_array(y, copy=False) else: lbin = LabelBinarizer() Y_bin = lbin.fit_transform(y) if Y_bin.shape[1] == 1: Y_bin = np.hstack([1 - Y_bin, Y_bin]) w0 = np.zeros((Y_bin.shape[1], n_features + int(fit_intercept)), order='F') mask_classes = classes if class_weight == "auto": class_weight_ = compute_class_weight(class_weight, mask_classes, y) sample_weight = class_weight_[le.fit_transform(y)] if coef is not None: # it must work both giving the bias term and not if multi_class == 'ovr': if coef.size not in (n_features, w0.size): raise ValueError( 'Initialization coef is of shape %d, expected shape ' '%d or %d' % (coef.size, n_features, w0.size)) w0[:coef.size] = coef else: # For binary problems coef.shape[0] should be 1, otherwise it # should be classes.size. n_vectors = classes.size if n_vectors == 2: n_vectors = 1 if (coef.shape[0] != n_vectors or coef.shape[1] not in (n_features, n_features + 1)): raise ValueError( 'Initialization coef is of shape (%d, %d), expected ' 'shape (%d, %d) or (%d, %d)' % ( coef.shape[0], coef.shape[1], classes.size, n_features, classes.size, n_features + 1)) w0[:, :coef.shape[1]] = coef if multi_class == 'multinomial': # fmin_l_bfgs_b and newton-cg accepts only ravelled parameters. w0 = w0.ravel() target = Y_bin if solver == 'lbfgs': func = lambda x, *args: _multinomial_loss_grad(x, *args)[0:2] elif solver == 'newton-cg': func = lambda x, *args: _multinomial_loss(x, *args)[0] grad = lambda x, *args: _multinomial_loss_grad(x, *args)[1] hess = _multinomial_grad_hess else: target = y if solver == 'lbfgs': func = _logistic_loss_and_grad elif solver == 'newton-cg': func = _logistic_loss grad = lambda x, *args: _logistic_loss_and_grad(x, *args)[1] hess = _logistic_grad_hess coefs = list() for C in Cs: if solver == 'lbfgs': try: w0, loss, info = optimize.fmin_l_bfgs_b( func, w0, fprime=None, args=(X, target, 1. / C, sample_weight), iprint=(verbose > 0) - 1, pgtol=tol, maxiter=max_iter) except TypeError: # old scipy doesn't have maxiter w0, loss, info = optimize.fmin_l_bfgs_b( func, w0, fprime=None, args=(X, target, 1. / C, sample_weight), iprint=(verbose > 0) - 1, pgtol=tol) if info["warnflag"] == 1 and verbose > 0: warnings.warn("lbfgs failed to converge. Increase the number " "of iterations.") elif solver == 'newton-cg': args = (X, target, 1. / C, sample_weight) w0 = newton_cg(hess, func, grad, w0, args=args, maxiter=max_iter, tol=tol) elif solver == 'liblinear': coef_, intercept_, _, = _fit_liblinear( X, y, C, fit_intercept, intercept_scaling, class_weight, penalty, dual, verbose, max_iter, tol, random_state) if fit_intercept: w0 = np.concatenate([coef_.ravel(), intercept_]) else: w0 = coef_.ravel() else: raise ValueError("solver must be one of {'liblinear', 'lbfgs', " "'newton-cg'}, got '%s' instead" % solver) if multi_class == 'multinomial': multi_w0 = np.reshape(w0, (classes.size, -1)) if classes.size == 2: multi_w0 = multi_w0[1][np.newaxis, :] coefs.append(multi_w0) else: coefs.append(w0) return coefs, np.array(Cs) # helper function for LogisticCV def _log_reg_scoring_path(X, y, train, test, pos_class=None, Cs=10, scoring=None, fit_intercept=False, max_iter=100, tol=1e-4, class_weight=None, verbose=0, solver='lbfgs', penalty='l2', dual=False, copy=True, intercept_scaling=1., multi_class='ovr'): """Computes scores across logistic_regression_path Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data. y : array-like, shape (n_samples,) or (n_samples, n_targets) Target labels. train : list of indices The indices of the train set. test : list of indices The indices of the test set. pos_class : int, None The class with respect to which we perform a one-vs-all fit. If None, then it is assumed that the given problem is binary. Cs : list of floats | int Each of the values in Cs describes the inverse of regularization strength. If Cs is as an int, then a grid of Cs values are chosen in a logarithmic scale between 1e-4 and 1e4. If not provided, then a fixed set of values for Cs are used. scoring : callable For a list of scoring functions that can be used, look at :mod:`sklearn.metrics`. The default scoring option used is accuracy_score. fit_intercept : bool If False, then the bias term is set to zero. Else the last term of each coef_ gives us the intercept. max_iter : int Maximum number of iterations for the solver. tol : float Tolerance for stopping criteria. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. solver : {'lbfgs', 'newton-cg', 'liblinear'} Decides which solver to use. penalty : str, 'l1' or 'l2' Used to specify the norm used in the penalization. The newton-cg and lbfgs solvers support only l2 penalties. dual : bool Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features. intercept_scaling : float, default 1. This parameter is useful only when the solver 'liblinear' is used and self.fit_intercept is set to True. In this case, x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. multi_class : str, {'ovr', 'multinomial'} Multiclass option can be either 'ovr' or 'multinomial'. If the option chosen is 'ovr', then a binary problem is fit for each label. Else the loss minimised is the multinomial loss fit across the entire probability distribution. Works only for the 'lbfgs' solver. copy : bool, default True Whether or not to produce a copy of the data. Setting this to True will be useful in cases, when ``_log_reg_scoring_path`` is called repeatedly with the same data, as y is modified along the path. Returns ------- coefs : ndarray, shape (n_cs, n_features) or (n_cs, n_features + 1) List of coefficients for the Logistic Regression model. If fit_intercept is set to True then the second dimension will be n_features + 1, where the last item represents the intercept. Cs : ndarray Grid of Cs used for cross-validation. scores : ndarray, shape (n_cs,) Scores obtained for each Cs. """ _check_solver_option(solver, multi_class, penalty, dual) log_reg = LogisticRegression(fit_intercept=fit_intercept) X_train = X[train] X_test = X[test] y_train = y[train] y_test = y[test] # The score method of Logistic Regression has a classes_ attribute. if multi_class == 'ovr': log_reg.classes_ = np.array([-1, 1]) elif multi_class == 'multinomial': log_reg.classes_ = np.unique(y_train) else: raise ValueError("multi_class should be either multinomial or ovr, " "got %d" % multi_class) if pos_class is not None: mask = (y_test == pos_class) y_test[mask] = 1 y_test[~mask] = -1 # To deal with object dtypes, we need to convert into an array of floats. y_test = as_float_array(y_test, copy=False) coefs, Cs = logistic_regression_path(X_train, y_train, Cs=Cs, fit_intercept=fit_intercept, solver=solver, max_iter=max_iter, class_weight=class_weight, copy=copy, pos_class=pos_class, multi_class=multi_class, tol=tol, verbose=verbose, dual=dual, penalty=penalty, intercept_scaling=intercept_scaling) scores = list() if isinstance(scoring, six.string_types): scoring = SCORERS[scoring] for w in coefs: if multi_class == 'ovr': w = w[np.newaxis, :] if fit_intercept: log_reg.coef_ = w[:, :-1] log_reg.intercept_ = w[:, -1] else: log_reg.coef_ = w log_reg.intercept_ = 0. if scoring is None: scores.append(log_reg.score(X_test, y_test)) else: scores.append(scoring(log_reg, X_test, y_test)) return coefs, Cs, np.array(scores) class LogisticRegression(BaseEstimator, LinearClassifierMixin, _LearntSelectorMixin, SparseCoefMixin): """Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr' and uses the cross-entropy loss, if the 'multi_class' option is set to 'multinomial'. (Currently the 'multinomial' option is supported only by the 'lbfgs' and 'newton-cg' solvers.) This class implements regularized logistic regression using the `liblinear` library, newton-cg and lbfgs solvers. It can handle both dense and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit floats for optimal performance; any other input format will be converted (and copied). The newton-cg and lbfgs solvers support only L2 regularization with primal formulation. The liblinear solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. Read more in the :ref:`User Guide <logistic_regression>`. Parameters ---------- penalty : str, 'l1' or 'l2' Used to specify the norm used in the penalization. The newton-cg and lbfgs solvers support only l2 penalties. dual : bool Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features. C : float, optional (default=1.0) Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization. fit_intercept : bool, default: True Specifies if a constant (a.k.a. bias or intercept) should be added the decision function. intercept_scaling : float, default: 1 Useful only if solver is liblinear. when self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` max_iter : int Useful only for the newton-cg and lbfgs solvers. Maximum number of iterations taken for the solvers to converge. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use when shuffling the data. solver : {'newton-cg', 'lbfgs', 'liblinear'} Algorithm to use in the optimization problem. tol : float, optional Tolerance for stopping criteria. multi_class : str, {'ovr', 'multinomial'} Multiclass option can be either 'ovr' or 'multinomial'. If the option chosen is 'ovr', then a binary problem is fit for each label. Else the loss minimised is the multinomial loss fit across the entire probability distribution. Works only for the 'lbfgs' solver. verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. Attributes ---------- coef_ : array, shape (n_classes, n_features) Coefficient of the features in the decision function. intercept_ : array, shape (n_classes,) Intercept (a.k.a. bias) added to the decision function. If `fit_intercept` is set to False, the intercept is set to zero. n_iter_ : int Maximum of the actual number of iterations across all classes. Valid only for the liblinear solver. See also -------- SGDClassifier : incrementally trained logistic regression (when given the parameter ``loss="log"``). sklearn.svm.LinearSVC : learns SVM models using the same algorithm. Notes ----- The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon, to have slightly different results for the same input data. If that happens, try with a smaller tol parameter. Predict output may not match that of standalone liblinear in certain cases. See :ref:`differences from liblinear <liblinear_differences>` in the narrative documentation. References ---------- LIBLINEAR -- A Library for Large Linear Classification http://www.csie.ntu.edu.tw/~cjlin/liblinear/ Hsiang-Fu Yu, Fang-Lan Huang, Chih-Jen Lin (2011). Dual coordinate descent methods for logistic regression and maximum entropy models. Machine Learning 85(1-2):41-75. http://www.csie.ntu.edu.tw/~cjlin/papers/maxent_dual.pdf See also -------- sklearn.linear_model.SGDClassifier """ def __init__(self, penalty='l2', dual=False, tol=1e-4, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='liblinear', max_iter=100, multi_class='ovr', verbose=0): self.penalty = penalty self.dual = dual self.tol = tol self.C = C self.fit_intercept = fit_intercept self.intercept_scaling = intercept_scaling self.class_weight = class_weight self.random_state = random_state self.solver = solver self.max_iter = max_iter self.multi_class = multi_class self.verbose = verbose def fit(self, X, y): """Fit the model according to the given training data. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Training vector, where n_samples in the number of samples and n_features is the number of features. y : array-like, shape (n_samples,) Target vector relative to X. Returns ------- self : object Returns self. """ if not isinstance(self.C, numbers.Number) or self.C < 0: raise ValueError("Penalty term must be positive; got (C=%r)" % self.C) if not isinstance(self.max_iter, numbers.Number) or self.max_iter < 0: raise ValueError("Maximum number of iteration must be positive;" " got (max_iter=%r)" % self.max_iter) if not isinstance(self.tol, numbers.Number) or self.tol < 0: raise ValueError("Tolerance for stopping criteria must be " "positive; got (tol=%r)" % self.tol) X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64, order="C") self.classes_ = np.unique(y) _check_solver_option(self.solver, self.multi_class, self.penalty, self.dual) if self.solver == 'liblinear': self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear( X, y, self.C, self.fit_intercept, self.intercept_scaling, self.class_weight, self.penalty, self.dual, self.verbose, self.max_iter, self.tol, self.random_state) return self n_classes = len(self.classes_) classes_ = self.classes_ if n_classes < 2: raise ValueError("This solver needs samples of at least 2 classes" " in the data, but the data contains only one" " class: %r" % classes_[0]) if len(self.classes_) == 2: n_classes = 1 classes_ = classes_[1:] self.coef_ = list() self.intercept_ = np.zeros(n_classes) # Hack so that we iterate only once for the multinomial case. if self.multi_class == 'multinomial': classes_ = [None] for ind, class_ in enumerate(classes_): coef_, _ = logistic_regression_path( X, y, pos_class=class_, Cs=[self.C], fit_intercept=self.fit_intercept, tol=self.tol, verbose=self.verbose, solver=self.solver, multi_class=self.multi_class, max_iter=self.max_iter, class_weight=self.class_weight) self.coef_.append(coef_[0]) self.coef_ = np.squeeze(self.coef_) # For the binary case, this get squeezed to a 1-D array. if self.coef_.ndim == 1: self.coef_ = self.coef_[np.newaxis, :] self.coef_ = np.asarray(self.coef_) if self.fit_intercept: self.intercept_ = self.coef_[:, -1] self.coef_ = self.coef_[:, :-1] return self def predict_proba(self, X): """Probability estimates. The returned estimates for all classes are ordered by the label of classes. Parameters ---------- X : array-like, shape = [n_samples, n_features] Returns ------- T : array-like, shape = [n_samples, n_classes] Returns the probability of the sample for each class in the model, where classes are ordered as they are in ``self.classes_``. """ return self._predict_proba_lr(X) def predict_log_proba(self, X): """Log of probability estimates. The returned estimates for all classes are ordered by the label of classes. Parameters ---------- X : array-like, shape = [n_samples, n_features] Returns ------- T : array-like, shape = [n_samples, n_classes] Returns the log-probability of the sample for each class in the model, where classes are ordered as they are in ``self.classes_``. """ return np.log(self.predict_proba(X)) class LogisticRegressionCV(LogisticRegression, BaseEstimator, LinearClassifierMixin, _LearntSelectorMixin): """Logistic Regression CV (aka logit, MaxEnt) classifier. This class implements logistic regression using liblinear, newton-cg or LBFGS optimizer. The newton-cg and lbfgs solvers support only L2 regularization with primal formulation. The liblinear solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. For the grid of Cs values (that are set by default to be ten values in a logarithmic scale between 1e-4 and 1e4), the best hyperparameter is selected by the cross-validator StratifiedKFold, but it can be changed using the cv parameter. In the case of newton-cg and lbfgs solvers, we warm start along the path i.e guess the initial coefficients of the present fit to be the coefficients got after convergence in the previous fit, so it is supposed to be faster for high-dimensional dense data. For a multiclass problem, the hyperparameters for each class are computed using the best scores got by doing a one-vs-rest in parallel across all folds and classes. Hence this is not the true multinomial loss. Read more in the :ref:`User Guide <logistic_regression>`. Parameters ---------- Cs : list of floats | int Each of the values in Cs describes the inverse of regularization strength. If Cs is as an int, then a grid of Cs values are chosen in a logarithmic scale between 1e-4 and 1e4. Like in support vector machines, smaller values specify stronger regularization. fit_intercept : bool, default: True Specifies if a constant (a.k.a. bias or intercept) should be added the decision function. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` cv : integer or cross-validation generator The default cross-validation generator used is Stratified K-Folds. If an integer is provided, then it is the number of folds used. See the module :mod:`sklearn.cross_validation` module for the list of possible cross-validation objects. penalty : str, 'l1' or 'l2' Used to specify the norm used in the penalization. The newton-cg and lbfgs solvers support only l2 penalties. dual : bool Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features. scoring : callabale Scoring function to use as cross-validation criteria. For a list of scoring functions that can be used, look at :mod:`sklearn.metrics`. The default scoring option used is accuracy_score. solver : {'newton-cg', 'lbfgs', 'liblinear'} Algorithm to use in the optimization problem. tol : float, optional Tolerance for stopping criteria. max_iter : int, optional Maximum number of iterations of the optimization algorithm. class_weight : dict or 'balanced', optional Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` n_jobs : int, optional Number of CPU cores used during the cross-validation loop. If given a value of -1, all cores are used. verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. refit : bool If set to True, the scores are averaged across all folds, and the coefs and the C that corresponds to the best score is taken, and a final refit is done using these parameters. Otherwise the coefs, intercepts and C that correspond to the best scores across folds are averaged. multi_class : str, {'ovr', 'multinomial'} Multiclass option can be either 'ovr' or 'multinomial'. If the option chosen is 'ovr', then a binary problem is fit for each label. Else the loss minimised is the multinomial loss fit across the entire probability distribution. Works only for the 'lbfgs' solver. intercept_scaling : float, default 1. Useful only if solver is liblinear. This parameter is useful only when the solver 'liblinear' is used and self.fit_intercept is set to True. In this case, x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. Attributes ---------- coef_ : array, shape (1, n_features) or (n_classes, n_features) Coefficient of the features in the decision function. `coef_` is of shape (1, n_features) when the given problem is binary. `coef_` is readonly property derived from `raw_coef_` that follows the internal memory layout of liblinear. intercept_ : array, shape (1,) or (n_classes,) Intercept (a.k.a. bias) added to the decision function. It is available only when parameter intercept is set to True and is of shape(1,) when the problem is binary. Cs_ : array Array of C i.e. inverse of regularization parameter values used for cross-validation. coefs_paths_ : array, shape ``(n_folds, len(Cs_), n_features)`` or \ ``(n_folds, len(Cs_), n_features + 1)`` dict with classes as the keys, and the path of coefficients obtained during cross-validating across each fold and then across each Cs after doing an OvR for the corresponding class as values. If the 'multi_class' option is set to 'multinomial', then the coefs_paths are the coefficients corresponding to each class. Each dict value has shape ``(n_folds, len(Cs_), n_features)`` or ``(n_folds, len(Cs_), n_features + 1)`` depending on whether the intercept is fit or not. scores_ : dict dict with classes as the keys, and the values as the grid of scores obtained during cross-validating each fold, after doing an OvR for the corresponding class. If the 'multi_class' option given is 'multinomial' then the same scores are repeated across all classes, since this is the multinomial class. Each dict value has shape (n_folds, len(Cs)) C_ : array, shape (n_classes,) or (n_classes - 1,) Array of C that maps to the best scores across every class. If refit is set to False, then for each class, the best C is the average of the C's that correspond to the best scores for each fold. See also -------- LogisticRegression """ def __init__(self, Cs=10, fit_intercept=True, cv=None, dual=False, penalty='l2', scoring=None, solver='lbfgs', tol=1e-4, max_iter=100, class_weight=None, n_jobs=1, verbose=0, refit=True, intercept_scaling=1., multi_class='ovr'): self.Cs = Cs self.fit_intercept = fit_intercept self.cv = cv self.dual = dual self.penalty = penalty self.scoring = scoring self.tol = tol self.max_iter = max_iter self.class_weight = class_weight self.n_jobs = n_jobs self.verbose = verbose self.solver = solver self.refit = refit self.intercept_scaling = intercept_scaling self.multi_class = multi_class def fit(self, X, y): """Fit the model according to the given training data. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Training vector, where n_samples in the number of samples and n_features is the number of features. y : array-like, shape (n_samples,) Target vector relative to X. Returns ------- self : object Returns self. """ _check_solver_option(self.solver, self.multi_class, self.penalty, self.dual) if not isinstance(self.max_iter, numbers.Number) or self.max_iter < 0: raise ValueError("Maximum number of iteration must be positive;" " got (max_iter=%r)" % self.max_iter) if not isinstance(self.tol, numbers.Number) or self.tol < 0: raise ValueError("Tolerance for stopping criteria must be " "positive; got (tol=%r)" % self.tol) X = check_array(X, accept_sparse='csr', dtype=np.float64) y = check_array(y, ensure_2d=False, dtype=None) if y.ndim == 2 and y.shape[1] == 1: warnings.warn( "A column-vector y was passed when a 1d array was" " expected. Please change the shape of y to " "(n_samples, ), for example using ravel().", DataConversionWarning) y = np.ravel(y) check_consistent_length(X, y) # init cross-validation generator cv = check_cv(self.cv, X, y, classifier=True) folds = list(cv) self._enc = LabelEncoder() self._enc.fit(y) labels = self.classes_ = np.unique(y) n_classes = len(labels) if n_classes < 2: raise ValueError("This solver needs samples of at least 2 classes" " in the data, but the data contains only one" " class: %r" % self.classes_[0]) if n_classes == 2: # OvR in case of binary problems is as good as fitting # the higher label n_classes = 1 labels = labels[1:] # We need this hack to iterate only once over labels, in the case of # multi_class = multinomial, without changing the value of the labels. iter_labels = labels if self.multi_class == 'multinomial': iter_labels = [None] if self.class_weight and not(isinstance(self.class_weight, dict) or self.class_weight in ['balanced', 'auto']): raise ValueError("class_weight provided should be a " "dict or 'balanced'") path_func = delayed(_log_reg_scoring_path) fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)( path_func(X, y, train, test, pos_class=label, Cs=self.Cs, fit_intercept=self.fit_intercept, penalty=self.penalty, dual=self.dual, solver=self.solver, tol=self.tol, max_iter=self.max_iter, verbose=self.verbose, class_weight=self.class_weight, scoring=self.scoring, multi_class=self.multi_class, intercept_scaling=self.intercept_scaling ) for label in iter_labels for train, test in folds) if self.multi_class == 'multinomial': multi_coefs_paths, Cs, multi_scores = zip(*fold_coefs_) multi_coefs_paths = np.asarray(multi_coefs_paths) multi_scores = np.asarray(multi_scores) # This is just to maintain API similarity between the ovr and # multinomial option. # Coefs_paths in now n_folds X len(Cs) X n_classes X n_features # we need it to be n_classes X len(Cs) X n_folds X n_features # to be similar to "ovr". coefs_paths = np.rollaxis(multi_coefs_paths, 2, 0) # Multinomial has a true score across all labels. Hence the # shape is n_folds X len(Cs). We need to repeat this score # across all labels for API similarity. scores = np.tile(multi_scores, (n_classes, 1, 1)) self.Cs_ = Cs[0] else: coefs_paths, Cs, scores = zip(*fold_coefs_) self.Cs_ = Cs[0] coefs_paths = np.reshape(coefs_paths, (n_classes, len(folds), len(self.Cs_), -1)) self.coefs_paths_ = dict(zip(labels, coefs_paths)) scores = np.reshape(scores, (n_classes, len(folds), -1)) self.scores_ = dict(zip(labels, scores)) self.C_ = list() self.coef_ = np.empty((n_classes, X.shape[1])) self.intercept_ = np.zeros(n_classes) # hack to iterate only once for multinomial case. if self.multi_class == 'multinomial': scores = multi_scores coefs_paths = multi_coefs_paths for index, label in enumerate(iter_labels): if self.multi_class == 'ovr': scores = self.scores_[label] coefs_paths = self.coefs_paths_[label] if self.refit: best_index = scores.sum(axis=0).argmax() C_ = self.Cs_[best_index] self.C_.append(C_) if self.multi_class == 'multinomial': coef_init = np.mean(coefs_paths[:, best_index, :, :], axis=0) else: coef_init = np.mean(coefs_paths[:, best_index, :], axis=0) w, _ = logistic_regression_path( X, y, pos_class=label, Cs=[C_], solver=self.solver, fit_intercept=self.fit_intercept, coef=coef_init, max_iter=self.max_iter, tol=self.tol, penalty=self.penalty, class_weight=self.class_weight, multi_class=self.multi_class, verbose=max(0, self.verbose - 1)) w = w[0] else: # Take the best scores across every fold and the average of all # coefficients corresponding to the best scores. best_indices = np.argmax(scores, axis=1) w = np.mean([coefs_paths[i][best_indices[i]] for i in range(len(folds))], axis=0) self.C_.append(np.mean(self.Cs_[best_indices])) if self.multi_class == 'multinomial': self.C_ = np.tile(self.C_, n_classes) self.coef_ = w[:, :X.shape[1]] if self.fit_intercept: self.intercept_ = w[:, -1] else: self.coef_[index] = w[: X.shape[1]] if self.fit_intercept: self.intercept_[index] = w[-1] self.C_ = np.asarray(self.C_) return self

bsd-3-clause

fredhusser/scikit-learn

benchmarks/bench_sparsify.py

323

3372

""" Benchmark SGD prediction time with dense/sparse coefficients. Invoke with ----------- $ kernprof.py -l sparsity_benchmark.py $ python -m line_profiler sparsity_benchmark.py.lprof Typical output -------------- input data sparsity: 0.050000 true coef sparsity: 0.000100 test data sparsity: 0.027400 model sparsity: 0.000024 r^2 on test data (dense model) : 0.233651 r^2 on test data (sparse model) : 0.233651 Wrote profile results to sparsity_benchmark.py.lprof Timer unit: 1e-06 s File: sparsity_benchmark.py Function: benchmark_dense_predict at line 51 Total time: 0.532979 s Line # Hits Time Per Hit % Time Line Contents ============================================================== 51 @profile 52 def benchmark_dense_predict(): 53 301 640 2.1 0.1 for _ in range(300): 54 300 532339 1774.5 99.9 clf.predict(X_test) File: sparsity_benchmark.py Function: benchmark_sparse_predict at line 56 Total time: 0.39274 s Line # Hits Time Per Hit % Time Line Contents ============================================================== 56 @profile 57 def benchmark_sparse_predict(): 58 1 10854 10854.0 2.8 X_test_sparse = csr_matrix(X_test) 59 301 477 1.6 0.1 for _ in range(300): 60 300 381409 1271.4 97.1 clf.predict(X_test_sparse) """ from scipy.sparse.csr import csr_matrix import numpy as np from sklearn.linear_model.stochastic_gradient import SGDRegressor from sklearn.metrics import r2_score np.random.seed(42) def sparsity_ratio(X): return np.count_nonzero(X) / float(n_samples * n_features) n_samples, n_features = 5000, 300 X = np.random.randn(n_samples, n_features) inds = np.arange(n_samples) np.random.shuffle(inds) X[inds[int(n_features / 1.2):]] = 0 # sparsify input print("input data sparsity: %f" % sparsity_ratio(X)) coef = 3 * np.random.randn(n_features) inds = np.arange(n_features) np.random.shuffle(inds) coef[inds[n_features/2:]] = 0 # sparsify coef print("true coef sparsity: %f" % sparsity_ratio(coef)) y = np.dot(X, coef) # add noise y += 0.01 * np.random.normal((n_samples,)) # Split data in train set and test set n_samples = X.shape[0] X_train, y_train = X[:n_samples / 2], y[:n_samples / 2] X_test, y_test = X[n_samples / 2:], y[n_samples / 2:] print("test data sparsity: %f" % sparsity_ratio(X_test)) ############################################################################### clf = SGDRegressor(penalty='l1', alpha=.2, fit_intercept=True, n_iter=2000) clf.fit(X_train, y_train) print("model sparsity: %f" % sparsity_ratio(clf.coef_)) def benchmark_dense_predict(): for _ in range(300): clf.predict(X_test) def benchmark_sparse_predict(): X_test_sparse = csr_matrix(X_test) for _ in range(300): clf.predict(X_test_sparse) def score(y_test, y_pred, case): r2 = r2_score(y_test, y_pred) print("r^2 on test data (%s) : %f" % (case, r2)) score(y_test, clf.predict(X_test), 'dense model') benchmark_dense_predict() clf.sparsify() score(y_test, clf.predict(X_test), 'sparse model') benchmark_sparse_predict()

bsd-3-clause

nbarba/py3DRec

image_sequence.py

1

2644

import numpy as np; import pandas as pd from PIL import Image, ImageDraw, ImageFont class ImageSequence: ''' Class to hold the necessary information about the image sequence ''' def __init__(self, filename): self.load_features(filename) def feat_2d(self): return self._feat_2d def length(self): return self._length def number_of_features(self): return self._number_of_features def width(self): return self._width def height(self): return self._height def load_features(self, filename): ''' Method that loads a txt file containing 2D coordinates of image features. The format of each line should be: [x y feature_number image_number image_filename] ''' features_df = pd.read_csv(filename, delimiter=r"\s+", index_col=False) # get length of sequence and number of features self._length = int(features_df['image_id'].max()) self._number_of_features = int(features_df['feature_id'].max()) # get the 2d features self.feat_2d = np.zeros(shape=[self._length, 4, self._number_of_features]) for i in range(1, self._length + 1): self.feat_2d[i - 1, :, :] = np.transpose(features_df.loc[features_df['image_id'] == i].values)[0:4] # keep the image filenames self._image_filenames = features_df.image_filename.unique() # get the image sequence width and height image = Image.open(self._image_filenames[0]) self._width = 1024 # image.width self._height = 768 # image.height def get_normalized_coordinates(self): ''' Method to normalize the coordinates to the range [-1,1] ''' mm = (self._width + self._height) / 2; rows = (self.feat_2d[:, 0] - np.ones(self.number_of_features) * self._width) / mm cols = (self.feat_2d[:, 1] - np.ones(self.number_of_features) * self._height) / mm return np.dstack((rows, cols)).swapaxes(1, 2) def show(self): ''' Method to display the sequence, with the 2D features superimposed ''' font = ImageFont.truetype('/Library/fonts/arial.ttf', 30) for i in range(0, self.length): filename = self._image_filenames[i] image = Image.open(filename) draw = ImageDraw.Draw(image) for j in range(0, self.number_of_features): x = self.feat_2d[i, :, j][0]; y = image.height - self.feat_2d[i, :, j][1] draw.text((x, y), "+" + str(j), font=font, fill=(0, 255, 0)) image.show()

mit

Titan-C/scikit-learn

sklearn/manifold/setup.py

43

1283

import os from os.path import join import numpy from numpy.distutils.misc_util import Configuration from sklearn._build_utils import get_blas_info def configuration(parent_package="", top_path=None): config = Configuration("manifold", parent_package, top_path) libraries = [] if os.name == 'posix': libraries.append('m') config.add_extension("_utils", sources=["_utils.pyx"], include_dirs=[numpy.get_include()], libraries=libraries, extra_compile_args=["-O3"]) cblas_libs, blas_info = get_blas_info() eca = blas_info.pop('extra_compile_args', []) eca.append("-O4") config.add_extension("_barnes_hut_tsne", libraries=cblas_libs, sources=["_barnes_hut_tsne.pyx"], include_dirs=[join('..', 'src', 'cblas'), numpy.get_include(), blas_info.pop('include_dirs', [])], extra_compile_args=eca, **blas_info) config.add_subpackage('tests') return config if __name__ == "__main__": from numpy.distutils.core import setup setup(**configuration().todict())

bsd-3-clause

sanketloke/scikit-learn

examples/neighbors/plot_approximate_nearest_neighbors_hyperparameters.py

102

5177

""" ================================================= Hyper-parameters of Approximate Nearest Neighbors ================================================= This example demonstrates the behaviour of the accuracy of the nearest neighbor queries of Locality Sensitive Hashing Forest as the number of candidates and the number of estimators (trees) vary. In the first plot, accuracy is measured with the number of candidates. Here, the term "number of candidates" refers to maximum bound for the number of distinct points retrieved from each tree to calculate the distances. Nearest neighbors are selected from this pool of candidates. Number of estimators is maintained at three fixed levels (1, 5, 10). In the second plot, the number of candidates is fixed at 50. Number of trees is varied and the accuracy is plotted against those values. To measure the accuracy, the true nearest neighbors are required, therefore :class:`sklearn.neighbors.NearestNeighbors` is used to compute the exact neighbors. """ from __future__ import division print(__doc__) # Author: Maheshakya Wijewardena <[email protected]> # # License: BSD 3 clause ############################################################################### import numpy as np from sklearn.datasets.samples_generator import make_blobs from sklearn.neighbors import LSHForest from sklearn.neighbors import NearestNeighbors import matplotlib.pyplot as plt # Initialize size of the database, iterations and required neighbors. n_samples = 10000 n_features = 100 n_queries = 30 rng = np.random.RandomState(42) # Generate sample data X, _ = make_blobs(n_samples=n_samples + n_queries, n_features=n_features, centers=10, random_state=0) X_index = X[:n_samples] X_query = X[n_samples:] # Get exact neighbors nbrs = NearestNeighbors(n_neighbors=1, algorithm='brute', metric='cosine').fit(X_index) neighbors_exact = nbrs.kneighbors(X_query, return_distance=False) # Set `n_candidate` values n_candidates_values = np.linspace(10, 500, 5).astype(np.int) n_estimators_for_candidate_value = [1, 5, 10] n_iter = 10 stds_accuracies = np.zeros((len(n_estimators_for_candidate_value), n_candidates_values.shape[0]), dtype=float) accuracies_c = np.zeros((len(n_estimators_for_candidate_value), n_candidates_values.shape[0]), dtype=float) # LSH Forest is a stochastic index: perform several iteration to estimate # expected accuracy and standard deviation displayed as error bars in # the plots for j, value in enumerate(n_estimators_for_candidate_value): for i, n_candidates in enumerate(n_candidates_values): accuracy_c = [] for seed in range(n_iter): lshf = LSHForest(n_estimators=value, n_candidates=n_candidates, n_neighbors=1, random_state=seed) # Build the LSH Forest index lshf.fit(X_index) # Get neighbors neighbors_approx = lshf.kneighbors(X_query, return_distance=False) accuracy_c.append(np.sum(np.equal(neighbors_approx, neighbors_exact)) / n_queries) stds_accuracies[j, i] = np.std(accuracy_c) accuracies_c[j, i] = np.mean(accuracy_c) # Set `n_estimators` values n_estimators_values = [1, 5, 10, 20, 30, 40, 50] accuracies_trees = np.zeros(len(n_estimators_values), dtype=float) # Calculate average accuracy for each value of `n_estimators` for i, n_estimators in enumerate(n_estimators_values): lshf = LSHForest(n_estimators=n_estimators, n_neighbors=1) # Build the LSH Forest index lshf.fit(X_index) # Get neighbors neighbors_approx = lshf.kneighbors(X_query, return_distance=False) accuracies_trees[i] = np.sum(np.equal(neighbors_approx, neighbors_exact))/n_queries ############################################################################### # Plot the accuracy variation with `n_candidates` plt.figure() colors = ['c', 'm', 'y'] for i, n_estimators in enumerate(n_estimators_for_candidate_value): label = 'n_estimators = %d ' % n_estimators plt.plot(n_candidates_values, accuracies_c[i, :], 'o-', c=colors[i], label=label) plt.errorbar(n_candidates_values, accuracies_c[i, :], stds_accuracies[i, :], c=colors[i]) plt.legend(loc='upper left', prop=dict(size='small')) plt.ylim([0, 1.2]) plt.xlim(min(n_candidates_values), max(n_candidates_values)) plt.ylabel("Accuracy") plt.xlabel("n_candidates") plt.grid(which='both') plt.title("Accuracy variation with n_candidates") # Plot the accuracy variation with `n_estimators` plt.figure() plt.scatter(n_estimators_values, accuracies_trees, c='k') plt.plot(n_estimators_values, accuracies_trees, c='g') plt.ylim([0, 1.2]) plt.xlim(min(n_estimators_values), max(n_estimators_values)) plt.ylabel("Accuracy") plt.xlabel("n_estimators") plt.grid(which='both') plt.title("Accuracy variation with n_estimators") plt.show()

bsd-3-clause

kernc/scikit-learn

examples/calibration/plot_calibration.py

33

4794

""" ====================================== Probability calibration of classifiers ====================================== When performing classification you often want to predict not only the class label, but also the associated probability. This probability gives you some kind of confidence on the prediction. However, not all classifiers provide well-calibrated probabilities, some being over-confident while others being under-confident. Thus, a separate calibration of predicted probabilities is often desirable as a postprocessing. This example illustrates two different methods for this calibration and evaluates the quality of the returned probabilities using Brier's score (see http://en.wikipedia.org/wiki/Brier_score). Compared are the estimated probability using a Gaussian naive Bayes classifier without calibration, with a sigmoid calibration, and with a non-parametric isotonic calibration. One can observe that only the non-parametric model is able to provide a probability calibration that returns probabilities close to the expected 0.5 for most of the samples belonging to the middle cluster with heterogeneous labels. This results in a significantly improved Brier score. """ print(__doc__) # Author: Mathieu Blondel <[email protected]> # Alexandre Gramfort <[email protected]> # Balazs Kegl <[email protected]> # Jan Hendrik Metzen <[email protected]> # License: BSD Style. import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from sklearn.datasets import make_blobs from sklearn.naive_bayes import GaussianNB from sklearn.metrics import brier_score_loss from sklearn.calibration import CalibratedClassifierCV from sklearn.model_selection import train_test_split n_samples = 50000 n_bins = 3 # use 3 bins for calibration_curve as we have 3 clusters here # Generate 3 blobs with 2 classes where the second blob contains # half positive samples and half negative samples. Probability in this # blob is therefore 0.5. centers = [(-5, -5), (0, 0), (5, 5)] X, y = make_blobs(n_samples=n_samples, n_features=2, cluster_std=1.0, centers=centers, shuffle=False, random_state=42) y[:n_samples // 2] = 0 y[n_samples // 2:] = 1 sample_weight = np.random.RandomState(42).rand(y.shape[0]) # split train, test for calibration X_train, X_test, y_train, y_test, sw_train, sw_test = \ train_test_split(X, y, sample_weight, test_size=0.9, random_state=42) # Gaussian Naive-Bayes with no calibration clf = GaussianNB() clf.fit(X_train, y_train) # GaussianNB itself does not support sample-weights prob_pos_clf = clf.predict_proba(X_test)[:, 1] # Gaussian Naive-Bayes with isotonic calibration clf_isotonic = CalibratedClassifierCV(clf, cv=2, method='isotonic') clf_isotonic.fit(X_train, y_train, sw_train) prob_pos_isotonic = clf_isotonic.predict_proba(X_test)[:, 1] # Gaussian Naive-Bayes with sigmoid calibration clf_sigmoid = CalibratedClassifierCV(clf, cv=2, method='sigmoid') clf_sigmoid.fit(X_train, y_train, sw_train) prob_pos_sigmoid = clf_sigmoid.predict_proba(X_test)[:, 1] print("Brier scores: (the smaller the better)") clf_score = brier_score_loss(y_test, prob_pos_clf, sw_test) print("No calibration: %1.3f" % clf_score) clf_isotonic_score = brier_score_loss(y_test, prob_pos_isotonic, sw_test) print("With isotonic calibration: %1.3f" % clf_isotonic_score) clf_sigmoid_score = brier_score_loss(y_test, prob_pos_sigmoid, sw_test) print("With sigmoid calibration: %1.3f" % clf_sigmoid_score) ############################################################################### # Plot the data and the predicted probabilities plt.figure() y_unique = np.unique(y) colors = cm.rainbow(np.linspace(0.0, 1.0, y_unique.size)) for this_y, color in zip(y_unique, colors): this_X = X_train[y_train == this_y] this_sw = sw_train[y_train == this_y] plt.scatter(this_X[:, 0], this_X[:, 1], s=this_sw * 50, c=color, alpha=0.5, label="Class %s" % this_y) plt.legend(loc="best") plt.title("Data") plt.figure() order = np.lexsort((prob_pos_clf, )) plt.plot(prob_pos_clf[order], 'r', label='No calibration (%1.3f)' % clf_score) plt.plot(prob_pos_isotonic[order], 'g', linewidth=3, label='Isotonic calibration (%1.3f)' % clf_isotonic_score) plt.plot(prob_pos_sigmoid[order], 'b', linewidth=3, label='Sigmoid calibration (%1.3f)' % clf_sigmoid_score) plt.plot(np.linspace(0, y_test.size, 51)[1::2], y_test[order].reshape(25, -1).mean(1), 'k', linewidth=3, label=r'Empirical') plt.ylim([-0.05, 1.05]) plt.xlabel("Instances sorted according to predicted probability " "(uncalibrated GNB)") plt.ylabel("P(y=1)") plt.legend(loc="upper left") plt.title("Gaussian naive Bayes probabilities") plt.show()

bsd-3-clause

ratschlab/oqtans_tools

EasySVM/0.3.3/build/lib.linux-x86_64-2.7/esvm/plots.py

4

8318

""" This module contains code for commonly used plots """ ############################################################################################# # # # This program is free software; you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation; either version 3 of the License, or # # (at your option) any later version. # # # # This program is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with this program; if not, see http://www.gnu.org/licenses # # or write to the Free Software Foundation, Inc., 51 Franklin Street, # # Fifth Floor, Boston, MA 02110-1301 USA # # # ############################################################################################# import sys import random import numpy import warnings import shutil from shogun.Features import BinaryLabels from shogun.Evaluation import * def plotroc(output, LTE, draw_random=False, figure_fname="", roc_label='ROC'): """Plot the receiver operating characteristic curve""" import pylab import matplotlib pylab.figure(1,dpi=300,figsize=(8,8)) fontdict=dict(family="serif", weight="bold",size=7,y=1.05) ; # family="cursive" pm=ROCEvaluation() pm.evaluate(BinaryLabels(numpy.array(output)), BinaryLabels(numpy.array(LTE))) points=pm.get_ROC() points=numpy.array(points).T # for pylab.plot pylab.plot(points[0], points[1], 'b-', label=roc_label) if draw_random: pylab.plot([0, 1], [0, 1], 'r-', label='random guessing') pylab.axis([0, 1, 0, 1]) ticks=numpy.arange(0., 1., .1, dtype=numpy.float64) pylab.xticks(ticks,size=10) pylab.yticks(ticks,size=10) pylab.xlabel('1 - specificity (false positive rate)',size=10) pylab.ylabel('sensitivity (true positive rate)',size=10) pylab.legend(loc='lower right') #, prop = matplotlib.font_manager.FontProperties('small')) if figure_fname!=None: warnings.filterwarnings('ignore','Could not match*') tempfname = figure_fname + '.png' pylab.savefig(tempfname) shutil.move(tempfname,figure_fname) auROC=pm.get_auROC() return auROC ; def plotprc(output, LTE, figure_fname="", prc_label='PRC'): """Plot the precision recall curve""" import pylab import matplotlib pylab.figure(2,dpi=300,figsize=(8,8)) pm=PRCEvaluation() pm.evaluate(BinaryLabels(numpy.array(output)), BinaryLabels(numpy.array(LTE))) points=pm.get_PRC() points=numpy.array(points).T # for pylab.plot pylab.plot(points[0], points[1], 'b-', label=prc_label) pylab.axis([0, 1, 0, 1]) ticks=numpy.arange(0., 1., .1, dtype=numpy.float64) pylab.xticks(ticks,size=10) pylab.yticks(ticks,size=10) pylab.xlabel('sensitivity (true positive rate)',size=10) pylab.ylabel('precision (1 - false discovery rate)',size=10) pylab.legend(loc='lower right') if figure_fname!=None: warnings.filterwarnings('ignore','Could not match*') tempfname = figure_fname + '.png' pylab.savefig(tempfname) shutil.move(tempfname,figure_fname) auPRC=pm.get_auPRC() return auPRC ; def plotcloud(cloud, figure_fname="", label='cloud'): """Plot the cloud of points (the first two dimensions only)""" import pylab import matplotlib pylab.figure(1,dpi=300,figsize=(8,8)) pos = [] neg = [] for i in xrange(len(cloud)): if cloud[i][0]==1: pos.append(cloud[i][1:]) elif cloud[i][0]==-1: neg.append(cloud[i][1:]) fontdict=dict(family="serif", weight="bold",size=10,y=1.05) ; # family="cursive" pylab.title(label, fontdict) points=numpy.array(pos).T # for pylab.plot pylab.plot(points[0], points[1], 'b+', label='positive') points=numpy.array(neg).T # for pylab.plot pylab.plot(points[0], points[1], 'rx', label='negative') #pylab.axis([0, 1, 0, 1]) #ticks=numpy.arange(0., 1., .1, dtype=numpy.float64) #pylab.xticks(ticks,size=10) #pylab.yticks(ticks,size=10) pylab.xlabel('dimension 1',size=10) pylab.ylabel('dimension 2',size=10) pylab.legend(loc='lower right') if figure_fname!=None: warnings.filterwarnings('ignore','Could not match*') tempfname = figure_fname + '.png' pylab.savefig(tempfname) shutil.move(tempfname,figure_fname) def plot_poims(poimfilename, poim, max_poim, diff_poim, poim_totalmass, poimdegree, max_len): """Plot a summary of the information in poims""" import pylab import matplotlib pylab.figure(3, dpi=300, figsize=(8,8)) # summary figures fontdict=dict(family="serif", weight="bold",size=7,y=1.05) ; # family="cursive" pylab.subplot(3,2,1) pylab.title('Total POIM Mass', fontdict) pylab.plot(poim_totalmass) ; pylab.ylabel('weight mass', size=5) pylab.subplot(3,2,3) pylab.title('POIMs', fontdict) pylab.pcolor(max_poim, shading='flat') ; pylab.subplot(3,2,5) pylab.title('Differential POIMs', fontdict) pylab.pcolor(diff_poim, shading='flat') ; for plot in [3, 5]: pylab.subplot(3,2,plot) ticks=numpy.arange(1., poimdegree+1, 1, dtype=numpy.float64) ticks_str = [] for i in xrange(0, poimdegree): ticks_str.append("%i" % (i+1)) ticks[i] = i + 0.5 pylab.yticks(ticks, ticks_str) pylab.ylabel('degree', size=5) # per k-mer figures fontdict=dict(family="serif", weight="bold",size=7,y=1.04) ; # family="cursive" # 1-mers pylab.subplot(3,2,2) pylab.title('1-mer Positional Importance', fontdict) pylab.pcolor(poim[0], shading='flat') ; ticks_str = ['A', 'C', 'G', 'T'] ticks = [0.5, 1.5, 2.5, 3.5] pylab.yticks(ticks, ticks_str, size=5) pylab.axis([0, max_len, 0, 4]) # 2-mers pylab.subplot(3,2,4) pylab.title('2-mer Positional Importance', fontdict) pylab.pcolor(poim[1], shading='flat') ; i=0 ; ticks=[] ; ticks_str=[] ; for l1 in ['A', 'C', 'G', 'T']: for l2 in ['A', 'C', 'G', 'T']: ticks_str.append(l1+l2) ticks.append(0.5+i) ; i+=1 ; pylab.yticks(ticks, ticks_str, fontsize=5) pylab.axis([0, max_len, 0, 16]) # 3-mers pylab.subplot(3,2,6) pylab.title('3-mer Positional Importance', fontdict) pylab.pcolor(poim[2], shading='flat') ; i=0 ; ticks=[] ; ticks_str=[] ; for l1 in ['A', 'C', 'G', 'T']: for l2 in ['A', 'C', 'G', 'T']: for l3 in ['A', 'C', 'G', 'T']: if numpy.mod(i,4)==0: ticks_str.append(l1+l2+l3) ticks.append(0.5+i) ; i+=1 ; pylab.yticks(ticks, ticks_str, fontsize=5) pylab.axis([0, max_len, 0, 64]) # x-axis on last two figures for plot in [5, 6]: pylab.subplot(3,2,plot) pylab.xlabel('sequence position', size=5) # finishing up for plot in xrange(0,6): pylab.subplot(3,2,plot+1) pylab.xticks(fontsize=5) for plot in [1,3,5]: pylab.subplot(3,2,plot) pylab.yticks(fontsize=5) pylab.subplots_adjust(hspace=0.35) ; # write to file warnings.filterwarnings('ignore','Could not match*') pylab.savefig('/tmp/temppylabfig.png') shutil.move('/tmp/temppylabfig.png',poimfilename)

mit

HoliestCow/ece692_deeplearning

project5/data/make_shuffling_integrations.py

1

7172

import numpy as np import matplotlib.pyplot as plt import os.path from rebin import rebin import glob from random import shuffle from joblib import Parallel, delayed # import time import h5py def label_datasets(): targetfile = '/home/holiestcow/Documents/zephyr/datasets/muse/trainingData/answers.csv' head, tail = os.path.split(targetfile) # filename = [] source_labels = {} id2string = {0: 'Background', 1: 'HEU', 2: 'WGPu', 3: 'I131', 4: 'Co60', 5: 'Tc99', 6: 'HEUandTc99'} f = open(targetfile, 'r') a = f.readlines() for i in range(len(a)): line = a[i].strip() if line[0] == 'R': continue parsed = line.split(',') filename = parsed[0] source = parsed[1] source_time = parsed[2] source_labels[filename] = {'source': id2string[int(source)], 'time': float(source_time)} f.close() return source_labels def parse_datafiles(targetfile, binno, outdir): item = targetfile # for item in filelist: f = open(item, 'r') a = f.readlines() binnumber = 1024 counter = 0 spectra = np.zeros((0, binnumber)) timetracker = 0 energy_deposited = [] for i in range(len(a)): b = a[i].strip() b_parsed = b.split(',') event_time = int(b_parsed[0]) energy_deposited += [float(b_parsed[1])] timetracker += event_time # print(timetracker) if timetracker >= 1E6: timetracker = 0 source_id = 0 counts, energy_edges = np.histogram(energy_deposited, bins=binnumber, range=(0.0, 3000.0)) spectra = np.vstack((spectra, counts)) counter += 1 # print(max(energy_deposited)) energy_deposited = [] # if counter >= 100: # break # print(np.sum(spectra[0, :])) time = np.linspace(0, counter, counter) time = time.reshape((time.shape[0], 1)) # print(time.shape, spectra.shape) tosave = np.hstack((time, spectra)) f.close() head, tail = os.path.split(item) print(tail, spectra.shape) # f = open(os.path.join('./integrations', tail), 'w') # np.savetxt(f, tosave, delimiter=',') # f.close() np.save(os.path.join(outdir, tail[:-4] + '.npy'), tosave) return def main(): # only need to do this once. binnumber = 1024 ncores = 4 nsamples = 50000 # nsamples = 0 filename = 'naive_dataset' id2string = {0: 'Background', 1: 'HEU', 2: 'WGPu', 3: 'I131', 4: 'Co60', 5: 'Tc99', 6: 'HEUandTc99'} string2id = {'Background': 0, 'HEU': 1, 'WGPu': 2, 'I131': 3, 'Co60': 4, 'Tc99': 5, 'HEUandTc99': 6} # sequence_length = 30 # 30 seconds used to guess the next one filelist = glob.glob('/home/holiestcow/Documents/zephyr/datasets/muse/trainingData/1*.csv') # shuffle(filelist) # Parallel(n_jobs=ncores)(delayed(parse_datafiles)(item, binnumber, './integrations') for item in filelist) # test_filelist = glob.glob('/home/holiestcow/Documents/zephyr/datasets/muse/testData/2*.csv') # HACK: RIGHT HERE test_filelist = glob.glob('./test_integrations/2*.npy') # Parallel(n_jobs=ncores)(delayed(parse_datafiles)(item, binnumber, './test_integrations') for item in test_filelist) labels = label_datasets() # NOTE: Slice for background segments f = h5py.File(filename + '.h5', 'w') train = f.create_group('training') test = f.create_group('testing') validation = f.create_group('validation') number_of_testing_files = 4800 number_of_training_files = len(labels.keys()) - number_of_testing_files # The last 10000 are for testing test2train_ratio = number_of_testing_files / number_of_training_files tostore_spectra = np.zeros((nsamples, 1024)) tostore_labels = np.zeros((nsamples, 1)) filelist = list(labels.keys()) for i in range(nsamples): # create training dataset random_file = filelist[np.random.randint(number_of_training_files)] if i % 100 == 0: print('training sample: {}'.format(i)) x = np.load('./integrations/' + random_file + '.npy') # time = x[:, 0] start = np.random.randint(x.shape[0]) source = 'Background' if labels[random_file]['source'] != 'Background' and start >= 30: start = int(labels[random_file]['time']) source = labels[random_file]['source'] spectra = x[start, 1:] tostore_spectra[i, :] = spectra tostore_labels[i] = int(string2id[source]) # g = train.create_group('sample_' + str(i)) # g.create_dataset('spectra', data=spectra, compression='gzip') # g.create_dataset('spectra', data=spectra) # g.create_dataset('label', data=int(string2id[source])) train.create_dataset('spectra', data=tostore_spectra, compression='gzip') train.create_dataset('labels', data=tostore_labels, compression='gzip') tostore_spectra = np.zeros((int(nsamples * test2train_ratio), 1024)) tostore_labels = np.zeros((int(nsamples * test2train_ratio), 1)) for i in range(int(nsamples * test2train_ratio)): # create training dataset random_file = filelist[number_of_training_files + np.random.randint(number_of_testing_files)] if i % 100 == 0: print('testing sample: {}'.format(i)) x = np.load('./integrations/' + random_file + '.npy') # time = x[:, 0] start = np.random.randint(x.shape[0]) source = 'Background' if labels[random_file]['source'] != 'Background' and start >= 30: start = int(labels[random_file]['time']) source = labels[random_file]['source'] spectra = x[start, 1:] tostore_spectra[i, :] = spectra tostore_labels[i] = int(string2id[source]) # g = test.create_group('sample_' + str(i)) # g.create_dataset('spectra', data=spectra, compression='gzip') # g.create_dataset('label', data=int(string2id[source])) test.create_dataset('spectra', data=tostore_spectra, compression='gzip') test.create_dataset('labels', data=tostore_labels, compression='gzip') # this is for the validation set, where i have to analyze # each file individual for i in range(len(test_filelist)): if i % 100 == 0: print('validation sample {}'.format(i)) filename = test_filelist[i] head, tail = os.path.split(filename) dataname = tail[:-4] x = np.load(os.path.join('./test_integrations', dataname + '.npy')) t = x[:, 0] spectra = x[:, 1:] file_sample = validation.create_group(dataname) file_sample.create_dataset('time', data=t, compression='gzip') file_sample.create_dataset('spectra', data=spectra, compression='gzip') f.close() return main()

mit

slinderman/pyhawkes

data/chalearn/make_figure.py

1

1779

import pickle import os import gzip import numpy as np import matplotlib.pyplot as plt from hips.plotting.layout import create_figure from hips.plotting.colormaps import harvard_colors def make_figure_a(S, F, C): """ Plot fluorescence traces, filtered fluorescence, and spike times for three neurons """ col = harvard_colors() dt = 0.02 T_start = 0 T_stop = 1 * 50 * 60 t = dt * np.arange(T_start, T_stop) ks = [0,1] nk = len(ks) fig = create_figure((3,3)) for ind,k in enumerate(ks): ax = fig.add_subplot(nk,1,ind+1) ax.plot(t, F[T_start:T_stop, k], color=col[1], label="$F$") # Plot the raw flourescence in blue ax.plot(t, C[T_start:T_stop, k], color=col[0], lw=1.5, label="$\widehat{F}$") # Plot the filtered flourescence in red spks = np.where(S[T_start:T_stop, k])[0] ax.plot(t[spks], C[spks,k], 'ko', label="S") # Plot the spike times in black # Make a legend if ind == 0: # Put a legend above plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=3, mode="expand", borderaxespad=0., prop={'size':9}) # Add labels ax.set_ylabel("$F_%d(t)$" % (k+1)) if ind == nk-1: ax.set_xlabel("Time $t$ [sec]") # Format the ticks ax.set_ylim([-0.1,1.0]) plt.locator_params(nbins=5, axis="y") plt.subplots_adjust(left=0.2, bottom=0.2) fig.savefig("figure3a.pdf") plt.show() data_path = os.path.join("data", "chalearn", "small", "network1_oopsi.pkl.gz") with gzip.open(data_path, 'r') as f: P, F, Cf, network, pos = pickle.load(f) S_full = (P > 0.1).astype(np.int) make_figure_a(S_full, F, Cf)

mit

zhonghualiu/FaST-LMM

fastlmm/pyplink/setup.py

1

2856

""" file to set up python package, see http://docs.python.org/2/distutils/setupscript.html for details. """ import platform import os import sys import shutil from distutils.core import setup from distutils.extension import Extension from distutils.command.clean import clean as Clean try: from Cython.Distutils import build_ext except Exception: print "cython needed for installation, please install cython first" sys.exit() try: import numpy except Exception: print "numpy needed for installation, please install numpy first" sys.exit() def readme(): with open('README.txt') as f: return f.read() class CleanCommand(Clean): description = "Remove build directories, and compiled files (including .pyc)" def run(self): Clean.run(self) if os.path.exists('build'): shutil.rmtree('build') for dirpath, dirnames, filenames in os.walk('fastlmm'): for filename in filenames: if (filename.endswith('.so') or filename.endswith('.pyd') #or filename.endswith('.dll') #or filename.endswith('.pyc') ): os.unlink(os.path.join(dirpath, filename)) # set up macro if platform.system() == "Darwin": macros = [("__APPLE__", "1")] elif "win" in platform.system().lower(): macros = [("_WIN32", "1")] ext = [Extension("fastlmm.util.stats.quadform.qfc_src.wrap_qfc", ["fastlmm/util/stats/quadform/qfc_src/wrap_qfc.pyx", "fastlmm/util/stats/quadform/qfc_src/QFC.cpp"], language="c++", define_macros=macros)] setup( name='fastlmm', version='0.1', description='Fast GWAS', long_description=readme(), keywords='gwas bioinformatics LMMs MLMs', url='', author='MSR', author_email='...', license='non-commercial (MSR-LA)', packages=[ "fastlmm/association/tests", "fastlmm/association", "fastlmm/external/sklearn/externals", "fastlmm/external/sklearn/metrics", "fastlmm/external/sklearn", "fastlmm/external/util", "fastlmm/external", "fastlmm/feature_selection", "fastlmm/inference/bingpc", "fastlmm/inference", "fastlmm/pyplink/altset_list", "fastlmm/pyplink/snpreader", "fastlmm/pyplink/snpset", "fastlmm/pyplink", "fastlmm/util/runner", "fastlmm/util/stats/quadform", "fastlmm/util/stats", "fastlmm/util", "fastlmm" ], install_requires=['cython', 'numpy', 'scipy', 'pandas', 'scikit-learn', 'matplotlib'], #zip_safe=False, # extensions cmdclass = {'build_ext': build_ext, 'clean': CleanCommand}, ext_modules = ext, include_dirs = [numpy.get_include()], )

apache-2.0

equialgo/scikit-learn

examples/svm/plot_svm_regression.py

120

1520

""" =================================================================== Support Vector Regression (SVR) using linear and non-linear kernels =================================================================== Toy example of 1D regression using linear, polynomial and RBF kernels. """ print(__doc__) import numpy as np from sklearn.svm import SVR import matplotlib.pyplot as plt ############################################################################### # Generate sample data X = np.sort(5 * np.random.rand(40, 1), axis=0) y = np.sin(X).ravel() ############################################################################### # Add noise to targets y[::5] += 3 * (0.5 - np.random.rand(8)) ############################################################################### # Fit regression model svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1) svr_lin = SVR(kernel='linear', C=1e3) svr_poly = SVR(kernel='poly', C=1e3, degree=2) y_rbf = svr_rbf.fit(X, y).predict(X) y_lin = svr_lin.fit(X, y).predict(X) y_poly = svr_poly.fit(X, y).predict(X) ############################################################################### # look at the results lw = 2 plt.scatter(X, y, color='darkorange', label='data') plt.hold('on') plt.plot(X, y_rbf, color='navy', lw=lw, label='RBF model') plt.plot(X, y_lin, color='c', lw=lw, label='Linear model') plt.plot(X, y_poly, color='cornflowerblue', lw=lw, label='Polynomial model') plt.xlabel('data') plt.ylabel('target') plt.title('Support Vector Regression') plt.legend() plt.show()

bsd-3-clause

ryklith/pyltesim

plotting/sinr_analysis_plot_ICC2013.py

1

1189

#!/usr/bin/env python ''' Plot a cdf from a csv file File: plot_CDF_from_file.py ''' __author__ = "Hauke Holtkamp" __credits__ = "Hauke Holtkamp" __license__ = "unknown" __version__ = "unknown" __maintainer__ = "Hauke Holtkamp" __email__ = "[email protected]" __status__ = "Development" def plot_cdf_from_file(filename): """Open file, store cdf to .pdf and .png""" import numpy as np import matplotlib.pyplot as plt data = np.genfromtxt(filename, delimiter=',') # convert zeros to nans and clear empty rows data[np.where(data==0)] = np.nan data = data[~np.isnan(data).all(1)] if not data.size: print 'No data in ' + str(filename) # SINR data is best presented in dB from utils import utils data = utils.WTodB(data) import cdf_plot label = [ "Iteration %d" %i for i in np.arange(data.shape[0])+1] cdf_plot.cdf_plot(data, '-', label=label) # plt.xlabel(xlabel) # plt.ylabel(ylabel) # plt.title(title) plt.savefig(filename+'.pdf', format='pdf') plt.savefig(filename+'.png', format='png') if __name__ == '__main__': import sys filename = sys.argv[1] plot_cdf_from_file(filename)

gpl-2.0

stuart-knock/bokeh

bokeh/charts/builder/donut_builder.py

31

8206

"""This is the Bokeh charts interface. It gives you a high level API to build complex plot is a simple way. This is the Donut class which lets you build your Donut charts just passing the arguments to the Chart class and calling the proper functions. It also add a new chained stacked method. """ #----------------------------------------------------------------------------- # Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved. # # Powered by the Bokeh Development Team. # # The full license is in the file LICENSE.txt, distributed with this software. #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- from __future__ import absolute_import, division from math import pi import pandas as pd from ..utils import cycle_colors, polar_to_cartesian from .._builder import Builder, create_and_build from ...models import ColumnDataSource, GlyphRenderer, Range1d from ...models.glyphs import AnnularWedge, Text, Wedge from ...properties import Any, Bool, Either, List #----------------------------------------------------------------------------- # Classes and functions #----------------------------------------------------------------------------- def Donut(values, cat=None, width=800, height=800, xgrid=False, ygrid=False, **kws): """ Creates a Donut chart using :class:`DonutBuilder <bokeh.charts.builder.donut_builder.DonutBuilder>` to render the geometry from values and cat. Args: values (iterable): iterable 2d representing the data series values matrix. cat (list or bool, optional): list of string representing the categories. Defaults to None. In addition the the parameters specific to this chart, :ref:`userguide_charts_generic_arguments` are also accepted as keyword parameters. Returns: a new :class:`Chart <bokeh.charts.Chart>` Examples: .. bokeh-plot:: :source-position: above from bokeh.charts import Donut, output_file, show # dict, OrderedDict, lists, arrays and DataFrames are valid inputs xyvalues = [[2., 5., 3.], [4., 1., 4.], [6., 4., 3.]] donut = Donut(xyvalues, ['cpu1', 'cpu2', 'cpu3']) output_file('donut.html') show(donut) """ return create_and_build( DonutBuilder, values, cat=cat, width=width, height=height, xgrid=xgrid, ygrid=ygrid, **kws ) class DonutBuilder(Builder): """This is the Donut class and it is in charge of plotting Donut chart in an easy and intuitive way. Essentially, it provides a way to ingest the data, make the proper calculations and push the references into a source object. We additionally make calculations for the donut slices and angles. And finally add the needed glyphs (Wedges and AnnularWedges) taking the references from the source. """ cat = Either(Bool, List(Any), help=""" List of string representing the categories. (Defaults to None.) """) def _process_data(self): """Take the chart data from self._values. It calculates the chart properties accordingly (start/end angles). Then build a dict containing references to all the calculated points to be used by the Wedge glyph inside the ``_yield_renderers`` method. """ dd = dict(zip(self._values.keys(), self._values.values())) self._df = df = pd.DataFrame(dd) self._groups = df.index = self.cat df.columns = self._values.keys() # Get the sum per category aggregated = df.T.sum() # Get the total (sum of all categories) self._total_units = total = aggregated.sum() radians = lambda x: 2*pi*(x/total) angles = aggregated.map(radians).cumsum() end_angles = angles.tolist() start_angles = [0] + end_angles[:-1] colors = cycle_colors(self.cat, self.palette) self.set_and_get("", "colors", colors) self.set_and_get("", "end", end_angles) self.set_and_get("", "start", start_angles) def _set_sources(self): """Push the Donut data into the ColumnDataSource and calculate the proper ranges. """ self._source = ColumnDataSource(self._data) self.x_range = Range1d(start=-2, end=2) self.y_range = Range1d(start=-2, end=2) def draw_central_wedge(self): """Draw the central part of the donut wedge from donut.source and its calculated start and end angles. """ glyph = Wedge( x=0, y=0, radius=1, start_angle="start", end_angle="end", line_color="white", line_width=2, fill_color="colors" ) yield GlyphRenderer(data_source=self._source, glyph=glyph) def draw_central_descriptions(self): """Draw the descriptions to be placed on the central part of the donut wedge """ text = ["%s" % cat for cat in self.cat] x, y = polar_to_cartesian(0.7, self._data["start"], self._data["end"]) text_source = ColumnDataSource(dict(text=text, x=x, y=y)) glyph = Text( x="x", y="y", text="text", text_align="center", text_baseline="middle" ) yield GlyphRenderer(data_source=text_source, glyph=glyph) def draw_external_ring(self, colors=None): """Draw the external part of the donut wedge from donut.source and its related descriptions """ if colors is None: colors = cycle_colors(self.cat, self.palette) first = True for i, (cat, start_angle, end_angle) in enumerate(zip( self.cat, self._data['start'], self._data['end'])): details = self._df.ix[i] radians = lambda x: 2*pi*(x/self._total_units) angles = details.map(radians).cumsum() + start_angle end = angles.tolist() + [end_angle] start = [start_angle] + end[:-1] base_color = colors[i] #fill = [ base_color.lighten(i*0.05) for i in range(len(details) + 1) ] fill = [base_color for i in range(len(details) + 1)] text = [rowlabel for rowlabel in details.index] x, y = polar_to_cartesian(1.25, start, end) source = ColumnDataSource(dict(start=start, end=end, fill=fill)) glyph = AnnularWedge( x=0, y=0, inner_radius=1, outer_radius=1.5, start_angle="start", end_angle="end", line_color="white", line_width=2, fill_color="fill" ) yield GlyphRenderer(data_source=source, glyph=glyph) text_angle = [(start[i]+end[i])/2 for i in range(len(start))] text_angle = [angle + pi if pi/2 < angle < 3*pi/2 else angle for angle in text_angle] if first and text: text.insert(0, '') offset = pi / 48 text_angle.insert(0, text_angle[0] - offset) start.insert(0, start[0] - offset) end.insert(0, end[0] - offset) x, y = polar_to_cartesian(1.25, start, end) first = False data = dict(text=text, x=x, y=y, angle=text_angle) text_source = ColumnDataSource(data) glyph = Text( x="x", y="y", text="text", angle="angle", text_align="center", text_baseline="middle" ) yield GlyphRenderer(data_source=text_source, glyph=glyph) def _yield_renderers(self): """Use the AnnularWedge and Wedge glyphs to display the wedges. Takes reference points from data loaded at the ColumnDataSurce. """ # build the central round area of the donut renderers = [] renderers += self.draw_central_wedge() # write central descriptions renderers += self.draw_central_descriptions() # build external donut ring renderers += self.draw_external_ring() return renderers

bsd-3-clause

CosmicFish/CosmicFish

camb/eftcamb/tests_EFT/python/CAMB_plots_lib/CAMB_comp_plots.py

1

31632

import numpy as np import matplotlib.pyplot as plt import math from CMB_plots import CMB_plots class CAMB_results_compare_plot: """ class that contains the necessary tools to plot the comparison of two CAMB run """ # general plot settings: color1 = 'red' # default color of the line of the first model color2 = 'blue' # default color of the line of the second model color_compa = 'green' # default color of the line of the difference x_size_inc = 8.30 # x size of the final plots, in inches y_size_inc = 11.7 # y size of the final plots, in inches def __init__(self, root1, root2, outpath, tensor=False, lensing=False, transfer=False, name1='', name2=''): """ Class constructor: root1 = name of the first CAMB run root2 = name of the second CAMB run outpath = path to the output folder tensor = (optional) specifies wether the results contains the tensor Cls lensing = (optional) specifies wether the results contains lensing transfer = (optional) specifies wether the results contains transfer functions name1 = (optional) specifies the name of the first model. Used for the legend. name2 = (optional) specifies the name of the second model. Used for the legend. """ # store the constructor options: self.root1 = root1 self.root2 = root2 self.outpath = outpath self.tensor = tensor self.lensing = lensing self.transfer = transfer # extract the name of the models from the roots: self.name1 = root1.split("/")[len(root1.split("/"))-1] self.name2 = root2.split("/")[len(root2.split("/"))-1] # set the human readable name if name1=='': self.name_h1 = ''.join(i for i in self.name1.replace('_',' ') if not i.isdigit()) else: self.name_h1 = name1 if name2=='': self.name_h2 = ''.join(i for i in self.name2.replace('_',' ') if not i.isdigit()) else: self.name_h2 = name2 # load the data: self.scalCls_1 = np.loadtxt(root1+'_scalCls.dat') self.scalCovCls_1 = np.loadtxt(root1+'_scalCovCls.dat') self.scalCls_2 = np.loadtxt(root2+'_scalCls.dat') self.scalCovCls_2 = np.loadtxt(root2+'_scalCovCls.dat') if self.lensing: self.lensedCls_1 = np.loadtxt(root1+'_lensedCls.dat') self.lenspotentialCls_1 = np.loadtxt(root1+'_lenspotentialCls.dat') self.lensedCls_2 = np.loadtxt(root2+'_lensedCls.dat') self.lenspotentialCls_2 = np.loadtxt(root2+'_lenspotentialCls.dat') if self.transfer: self.matterpower_1 = np.loadtxt(root1+'_matterpower.dat') self.transfer_func_1 = np.loadtxt(root1+'_transfer_out.dat') self.matterpower_2 = np.loadtxt(root2+'_matterpower.dat') self.transfer_func_2 = np.loadtxt(root2+'_transfer_out.dat') if self.tensor: self.tensCls_1 = np.loadtxt(root1+'_tensCls.dat') self.totCls_1 = np.loadtxt(root1+'_totCls.dat') self.tensCls_2 = np.loadtxt(root2+'_tensCls.dat') self.totCls_2 = np.loadtxt(root2+'_totCls.dat') if self.lensing and self.tensor: self.lensedtotCls_1 = np.loadtxt(root1+'_lensedtotCls.dat') self.lensedtotCls_2 = np.loadtxt(root2+'_lensedtotCls.dat') def plot_compare_scalCls(self): """ Plots and saves the comparison of all the scalar Cls in a unique image """ # number of Cls in the two files: num1 = self.scalCls_1.shape[1]-1 num2 = self.scalCls_2.shape[1]-1 # protection against different runs if num1!=num2: print 'wrong number of Cls' return if len(self.scalCls_1[:,0])!=len(self.scalCls_2[:,0]): print 'different lmax' return if len(self.matterpower_1[:,0])!=len(self.matterpower_2[:,0]): self.transfer = False # add the matter power spectrum if required: if self.transfer: num1 += 1 # values of l xvalues = self.scalCls_1[:,0] # set up the plots: plots_1 = CMB_plots() plots_2 = CMB_plots() plots_compa = CMB_plots() plots_1.color = self.color1 plots_2.color = self.color2 plots_compa.color = self.color_compa plots_compa.comparison = True plots_compa.axes_label_position = 'right' fig = plt.gcf() # do the plots: for ind in xrange(1,num1+1): # distribute the plots in the figure: temp = plt.subplot2grid((num1,2), (ind-1, 0)) temp_comp = plt.subplot2grid((num1,2), (ind-1, 1)) if ind == 1: # TT power spectrum: yvalues_1 = self.scalCls_1[:,ind] yvalues_2 = self.scalCls_2[:,ind] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0**(-16), [10.0**(-16)]) # make the plots: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) temp_comp.set_yscale('Log') temp.set_title('TT power spectrum') elif ind == 2: # EE power spectrum: yvalues_1 = self.scalCls_1[:,ind] yvalues_2 = self.scalCls_2[:,ind] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0**(-16), [10.0**(-16)]) # make the plots: plots_1.EE_plot(temp, xvalues, yvalues_1) plots_2.EE_plot(temp, xvalues, yvalues_2) plots_compa.EE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('EE power spectrum') elif ind == 3: # TE power spectrum: yvalues_1 = self.scalCls_1[:,ind] yvalues_2 = self.scalCls_2[:,ind] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0**(-16), [10.0**(-16)]) # make the plots: plots_1.TE_plot(temp, xvalues, yvalues_1) plots_2.TE_plot(temp, xvalues, yvalues_2) plots_compa.TE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('TE power spectrum') elif ind == 4 and self.lensing: # CMB lensing power spectrum: yvalues_1 = self.scalCls_1[:,ind] yvalues_2 = self.scalCls_2[:,ind] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0**(-16), [10.0**(-16)]) # make the plots: plots_1.Phi_plot(temp, xvalues, yvalues_1) plots_2.Phi_plot(temp, xvalues, yvalues_2) plots_compa.Phi_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('$\phi$ power spectrum') elif ind == 5 and self.lensing: # CMB lensing - Temperature power spectrum: yvalues_1 = self.scalCls_1[:,ind] yvalues_2 = self.scalCls_2[:,ind] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0**(-16), [10.0**(-16)]) # make the plots: plots_1.PhiT_plot(temp, xvalues, yvalues_1) plots_2.PhiT_plot(temp, xvalues, yvalues_2) plots_compa.PhiT_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('$\phi$T power spectrum') elif ind == num1 and self.transfer: # matter power spectrum: xvalues = self.matterpower_2[:,0] yvalues_1 = self.matterpower_1[:,1] yvalues_2 = self.matterpower_2[:,1] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0**(-16), [10.0**(-16)]) # make the plots: plots_1.Matter_plot(temp, xvalues, yvalues_1) plots_2.Matter_plot(temp, xvalues, yvalues_2) plots_compa.Matter_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('Matter power spectrum') else: # generic Cl comparison: yvalues_1 = self.scalCls_1[:,ind] yvalues_2 = self.scalCls_2[:,ind] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0**(-16), [10.0**(-16)]) # make the plots: plots_1.Generic_Cl(temp, xvalues, yvalues_1) plots_2.Generic_Cl(temp, xvalues, yvalues_2) plots_compa.Generic_Cl(temp_comp, xvalues, yvalues_comp) # set the size of the image fig.set_size_inches( self.x_size_inc, self.y_size_inc) # set a tight layout fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3) # set the global title plt.suptitle(self.name_h1+' VS '+self.name_h2+ ' comparison of scalar Cls', fontsize=16) # set the global legend fig.legend( handles = [plots_1.TT_p, plots_2.TT_p, plots_compa.CV_p], labels = [self.name_h1, self.name_h2, 'Cosmic variance'], loc='lower center', ncol=3 ,fancybox=True) # adjust the size of the plot fig.subplots_adjust(top=0.92, bottom=0.08) # save the result and close plt.savefig(self.outpath+self.name1+'_'+self.name2+'_scalCls_comp.pdf') plt.clf() plt.close("all") def plot_compare_scalCovCls(self): """ Plots and saves the comparison of all the scalar Cov Cls in a unique image """ # number of Cls: num1 = self.scalCovCls_1.shape[1]-1 num2 = self.scalCovCls_2.shape[1]-1 # protection against different runs if num1!=num2: print 'wrong number of Cls' return if len(self.scalCovCls_1[:,0])!=len(self.scalCovCls_2[:,0]): print 'different lmax' return # size of the Cl Cov matrix: num1 = int(math.sqrt(num1)) num_tot = sum(xrange(1,num1+1)) # set up the plots: plots_1 = CMB_plots() plots_2 = CMB_plots() plots_compa = CMB_plots() plots_1.color = self.color1 plots_2.color = self.color2 plots_compa.color = self.color_compa plots_compa.comparison = True plots_compa.axes_label_position = 'right' fig = plt.gcf() # setup a dictionary with the names of the Cls dict = { 1: 'T', 2: 'E', 3: '$\phi$'} for i in xrange(4, num1+1): dict[i] = 'W'+str(i) # values of the multipoles: xvalues = self.scalCovCls_1[:,0] # other stuff: ind_tot = 0 # do the plots: for ind in xrange(1,num1+1): for ind2 in xrange(1, ind+1): ind_tot += 1 # place the plots: temp = plt.subplot2grid((num_tot,2), (ind_tot-1, 0)) temp_comp = plt.subplot2grid((num_tot,2), (ind_tot-1, 1)) # values of the Cls: col = ind + num1*(ind2-1) yvalues_1 = self.scalCovCls_1[:,col] yvalues_2 = self.scalCovCls_2[:,col] # protection against values equal to zero: yvalues_temp = np.array(map(abs, yvalues_1)) min2val = np.min(yvalues_temp[np.nonzero(yvalues_temp)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) # computation of the percentual comparison: yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100 # protection against values too small: np.place(yvalues_comp, abs(yvalues_comp)<10.0**(-16), [10.0**(-16)]) # make the plots: plots_1.Generic_Cl(temp, xvalues, yvalues_1) plots_2.Generic_Cl(temp, xvalues, yvalues_2) plots_compa.Generic_Cl(temp_comp, xvalues, yvalues_comp) temp.set_title(dict[ind2]+dict[ind]+' power spectrum') # set the size of the image fig.set_size_inches( self.x_size_inc, self.y_size_inc/5.0*num_tot) # set a tight layout fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3) # set the global legend fig.legend( handles = [plots_1.Generic_Cl_plot, plots_2.Generic_Cl_plot, plots_compa.CV_p], labels = [self.name_h1, self.name_h2, 'Cosmic variance'], loc='lower center', ncol=3 ,fancybox=True) # set the global title plt.suptitle(self.name_h1+' VS '+self.name_h2+ ' comparison of scalar Cov Cls', fontsize=16) # adjust the size of the plot fig.subplots_adjust(top=0.92, bottom=0.08) # fig.subplots_adjust(top=0.96, bottom=0.01) # save the result and close plt.savefig(self.outpath+self.name1+'_'+self.name2+'_scalCovCls_comp.pdf') plt.clf() plt.close("all") def plot_compare_lensedCls(self): """ Plots and saves the comparison of all the lensed Cls in a unique image """ # protection from direct calls if lensing is not included if not self.lensing: return # number of Cls: num1 = self.lensedCls_1.shape[1]-1 num2 = self.lensedCls_2.shape[1]-1 # protection against different runs if num1!=num2: print 'wrong number of Cls' return if len(self.lensedCls_1[:,0])!=len(self.lensedCls_2[:,0]): print 'different lmax' return xvalues = self.lensedCls_1[:,0] # set up the plots: plots_1 = CMB_plots() plots_2 = CMB_plots() plots_compa = CMB_plots() plots_1.color = self.color1 plots_2.color = self.color2 plots_compa.color = self.color_compa plots_compa.comparison = True plots_compa.axes_label_position = 'right' fig = plt.gcf() # do the plots: for ind in xrange(1,num1+1): temp = plt.subplot2grid((num1,2), (ind-1, 0)) temp_comp = plt.subplot2grid((num1,2), (ind-1, 1)) yvalues_1 = self.lensedCls_1[:,ind] yvalues_2 = self.lensedCls_2[:,ind] min2val = np.min(yvalues_1[np.nonzero(yvalues_1)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100 # Protection against all zero: put to machine precision the values that are zero np.place(yvalues_comp, abs(yvalues_comp)<10.0**(-16), [10.0**(-16)]) if ind == 1: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) temp_comp.set_yscale('Log') temp.set_title('TT power spectrum') elif ind == 2: plots_1.EE_plot(temp, xvalues, yvalues_1) plots_2.EE_plot(temp, xvalues, yvalues_2) plots_compa.EE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('EE power spectrum') elif ind == 3: plots_1.BB_plot(temp, xvalues, yvalues_1) plots_2.BB_plot(temp, xvalues, yvalues_2) plots_compa.BB_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('BB power spectrum') elif ind == 4: plots_1.TE_plot(temp, xvalues, yvalues_1) plots_2.TE_plot(temp, xvalues, yvalues_2) plots_compa.TE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('TE power spectrum') else: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) # set the size of the image fig.set_size_inches( self.x_size_inc, self.y_size_inc) # set a tight layout fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3) # set the global title plt.suptitle(self.name_h1+' VS '+self.name_h2+ ' comparison of lensed Cls', fontsize=16) # set the global legend fig.legend( handles = [plots_1.TT_p, plots_2.TT_p, plots_compa.CV_p], labels = [self.name_h1, self.name_h2, 'Cosmic variance'], loc='lower center', ncol=3 ,fancybox=True) # adjust the size of the plot fig.subplots_adjust(top=0.92, bottom=0.08) # save the result and close plt.savefig(self.outpath+self.name1+'_'+self.name2+'_lensedCls_comp.pdf') plt.clf() plt.close("all") def plot_compare_tensCls(self): """ Plots and saves the comparison of all the tensor Cls in a unique image """ # protection from direct calls if tensors are not included if not self.tensor: return # number of Cls: num1 = self.tensCls_1.shape[1]-1 num2 = self.tensCls_2.shape[1]-1 # protection against different runs if num1!=num2: print 'wrong number of Cls' return if len(self.tensCls_1[:,0])!=len(self.tensCls_2[:,0]): print 'different lmax' return xvalues = self.tensCls_1[:,0] # set up the plots: plots_1 = CMB_plots() plots_2 = CMB_plots() plots_compa = CMB_plots() plots_1.color = self.color1 plots_2.color = self.color2 plots_compa.color = self.color_compa plots_compa.comparison = True plots_compa.axes_label_position = 'right' fig = plt.gcf() # do the plots: for ind in xrange(1,num1+1): temp = plt.subplot2grid((num1,2), (ind-1, 0)) temp_comp = plt.subplot2grid((num1,2), (ind-1, 1)) yvalues_1 = self.tensCls_1[:,ind] yvalues_2 = self.tensCls_2[:,ind] min2val = np.min(yvalues_1[np.nonzero(yvalues_1)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100 # Protection against all zero: put to machine precision the values that are zero np.place(yvalues_comp, abs(yvalues_comp)<10.0**(-16), [10.0**(-16)]) if ind == 1: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) temp.set_yscale('Log') temp.set_title('TT power spectrum') elif ind == 2: plots_1.EE_plot(temp, xvalues, yvalues_1) plots_2.EE_plot(temp, xvalues, yvalues_2) plots_compa.EE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('EE power spectrum') elif ind == 3: plots_1.BB_plot(temp, xvalues, yvalues_1) plots_2.BB_plot(temp, xvalues, yvalues_2) plots_compa.BB_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('BB power spectrum') elif ind == 4: plots_1.TE_plot(temp, xvalues, yvalues_1) plots_2.TE_plot(temp, xvalues, yvalues_2) plots_compa.TE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('TE power spectrum') else: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) # set the size of the image fig.set_size_inches( self.x_size_inc, self.y_size_inc) # set a tight layout fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3) # set the global title plt.suptitle(self.name_h1+' VS '+self.name_h2+ ' comparison of tensor Cls', fontsize=16) # set the global legend fig.legend( handles = [plots_1.TT_p, plots_2.TT_p, plots_compa.CV_p], labels = [self.name_h1, self.name_h2, 'Cosmic variance'], loc='lower center', ncol=3 ,fancybox=True) # adjust the size of the plot fig.subplots_adjust(top=0.92, bottom=0.08) # save the result and close plt.savefig(self.outpath+self.name1+'_'+self.name2+'_tensCls_comp.pdf') plt.clf() plt.close("all") def plot_compare_totalCls(self): """ Plots and saves the comparison of all the total (scalar + tensor) Cls in a unique image If lensing is included lensed Cls are used. """ # protection from direct calls if tensors are not included if not self.tensor: return # decide what data to use: if self.lensing: data1 = self.lensedtotCls_1 data2 = self.lensedtotCls_2 else: data1 = self.totCls_1 data2 = self.totCls_2 # number of Cls: num1 = data1.shape[1]-1 num2 = data2.shape[1]-1 # protection against different runs if num1!=num2: print 'wrong number of Cls' return if len(data1[:,0])!=len(data2[:,0]): print 'different lmax' return xvalues = data1[:,0] # set up the plots: plots_1 = CMB_plots() plots_2 = CMB_plots() plots_compa = CMB_plots() plots_1.color = self.color1 plots_2.color = self.color2 plots_compa.color = self.color_compa plots_compa.comparison = True plots_compa.axes_label_position = 'right' fig = plt.gcf() # do the plots: for ind in xrange(1,num1+1): temp = plt.subplot2grid((num1,2), (ind-1, 0)) temp_comp = plt.subplot2grid((num1,2), (ind-1, 1)) yvalues_1 = data1[:,ind] yvalues_2 = data2[:,ind] min2val = np.min(yvalues_1[np.nonzero(yvalues_1)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100 # Protection against all zero: put to machine precision the values that are zero np.place(yvalues_comp, abs(yvalues_comp)<10.0**(-16), [10.0**(-16)]) if ind == 1: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) temp.set_yscale('Log') temp.set_title('TT power spectrum') elif ind == 2: plots_1.EE_plot(temp, xvalues, yvalues_1) plots_2.EE_plot(temp, xvalues, yvalues_2) plots_compa.EE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('EE power spectrum') elif ind == 3: plots_1.BB_plot(temp, xvalues, yvalues_1) plots_2.BB_plot(temp, xvalues, yvalues_2) plots_compa.BB_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('BB power spectrum') elif ind == 4: plots_1.TE_plot(temp, xvalues, yvalues_1) plots_2.TE_plot(temp, xvalues, yvalues_2) plots_compa.TE_plot(temp_comp, xvalues, yvalues_comp) temp.set_title('TE power spectrum') else: plots_1.TT_plot(temp, xvalues, yvalues_1) plots_2.TT_plot(temp, xvalues, yvalues_2) plots_compa.TT_plot(temp_comp, xvalues, yvalues_comp) # set the size of the image fig.set_size_inches( self.x_size_inc, self.y_size_inc) # set a tight layout fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3) # set the global title if self.lensing: plt.suptitle(self.name_h1+' VS '+self.name_h2+' comparison of total lensed Cls', fontsize=16) else: plt.suptitle(self.name_h1+' VS '+self.name_h2+' comparison of total Cls', fontsize=16) # set the global legend fig.legend( handles = [plots_1.TT_p, plots_2.TT_p, plots_compa.CV_p], labels = [self.name_h1, self.name_h2, 'Cosmic variance'], loc='lower center', ncol=3 ,fancybox=True) # adjust the size of the plot fig.subplots_adjust(top=0.92, bottom=0.08) # save the result and close plt.savefig(self.outpath+self.name1+'_'+self.name2+'_totCls_comp.pdf') plt.clf() plt.close("all") def plot_compare_Transfer(self): """ Plots and saves the comparison of all the transfer functions in a unique image """ # protection from direct calls if transfer functions are not included if not self.transfer: return data1 = self.transfer_func_1 data2 = self.transfer_func_2 # number of transfer functions: num1 = data1.shape[1]-1 num2 = data2.shape[1]-1 # protection against different runs if num1!=num2: print 'wrong number of transfer functions' return if len(data1[:,0])!=len(data2[:,0]): print 'Different values of k' return xvalues = data1[:,0] # set up the plots: plots_1 = CMB_plots() plots_2 = CMB_plots() plots_compa = CMB_plots() plots_1.color = self.color1 plots_2.color = self.color2 plots_compa.color = self.color_compa plots_compa.comparison = True plots_compa.axes_label_position = 'right' fig = plt.gcf() labels = [ 'CDM', 'baryons', 'photons', 'massless neutrinos', 'massive neutrinos', 'CDM+baryons+massive neutrinos', 'CDM+baryons', 'CDM+baryons+massive neutrinos+ de', 'The Weyl potential', 'vel_Newt_cdm', 'vel_Newt_b', 'relative baryon-CDM velocity' ] if not len(labels) == num1: print 'Not enough transfer functions' return # do the plots: for ind in xrange(1,num1+1): temp = plt.subplot2grid((num1,2), (ind-1, 0)) temp_comp = plt.subplot2grid((num1,2), (ind-1, 1)) yvalues_1 = data1[:,ind] yvalues_2 = data2[:,ind] min2val = np.min(yvalues_1[np.nonzero(yvalues_1)]) np.place(yvalues_1, yvalues_1 == 0.0, min2val) yvalues_comp = (yvalues_1 - yvalues_2)/abs(yvalues_1)*100 # Protection against all zero: put to machine precision the values that are zero np.place(yvalues_comp, abs(yvalues_comp)<10.0**(-16), [10.0**(-16)]) plots_1.Transfer_plot(temp, xvalues, yvalues_1) plots_2.Transfer_plot(temp, xvalues, yvalues_2) plots_compa.Transfer_plot(temp_comp, xvalues, yvalues_comp) temp.set_title(labels[ind-1]) # set the size of the image fig.set_size_inches( self.x_size_inc, 1.61803398875*self.x_size_inc/6.*num1 ) # set a tight layout fig.tight_layout(pad=0.3, h_pad=0.3, w_pad=0.3) # set the global title plt.suptitle(self.name_h1+' VS '+self.name_h2+' comparison of transfer functions', fontsize=16) # set the global legend fig.legend( handles = [plots_1.Transfer_p, plots_2.Transfer_p], labels = [self.name_h1, self.name_h2], loc='lower center', ncol=3 ,fancybox=True) # adjust the size of the plot fig.subplots_adjust(top=0.95, bottom=0.05) # save the result and close plt.savefig(self.outpath+self.name1+'_'+self.name2+'_transfer_comp.pdf') plt.clf() plt.close("all")

gpl-3.0

DonBeo/scikit-learn

sklearn/cluster/tests/test_dbscan.py

7

10974

""" Tests for DBSCAN clustering algorithm """ import pickle import numpy as np from scipy.spatial import distance from scipy import sparse from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_in from sklearn.utils.testing import assert_not_in from sklearn.cluster.dbscan_ import DBSCAN from sklearn.cluster.dbscan_ import dbscan from sklearn.cluster.tests.common import generate_clustered_data from sklearn.metrics.pairwise import pairwise_distances n_clusters = 3 X = generate_clustered_data(n_clusters=n_clusters) def test_dbscan_similarity(): # Tests the DBSCAN algorithm with a similarity array. # Parameters chosen specifically for this task. eps = 0.15 min_samples = 10 # Compute similarities D = distance.squareform(distance.pdist(X)) D /= np.max(D) # Compute DBSCAN core_samples, labels = dbscan(D, metric="precomputed", eps=eps, min_samples=min_samples) # number of clusters, ignoring noise if present n_clusters_1 = len(set(labels)) - (1 if -1 in labels else 0) assert_equal(n_clusters_1, n_clusters) db = DBSCAN(metric="precomputed", eps=eps, min_samples=min_samples) labels = db.fit(D).labels_ n_clusters_2 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_2, n_clusters) def test_dbscan_feature(): # Tests the DBSCAN algorithm with a feature vector array. # Parameters chosen specifically for this task. # Different eps to other test, because distance is not normalised. eps = 0.8 min_samples = 10 metric = 'euclidean' # Compute DBSCAN # parameters chosen for task core_samples, labels = dbscan(X, metric=metric, eps=eps, min_samples=min_samples) # number of clusters, ignoring noise if present n_clusters_1 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_1, n_clusters) db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples) labels = db.fit(X).labels_ n_clusters_2 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_2, n_clusters) def test_dbscan_sparse(): core_sparse, labels_sparse = dbscan(sparse.lil_matrix(X), eps=.8, min_samples=10) core_dense, labels_dense = dbscan(X, eps=.8, min_samples=10) assert_array_equal(core_dense, core_sparse) assert_array_equal(labels_dense, labels_sparse) def test_dbscan_no_core_samples(): rng = np.random.RandomState(0) X = rng.rand(40, 10) X[X < .8] = 0 for X_ in [X, sparse.csr_matrix(X)]: db = DBSCAN(min_samples=6).fit(X_) assert_array_equal(db.components_, np.empty((0, X_.shape[1]))) assert_array_equal(db.labels_, -1) assert_equal(db.core_sample_indices_.shape, (0,)) def test_dbscan_callable(): # Tests the DBSCAN algorithm with a callable metric. # Parameters chosen specifically for this task. # Different eps to other test, because distance is not normalised. eps = 0.8 min_samples = 10 # metric is the function reference, not the string key. metric = distance.euclidean # Compute DBSCAN # parameters chosen for task core_samples, labels = dbscan(X, metric=metric, eps=eps, min_samples=min_samples, algorithm='ball_tree') # number of clusters, ignoring noise if present n_clusters_1 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_1, n_clusters) db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples, algorithm='ball_tree') labels = db.fit(X).labels_ n_clusters_2 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_2, n_clusters) def test_dbscan_balltree(): # Tests the DBSCAN algorithm with balltree for neighbor calculation. eps = 0.8 min_samples = 10 D = pairwise_distances(X) core_samples, labels = dbscan(D, metric="precomputed", eps=eps, min_samples=min_samples) # number of clusters, ignoring noise if present n_clusters_1 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_1, n_clusters) db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm='ball_tree') labels = db.fit(X).labels_ n_clusters_2 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_2, n_clusters) db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm='kd_tree') labels = db.fit(X).labels_ n_clusters_3 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_3, n_clusters) db = DBSCAN(p=1.0, eps=eps, min_samples=min_samples, algorithm='ball_tree') labels = db.fit(X).labels_ n_clusters_4 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_4, n_clusters) db = DBSCAN(leaf_size=20, eps=eps, min_samples=min_samples, algorithm='ball_tree') labels = db.fit(X).labels_ n_clusters_5 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_5, n_clusters) def test_input_validation(): # DBSCAN.fit should accept a list of lists. X = [[1., 2.], [3., 4.]] DBSCAN().fit(X) # must not raise exception def test_dbscan_badargs(): # Test bad argument values: these should all raise ValueErrors assert_raises(ValueError, dbscan, X, eps=-1.0) assert_raises(ValueError, dbscan, X, algorithm='blah') assert_raises(ValueError, dbscan, X, metric='blah') assert_raises(ValueError, dbscan, X, leaf_size=-1) assert_raises(ValueError, dbscan, X, p=-1) def test_pickle(): obj = DBSCAN() s = pickle.dumps(obj) assert_equal(type(pickle.loads(s)), obj.__class__) def test_boundaries(): # ensure min_samples is inclusive of core point core, _ = dbscan([[0], [1]], eps=2, min_samples=2) assert_in(0, core) # ensure eps is inclusive of circumference core, _ = dbscan([[0], [1], [1]], eps=1, min_samples=2) assert_in(0, core) core, _ = dbscan([[0], [1], [1]], eps=.99, min_samples=2) assert_not_in(0, core) def test_weighted_dbscan(): # ensure sample_weight is validated assert_raises(ValueError, dbscan, [[0], [1]], sample_weight=[2]) assert_raises(ValueError, dbscan, [[0], [1]], sample_weight=[2, 3, 4]) # ensure sample_weight has an effect assert_array_equal([], dbscan([[0], [1]], sample_weight=None, min_samples=6)[0]) assert_array_equal([], dbscan([[0], [1]], sample_weight=[5, 5], min_samples=6)[0]) assert_array_equal([0], dbscan([[0], [1]], sample_weight=[6, 5], min_samples=6)[0]) assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[6, 6], min_samples=6)[0]) # points within eps of each other: assert_array_equal([0, 1], dbscan([[0], [1]], eps=1.5, sample_weight=[5, 1], min_samples=6)[0]) # and effect of non-positive and non-integer sample_weight: assert_array_equal([], dbscan([[0], [1]], sample_weight=[5, 0], eps=1.5, min_samples=6)[0]) assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[5.9, 0.1], eps=1.5, min_samples=6)[0]) assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[6, 0], eps=1.5, min_samples=6)[0]) assert_array_equal([], dbscan([[0], [1]], sample_weight=[6, -1], eps=1.5, min_samples=6)[0]) # for non-negative sample_weight, cores should be identical to repetition rng = np.random.RandomState(42) sample_weight = rng.randint(0, 5, X.shape[0]) core1, label1 = dbscan(X, sample_weight=sample_weight) assert_equal(len(label1), len(X)) X_repeated = np.repeat(X, sample_weight, axis=0) core_repeated, label_repeated = dbscan(X_repeated) core_repeated_mask = np.zeros(X_repeated.shape[0], dtype=bool) core_repeated_mask[core_repeated] = True core_mask = np.zeros(X.shape[0], dtype=bool) core_mask[core1] = True assert_array_equal(np.repeat(core_mask, sample_weight), core_repeated_mask) # sample_weight should work with precomputed distance matrix D = pairwise_distances(X) core3, label3 = dbscan(D, sample_weight=sample_weight, metric='precomputed') assert_array_equal(core1, core3) assert_array_equal(label1, label3) # sample_weight should work with estimator est = DBSCAN().fit(X, sample_weight=sample_weight) core4 = est.core_sample_indices_ label4 = est.labels_ assert_array_equal(core1, core4) assert_array_equal(label1, label4) est = DBSCAN() label5 = est.fit_predict(X, sample_weight=sample_weight) core5 = est.core_sample_indices_ assert_array_equal(core1, core5) assert_array_equal(label1, label5) assert_array_equal(label1, est.labels_) def test_dbscan_core_samples_toy(): X = [[0], [2], [3], [4], [6], [8], [10]] n_samples = len(X) for algorithm in ['brute', 'kd_tree', 'ball_tree']: # Degenerate case: every sample is a core sample, either with its own # cluster or including other close core samples. core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=1) assert_array_equal(core_samples, np.arange(n_samples)) assert_array_equal(labels, [0, 1, 1, 1, 2, 3, 4]) # With eps=1 and min_samples=2 only the 3 samples from the denser area # are core samples. All other points are isolated and considered noise. core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=2) assert_array_equal(core_samples, [1, 2, 3]) assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1]) # Only the sample in the middle of the dense area is core. Its two # neighbors are edge samples. Remaining samples are noise. core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=3) assert_array_equal(core_samples, [2]) assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1]) # It's no longer possible to extract core samples with eps=1: # everything is noise. core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=4) assert_array_equal(core_samples, []) assert_array_equal(labels, -np.ones(n_samples))

bsd-3-clause

sssllliang/BuildingMachineLearningSystemsWithPython

ch02/chapter.py

17

4700

# This code is supporting material for the book # Building Machine Learning Systems with Python # by Willi Richert and Luis Pedro Coelho # published by PACKT Publishing # # It is made available under the MIT License from matplotlib import pyplot as plt import numpy as np # We load the data with load_iris from sklearn from sklearn.datasets import load_iris data = load_iris() # load_iris returns an object with several fields features = data.data feature_names = data.feature_names target = data.target target_names = data.target_names for t in range(3): if t == 0: c = 'r' marker = '>' elif t == 1: c = 'g' marker = 'o' elif t == 2: c = 'b' marker = 'x' plt.scatter(features[target == t, 0], features[target == t, 1], marker=marker, c=c) # We use NumPy fancy indexing to get an array of strings: labels = target_names[target] # The petal length is the feature at position 2 plength = features[:, 2] # Build an array of booleans: is_setosa = (labels == 'setosa') # This is the important step: max_setosa =plength[is_setosa].max() min_non_setosa = plength[~is_setosa].min() print('Maximum of setosa: {0}.'.format(max_setosa)) print('Minimum of others: {0}.'.format(min_non_setosa)) # ~ is the boolean negation operator features = features[~is_setosa] labels = labels[~is_setosa] # Build a new target variable, is_virigina is_virginica = (labels == 'virginica') # Initialize best_acc to impossibly low value best_acc = -1.0 for fi in range(features.shape[1]): # We are going to test all possible thresholds thresh = features[:,fi] for t in thresh: # Get the vector for feature `fi` feature_i = features[:, fi] # apply threshold `t` pred = (feature_i > t) acc = (pred == is_virginica).mean() rev_acc = (pred == ~is_virginica).mean() if rev_acc > acc: reverse = True acc = rev_acc else: reverse = False if acc > best_acc: best_acc = acc best_fi = fi best_t = t best_reverse = reverse print(best_fi, best_t, best_reverse, best_acc) def is_virginica_test(fi, t, reverse, example): 'Apply threshold model to a new example' test = example[fi] > t if reverse: test = not test return test from threshold import fit_model, predict # ning accuracy was 96.0%. # ing accuracy was 90.0% (N = 50). correct = 0.0 for ei in range(len(features)): # select all but the one at position `ei`: training = np.ones(len(features), bool) training[ei] = False testing = ~training model = fit_model(features[training], is_virginica[training]) predict(model, features[testing]) predictions = predict(model, features[testing]) correct += np.sum(predictions == is_virginica[testing]) acc = correct/float(len(features)) print('Accuracy: {0:.1%}'.format(acc)) ########################################### ############## SEEDS DATASET ############## ########################################### from load import load_dataset feature_names = [ 'area', 'perimeter', 'compactness', 'length of kernel', 'width of kernel', 'asymmetry coefficien', 'length of kernel groove', ] features, labels = load_dataset('seeds') from sklearn.neighbors import KNeighborsClassifier classifier = KNeighborsClassifier(n_neighbors=1) from sklearn.cross_validation import KFold kf = KFold(len(features), n_folds=5, shuffle=True) means = [] for training,testing in kf: # We learn a model for this fold with `fit` and then apply it to the # testing data with `predict`: classifier.fit(features[training], labels[training]) prediction = classifier.predict(features[testing]) # np.mean on an array of booleans returns fraction # of correct decisions for this fold: curmean = np.mean(prediction == labels[testing]) means.append(curmean) print('Mean accuracy: {:.1%}'.format(np.mean(means))) from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler classifier = KNeighborsClassifier(n_neighbors=1) classifier = Pipeline([('norm', StandardScaler()), ('knn', classifier)]) means = [] for training,testing in kf: # We learn a model for this fold with `fit` and then apply it to the # testing data with `predict`: classifier.fit(features[training], labels[training]) prediction = classifier.predict(features[testing]) # np.mean on an array of booleans returns fraction # of correct decisions for this fold: curmean = np.mean(prediction == labels[testing]) means.append(curmean) print('Mean accuracy: {:.1%}'.format(np.mean(means)))

mit

nikitasingh981/scikit-learn

sklearn/metrics/tests/test_classification.py

8

57459

from __future__ import division, print_function import numpy as np from scipy import linalg from functools import partial from itertools import product import warnings from sklearn import datasets from sklearn import svm from sklearn.datasets import make_multilabel_classification from sklearn.preprocessing import label_binarize from sklearn.utils.fixes import np_version from sklearn.utils.validation import check_random_state from sklearn.utils.testing import assert_raises, clean_warning_registry from sklearn.utils.testing import assert_raise_message from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_warns from sklearn.utils.testing import assert_no_warnings from sklearn.utils.testing import assert_warns_message from sklearn.utils.testing import assert_not_equal from sklearn.utils.testing import ignore_warnings from sklearn.utils.mocking import MockDataFrame from sklearn.metrics import accuracy_score from sklearn.metrics import average_precision_score from sklearn.metrics import classification_report from sklearn.metrics import cohen_kappa_score from sklearn.metrics import confusion_matrix from sklearn.metrics import f1_score from sklearn.metrics import fbeta_score from sklearn.metrics import hamming_loss from sklearn.metrics import hinge_loss from sklearn.metrics import jaccard_similarity_score from sklearn.metrics import log_loss from sklearn.metrics import matthews_corrcoef from sklearn.metrics import precision_recall_fscore_support from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import zero_one_loss from sklearn.metrics import brier_score_loss from sklearn.metrics.classification import _check_targets from sklearn.exceptions import UndefinedMetricWarning from scipy.spatial.distance import hamming as sp_hamming ############################################################################### # Utilities for testing def make_prediction(dataset=None, binary=False): """Make some classification predictions on a toy dataset using a SVC If binary is True restrict to a binary classification problem instead of a multiclass classification problem """ if dataset is None: # import some data to play with dataset = datasets.load_iris() X = dataset.data y = dataset.target if binary: # restrict to a binary classification task X, y = X[y < 2], y[y < 2] n_samples, n_features = X.shape p = np.arange(n_samples) rng = check_random_state(37) rng.shuffle(p) X, y = X[p], y[p] half = int(n_samples / 2) # add noisy features to make the problem harder and avoid perfect results rng = np.random.RandomState(0) X = np.c_[X, rng.randn(n_samples, 200 * n_features)] # run classifier, get class probabilities and label predictions clf = svm.SVC(kernel='linear', probability=True, random_state=0) probas_pred = clf.fit(X[:half], y[:half]).predict_proba(X[half:]) if binary: # only interested in probabilities of the positive case # XXX: do we really want a special API for the binary case? probas_pred = probas_pred[:, 1] y_pred = clf.predict(X[half:]) y_true = y[half:] return y_true, y_pred, probas_pred ############################################################################### # Tests def test_multilabel_accuracy_score_subset_accuracy(): # Dense label indicator matrix format y1 = np.array([[0, 1, 1], [1, 0, 1]]) y2 = np.array([[0, 0, 1], [1, 0, 1]]) assert_equal(accuracy_score(y1, y2), 0.5) assert_equal(accuracy_score(y1, y1), 1) assert_equal(accuracy_score(y2, y2), 1) assert_equal(accuracy_score(y2, np.logical_not(y2)), 0) assert_equal(accuracy_score(y1, np.logical_not(y1)), 0) assert_equal(accuracy_score(y1, np.zeros(y1.shape)), 0) assert_equal(accuracy_score(y2, np.zeros(y1.shape)), 0) def test_precision_recall_f1_score_binary(): # Test Precision Recall and F1 Score for binary classification task y_true, y_pred, _ = make_prediction(binary=True) # detailed measures for each class p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None) assert_array_almost_equal(p, [0.73, 0.85], 2) assert_array_almost_equal(r, [0.88, 0.68], 2) assert_array_almost_equal(f, [0.80, 0.76], 2) assert_array_equal(s, [25, 25]) # individual scoring function that can be used for grid search: in the # binary class case the score is the value of the measure for the positive # class (e.g. label == 1). This is deprecated for average != 'binary'. for kwargs, my_assert in [({}, assert_no_warnings), ({'average': 'binary'}, assert_no_warnings)]: ps = my_assert(precision_score, y_true, y_pred, **kwargs) assert_array_almost_equal(ps, 0.85, 2) rs = my_assert(recall_score, y_true, y_pred, **kwargs) assert_array_almost_equal(rs, 0.68, 2) fs = my_assert(f1_score, y_true, y_pred, **kwargs) assert_array_almost_equal(fs, 0.76, 2) assert_almost_equal(my_assert(fbeta_score, y_true, y_pred, beta=2, **kwargs), (1 + 2 ** 2) * ps * rs / (2 ** 2 * ps + rs), 2) def test_precision_recall_f_binary_single_class(): # Test precision, recall and F1 score behave with a single positive or # negative class # Such a case may occur with non-stratified cross-validation assert_equal(1., precision_score([1, 1], [1, 1])) assert_equal(1., recall_score([1, 1], [1, 1])) assert_equal(1., f1_score([1, 1], [1, 1])) assert_equal(0., precision_score([-1, -1], [-1, -1])) assert_equal(0., recall_score([-1, -1], [-1, -1])) assert_equal(0., f1_score([-1, -1], [-1, -1])) @ignore_warnings def test_precision_recall_f_extra_labels(): # Test handling of explicit additional (not in input) labels to PRF y_true = [1, 3, 3, 2] y_pred = [1, 1, 3, 2] y_true_bin = label_binarize(y_true, classes=np.arange(5)) y_pred_bin = label_binarize(y_pred, classes=np.arange(5)) data = [(y_true, y_pred), (y_true_bin, y_pred_bin)] for i, (y_true, y_pred) in enumerate(data): # No average: zeros in array actual = recall_score(y_true, y_pred, labels=[0, 1, 2, 3, 4], average=None) assert_array_almost_equal([0., 1., 1., .5, 0.], actual) # Macro average is changed actual = recall_score(y_true, y_pred, labels=[0, 1, 2, 3, 4], average='macro') assert_array_almost_equal(np.mean([0., 1., 1., .5, 0.]), actual) # No effect otheriwse for average in ['micro', 'weighted', 'samples']: if average == 'samples' and i == 0: continue assert_almost_equal(recall_score(y_true, y_pred, labels=[0, 1, 2, 3, 4], average=average), recall_score(y_true, y_pred, labels=None, average=average)) # Error when introducing invalid label in multilabel case # (although it would only affect performance if average='macro'/None) for average in [None, 'macro', 'micro', 'samples']: assert_raises(ValueError, recall_score, y_true_bin, y_pred_bin, labels=np.arange(6), average=average) assert_raises(ValueError, recall_score, y_true_bin, y_pred_bin, labels=np.arange(-1, 4), average=average) @ignore_warnings def test_precision_recall_f_ignored_labels(): # Test a subset of labels may be requested for PRF y_true = [1, 1, 2, 3] y_pred = [1, 3, 3, 3] y_true_bin = label_binarize(y_true, classes=np.arange(5)) y_pred_bin = label_binarize(y_pred, classes=np.arange(5)) data = [(y_true, y_pred), (y_true_bin, y_pred_bin)] for i, (y_true, y_pred) in enumerate(data): recall_13 = partial(recall_score, y_true, y_pred, labels=[1, 3]) recall_all = partial(recall_score, y_true, y_pred, labels=None) assert_array_almost_equal([.5, 1.], recall_13(average=None)) assert_almost_equal((.5 + 1.) / 2, recall_13(average='macro')) assert_almost_equal((.5 * 2 + 1. * 1) / 3, recall_13(average='weighted')) assert_almost_equal(2. / 3, recall_13(average='micro')) # ensure the above were meaningful tests: for average in ['macro', 'weighted', 'micro']: assert_not_equal(recall_13(average=average), recall_all(average=average)) def test_average_precision_score_score_non_binary_class(): # Test that average_precision_score function returns an error when trying # to compute average_precision_score for multiclass task. rng = check_random_state(404) y_pred = rng.rand(10) # y_true contains three different class values y_true = rng.randint(0, 3, size=10) assert_raise_message(ValueError, "multiclass format is not supported", average_precision_score, y_true, y_pred) def test_average_precision_score_duplicate_values(): # Duplicate values with precision-recall require a different # processing than when computing the AUC of a ROC, because the # precision-recall curve is a decreasing curve # The following situation corresponds to a perfect # test statistic, the average_precision_score should be 1 y_true = [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1] y_score = [0, .1, .1, .4, .5, .6, .6, .9, .9, 1, 1] assert_equal(average_precision_score(y_true, y_score), 1) def test_average_precision_score_tied_values(): # Here if we go from left to right in y_true, the 0 values are # are separated from the 1 values, so it appears that we've # Correctly sorted our classifications. But in fact the first two # values have the same score (0.5) and so the first two values # could be swapped around, creating an imperfect sorting. This # imperfection should come through in the end score, making it less # than one. y_true = [0, 1, 1] y_score = [.5, .5, .6] assert_not_equal(average_precision_score(y_true, y_score), 1.) @ignore_warnings def test_precision_recall_fscore_support_errors(): y_true, y_pred, _ = make_prediction(binary=True) # Bad beta assert_raises(ValueError, precision_recall_fscore_support, y_true, y_pred, beta=0.0) # Bad pos_label assert_raises(ValueError, precision_recall_fscore_support, y_true, y_pred, pos_label=2, average='binary') # Bad average option assert_raises(ValueError, precision_recall_fscore_support, [0, 1, 2], [1, 2, 0], average='mega') def test_precision_recall_f_unused_pos_label(): # Check warning that pos_label unused when set to non-default value # but average != 'binary'; even if data is binary. assert_warns_message(UserWarning, "Note that pos_label (set to 2) is " "ignored when average != 'binary' (got 'macro'). You " "may use labels=[pos_label] to specify a single " "positive class.", precision_recall_fscore_support, [1, 2, 1], [1, 2, 2], pos_label=2, average='macro') def test_confusion_matrix_binary(): # Test confusion matrix - binary classification case y_true, y_pred, _ = make_prediction(binary=True) def test(y_true, y_pred): cm = confusion_matrix(y_true, y_pred) assert_array_equal(cm, [[22, 3], [8, 17]]) tp, fp, fn, tn = cm.flatten() num = (tp * tn - fp * fn) den = np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)) true_mcc = 0 if den == 0 else num / den mcc = matthews_corrcoef(y_true, y_pred) assert_array_almost_equal(mcc, true_mcc, decimal=2) assert_array_almost_equal(mcc, 0.57, decimal=2) test(y_true, y_pred) test([str(y) for y in y_true], [str(y) for y in y_pred]) def test_cohen_kappa(): # These label vectors reproduce the contingency matrix from Artstein and # Poesio (2008), Table 1: np.array([[20, 20], [10, 50]]). y1 = np.array([0] * 40 + [1] * 60) y2 = np.array([0] * 20 + [1] * 20 + [0] * 10 + [1] * 50) kappa = cohen_kappa_score(y1, y2) assert_almost_equal(kappa, .348, decimal=3) assert_equal(kappa, cohen_kappa_score(y2, y1)) # Add spurious labels and ignore them. y1 = np.append(y1, [2] * 4) y2 = np.append(y2, [2] * 4) assert_equal(cohen_kappa_score(y1, y2, labels=[0, 1]), kappa) assert_almost_equal(cohen_kappa_score(y1, y1), 1.) # Multiclass example: Artstein and Poesio, Table 4. y1 = np.array([0] * 46 + [1] * 44 + [2] * 10) y2 = np.array([0] * 52 + [1] * 32 + [2] * 16) assert_almost_equal(cohen_kappa_score(y1, y2), .8013, decimal=4) # Weighting example: none, linear, quadratic. y1 = np.array([0] * 46 + [1] * 44 + [2] * 10) y2 = np.array([0] * 50 + [1] * 40 + [2] * 10) assert_almost_equal(cohen_kappa_score(y1, y2), .9315, decimal=4) assert_almost_equal(cohen_kappa_score(y1, y2, weights="linear"), .9412, decimal=4) assert_almost_equal(cohen_kappa_score(y1, y2, weights="quadratic"), .9541, decimal=4) @ignore_warnings def test_matthews_corrcoef_nan(): assert_equal(matthews_corrcoef([0], [1]), 0.0) assert_equal(matthews_corrcoef([0, 0], [0, 1]), 0.0) def test_matthews_corrcoef_against_numpy_corrcoef(): rng = np.random.RandomState(0) y_true = rng.randint(0, 2, size=20) y_pred = rng.randint(0, 2, size=20) assert_almost_equal(matthews_corrcoef(y_true, y_pred), np.corrcoef(y_true, y_pred)[0, 1], 10) def test_matthews_corrcoef(): rng = np.random.RandomState(0) y_true = ["a" if i == 0 else "b" for i in rng.randint(0, 2, size=20)] # corrcoef of same vectors must be 1 assert_almost_equal(matthews_corrcoef(y_true, y_true), 1.0) # corrcoef, when the two vectors are opposites of each other, should be -1 y_true_inv = ["b" if i == "a" else "a" for i in y_true] assert_almost_equal(matthews_corrcoef(y_true, y_true_inv), -1) y_true_inv2 = label_binarize(y_true, ["a", "b"]) y_true_inv2 = np.where(y_true_inv2, 'a', 'b') assert_almost_equal(matthews_corrcoef(y_true, y_true_inv2), -1) # For the zero vector case, the corrcoef cannot be calculated and should # result in a RuntimeWarning mcc = assert_warns_message(RuntimeWarning, 'invalid value encountered', matthews_corrcoef, [0, 0, 0, 0], [0, 0, 0, 0]) # But will output 0 assert_almost_equal(mcc, 0.) # And also for any other vector with 0 variance mcc = assert_warns_message(RuntimeWarning, 'invalid value encountered', matthews_corrcoef, y_true, ['a'] * len(y_true)) # But will output 0 assert_almost_equal(mcc, 0.) # These two vectors have 0 correlation and hence mcc should be 0 y_1 = [1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1] y_2 = [1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1] assert_almost_equal(matthews_corrcoef(y_1, y_2), 0.) # Check that sample weight is able to selectively exclude mask = [1] * 10 + [0] * 10 # Now the first half of the vector elements are alone given a weight of 1 # and hence the mcc will not be a perfect 0 as in the previous case assert_raises(AssertionError, assert_almost_equal, matthews_corrcoef(y_1, y_2, sample_weight=mask), 0.) def test_precision_recall_f1_score_multiclass(): # Test Precision Recall and F1 Score for multiclass classification task y_true, y_pred, _ = make_prediction(binary=False) # compute scores with default labels introspection p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None) assert_array_almost_equal(p, [0.83, 0.33, 0.42], 2) assert_array_almost_equal(r, [0.79, 0.09, 0.90], 2) assert_array_almost_equal(f, [0.81, 0.15, 0.57], 2) assert_array_equal(s, [24, 31, 20]) # averaging tests ps = precision_score(y_true, y_pred, pos_label=1, average='micro') assert_array_almost_equal(ps, 0.53, 2) rs = recall_score(y_true, y_pred, average='micro') assert_array_almost_equal(rs, 0.53, 2) fs = f1_score(y_true, y_pred, average='micro') assert_array_almost_equal(fs, 0.53, 2) ps = precision_score(y_true, y_pred, average='macro') assert_array_almost_equal(ps, 0.53, 2) rs = recall_score(y_true, y_pred, average='macro') assert_array_almost_equal(rs, 0.60, 2) fs = f1_score(y_true, y_pred, average='macro') assert_array_almost_equal(fs, 0.51, 2) ps = precision_score(y_true, y_pred, average='weighted') assert_array_almost_equal(ps, 0.51, 2) rs = recall_score(y_true, y_pred, average='weighted') assert_array_almost_equal(rs, 0.53, 2) fs = f1_score(y_true, y_pred, average='weighted') assert_array_almost_equal(fs, 0.47, 2) assert_raises(ValueError, precision_score, y_true, y_pred, average="samples") assert_raises(ValueError, recall_score, y_true, y_pred, average="samples") assert_raises(ValueError, f1_score, y_true, y_pred, average="samples") assert_raises(ValueError, fbeta_score, y_true, y_pred, average="samples", beta=0.5) # same prediction but with and explicit label ordering p, r, f, s = precision_recall_fscore_support( y_true, y_pred, labels=[0, 2, 1], average=None) assert_array_almost_equal(p, [0.83, 0.41, 0.33], 2) assert_array_almost_equal(r, [0.79, 0.90, 0.10], 2) assert_array_almost_equal(f, [0.81, 0.57, 0.15], 2) assert_array_equal(s, [24, 20, 31]) def test_precision_refcall_f1_score_multilabel_unordered_labels(): # test that labels need not be sorted in the multilabel case y_true = np.array([[1, 1, 0, 0]]) y_pred = np.array([[0, 0, 1, 1]]) for average in ['samples', 'micro', 'macro', 'weighted', None]: p, r, f, s = precision_recall_fscore_support( y_true, y_pred, labels=[3, 0, 1, 2], warn_for=[], average=average) assert_array_equal(p, 0) assert_array_equal(r, 0) assert_array_equal(f, 0) if average is None: assert_array_equal(s, [0, 1, 1, 0]) def test_precision_recall_f1_score_binary_averaged(): y_true = np.array([0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1]) y_pred = np.array([1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1]) # compute scores with default labels introspection ps, rs, fs, _ = precision_recall_fscore_support(y_true, y_pred, average=None) p, r, f, _ = precision_recall_fscore_support(y_true, y_pred, average='macro') assert_equal(p, np.mean(ps)) assert_equal(r, np.mean(rs)) assert_equal(f, np.mean(fs)) p, r, f, _ = precision_recall_fscore_support(y_true, y_pred, average='weighted') support = np.bincount(y_true) assert_equal(p, np.average(ps, weights=support)) assert_equal(r, np.average(rs, weights=support)) assert_equal(f, np.average(fs, weights=support)) def test_zero_precision_recall(): # Check that pathological cases do not bring NaNs old_error_settings = np.seterr(all='raise') try: y_true = np.array([0, 1, 2, 0, 1, 2]) y_pred = np.array([2, 0, 1, 1, 2, 0]) assert_almost_equal(precision_score(y_true, y_pred, average='macro'), 0.0, 2) assert_almost_equal(recall_score(y_true, y_pred, average='macro'), 0.0, 2) assert_almost_equal(f1_score(y_true, y_pred, average='macro'), 0.0, 2) finally: np.seterr(**old_error_settings) def test_confusion_matrix_multiclass(): # Test confusion matrix - multi-class case y_true, y_pred, _ = make_prediction(binary=False) def test(y_true, y_pred, string_type=False): # compute confusion matrix with default labels introspection cm = confusion_matrix(y_true, y_pred) assert_array_equal(cm, [[19, 4, 1], [4, 3, 24], [0, 2, 18]]) # compute confusion matrix with explicit label ordering labels = ['0', '2', '1'] if string_type else [0, 2, 1] cm = confusion_matrix(y_true, y_pred, labels=labels) assert_array_equal(cm, [[19, 1, 4], [0, 18, 2], [4, 24, 3]]) test(y_true, y_pred) test(list(str(y) for y in y_true), list(str(y) for y in y_pred), string_type=True) def test_confusion_matrix_sample_weight(): """Test confusion matrix - case with sample_weight""" y_true, y_pred, _ = make_prediction(binary=False) weights = [.1] * 25 + [.2] * 25 + [.3] * 25 cm = confusion_matrix(y_true, y_pred, sample_weight=weights) true_cm = (.1 * confusion_matrix(y_true[:25], y_pred[:25]) + .2 * confusion_matrix(y_true[25:50], y_pred[25:50]) + .3 * confusion_matrix(y_true[50:], y_pred[50:])) assert_array_almost_equal(cm, true_cm) assert_raises( ValueError, confusion_matrix, y_true, y_pred, sample_weight=weights[:-1]) def test_confusion_matrix_multiclass_subset_labels(): # Test confusion matrix - multi-class case with subset of labels y_true, y_pred, _ = make_prediction(binary=False) # compute confusion matrix with only first two labels considered cm = confusion_matrix(y_true, y_pred, labels=[0, 1]) assert_array_equal(cm, [[19, 4], [4, 3]]) # compute confusion matrix with explicit label ordering for only subset # of labels cm = confusion_matrix(y_true, y_pred, labels=[2, 1]) assert_array_equal(cm, [[18, 2], [24, 3]]) # a label not in y_true should result in zeros for that row/column extra_label = np.max(y_true) + 1 cm = confusion_matrix(y_true, y_pred, labels=[2, extra_label]) assert_array_equal(cm, [[18, 0], [0, 0]]) # check for exception when none of the specified labels are in y_true assert_raises(ValueError, confusion_matrix, y_true, y_pred, labels=[extra_label, extra_label + 1]) def test_classification_report_multiclass(): # Test performance report iris = datasets.load_iris() y_true, y_pred, _ = make_prediction(dataset=iris, binary=False) # print classification report with class names expected_report = """\ precision recall f1-score support setosa 0.83 0.79 0.81 24 versicolor 0.33 0.10 0.15 31 virginica 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ report = classification_report( y_true, y_pred, labels=np.arange(len(iris.target_names)), target_names=iris.target_names) assert_equal(report, expected_report) # print classification report with label detection expected_report = """\ precision recall f1-score support 0 0.83 0.79 0.81 24 1 0.33 0.10 0.15 31 2 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ report = classification_report(y_true, y_pred) assert_equal(report, expected_report) def test_classification_report_multiclass_with_digits(): # Test performance report with added digits in floating point values iris = datasets.load_iris() y_true, y_pred, _ = make_prediction(dataset=iris, binary=False) # print classification report with class names expected_report = """\ precision recall f1-score support setosa 0.82609 0.79167 0.80851 24 versicolor 0.33333 0.09677 0.15000 31 virginica 0.41860 0.90000 0.57143 20 avg / total 0.51375 0.53333 0.47310 75 """ report = classification_report( y_true, y_pred, labels=np.arange(len(iris.target_names)), target_names=iris.target_names, digits=5) assert_equal(report, expected_report) # print classification report with label detection expected_report = """\ precision recall f1-score support 0 0.83 0.79 0.81 24 1 0.33 0.10 0.15 31 2 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ report = classification_report(y_true, y_pred) assert_equal(report, expected_report) def test_classification_report_multiclass_with_string_label(): y_true, y_pred, _ = make_prediction(binary=False) y_true = np.array(["blue", "green", "red"])[y_true] y_pred = np.array(["blue", "green", "red"])[y_pred] expected_report = """\ precision recall f1-score support blue 0.83 0.79 0.81 24 green 0.33 0.10 0.15 31 red 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ report = classification_report(y_true, y_pred) assert_equal(report, expected_report) expected_report = """\ precision recall f1-score support a 0.83 0.79 0.81 24 b 0.33 0.10 0.15 31 c 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ report = classification_report(y_true, y_pred, target_names=["a", "b", "c"]) assert_equal(report, expected_report) def test_classification_report_multiclass_with_unicode_label(): y_true, y_pred, _ = make_prediction(binary=False) labels = np.array([u"blue\xa2", u"green\xa2", u"red\xa2"]) y_true = labels[y_true] y_pred = labels[y_pred] expected_report = u"""\ precision recall f1-score support blue\xa2 0.83 0.79 0.81 24 green\xa2 0.33 0.10 0.15 31 red\xa2 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ if np_version[:3] < (1, 7, 0): expected_message = ("NumPy < 1.7.0 does not implement" " searchsorted on unicode data correctly.") assert_raise_message(RuntimeError, expected_message, classification_report, y_true, y_pred) else: report = classification_report(y_true, y_pred) assert_equal(report, expected_report) def test_classification_report_multiclass_with_long_string_label(): y_true, y_pred, _ = make_prediction(binary=False) labels = np.array(["blue", "green"*5, "red"]) y_true = labels[y_true] y_pred = labels[y_pred] expected_report = """\ precision recall f1-score support blue 0.83 0.79 0.81 24 greengreengreengreengreen 0.33 0.10 0.15 31 red 0.42 0.90 0.57 20 avg / total 0.51 0.53 0.47 75 """ report = classification_report(y_true, y_pred) assert_equal(report, expected_report) def test_classification_report_labels_target_names_unequal_length(): y_true = [0, 0, 2, 0, 0] y_pred = [0, 2, 2, 0, 0] target_names = ['class 0', 'class 1', 'class 2'] assert_warns_message(UserWarning, "labels size, 2, does not " "match size of target_names, 3", classification_report, y_true, y_pred, target_names=target_names) def test_multilabel_classification_report(): n_classes = 4 n_samples = 50 _, y_true = make_multilabel_classification(n_features=1, n_samples=n_samples, n_classes=n_classes, random_state=0) _, y_pred = make_multilabel_classification(n_features=1, n_samples=n_samples, n_classes=n_classes, random_state=1) expected_report = """\ precision recall f1-score support 0 0.50 0.67 0.57 24 1 0.51 0.74 0.61 27 2 0.29 0.08 0.12 26 3 0.52 0.56 0.54 27 avg / total 0.45 0.51 0.46 104 """ report = classification_report(y_true, y_pred) assert_equal(report, expected_report) def test_multilabel_zero_one_loss_subset(): # Dense label indicator matrix format y1 = np.array([[0, 1, 1], [1, 0, 1]]) y2 = np.array([[0, 0, 1], [1, 0, 1]]) assert_equal(zero_one_loss(y1, y2), 0.5) assert_equal(zero_one_loss(y1, y1), 0) assert_equal(zero_one_loss(y2, y2), 0) assert_equal(zero_one_loss(y2, np.logical_not(y2)), 1) assert_equal(zero_one_loss(y1, np.logical_not(y1)), 1) assert_equal(zero_one_loss(y1, np.zeros(y1.shape)), 1) assert_equal(zero_one_loss(y2, np.zeros(y1.shape)), 1) def test_multilabel_hamming_loss(): # Dense label indicator matrix format y1 = np.array([[0, 1, 1], [1, 0, 1]]) y2 = np.array([[0, 0, 1], [1, 0, 1]]) w = np.array([1, 3]) assert_equal(hamming_loss(y1, y2), 1 / 6) assert_equal(hamming_loss(y1, y1), 0) assert_equal(hamming_loss(y2, y2), 0) assert_equal(hamming_loss(y2, 1 - y2), 1) assert_equal(hamming_loss(y1, 1 - y1), 1) assert_equal(hamming_loss(y1, np.zeros(y1.shape)), 4 / 6) assert_equal(hamming_loss(y2, np.zeros(y1.shape)), 0.5) assert_equal(hamming_loss(y1, y2, sample_weight=w), 1. / 12) assert_equal(hamming_loss(y1, 1-y2, sample_weight=w), 11. / 12) assert_equal(hamming_loss(y1, np.zeros_like(y1), sample_weight=w), 2. / 3) # sp_hamming only works with 1-D arrays assert_equal(hamming_loss(y1[0], y2[0]), sp_hamming(y1[0], y2[0])) assert_warns(DeprecationWarning, hamming_loss, y1, y2, classes=[0, 1]) def test_multilabel_jaccard_similarity_score(): # Dense label indicator matrix format y1 = np.array([[0, 1, 1], [1, 0, 1]]) y2 = np.array([[0, 0, 1], [1, 0, 1]]) # size(y1 \inter y2) = [1, 2] # size(y1 \union y2) = [2, 2] assert_equal(jaccard_similarity_score(y1, y2), 0.75) assert_equal(jaccard_similarity_score(y1, y1), 1) assert_equal(jaccard_similarity_score(y2, y2), 1) assert_equal(jaccard_similarity_score(y2, np.logical_not(y2)), 0) assert_equal(jaccard_similarity_score(y1, np.logical_not(y1)), 0) assert_equal(jaccard_similarity_score(y1, np.zeros(y1.shape)), 0) assert_equal(jaccard_similarity_score(y2, np.zeros(y1.shape)), 0) @ignore_warnings def test_precision_recall_f1_score_multilabel_1(): # Test precision_recall_f1_score on a crafted multilabel example # First crafted example y_true = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 1]]) y_pred = np.array([[0, 1, 0, 0], [0, 1, 0, 0], [1, 0, 1, 0]]) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None) # tp = [0, 1, 1, 0] # fn = [1, 0, 0, 1] # fp = [1, 1, 0, 0] # Check per class assert_array_almost_equal(p, [0.0, 0.5, 1.0, 0.0], 2) assert_array_almost_equal(r, [0.0, 1.0, 1.0, 0.0], 2) assert_array_almost_equal(f, [0.0, 1 / 1.5, 1, 0.0], 2) assert_array_almost_equal(s, [1, 1, 1, 1], 2) f2 = fbeta_score(y_true, y_pred, beta=2, average=None) support = s assert_array_almost_equal(f2, [0, 0.83, 1, 0], 2) # Check macro p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="macro") assert_almost_equal(p, 1.5 / 4) assert_almost_equal(r, 0.5) assert_almost_equal(f, 2.5 / 1.5 * 0.25) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="macro"), np.mean(f2)) # Check micro p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="micro") assert_almost_equal(p, 0.5) assert_almost_equal(r, 0.5) assert_almost_equal(f, 0.5) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="micro"), (1 + 4) * p * r / (4 * p + r)) # Check weighted p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="weighted") assert_almost_equal(p, 1.5 / 4) assert_almost_equal(r, 0.5) assert_almost_equal(f, 2.5 / 1.5 * 0.25) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="weighted"), np.average(f2, weights=support)) # Check samples # |h(x_i) inter y_i | = [0, 1, 1] # |y_i| = [1, 1, 2] # |h(x_i)| = [1, 1, 2] p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="samples") assert_almost_equal(p, 0.5) assert_almost_equal(r, 0.5) assert_almost_equal(f, 0.5) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="samples"), 0.5) @ignore_warnings def test_precision_recall_f1_score_multilabel_2(): # Test precision_recall_f1_score on a crafted multilabel example 2 # Second crafted example y_true = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 0]]) y_pred = np.array([[0, 0, 0, 1], [0, 0, 0, 1], [1, 1, 0, 0]]) # tp = [ 0. 1. 0. 0.] # fp = [ 1. 0. 0. 2.] # fn = [ 1. 1. 1. 0.] p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None) assert_array_almost_equal(p, [0.0, 1.0, 0.0, 0.0], 2) assert_array_almost_equal(r, [0.0, 0.5, 0.0, 0.0], 2) assert_array_almost_equal(f, [0.0, 0.66, 0.0, 0.0], 2) assert_array_almost_equal(s, [1, 2, 1, 0], 2) f2 = fbeta_score(y_true, y_pred, beta=2, average=None) support = s assert_array_almost_equal(f2, [0, 0.55, 0, 0], 2) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="micro") assert_almost_equal(p, 0.25) assert_almost_equal(r, 0.25) assert_almost_equal(f, 2 * 0.25 * 0.25 / 0.5) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="micro"), (1 + 4) * p * r / (4 * p + r)) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="macro") assert_almost_equal(p, 0.25) assert_almost_equal(r, 0.125) assert_almost_equal(f, 2 / 12) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="macro"), np.mean(f2)) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="weighted") assert_almost_equal(p, 2 / 4) assert_almost_equal(r, 1 / 4) assert_almost_equal(f, 2 / 3 * 2 / 4) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="weighted"), np.average(f2, weights=support)) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="samples") # Check samples # |h(x_i) inter y_i | = [0, 0, 1] # |y_i| = [1, 1, 2] # |h(x_i)| = [1, 1, 2] assert_almost_equal(p, 1 / 6) assert_almost_equal(r, 1 / 6) assert_almost_equal(f, 2 / 4 * 1 / 3) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="samples"), 0.1666, 2) @ignore_warnings def test_precision_recall_f1_score_with_an_empty_prediction(): y_true = np.array([[0, 1, 0, 0], [1, 0, 0, 0], [0, 1, 1, 0]]) y_pred = np.array([[0, 0, 0, 0], [0, 0, 0, 1], [0, 1, 1, 0]]) # true_pos = [ 0. 1. 1. 0.] # false_pos = [ 0. 0. 0. 1.] # false_neg = [ 1. 1. 0. 0.] p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None) assert_array_almost_equal(p, [0.0, 1.0, 1.0, 0.0], 2) assert_array_almost_equal(r, [0.0, 0.5, 1.0, 0.0], 2) assert_array_almost_equal(f, [0.0, 1 / 1.5, 1, 0.0], 2) assert_array_almost_equal(s, [1, 2, 1, 0], 2) f2 = fbeta_score(y_true, y_pred, beta=2, average=None) support = s assert_array_almost_equal(f2, [0, 0.55, 1, 0], 2) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="macro") assert_almost_equal(p, 0.5) assert_almost_equal(r, 1.5 / 4) assert_almost_equal(f, 2.5 / (4 * 1.5)) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="macro"), np.mean(f2)) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="micro") assert_almost_equal(p, 2 / 3) assert_almost_equal(r, 0.5) assert_almost_equal(f, 2 / 3 / (2 / 3 + 0.5)) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="micro"), (1 + 4) * p * r / (4 * p + r)) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="weighted") assert_almost_equal(p, 3 / 4) assert_almost_equal(r, 0.5) assert_almost_equal(f, (2 / 1.5 + 1) / 4) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="weighted"), np.average(f2, weights=support)) p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="samples") # |h(x_i) inter y_i | = [0, 0, 2] # |y_i| = [1, 1, 2] # |h(x_i)| = [0, 1, 2] assert_almost_equal(p, 1 / 3) assert_almost_equal(r, 1 / 3) assert_almost_equal(f, 1 / 3) assert_equal(s, None) assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="samples"), 0.333, 2) def test_precision_recall_f1_no_labels(): y_true = np.zeros((20, 3)) y_pred = np.zeros_like(y_true) # tp = [0, 0, 0] # fn = [0, 0, 0] # fp = [0, 0, 0] # support = [0, 0, 0] # |y_hat_i inter y_i | = [0, 0, 0] # |y_i| = [0, 0, 0] # |y_hat_i| = [0, 0, 0] for beta in [1]: p, r, f, s = assert_warns(UndefinedMetricWarning, precision_recall_fscore_support, y_true, y_pred, average=None, beta=beta) assert_array_almost_equal(p, [0, 0, 0], 2) assert_array_almost_equal(r, [0, 0, 0], 2) assert_array_almost_equal(f, [0, 0, 0], 2) assert_array_almost_equal(s, [0, 0, 0], 2) fbeta = assert_warns(UndefinedMetricWarning, fbeta_score, y_true, y_pred, beta=beta, average=None) assert_array_almost_equal(fbeta, [0, 0, 0], 2) for average in ["macro", "micro", "weighted", "samples"]: p, r, f, s = assert_warns(UndefinedMetricWarning, precision_recall_fscore_support, y_true, y_pred, average=average, beta=beta) assert_almost_equal(p, 0) assert_almost_equal(r, 0) assert_almost_equal(f, 0) assert_equal(s, None) fbeta = assert_warns(UndefinedMetricWarning, fbeta_score, y_true, y_pred, beta=beta, average=average) assert_almost_equal(fbeta, 0) def test_prf_warnings(): # average of per-label scores f, w = precision_recall_fscore_support, UndefinedMetricWarning my_assert = assert_warns_message for average in [None, 'weighted', 'macro']: msg = ('Precision and F-score are ill-defined and ' 'being set to 0.0 in labels with no predicted samples.') my_assert(w, msg, f, [0, 1, 2], [1, 1, 2], average=average) msg = ('Recall and F-score are ill-defined and ' 'being set to 0.0 in labels with no true samples.') my_assert(w, msg, f, [1, 1, 2], [0, 1, 2], average=average) # average of per-sample scores msg = ('Precision and F-score are ill-defined and ' 'being set to 0.0 in samples with no predicted labels.') my_assert(w, msg, f, np.array([[1, 0], [1, 0]]), np.array([[1, 0], [0, 0]]), average='samples') msg = ('Recall and F-score are ill-defined and ' 'being set to 0.0 in samples with no true labels.') my_assert(w, msg, f, np.array([[1, 0], [0, 0]]), np.array([[1, 0], [1, 0]]), average='samples') # single score: micro-average msg = ('Precision and F-score are ill-defined and ' 'being set to 0.0 due to no predicted samples.') my_assert(w, msg, f, np.array([[1, 1], [1, 1]]), np.array([[0, 0], [0, 0]]), average='micro') msg = ('Recall and F-score are ill-defined and ' 'being set to 0.0 due to no true samples.') my_assert(w, msg, f, np.array([[0, 0], [0, 0]]), np.array([[1, 1], [1, 1]]), average='micro') # single postive label msg = ('Precision and F-score are ill-defined and ' 'being set to 0.0 due to no predicted samples.') my_assert(w, msg, f, [1, 1], [-1, -1], average='binary') msg = ('Recall and F-score are ill-defined and ' 'being set to 0.0 due to no true samples.') my_assert(w, msg, f, [-1, -1], [1, 1], average='binary') def test_recall_warnings(): assert_no_warnings(recall_score, np.array([[1, 1], [1, 1]]), np.array([[0, 0], [0, 0]]), average='micro') clean_warning_registry() with warnings.catch_warnings(record=True) as record: warnings.simplefilter('always') recall_score(np.array([[0, 0], [0, 0]]), np.array([[1, 1], [1, 1]]), average='micro') assert_equal(str(record.pop().message), 'Recall is ill-defined and ' 'being set to 0.0 due to no true samples.') def test_precision_warnings(): clean_warning_registry() with warnings.catch_warnings(record=True) as record: warnings.simplefilter('always') precision_score(np.array([[1, 1], [1, 1]]), np.array([[0, 0], [0, 0]]), average='micro') assert_equal(str(record.pop().message), 'Precision is ill-defined and ' 'being set to 0.0 due to no predicted samples.') assert_no_warnings(precision_score, np.array([[0, 0], [0, 0]]), np.array([[1, 1], [1, 1]]), average='micro') def test_fscore_warnings(): clean_warning_registry() with warnings.catch_warnings(record=True) as record: warnings.simplefilter('always') for score in [f1_score, partial(fbeta_score, beta=2)]: score(np.array([[1, 1], [1, 1]]), np.array([[0, 0], [0, 0]]), average='micro') assert_equal(str(record.pop().message), 'F-score is ill-defined and ' 'being set to 0.0 due to no predicted samples.') score(np.array([[0, 0], [0, 0]]), np.array([[1, 1], [1, 1]]), average='micro') assert_equal(str(record.pop().message), 'F-score is ill-defined and ' 'being set to 0.0 due to no true samples.') def test_prf_average_binary_data_non_binary(): # Error if user does not explicitly set non-binary average mode y_true_mc = [1, 2, 3, 3] y_pred_mc = [1, 2, 3, 1] y_true_ind = np.array([[0, 1, 1], [1, 0, 0], [0, 0, 1]]) y_pred_ind = np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]]) for y_true, y_pred, y_type in [ (y_true_mc, y_pred_mc, 'multiclass'), (y_true_ind, y_pred_ind, 'multilabel-indicator'), ]: for metric in [precision_score, recall_score, f1_score, partial(fbeta_score, beta=2)]: assert_raise_message(ValueError, "Target is %s but average='binary'. Please " "choose another average setting." % y_type, metric, y_true, y_pred) def test__check_targets(): # Check that _check_targets correctly merges target types, squeezes # output and fails if input lengths differ. IND = 'multilabel-indicator' MC = 'multiclass' BIN = 'binary' CNT = 'continuous' MMC = 'multiclass-multioutput' MCN = 'continuous-multioutput' # all of length 3 EXAMPLES = [ (IND, np.array([[0, 1, 1], [1, 0, 0], [0, 0, 1]])), # must not be considered binary (IND, np.array([[0, 1], [1, 0], [1, 1]])), (MC, [2, 3, 1]), (BIN, [0, 1, 1]), (CNT, [0., 1.5, 1.]), (MC, np.array([[2], [3], [1]])), (BIN, np.array([[0], [1], [1]])), (CNT, np.array([[0.], [1.5], [1.]])), (MMC, np.array([[0, 2], [1, 3], [2, 3]])), (MCN, np.array([[0.5, 2.], [1.1, 3.], [2., 3.]])), ] # expected type given input types, or None for error # (types will be tried in either order) EXPECTED = { (IND, IND): IND, (MC, MC): MC, (BIN, BIN): BIN, (MC, IND): None, (BIN, IND): None, (BIN, MC): MC, # Disallowed types (CNT, CNT): None, (MMC, MMC): None, (MCN, MCN): None, (IND, CNT): None, (MC, CNT): None, (BIN, CNT): None, (MMC, CNT): None, (MCN, CNT): None, (IND, MMC): None, (MC, MMC): None, (BIN, MMC): None, (MCN, MMC): None, (IND, MCN): None, (MC, MCN): None, (BIN, MCN): None, } for (type1, y1), (type2, y2) in product(EXAMPLES, repeat=2): try: expected = EXPECTED[type1, type2] except KeyError: expected = EXPECTED[type2, type1] if expected is None: assert_raises(ValueError, _check_targets, y1, y2) if type1 != type2: assert_raise_message( ValueError, "Can't handle mix of {0} and {1}".format(type1, type2), _check_targets, y1, y2) else: if type1 not in (BIN, MC, IND): assert_raise_message(ValueError, "{0} is not supported".format(type1), _check_targets, y1, y2) else: merged_type, y1out, y2out = _check_targets(y1, y2) assert_equal(merged_type, expected) if merged_type.startswith('multilabel'): assert_equal(y1out.format, 'csr') assert_equal(y2out.format, 'csr') else: assert_array_equal(y1out, np.squeeze(y1)) assert_array_equal(y2out, np.squeeze(y2)) assert_raises(ValueError, _check_targets, y1[:-1], y2) # Make sure seq of seq is not supported y1 = [(1, 2,), (0, 2, 3)] y2 = [(2,), (0, 2,)] msg = ('You appear to be using a legacy multi-label data representation. ' 'Sequence of sequences are no longer supported; use a binary array' ' or sparse matrix instead.') assert_raise_message(ValueError, msg, _check_targets, y1, y2) def test__check_targets_multiclass_with_both_y_true_and_y_pred_binary(): # https://github.com/scikit-learn/scikit-learn/issues/8098 y_true = [0, 1] y_pred = [0, -1] assert_equal(_check_targets(y_true, y_pred)[0], 'multiclass') def test_hinge_loss_binary(): y_true = np.array([-1, 1, 1, -1]) pred_decision = np.array([-8.5, 0.5, 1.5, -0.3]) assert_equal(hinge_loss(y_true, pred_decision), 1.2 / 4) y_true = np.array([0, 2, 2, 0]) pred_decision = np.array([-8.5, 0.5, 1.5, -0.3]) assert_equal(hinge_loss(y_true, pred_decision), 1.2 / 4) def test_hinge_loss_multiclass(): pred_decision = np.array([ [+0.36, -0.17, -0.58, -0.99], [-0.54, -0.37, -0.48, -0.58], [-1.45, -0.58, -0.38, -0.17], [-0.54, -0.38, -0.48, -0.58], [-2.36, -0.79, -0.27, +0.24], [-1.45, -0.58, -0.38, -0.17] ]) y_true = np.array([0, 1, 2, 1, 3, 2]) dummy_losses = np.array([ 1 - pred_decision[0][0] + pred_decision[0][1], 1 - pred_decision[1][1] + pred_decision[1][2], 1 - pred_decision[2][2] + pred_decision[2][3], 1 - pred_decision[3][1] + pred_decision[3][2], 1 - pred_decision[4][3] + pred_decision[4][2], 1 - pred_decision[5][2] + pred_decision[5][3] ]) dummy_losses[dummy_losses <= 0] = 0 dummy_hinge_loss = np.mean(dummy_losses) assert_equal(hinge_loss(y_true, pred_decision), dummy_hinge_loss) def test_hinge_loss_multiclass_missing_labels_with_labels_none(): y_true = np.array([0, 1, 2, 2]) pred_decision = np.array([ [+1.27, 0.034, -0.68, -1.40], [-1.45, -0.58, -0.38, -0.17], [-2.36, -0.79, -0.27, +0.24], [-2.36, -0.79, -0.27, +0.24] ]) error_message = ("Please include all labels in y_true " "or pass labels as third argument") assert_raise_message(ValueError, error_message, hinge_loss, y_true, pred_decision) def test_hinge_loss_multiclass_with_missing_labels(): pred_decision = np.array([ [+0.36, -0.17, -0.58, -0.99], [-0.55, -0.38, -0.48, -0.58], [-1.45, -0.58, -0.38, -0.17], [-0.55, -0.38, -0.48, -0.58], [-1.45, -0.58, -0.38, -0.17] ]) y_true = np.array([0, 1, 2, 1, 2]) labels = np.array([0, 1, 2, 3]) dummy_losses = np.array([ 1 - pred_decision[0][0] + pred_decision[0][1], 1 - pred_decision[1][1] + pred_decision[1][2], 1 - pred_decision[2][2] + pred_decision[2][3], 1 - pred_decision[3][1] + pred_decision[3][2], 1 - pred_decision[4][2] + pred_decision[4][3] ]) dummy_losses[dummy_losses <= 0] = 0 dummy_hinge_loss = np.mean(dummy_losses) assert_equal(hinge_loss(y_true, pred_decision, labels=labels), dummy_hinge_loss) def test_hinge_loss_multiclass_invariance_lists(): # Currently, invariance of string and integer labels cannot be tested # in common invariance tests because invariance tests for multiclass # decision functions is not implemented yet. y_true = ['blue', 'green', 'red', 'green', 'white', 'red'] pred_decision = [ [+0.36, -0.17, -0.58, -0.99], [-0.55, -0.38, -0.48, -0.58], [-1.45, -0.58, -0.38, -0.17], [-0.55, -0.38, -0.48, -0.58], [-2.36, -0.79, -0.27, +0.24], [-1.45, -0.58, -0.38, -0.17]] dummy_losses = np.array([ 1 - pred_decision[0][0] + pred_decision[0][1], 1 - pred_decision[1][1] + pred_decision[1][2], 1 - pred_decision[2][2] + pred_decision[2][3], 1 - pred_decision[3][1] + pred_decision[3][2], 1 - pred_decision[4][3] + pred_decision[4][2], 1 - pred_decision[5][2] + pred_decision[5][3] ]) dummy_losses[dummy_losses <= 0] = 0 dummy_hinge_loss = np.mean(dummy_losses) assert_equal(hinge_loss(y_true, pred_decision), dummy_hinge_loss) def test_log_loss(): # binary case with symbolic labels ("no" < "yes") y_true = ["no", "no", "no", "yes", "yes", "yes"] y_pred = np.array([[0.5, 0.5], [0.1, 0.9], [0.01, 0.99], [0.9, 0.1], [0.75, 0.25], [0.001, 0.999]]) loss = log_loss(y_true, y_pred) assert_almost_equal(loss, 1.8817971) # multiclass case; adapted from http://bit.ly/RJJHWA y_true = [1, 0, 2] y_pred = [[0.2, 0.7, 0.1], [0.6, 0.2, 0.2], [0.6, 0.1, 0.3]] loss = log_loss(y_true, y_pred, normalize=True) assert_almost_equal(loss, 0.6904911) # check that we got all the shapes and axes right # by doubling the length of y_true and y_pred y_true *= 2 y_pred *= 2 loss = log_loss(y_true, y_pred, normalize=False) assert_almost_equal(loss, 0.6904911 * 6, decimal=6) # check eps and handling of absolute zero and one probabilities y_pred = np.asarray(y_pred) > .5 loss = log_loss(y_true, y_pred, normalize=True, eps=.1) assert_almost_equal(loss, log_loss(y_true, np.clip(y_pred, .1, .9))) # raise error if number of classes are not equal. y_true = [1, 0, 2] y_pred = [[0.2, 0.7], [0.6, 0.5], [0.4, 0.1]] assert_raises(ValueError, log_loss, y_true, y_pred) # case when y_true is a string array object y_true = ["ham", "spam", "spam", "ham"] y_pred = [[0.2, 0.7], [0.6, 0.5], [0.4, 0.1], [0.7, 0.2]] loss = log_loss(y_true, y_pred) assert_almost_equal(loss, 1.0383217, decimal=6) # test labels option y_true = [2, 2] y_pred = [[0.2, 0.7], [0.6, 0.5]] y_score = np.array([[0.1, 0.9], [0.1, 0.9]]) error_str = ('y_true contains only one label (2). Please provide ' 'the true labels explicitly through the labels argument.') assert_raise_message(ValueError, error_str, log_loss, y_true, y_pred) y_pred = [[0.2, 0.7], [0.6, 0.5], [0.2, 0.3]] error_str = ('Found input variables with inconsistent numbers of samples: ' '[3, 2]') assert_raise_message(ValueError, error_str, log_loss, y_true, y_pred) # works when the labels argument is used true_log_loss = -np.mean(np.log(y_score[:, 1])) calculated_log_loss = log_loss(y_true, y_score, labels=[1, 2]) assert_almost_equal(calculated_log_loss, true_log_loss) # ensure labels work when len(np.unique(y_true)) != y_pred.shape[1] y_true = [1, 2, 2] y_score2 = [[0.2, 0.7, 0.3], [0.6, 0.5, 0.3], [0.3, 0.9, 0.1]] loss = log_loss(y_true, y_score2, labels=[1, 2, 3]) assert_almost_equal(loss, 1.0630345, decimal=6) def test_log_loss_pandas_input(): # case when input is a pandas series and dataframe gh-5715 y_tr = np.array(["ham", "spam", "spam", "ham"]) y_pr = np.array([[0.2, 0.7], [0.6, 0.5], [0.4, 0.1], [0.7, 0.2]]) types = [(MockDataFrame, MockDataFrame)] try: from pandas import Series, DataFrame types.append((Series, DataFrame)) except ImportError: pass for TrueInputType, PredInputType in types: # y_pred dataframe, y_true series y_true, y_pred = TrueInputType(y_tr), PredInputType(y_pr) loss = log_loss(y_true, y_pred) assert_almost_equal(loss, 1.0383217, decimal=6) def test_brier_score_loss(): # Check brier_score_loss function y_true = np.array([0, 1, 1, 0, 1, 1]) y_pred = np.array([0.1, 0.8, 0.9, 0.3, 1., 0.95]) true_score = linalg.norm(y_true - y_pred) ** 2 / len(y_true) assert_almost_equal(brier_score_loss(y_true, y_true), 0.0) assert_almost_equal(brier_score_loss(y_true, y_pred), true_score) assert_almost_equal(brier_score_loss(1. + y_true, y_pred), true_score) assert_almost_equal(brier_score_loss(2 * y_true - 1, y_pred), true_score) assert_raises(ValueError, brier_score_loss, y_true, y_pred[1:]) assert_raises(ValueError, brier_score_loss, y_true, y_pred + 1.) assert_raises(ValueError, brier_score_loss, y_true, y_pred - 1.) # calculate even if only single class in y_true (#6980) assert_almost_equal(brier_score_loss([0], [0.5]), 0.25) assert_almost_equal(brier_score_loss([1], [0.5]), 0.25)

bsd-3-clause

fumitoh/modelx

modelx/tests/io/test_pandas.py

1

5359

import modelx as mx import pandas as pd import numpy as np import pytest _pd_ver = tuple(int(i) for i in pd.__version__.split("."))[:-1] @pytest.fixture def testspace(): model = mx.new_model() space = model.new_space() def f0(): return 3 def f1(x): return 2 * x def f2(x, y=1): return x + y f0, f1, f2 = mx.defcells(f0, f1, f2) return space @pytest.fixture def space_with_string_index(): model = mx.new_model() space = model.new_space() def f0(strind): return strind def f1(): return 3 mx.defcells(f0, f1) return space # ------------------------------------------------------------------------- # Test Conversion from Cells to DataFrame and Series def test_cells_empty(testspace): for c in ["f0", "f1", "f2"]: assert testspace.cells[c].to_series().empty assert testspace.cells[c].series.empty assert testspace.cells[c].to_frame().empty assert testspace.cells[c].frame.empty @pytest.mark.parametrize( "cells, args, length", [ ["f0", ((),), 1], ["f1", (1, 2, 3), 3], ["f1", ((1, 2, 3),), 3], ["f2", ((1, 2), (3, 4), (5, 6)), 3], ["f2", (((1, 2), (3, 4), (5, 6))), 3], ], ) def test_cells_to_frame_with_args(testspace, cells, args, length): assert len(testspace.cells[cells].to_frame(*args).index) == length assert len(testspace.cells[cells].to_frame()) == length assert len(testspace.cells[cells].frame) == length @pytest.mark.parametrize( "cells, args, length", [ ["f0", ((),), 1], ["f1", (1, 2, 3), 3], ["f1", ((1, 2, 3),), 3], ["f2", ((1, 2), (3, 4), (5, 6)), 3], ["f2", (((1, 2), (3, 4), (5, 6)),), 3], ], ) def test_cells_to_series_with_args(testspace, cells, args, length): assert len(testspace.cells[cells].to_series(*args).index) == length assert len(testspace.cells[cells].to_series()) == length assert len(testspace.cells[cells].series) == length # ------------------------------------------------------------------------- # Test Conversion from Space to DataFrame def test_space_to_frame_empty(testspace): assert testspace.to_frame().empty assert testspace.frame.empty if _pd_ver >= (0, 20): @pytest.mark.parametrize( "args, idxlen, cols", [ [((1, 2), (3, 4), (5, 6)), 7, {"f0", "f1", "f2"}], [(((1, 2), (3, 4), (5, 6)),), 7, {"f0", "f1", "f2"}], ], ) def test_space_to_frame_args(testspace, args, idxlen, cols): assert testspace.to_frame().empty df = testspace.to_frame(*args) assert set(df.columns) == cols assert len(df.index) == idxlen if len(args) == 1: args = args[0] for arg in args: dfx = df.xs(arg[0], level="x") assert int(dfx.loc[dfx.index.isnull(), "f1"]) == testspace.f1( arg[0] ) assert df.loc[arg, "f2"] == testspace.f2(*arg) @pytest.mark.parametrize( "args, idxlen, cols", [ [(1, 2, 3), 7, {"f0", "f1", "f2"}], [((1, 2, 3),), 7, {"f0", "f1", "f2"}], ], ) def test_space_to_frame_args_defaults(testspace, args, idxlen, cols): assert testspace.to_frame().empty df = testspace.to_frame(*args) assert set(df.columns) == cols assert len(df.index) == idxlen if isinstance(args[0], tuple): args = args[0] for arg in args: assert df.loc[(arg, 1), "f2"] == testspace.f2(arg, 1) def test_space_with_string_index_to_frame(space_with_string_index): """When index contains string and NaN""" s = space_with_string_index s.f0("foo") s.f1() df = pd.DataFrame( data={"f0": ["foo", np.NaN], "f1": [np.NaN, 3.0]}, index=pd.Index(["foo", np.NaN], name="strind"), ) assert s.frame.equals(df) # ------------------------------------------------------------------------- # Test Conversion from CellsView to DataFrame if _pd_ver >= (0, 20): @pytest.mark.parametrize( "args, idxlen, cols", [ [((1, 2), (3, 4), (5, 6)), 7, ["f0", "f1", "f2"]], [(((1, 2), (3, 4), (5, 6)),), 7, ["f0", "f1", "f2"]], ], ) def test_cellsview_to_frame_args(testspace, args, idxlen, cols): assert testspace.cells[cols].to_frame().empty df = testspace.cells[cols].to_frame(*args) assert set(df.columns) == set(cols) assert len(df.index) == idxlen if len(args) == 1: args = args[0] for arg in args: dfx = df.xs(arg[0], level="x") assert int(dfx.loc[dfx.index.isnull(), "f1"]) == testspace.f1( arg[0] ) assert df.loc[arg, "f2"] == testspace.f2(*arg) @pytest.mark.parametrize( "args, idxlen, cols", [ [(1, 2, 3), 7, ["f0", "f1", "f2"]], [((1, 2, 3),), 7, ["f0", "f1", "f2"]], ], ) def test_cellsview_to_frame_args_defaults(testspace, args, idxlen, cols): assert testspace.cells[cols].to_frame().empty df = testspace.cells[cols].to_frame(*args) assert set(df.columns) == set(cols) assert len(df.index) == idxlen if isinstance(args[0], tuple): args = args[0] for arg in args: assert df.loc[(arg, 1), "f2"] == testspace.f2(arg, 1)

gpl-3.0

davemccormick/pyAnimalTrack

src/pyAnimalTrack/ui/Model/TableModel.py

1

4032

from PyQt5.QtCore import QAbstractTableModel, QVariant from PyQt5.Qt import Qt from pyAnimalTrack.backend.filehandlers.input_data import InputData class TableModel(QAbstractTableModel): def __init__(self, input_data): """ Constructor :returns: void """ super(TableModel, self).__init__() self.__dataFile = input_data self.__dataSet = self.__dataFile.getData() def rowCount(self, QModelIndex_parent=None, *args, **kwargs): """ Gets the number of data rows. Used by PyQt. :param QModelIndex_parent: - :param args: - :param kwargs: - :returns: The number of data rows """ return len(self.__dataSet.index) def columnCount(self, QModelIndex_parent=None, *args, **kwargs): """ Gets the number of columns used in the dataset Used by PyQt. :param QModelIndex_parent: - :param args: - :param kwargs: - :returns: The number of columns for the dataset """ return len(self.__dataSet.columns) def headerData(self, index, Qt_Orientation, role=None): """ Gets a header for a row/column of data. Used by PyQt. :param index: The column/row index :param Qt_Orientation: The alignment of the header, Qt.Horizontal or Qt.Vertical :param role: ? :returns: A string containing the text to show as the header """ if role == Qt.DisplayRole: if Qt_Orientation == Qt.Horizontal: return self.__dataFile.getReadableColumns()[index] else: return index + 1 else: return QVariant() def data(self, QModelIndex, role=None): """ Gets an individual cell's value. Used by PyQt. :param QModelIndex: An object with a row() and column() function, used to determine the correct cell :param role: :returns: A string representation of the cell's value """ if role == Qt.DisplayRole: return str(self.__dataSet.iloc[QModelIndex.row()][QModelIndex.column()]) else: return QVariant() def get_dataset(self): """ Retrieve the entire dataset :returns: A pandas dataframe of the entire dataset """ return self.__dataSet def get_epoch_dataset(self, start=0, end=0, step=1, isMilliseconds=False, sampleRatePerSecond=10): """ Retrieve a subset of the dataset, by rows or milliseconds :param start: The first row (or millisecond) to get :param end: The last row (or millisecond) to get :param step: How far between each row to return :param isMilliseconds: To go by row, or by time :param sampleRatePerSecond: If working in milliseconds, how many samples per second were taken :returns: A pandas dataframe, sliced to the requested rows """ # Make sure we are working with integer values for the numerical parameters try: start = int(start) except: start = 0 try: end = int(end) except: end = 0 try: step = int(step) except: step = 1 try: sampleRatePerSecond = int(sampleRatePerSecond) except: sampleRatePerSecond = 10 # If working time based, we need a conversion if isMilliseconds: start = int((start / 1000.0) * sampleRatePerSecond) end = int((end / 1000.0) * sampleRatePerSecond) # Sanity checks if end > len(self.__dataSet): end = len(self.__dataSet) elif end < 0: end = 0 # Correct the end first, so the start doesn't get left incorrect if modified if start > end: start = end elif start < 0: start = 0 # If the given end is 0, we actually want everything if end == 0: end = -1 return self.__dataSet[start:end:step]

gpl-3.0

baseband-geek/singlepulse-visualizer

singlepulse_tools.py

1

16869

#!/usr/bin/python # DM Sigma Time (s) Sample Downfact import numpy as np import matplotlib import matplotlib.patches as patches import matplotlib.pyplot as plt from pulsar_tools import disp_delay import math import sys class SinglePulse: """ A class to contain all the relevant information for each single pulse detection (i.e. S/N, box-car window size, DM, etc.). This is for ease of access during plotting/other interactive stuff. """ def __init__(self, DM, sig, t, samp, dfact, inf): self.dm = DM self.sigma = sig self.time = t self.sample = samp self.downfact = dfact self.inf_file = inf def print_params(self): print "DM:",self.dm print "Sigma:",self.sigma print "Time:",self.time print "Sample:",self.sample print "Downfactor:",self.downfact print "inf_file:",self.inf_file class SPList: """ A class to contain a number of SinglePulse objects in a numpy.array and grant easy acces to paramter lists of those objects. Contains the original list of SinglePulse objects, and a list of each object's: DM, sigma, time, sample, downfactor and inf_file name. """ def __init__(self, sp_list): self.list = np.array(sp_list) self.dm_list = np.array([sp.dm for sp in sp_list]) self.sigma_list = np.array([sp.sigma for sp in sp_list]) self.time_list = np.array([sp.time for sp in sp_list]) self.sample_list = np.array([sp.sample for sp in sp_list]) self.downfact_list = np.array([sp.downfact for sp in sp_list]) self.inf_list = np.array([sp.inf_file for sp in sp_list]) def print_lists(self): print "DM list:",self.dm_list print "Sigma list:",self.sigma_list print "Time list:",self.time_list print "Sample list:",self.sample_list print "Downfactor list:",self.downfact_list print "Inf_file list:",self.inf_list def load_file(filename): if filename==None: print "No filename supplied to read..." elif filename.endswith('.singlepulse'): DM = np.genfromtxt(filename, comments="#", autostrip=True, usecols=0, skip_header=1) Sigma = np.genfromtxt(filename, comments="#", autostrip=True, usecols=1, skip_header=1) Time = np.genfromtxt(filename, comments="#", autostrip=True, usecols=2, skip_header=1) Sample = np.genfromtxt(filename, comments="#", autostrip=True, usecols=3, skip_header=1) Downfact = np.genfromtxt(filename, comments="#", autostrip=True, usecols=4, skip_header=1) inf_file = np.genfromtxt(filename, comments="#", autostrip=True, usecols=5, dtype=str, skip_header=1) sp = [SinglePulse(dm, sig, time, samp, dfact, inf) for dm, sig, time, samp, dfact, inf \ in zip(DM, Sigma, Time, Sample, Downfact, inf_file)] return SPList(sp) #return sp elif filename.endswith('.flag'): flags = np.genfromtxt(filename ,comments="#", autostrip=True) if len(flags) == 0: print "No flags/bad times provided. Not times in final output will be masked." return flags else: print "File name suplied is not recognised. Must be either .singlepulse, .bad or .flag" #def load_flags(filename): # if filename==None: # print "No filename supplied to read into flags..." # # flags = np.genfromtxt(filename ,comments="#", autostrip=True) # if len(flags)==0: # print "No flags provided. Not times in final output will be hidden." # # return flags def obs_stats(time, flags): # Not doing total time correctly, depends on last single pulse detection instead of observation time flag_time = 0 # BWM: if there is only 1 masked region, flags is a list, # if there are 2+ masked regions, flags is a list of lists. if any(isinstance(l, np.ndarray) for l in flags): for flag in flags: flag_time += (float(flag[1]) - float(flag[0])) else: flag_time = float(flags[1]) - float(flags[0]) print "%.2f seconds flagged from %.2f seconds of data (%.2f percent)" % ( flag_time, time[-1], flag_time/time[-1]*100) def flagfile(basename, max_DM=2097.2, freq_l=0.169615, freq_h=0.200335, padding=3): """ This function takes in a text file of bad 0 DM times and writes out one flagged over the correct de-dispersive smearing times, looking for overlaps along the way. There must be a text file named basename.bad with rows indicating bad times for this to work. """ from subprocess import check_call # BWM: originally planned to move this to the load_file function, # but left it incase we JUST want to call flagfile bads = np.genfromtxt(basename+'.bad', comments='#', autostrip=True) # BWM: again because how np.genfromtxt works, if there is only 1 bad line, we get a list, # if there are 2+ bad lines we get a list of lists. So have to check for np.ndarray # instances and change method accordingly. i = 0 # initialize counter for new list flags = [] if any(isinstance(b, np.ndarray) for b in bads): for bad in bads: start = bad[0] - (padding + disp_delay(freq1=freq_l, freq2=freq_h, DM=max_DM)/1000.0) if start < 0: start = 0 stop = bad[1] + padding if len(flags) > 0: if start <= flags[-1][1]: flags[-1][1] = stop else: flags.append([start, stop]) else: flags.append([start, stop]) else: start = bads[0] - (padding + disp_delay(freq1=freq_l, freq2=freq_h, DM=max_DM)/1000.0) if start < 0: start = 0 stop = bads[1] + padding if len(flags) > 0: if start <= flags[-1][1]: flags[-1][1] = stop else: flags.append([start, stop]) else: flags.append([start, stop]) # save new file as basename.flag np.savetxt(basename+'.flag', flags, fmt='%d') # call flag.sh script to creat masked singlepulse file check_call(['flag.sh', basename]) #Popen(['flag.sh', basename]).communicate()[0] def singlepulse_plot(basename=None, DMvTime=1, StatPlots=False, raw = False, threshold=5.0, movie=False): """ Plots up the flagged data, should switch to using genfromtxt when I have the time. BWM: switched to using load_file to load singlepulse and flags. Uses genfromtxt. """ print "Make sure you have run sort_singlepulse.py to gather the single pulse events into the one file {0}.singelpulse".format(basename) if raw: data = load_file(basename + '.singlepulse') #flag_times = False else: #flag_times = load_file(basename+'.bad') try: flagfile(basename) # BWM: should we be providing appropriate freqs and DM for this? except: print "No {}.bad file given. Creating one with entry [0 0]".format(basename) f=open('{}.bad'.format(basename),'w') f.write('0 0') f.close() print "Saved {}.bad".format(basename) print "Retrying..." flagfile(basename) data = load_file(basename + '_flagged.singlepulse') flags = load_file(basename + '.flag') #for a in vars(data).items(): # print a[0] #print [v[0] for v in vars(data).items()] #sys.exit(0) data = SPList(data.list[np.where(data.sigma_list >= threshold)]) #DM = [float(row.split()[0]) for row in data if float(row.split()[1]) >= threshold] #Sigma = [float(row.split()[1]) for row in data if float(row.split()[1]) >= threshold] #Time = [float(row.split()[2]) for row in data if float(row.split()[1]) >= threshold] #Sample = [int(row.split()[3]) for row in data if float(row.split()[1]) >= threshold] #Downfact = [int(row.split()[4]) for row in data if float(row.split()[1]) >= threshold] #DM = data.dm_list #Sigma = data.sigma_list #Time = data.time_list #Downfact = data.downfact_list Downfact_float = data.downfact_list.astype(float) fig = plt.figure() cm = plt.cm.get_cmap('gist_rainbow') if StatPlots: ax0 = fig.add_subplot(231) plt.hist(data.sigma_list, histtype='step', bins=int(0.2 * len(set(data.sigma_list)))) ax0.set_xlabel('Signal-to-Noise', fontsize=18) ax0.set_ylabel('Number of Pulses', fontsize=18) ax0.set_xlim([data.sigma_list.min(), data.sigma_list.max()]) ax1 = fig.add_subplot(232) plt.hist(data.dm_list, histtype='step', bins=int(0.5 * len(set(data.dm_list)))) ax1.set_xlabel('DM ($\mathrm{pc\, cm^{-3}}$)', fontsize=18) ax1.set_ylabel('Number of Pulses', fontsize=18) ax1.set_xlim([data.dm_list.min(), data.dm_list.max()]) ax2 = fig.add_subplot(233, sharex=ax1) # BWM: now shares x-axis with ax1, so changing DM on one will change range on the other plt.scatter(data.dm_list, data.sigma_list, c=Downfact_float, cmap=cm, alpha=0.9) ax2.set_ylabel('Signal-to-Noise', fontsize=18) ax2.set_xlabel('DM ($\mathrm{p\, cm^{-3}}$)', fontsize=18) ax2.set_xlim([data.dm_list.min(), data.dm_list.max()]) ax2.set_ylim([data.sigma_list.min(), data.sigma_list.max()]) ax3 = fig.add_subplot(212) else: ax3 = fig.add_subplot(111) # TODO: need to figure out how (if at all) we can make the axis sharing work # for x-axis to y-axis share # ax3.set_title("Single Pulse Sigma") ax3.set_xlabel('Time (s)', fontsize=18) ax3.set_ylabel('DM ($\mathrm{pc\, cm^{-3}}$)', fontsize=18) ax3.set_ylim([data.dm_list.min(), data.dm_list.max()]) ax3.set_xlim([data.time_list.min(), data.time_list.max()]) #ax3.set_ylabel('Time (s)', fontsize=18) #ax3.set_xlabel('DM ($\mathrm{pc\, cm^{-3}}$)', fontsize=18) #ax3.set_ylim([data.time_list.min(), data.time_list.max()]) #ax3.set_xlim([data.dm_list.min(), data.dm_list.max()]) #cm = plt.cm.get_cmap('gist_rainbow') # grab axis3 size to allocate marker sizes bbox_pix = ax3.get_window_extent().transformed(fig.dpi_scale_trans.inverted()) width, height = bbox_pix.width, bbox_pix.height area = width * height # axes area in inches^2 (apparently) #TODO: need to try and use something like percentiles to make sure that just one # big pulse doesn't swamp the sizes or colorbars. print data.sigma_list.min() print data.sigma_list.max() print np.percentile(data.sigma_list, 99.5) Size = (3. * area / 2.) * (data.sigma_list**2 / np.percentile(data.sigma_list, 99.5)) Size[np.where(Size > np.percentile(data.sigma_list, 99.5))] = (3. * area / 2.) * np.percentile(data.sigma_list, 99.5) #print len(Size[np.where(data.sigma_list>np.percentile(data.sigma_list, 99.5))]) print Size.min() print Size.max() obs_stats(data.time_list, flags) # sc=ax3.scatter(Time,DM, s=Size, c=Sigma, vmin=min(Sigma), vmax=max(Sigma),\ # cmap=cm, picker=1) sc = ax3.scatter(data.time_list, data.dm_list, s=Size, c=Downfact_float, cmap=cm, \ vmin=Downfact_float.min(), vmax=Downfact_float.max(), facecolors='none') # sc = ax3.scatter(data.dm_list, data.time_list, s=Size, c=Downfact_float, cmap=cm, \ # vmin=Downfact_float.min(), vmax=Downfact_float.max(), picker=1, facecolor='none') # leg = ax1.legend() #plt.colorbar(sc, label="Sigma", pad=0.01) #plt.colorbar(sc, label="Downfact", pad=0.01) # BWM: can't seem to get the bottom plot to extend the entire width when the color bar is active. fig.subplots_adjust(hspace=0.2, wspace=0.5) if not raw: if any(isinstance(l, np.ndarray) for l in flags): for flag in flags: flag_area = patches.Rectangle((float(flag[0]), data.dm_list.min()), \ (float(flag[1]) - float(flag[0])), \ (data.dm_list.max() - data.dm_list.min()), \ edgecolor='0', facecolor='0.66') ax3.add_patch(flag_area) else: flag_area = patches.Rectangle((float(flags[0]), data.dm_list.min()), \ (float(flags[1]) - float(flags[0])), \ (data.dm_list.max() - data.dm_list.min()), \ edgecolor='0', facecolor='0.66') ax3.add_patch(flag_area) def onpick(event): points = event.artist ind = event.ind mouseevent = event.mouseevent print '\n' print "Information for data points around click event %.4f, %.4f:" % (mouseevent.xdata, mouseevent.ydata) for i in ind: # These are fudge factors to turn samples into ms. if ( data.dm_list[i] < 150): boxcar = data.downfact_list[i] elif ( 150<= data.dm_list[i] < 823.2 ): boxcar = data.downfact_list[i] * 2 elif ( 823.2 <= data.dm_list[i] < 1486.2): boxcar = data.downfact_list[i] * 2 elif ( 1426.2 <= data.dm_list[i] < 2100): boxcar = data.downfact_list[i] * 2 print "%.2f seconds, %.2f Sigma event detected at a DM of %.2f with a boxcar of: %d ms" % (data.time_list[i], data.sigma_list[i], data.dm_list[i], boxcar) fig.canvas.mpl_connect('pick_event', onpick) ''' ax2 = fig.add_subplot(122) ax2.set_title("Single Pulse Boxcar") ax2.set_xlabel('Time (s)') ax2.set_ylabel('DM (pc cm^-3)') cm = plt.cm.get_cmap('RdYlBu') sc2=ax2.scatter(Time,DM, c=Downfact_float, vmin=min(Downfact_float), vmax=max(Downfact_float), cmap=cm) # leg = ax1.legend() plt.colorbar(sc2) if not raw: for flag in flags: flag_area = matplotlib.patches.Rectangle((float(flag.split()[0]), min(DM)), float(flag.split()[1])-float(flag.split()[0]), max(DM)-min(DM), edgecolor='0', facecolor='0.66') ax2.add_patch(flag_area) ''' fig.suptitle('Single Pulse Search results for ' + basename) #plt.tight_layout(w_pad=0.1, h_pad=0.1) #plt.savefig('test.png') #plt.close(fig) plt.show() #obs_stats(Time, flags) #def slice(infile, dm=None, timerange=None, sigma=None, downfact=None): # # Not properly implemented yet # # data = read_singlepulse(infile) # # slices = [None]*5 # # slice_map = {'dm':0, 'sigma':1, 'timerange':2, 'sample':3, 'downfact':4} # # # # # DM = [row.split()[0] for row in data] # Sigma = [row.split()[1] for row in data] # Time = [row.split()[2] for row in data] # Sample = [row.split()[3] for row in data] # Downfact = [row.split()[4] for row in data] # # if dm: # if type(dm) == type(0) or type(0.0): # data = [row for row in data if dm <= row.split()[0]] # elif type(dm) == type([]): # data = [row for row in data if dm[0] <= row.split()[0] <= dm[1]] # if sigma: # if type(sigma) == type(0) or type(0.0): # data = [row for row in data if sigma <= row.split()[1] ] # elif type(sigma) == type([]): # data = [row for row in data if sigma[0] <= row.split()[1] <= sigma[1]] if __name__ == '__main__': modes = ['interactive','movie'] from optparse import OptionParser, OptionGroup parser = OptionParser(description="A python tool to plot, flag, and do otherwise with singlepulse search data from PRESTO") parser.add_option("-m", "--mode", type="choice", choices=['interactive','movie'], help="Mode you want to run. {0}".format(modes)) parser.add_option("--dm_range", action="store", type="string", nargs=2, default=(0,2000), help="(Not yet implemented) The lowest and highest DM to plot. [default=%default]") parser.add_option("--obsid", action="store", type="string", help="Observation ID or other basename for files. [No default]") parser.add_option("--threshold", action="store", type="float", default=5.0, help="S/N threshold. [default=%default]") (opts, args) = parser.parse_args() if opts.mode == 'movie': singlepulse_plot(basename=opts.obsid, DMvTime=1, StatPlots=True, raw = False, threshold=opts.threshold, movie=True) elif opts.mode == 'interactive': singlepulse_plot(basename=opts.obsid, DMvTime=1, StatPlots=True, raw=False, threshold=opts.threshold, movie=False) else: print "Somehow your non-standard mode snuck through. Try again with one of {0}".format(modes) quit()

mit

ctogle/modular

src/dstoolm4/src/dstoolm4/writer.py

2

3313

import modular4.mpi as mmpi import numpy import PyDSTool as dst import matplotlib.pyplot as plt import pdb def convert_reactions(ss,rs,vs,fs,es): vns,vvs = zip(*vs) if vs else ([],[]) fns,fvs = zip(*fs) if fs else ([],[]) ens,evs = zip(*es) if es else ([],[]) def rxr(r): if r in vns:return r elif r in fns: r = fvs[fns.index(r)] return '('+str(r)+')' else: print('reaction rate is neither a function nor a variable!') raise ValueError def rxustr(rr,ru): rxu = ' * '.join((u[1]+'**'+str(u[0]) if u[0] > 1 else u[1] for u in ru)) rxs = rxr(rr) if rxu:rxs = rxu+' * '+rxs return rxs rhs,afs = {},{} for sn,sv in ss: if sn in ens: base = evs[ens.index(sn)] for fn in fns: base = base.replace(fn,rxr(fn)) else:base = '' rhs[sn] = base for rr,ru,rp,rn in rs: term = rxustr(rr,ru) uvs,uns = zip(*ru) if ru else ([],[]) pvs,pns = zip(*rp) if rp else ([],[]) for sn,sv in ss: m = 0 if sn in uns:m -= uvs[uns.index(sn)] if sn in pns:m += pvs[pns.index(sn)] if not m == 0: smv = str(abs(m))+' * ' if abs(m) > 1 else '' sms = ' - ' if m < 0 else ' + ' rhs[sn] += sms+smv+term for sn,sv in ss: if rhs[sn].startswith(' + '): rhs[sn] = rhs[sn].replace(' + ','',1) return rhs,afs def get_simulator(e): esp = e.simparameters etime = e.end ctime = e.capture axes = e.pspace.axes rhs,afs = convert_reactions( esp['species'],esp['reactions'], esp['variables'],esp['functions'], esp['equations']) dtargs = e.targets[:] dtargs[0] = 't' dshape = (len(dtargs),int(etime/ctime)+1) if mmpi.root(): print('\n'+'-'*50) print('converted rhs:') for r in rhs:print('\t'+r+': '+rhs[r]) print('-'*50+'\n') algparams = { 'atol': 1e-2, #'stiff': False, #'max_step': 0.0, ## CVODE INTERNAL USE ONLY #'min_step': 0.0, ## CVODE INTERNAL USE ONLY #'init_step': 0.01, ## DICTATES DT FOR FIXED OUTPUT MESH 'init_step':ctime*0.8, ## DICTATES DT FOR FIXED OUTPUT MESH } def simf(*args): DSargs = dst.args(name = 'dstoolm_test') dspars,dsics,dsvarspecs,dsfnspecs = {},{},{},{} for vn,vv in esp['variables']: if vn in axes:vv = args[axes.index(vn)+1] dspars[vn] = vv for sn,si in esp['species']: if sn in axes:si = args[axes.index(sn)+1] dsics[sn] = si dsvarspecs[sn] = rhs[sn] for fn,ft in afs:dsfnspecs[fn] = ft DSargs.algparams = algparams DSargs.pars = dspars DSargs.fnspecs = dsfnspecs DSargs.varspecs = dsvarspecs DSargs.ics = dsics DSargs.tdomain = [0,etime] ode = dst.Generator.Vode_ODEsystem(DSargs) traj = ode.compute('trajectory') pts = traj.sample(dt = ctime) data = numpy.zeros(dshape,dtype = numpy.float) for dtx in range(len(dtargs)): data[dtx] = pts[dtargs[dtx]] return data return simf

mit

kernc/scikit-learn

sklearn/cross_decomposition/pls_.py

34

30531

""" The :mod:`sklearn.pls` module implements Partial Least Squares (PLS). """ # Author: Edouard Duchesnay <[email protected]> # License: BSD 3 clause from distutils.version import LooseVersion from sklearn.utils.extmath import svd_flip from ..base import BaseEstimator, RegressorMixin, TransformerMixin from ..utils import check_array, check_consistent_length from ..externals import six import warnings from abc import ABCMeta, abstractmethod import numpy as np from scipy import linalg from ..utils import arpack from ..utils.validation import check_is_fitted, FLOAT_DTYPES __all__ = ['PLSCanonical', 'PLSRegression', 'PLSSVD'] import scipy pinv2_args = {} if LooseVersion(scipy.__version__) >= LooseVersion('0.12'): # check_finite=False is an optimization available only in scipy >=0.12 pinv2_args = {'check_finite': False} def _nipals_twoblocks_inner_loop(X, Y, mode="A", max_iter=500, tol=1e-06, norm_y_weights=False): """Inner loop of the iterative NIPALS algorithm. Provides an alternative to the svd(X'Y); returns the first left and right singular vectors of X'Y. See PLS for the meaning of the parameters. It is similar to the Power method for determining the eigenvectors and eigenvalues of a X'Y. """ y_score = Y[:, [0]] x_weights_old = 0 ite = 1 X_pinv = Y_pinv = None eps = np.finfo(X.dtype).eps # Inner loop of the Wold algo. while True: # 1.1 Update u: the X weights if mode == "B": if X_pinv is None: # We use slower pinv2 (same as np.linalg.pinv) for stability # reasons X_pinv = linalg.pinv2(X, **pinv2_args) x_weights = np.dot(X_pinv, y_score) else: # mode A # Mode A regress each X column on y_score x_weights = np.dot(X.T, y_score) / np.dot(y_score.T, y_score) # 1.2 Normalize u x_weights /= np.sqrt(np.dot(x_weights.T, x_weights)) + eps # 1.3 Update x_score: the X latent scores x_score = np.dot(X, x_weights) # 2.1 Update y_weights if mode == "B": if Y_pinv is None: Y_pinv = linalg.pinv2(Y, **pinv2_args) # compute once pinv(Y) y_weights = np.dot(Y_pinv, x_score) else: # Mode A regress each Y column on x_score y_weights = np.dot(Y.T, x_score) / np.dot(x_score.T, x_score) # 2.2 Normalize y_weights if norm_y_weights: y_weights /= np.sqrt(np.dot(y_weights.T, y_weights)) + eps # 2.3 Update y_score: the Y latent scores y_score = np.dot(Y, y_weights) / (np.dot(y_weights.T, y_weights) + eps) # y_score = np.dot(Y, y_weights) / np.dot(y_score.T, y_score) ## BUG x_weights_diff = x_weights - x_weights_old if np.dot(x_weights_diff.T, x_weights_diff) < tol or Y.shape[1] == 1: break if ite == max_iter: warnings.warn('Maximum number of iterations reached') break x_weights_old = x_weights ite += 1 return x_weights, y_weights, ite def _svd_cross_product(X, Y): C = np.dot(X.T, Y) U, s, Vh = linalg.svd(C, full_matrices=False) u = U[:, [0]] v = Vh.T[:, [0]] return u, v def _center_scale_xy(X, Y, scale=True): """ Center X, Y and scale if the scale parameter==True Returns ------- X, Y, x_mean, y_mean, x_std, y_std """ # center x_mean = X.mean(axis=0) X -= x_mean y_mean = Y.mean(axis=0) Y -= y_mean # scale if scale: x_std = X.std(axis=0, ddof=1) x_std[x_std == 0.0] = 1.0 X /= x_std y_std = Y.std(axis=0, ddof=1) y_std[y_std == 0.0] = 1.0 Y /= y_std else: x_std = np.ones(X.shape[1]) y_std = np.ones(Y.shape[1]) return X, Y, x_mean, y_mean, x_std, y_std class _PLS(six.with_metaclass(ABCMeta), BaseEstimator, TransformerMixin, RegressorMixin): """Partial Least Squares (PLS) This class implements the generic PLS algorithm, constructors' parameters allow to obtain a specific implementation such as: - PLS2 regression, i.e., PLS 2 blocks, mode A, with asymmetric deflation and unnormalized y weights such as defined by [Tenenhaus 1998] p. 132. With univariate response it implements PLS1. - PLS canonical, i.e., PLS 2 blocks, mode A, with symmetric deflation and normalized y weights such as defined by [Tenenhaus 1998] (p. 132) and [Wegelin et al. 2000]. This parametrization implements the original Wold algorithm. We use the terminology defined by [Wegelin et al. 2000]. This implementation uses the PLS Wold 2 blocks algorithm based on two nested loops: (i) The outer loop iterate over components. (ii) The inner loop estimates the weights vectors. This can be done with two algo. (a) the inner loop of the original NIPALS algo. or (b) a SVD on residuals cross-covariance matrices. n_components : int, number of components to keep. (default 2). scale : boolean, scale data? (default True) deflation_mode : str, "canonical" or "regression". See notes. mode : "A" classical PLS and "B" CCA. See notes. norm_y_weights: boolean, normalize Y weights to one? (default False) algorithm : string, "nipals" or "svd" The algorithm used to estimate the weights. It will be called n_components times, i.e. once for each iteration of the outer loop. max_iter : an integer, the maximum number of iterations (default 500) of the NIPALS inner loop (used only if algorithm="nipals") tol : non-negative real, default 1e-06 The tolerance used in the iterative algorithm. copy : boolean, default True Whether the deflation should be done on a copy. Let the default value to True unless you don't care about side effects. Attributes ---------- x_weights_ : array, [p, n_components] X block weights vectors. y_weights_ : array, [q, n_components] Y block weights vectors. x_loadings_ : array, [p, n_components] X block loadings vectors. y_loadings_ : array, [q, n_components] Y block loadings vectors. x_scores_ : array, [n_samples, n_components] X scores. y_scores_ : array, [n_samples, n_components] Y scores. x_rotations_ : array, [p, n_components] X block to latents rotations. y_rotations_ : array, [q, n_components] Y block to latents rotations. coef_: array, [p, q] The coefficients of the linear model: ``Y = X coef_ + Err`` n_iter_ : array-like Number of iterations of the NIPALS inner loop for each component. Not useful if the algorithm given is "svd". References ---------- Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000. In French but still a reference: Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic. See also -------- PLSCanonical PLSRegression CCA PLS_SVD """ @abstractmethod def __init__(self, n_components=2, scale=True, deflation_mode="regression", mode="A", algorithm="nipals", norm_y_weights=False, max_iter=500, tol=1e-06, copy=True): self.n_components = n_components self.deflation_mode = deflation_mode self.mode = mode self.norm_y_weights = norm_y_weights self.scale = scale self.algorithm = algorithm self.max_iter = max_iter self.tol = tol self.copy = copy def fit(self, X, Y): """Fit model to data. Parameters ---------- X : array-like, shape = [n_samples, n_features] Training vectors, where n_samples in the number of samples and n_features is the number of predictors. Y : array-like of response, shape = [n_samples, n_targets] Target vectors, where n_samples in the number of samples and n_targets is the number of response variables. """ # copy since this will contains the residuals (deflated) matrices check_consistent_length(X, Y) X = check_array(X, dtype=np.float64, copy=self.copy) Y = check_array(Y, dtype=np.float64, copy=self.copy, ensure_2d=False) if Y.ndim == 1: Y = Y.reshape(-1, 1) n = X.shape[0] p = X.shape[1] q = Y.shape[1] if self.n_components < 1 or self.n_components > p: raise ValueError('Invalid number of components: %d' % self.n_components) if self.algorithm not in ("svd", "nipals"): raise ValueError("Got algorithm %s when only 'svd' " "and 'nipals' are known" % self.algorithm) if self.algorithm == "svd" and self.mode == "B": raise ValueError('Incompatible configuration: mode B is not ' 'implemented with svd algorithm') if self.deflation_mode not in ["canonical", "regression"]: raise ValueError('The deflation mode is unknown') # Scale (in place) X, Y, self.x_mean_, self.y_mean_, self.x_std_, self.y_std_ = ( _center_scale_xy(X, Y, self.scale)) # Residuals (deflated) matrices Xk = X Yk = Y # Results matrices self.x_scores_ = np.zeros((n, self.n_components)) self.y_scores_ = np.zeros((n, self.n_components)) self.x_weights_ = np.zeros((p, self.n_components)) self.y_weights_ = np.zeros((q, self.n_components)) self.x_loadings_ = np.zeros((p, self.n_components)) self.y_loadings_ = np.zeros((q, self.n_components)) self.n_iter_ = [] # NIPALS algo: outer loop, over components for k in range(self.n_components): if np.all(np.dot(Yk.T, Yk) < np.finfo(np.double).eps): # Yk constant warnings.warn('Y residual constant at iteration %s' % k) break # 1) weights estimation (inner loop) # ----------------------------------- if self.algorithm == "nipals": x_weights, y_weights, n_iter_ = \ _nipals_twoblocks_inner_loop( X=Xk, Y=Yk, mode=self.mode, max_iter=self.max_iter, tol=self.tol, norm_y_weights=self.norm_y_weights) self.n_iter_.append(n_iter_) elif self.algorithm == "svd": x_weights, y_weights = _svd_cross_product(X=Xk, Y=Yk) # Forces sign stability of x_weights and y_weights # Sign undeterminacy issue from svd if algorithm == "svd" # and from platform dependent computation if algorithm == 'nipals' x_weights, y_weights = svd_flip(x_weights, y_weights.T) y_weights = y_weights.T # compute scores x_scores = np.dot(Xk, x_weights) if self.norm_y_weights: y_ss = 1 else: y_ss = np.dot(y_weights.T, y_weights) y_scores = np.dot(Yk, y_weights) / y_ss # test for null variance if np.dot(x_scores.T, x_scores) < np.finfo(np.double).eps: warnings.warn('X scores are null at iteration %s' % k) break # 2) Deflation (in place) # ---------------------- # Possible memory footprint reduction may done here: in order to # avoid the allocation of a data chunk for the rank-one # approximations matrix which is then subtracted to Xk, we suggest # to perform a column-wise deflation. # # - regress Xk's on x_score x_loadings = np.dot(Xk.T, x_scores) / np.dot(x_scores.T, x_scores) # - subtract rank-one approximations to obtain remainder matrix Xk -= np.dot(x_scores, x_loadings.T) if self.deflation_mode == "canonical": # - regress Yk's on y_score, then subtract rank-one approx. y_loadings = (np.dot(Yk.T, y_scores) / np.dot(y_scores.T, y_scores)) Yk -= np.dot(y_scores, y_loadings.T) if self.deflation_mode == "regression": # - regress Yk's on x_score, then subtract rank-one approx. y_loadings = (np.dot(Yk.T, x_scores) / np.dot(x_scores.T, x_scores)) Yk -= np.dot(x_scores, y_loadings.T) # 3) Store weights, scores and loadings # Notation: self.x_scores_[:, k] = x_scores.ravel() # T self.y_scores_[:, k] = y_scores.ravel() # U self.x_weights_[:, k] = x_weights.ravel() # W self.y_weights_[:, k] = y_weights.ravel() # C self.x_loadings_[:, k] = x_loadings.ravel() # P self.y_loadings_[:, k] = y_loadings.ravel() # Q # Such that: X = TP' + Err and Y = UQ' + Err # 4) rotations from input space to transformed space (scores) # T = X W(P'W)^-1 = XW* (W* : p x k matrix) # U = Y C(Q'C)^-1 = YC* (W* : q x k matrix) self.x_rotations_ = np.dot( self.x_weights_, linalg.pinv2(np.dot(self.x_loadings_.T, self.x_weights_), **pinv2_args)) if Y.shape[1] > 1: self.y_rotations_ = np.dot( self.y_weights_, linalg.pinv2(np.dot(self.y_loadings_.T, self.y_weights_), **pinv2_args)) else: self.y_rotations_ = np.ones(1) if True or self.deflation_mode == "regression": # FIXME what's with the if? # Estimate regression coefficient # Regress Y on T # Y = TQ' + Err, # Then express in function of X # Y = X W(P'W)^-1Q' + Err = XB + Err # => B = W*Q' (p x q) self.coef_ = np.dot(self.x_rotations_, self.y_loadings_.T) self.coef_ = (1. / self.x_std_.reshape((p, 1)) * self.coef_ * self.y_std_) return self def transform(self, X, Y=None, copy=True): """Apply the dimension reduction learned on the train data. Parameters ---------- X : array-like of predictors, shape = [n_samples, p] Training vectors, where n_samples in the number of samples and p is the number of predictors. Y : array-like of response, shape = [n_samples, q], optional Training vectors, where n_samples in the number of samples and q is the number of response variables. copy : boolean, default True Whether to copy X and Y, or perform in-place normalization. Returns ------- x_scores if Y is not given, (x_scores, y_scores) otherwise. """ check_is_fitted(self, 'x_mean_') X = check_array(X, copy=copy, dtype=FLOAT_DTYPES) # Normalize X -= self.x_mean_ X /= self.x_std_ # Apply rotation x_scores = np.dot(X, self.x_rotations_) if Y is not None: Y = check_array(Y, ensure_2d=False, copy=copy, dtype=FLOAT_DTYPES) if Y.ndim == 1: Y = Y.reshape(-1, 1) Y -= self.y_mean_ Y /= self.y_std_ y_scores = np.dot(Y, self.y_rotations_) return x_scores, y_scores return x_scores def predict(self, X, copy=True): """Apply the dimension reduction learned on the train data. Parameters ---------- X : array-like of predictors, shape = [n_samples, p] Training vectors, where n_samples in the number of samples and p is the number of predictors. copy : boolean, default True Whether to copy X and Y, or perform in-place normalization. Notes ----- This call requires the estimation of a p x q matrix, which may be an issue in high dimensional space. """ check_is_fitted(self, 'x_mean_') X = check_array(X, copy=copy, dtype=FLOAT_DTYPES) # Normalize X -= self.x_mean_ X /= self.x_std_ Ypred = np.dot(X, self.coef_) return Ypred + self.y_mean_ def fit_transform(self, X, y=None, **fit_params): """Learn and apply the dimension reduction on the train data. Parameters ---------- X : array-like of predictors, shape = [n_samples, p] Training vectors, where n_samples in the number of samples and p is the number of predictors. Y : array-like of response, shape = [n_samples, q], optional Training vectors, where n_samples in the number of samples and q is the number of response variables. copy : boolean, default True Whether to copy X and Y, or perform in-place normalization. Returns ------- x_scores if Y is not given, (x_scores, y_scores) otherwise. """ return self.fit(X, y, **fit_params).transform(X, y) class PLSRegression(_PLS): """PLS regression PLSRegression implements the PLS 2 blocks regression known as PLS2 or PLS1 in case of one dimensional response. This class inherits from _PLS with mode="A", deflation_mode="regression", norm_y_weights=False and algorithm="nipals". Read more in the :ref:`User Guide <cross_decomposition>`. Parameters ---------- n_components : int, (default 2) Number of components to keep. scale : boolean, (default True) whether to scale the data max_iter : an integer, (default 500) the maximum number of iterations of the NIPALS inner loop (used only if algorithm="nipals") tol : non-negative real Tolerance used in the iterative algorithm default 1e-06. copy : boolean, default True Whether the deflation should be done on a copy. Let the default value to True unless you don't care about side effect Attributes ---------- x_weights_ : array, [p, n_components] X block weights vectors. y_weights_ : array, [q, n_components] Y block weights vectors. x_loadings_ : array, [p, n_components] X block loadings vectors. y_loadings_ : array, [q, n_components] Y block loadings vectors. x_scores_ : array, [n_samples, n_components] X scores. y_scores_ : array, [n_samples, n_components] Y scores. x_rotations_ : array, [p, n_components] X block to latents rotations. y_rotations_ : array, [q, n_components] Y block to latents rotations. coef_: array, [p, q] The coefficients of the linear model: ``Y = X coef_ + Err`` n_iter_ : array-like Number of iterations of the NIPALS inner loop for each component. Notes ----- Matrices:: T: x_scores_ U: y_scores_ W: x_weights_ C: y_weights_ P: x_loadings_ Q: y_loadings__ Are computed such that:: X = T P.T + Err and Y = U Q.T + Err T[:, k] = Xk W[:, k] for k in range(n_components) U[:, k] = Yk C[:, k] for k in range(n_components) x_rotations_ = W (P.T W)^(-1) y_rotations_ = C (Q.T C)^(-1) where Xk and Yk are residual matrices at iteration k. `Slides explaining PLS <http://www.eigenvector.com/Docs/Wise_pls_properties.pdf>` For each component k, find weights u, v that optimizes: ``max corr(Xk u, Yk v) * std(Xk u) std(Yk u)``, such that ``|u| = 1`` Note that it maximizes both the correlations between the scores and the intra-block variances. The residual matrix of X (Xk+1) block is obtained by the deflation on the current X score: x_score. The residual matrix of Y (Yk+1) block is obtained by deflation on the current X score. This performs the PLS regression known as PLS2. This mode is prediction oriented. This implementation provides the same results that 3 PLS packages provided in the R language (R-project): - "mixOmics" with function pls(X, Y, mode = "regression") - "plspm " with function plsreg2(X, Y) - "pls" with function oscorespls.fit(X, Y) Examples -------- >>> from sklearn.cross_decomposition import PLSRegression >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> pls2 = PLSRegression(n_components=2) >>> pls2.fit(X, Y) ... # doctest: +NORMALIZE_WHITESPACE PLSRegression(copy=True, max_iter=500, n_components=2, scale=True, tol=1e-06) >>> Y_pred = pls2.predict(X) References ---------- Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000. In french but still a reference: Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic. """ def __init__(self, n_components=2, scale=True, max_iter=500, tol=1e-06, copy=True): super(PLSRegression, self).__init__( n_components=n_components, scale=scale, deflation_mode="regression", mode="A", norm_y_weights=False, max_iter=max_iter, tol=tol, copy=copy) class PLSCanonical(_PLS): """ PLSCanonical implements the 2 blocks canonical PLS of the original Wold algorithm [Tenenhaus 1998] p.204, referred as PLS-C2A in [Wegelin 2000]. This class inherits from PLS with mode="A" and deflation_mode="canonical", norm_y_weights=True and algorithm="nipals", but svd should provide similar results up to numerical errors. Read more in the :ref:`User Guide <cross_decomposition>`. Parameters ---------- scale : boolean, scale data? (default True) algorithm : string, "nipals" or "svd" The algorithm used to estimate the weights. It will be called n_components times, i.e. once for each iteration of the outer loop. max_iter : an integer, (default 500) the maximum number of iterations of the NIPALS inner loop (used only if algorithm="nipals") tol : non-negative real, default 1e-06 the tolerance used in the iterative algorithm copy : boolean, default True Whether the deflation should be done on a copy. Let the default value to True unless you don't care about side effect n_components : int, number of components to keep. (default 2). Attributes ---------- x_weights_ : array, shape = [p, n_components] X block weights vectors. y_weights_ : array, shape = [q, n_components] Y block weights vectors. x_loadings_ : array, shape = [p, n_components] X block loadings vectors. y_loadings_ : array, shape = [q, n_components] Y block loadings vectors. x_scores_ : array, shape = [n_samples, n_components] X scores. y_scores_ : array, shape = [n_samples, n_components] Y scores. x_rotations_ : array, shape = [p, n_components] X block to latents rotations. y_rotations_ : array, shape = [q, n_components] Y block to latents rotations. n_iter_ : array-like Number of iterations of the NIPALS inner loop for each component. Not useful if the algorithm provided is "svd". Notes ----- Matrices:: T: x_scores_ U: y_scores_ W: x_weights_ C: y_weights_ P: x_loadings_ Q: y_loadings__ Are computed such that:: X = T P.T + Err and Y = U Q.T + Err T[:, k] = Xk W[:, k] for k in range(n_components) U[:, k] = Yk C[:, k] for k in range(n_components) x_rotations_ = W (P.T W)^(-1) y_rotations_ = C (Q.T C)^(-1) where Xk and Yk are residual matrices at iteration k. `Slides explaining PLS <http://www.eigenvector.com/Docs/Wise_pls_properties.pdf>` For each component k, find weights u, v that optimize:: max corr(Xk u, Yk v) * std(Xk u) std(Yk u), such that ``|u| = |v| = 1`` Note that it maximizes both the correlations between the scores and the intra-block variances. The residual matrix of X (Xk+1) block is obtained by the deflation on the current X score: x_score. The residual matrix of Y (Yk+1) block is obtained by deflation on the current Y score. This performs a canonical symmetric version of the PLS regression. But slightly different than the CCA. This is mostly used for modeling. This implementation provides the same results that the "plspm" package provided in the R language (R-project), using the function plsca(X, Y). Results are equal or collinear with the function ``pls(..., mode = "canonical")`` of the "mixOmics" package. The difference relies in the fact that mixOmics implementation does not exactly implement the Wold algorithm since it does not normalize y_weights to one. Examples -------- >>> from sklearn.cross_decomposition import PLSCanonical >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> plsca = PLSCanonical(n_components=2) >>> plsca.fit(X, Y) ... # doctest: +NORMALIZE_WHITESPACE PLSCanonical(algorithm='nipals', copy=True, max_iter=500, n_components=2, scale=True, tol=1e-06) >>> X_c, Y_c = plsca.transform(X, Y) References ---------- Jacob A. Wegelin. A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case. Technical Report 371, Department of Statistics, University of Washington, Seattle, 2000. Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic. See also -------- CCA PLSSVD """ def __init__(self, n_components=2, scale=True, algorithm="nipals", max_iter=500, tol=1e-06, copy=True): super(PLSCanonical, self).__init__( n_components=n_components, scale=scale, deflation_mode="canonical", mode="A", norm_y_weights=True, algorithm=algorithm, max_iter=max_iter, tol=tol, copy=copy) class PLSSVD(BaseEstimator, TransformerMixin): """Partial Least Square SVD Simply perform a svd on the crosscovariance matrix: X'Y There are no iterative deflation here. Read more in the :ref:`User Guide <cross_decomposition>`. Parameters ---------- n_components : int, default 2 Number of components to keep. scale : boolean, default True Whether to scale X and Y. copy : boolean, default True Whether to copy X and Y, or perform in-place computations. Attributes ---------- x_weights_ : array, [p, n_components] X block weights vectors. y_weights_ : array, [q, n_components] Y block weights vectors. x_scores_ : array, [n_samples, n_components] X scores. y_scores_ : array, [n_samples, n_components] Y scores. See also -------- PLSCanonical CCA """ def __init__(self, n_components=2, scale=True, copy=True): self.n_components = n_components self.scale = scale self.copy = copy def fit(self, X, Y): # copy since this will contains the centered data check_consistent_length(X, Y) X = check_array(X, dtype=np.float64, copy=self.copy) Y = check_array(Y, dtype=np.float64, copy=self.copy, ensure_2d=False) if Y.ndim == 1: Y = Y.reshape(-1, 1) if self.n_components > max(Y.shape[1], X.shape[1]): raise ValueError("Invalid number of components n_components=%d" " with X of shape %s and Y of shape %s." % (self.n_components, str(X.shape), str(Y.shape))) # Scale (in place) X, Y, self.x_mean_, self.y_mean_, self.x_std_, self.y_std_ = ( _center_scale_xy(X, Y, self.scale)) # svd(X'Y) C = np.dot(X.T, Y) # The arpack svds solver only works if the number of extracted # components is smaller than rank(X) - 1. Hence, if we want to extract # all the components (C.shape[1]), we have to use another one. Else, # let's use arpacks to compute only the interesting components. if self.n_components >= np.min(C.shape): U, s, V = linalg.svd(C, full_matrices=False) else: U, s, V = arpack.svds(C, k=self.n_components) # Deterministic output U, V = svd_flip(U, V) V = V.T self.x_scores_ = np.dot(X, U) self.y_scores_ = np.dot(Y, V) self.x_weights_ = U self.y_weights_ = V return self def transform(self, X, Y=None): """Apply the dimension reduction learned on the train data.""" check_is_fitted(self, 'x_mean_') X = check_array(X, dtype=np.float64) Xr = (X - self.x_mean_) / self.x_std_ x_scores = np.dot(Xr, self.x_weights_) if Y is not None: if Y.ndim == 1: Y = Y.reshape(-1, 1) Yr = (Y - self.y_mean_) / self.y_std_ y_scores = np.dot(Yr, self.y_weights_) return x_scores, y_scores return x_scores def fit_transform(self, X, y=None, **fit_params): """Learn and apply the dimension reduction on the train data. Parameters ---------- X : array-like of predictors, shape = [n_samples, p] Training vectors, where n_samples in the number of samples and p is the number of predictors. Y : array-like of response, shape = [n_samples, q], optional Training vectors, where n_samples in the number of samples and q is the number of response variables. Returns ------- x_scores if Y is not given, (x_scores, y_scores) otherwise. """ return self.fit(X, y, **fit_params).transform(X, y)

bsd-3-clause

jmargeta/scikit-learn

examples/linear_model/plot_lasso_coordinate_descent_path.py

4

2823

""" ===================== Lasso and Elastic Net ===================== Lasso and elastic net (L1 and L2 penalisation) implemented using a coordinate descent. The coefficients can be forced to be positive. """ print(__doc__) # Author: Alexandre Gramfort <[email protected]> # License: BSD Style. import numpy as np import pylab as pl from sklearn.linear_model import lasso_path, enet_path from sklearn import datasets diabetes = datasets.load_diabetes() X = diabetes.data y = diabetes.target X /= X.std(0) # Standardize data (easier to set the l1_ratio parameter) ############################################################################### # Compute paths eps = 5e-3 # the smaller it is the longer is the path print("Computing regularization path using the lasso...") models = lasso_path(X, y, eps=eps) alphas_lasso = np.array([model.alpha for model in models]) coefs_lasso = np.array([model.coef_ for model in models]) print("Computing regularization path using the positive lasso...") models = lasso_path(X, y, eps=eps, positive=True) alphas_positive_lasso = np.array([model.alpha for model in models]) coefs_positive_lasso = np.array([model.coef_ for model in models]) print("Computing regularization path using the elastic net...") models = enet_path(X, y, eps=eps, l1_ratio=0.8) alphas_enet = np.array([model.alpha for model in models]) coefs_enet = np.array([model.coef_ for model in models]) print("Computing regularization path using the positve elastic net...") models = enet_path(X, y, eps=eps, l1_ratio=0.8, positive=True) alphas_positive_enet = np.array([model.alpha for model in models]) coefs_positive_enet = np.array([model.coef_ for model in models]) ############################################################################### # Display results pl.figure(1) ax = pl.gca() ax.set_color_cycle(2 * ['b', 'r', 'g', 'c', 'k']) l1 = pl.plot(coefs_lasso) l2 = pl.plot(coefs_enet, linestyle='--') pl.xlabel('-Log(lambda)') pl.ylabel('weights') pl.title('Lasso and Elastic-Net Paths') pl.legend((l1[-1], l2[-1]), ('Lasso', 'Elastic-Net'), loc='lower left') pl.axis('tight') pl.figure(2) ax = pl.gca() ax.set_color_cycle(2 * ['b', 'r', 'g', 'c', 'k']) l1 = pl.plot(coefs_lasso) l2 = pl.plot(coefs_positive_lasso, linestyle='--') pl.xlabel('-Log(lambda)') pl.ylabel('weights') pl.title('Lasso and positive Lasso') pl.legend((l1[-1], l2[-1]), ('Lasso', 'positive Lasso'), loc='lower left') pl.axis('tight') pl.figure(3) ax = pl.gca() ax.set_color_cycle(2 * ['b', 'r', 'g', 'c', 'k']) l1 = pl.plot(coefs_enet) l2 = pl.plot(coefs_positive_enet, linestyle='--') pl.xlabel('-Log(lambda)') pl.ylabel('weights') pl.title('Elastic-Net and positive Elastic-Net') pl.legend((l1[-1], l2[-1]), ('Elastic-Net', 'positive Elastic-Net'), loc='lower left') pl.axis('tight') pl.show()

bsd-3-clause

neale/CS-program

434-MachineLearning/final_project/linearClassifier/sklearn/linear_model/tests/test_base.py

83

15089

# Author: Alexandre Gramfort <[email protected]> # Fabian Pedregosa <[email protected]> # # License: BSD 3 clause import numpy as np from scipy import sparse from scipy import linalg from itertools import product from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_equal from sklearn.utils.testing import ignore_warnings from sklearn.linear_model.base import LinearRegression from sklearn.linear_model.base import _preprocess_data from sklearn.linear_model.base import sparse_center_data, center_data from sklearn.linear_model.base import _rescale_data from sklearn.utils import check_random_state from sklearn.utils.testing import assert_greater from sklearn.datasets.samples_generator import make_sparse_uncorrelated from sklearn.datasets.samples_generator import make_regression rng = np.random.RandomState(0) def test_linear_regression(): # Test LinearRegression on a simple dataset. # a simple dataset X = [[1], [2]] Y = [1, 2] reg = LinearRegression() reg.fit(X, Y) assert_array_almost_equal(reg.coef_, [1]) assert_array_almost_equal(reg.intercept_, [0]) assert_array_almost_equal(reg.predict(X), [1, 2]) # test it also for degenerate input X = [[1]] Y = [0] reg = LinearRegression() reg.fit(X, Y) assert_array_almost_equal(reg.coef_, [0]) assert_array_almost_equal(reg.intercept_, [0]) assert_array_almost_equal(reg.predict(X), [0]) def test_linear_regression_sample_weights(): # TODO: loop over sparse data as well rng = np.random.RandomState(0) # It would not work with under-determined systems for n_samples, n_features in ((6, 5), ): y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) sample_weight = 1.0 + rng.rand(n_samples) for intercept in (True, False): # LinearRegression with explicit sample_weight reg = LinearRegression(fit_intercept=intercept) reg.fit(X, y, sample_weight=sample_weight) coefs1 = reg.coef_ inter1 = reg.intercept_ assert_equal(reg.coef_.shape, (X.shape[1], )) # sanity checks assert_greater(reg.score(X, y), 0.5) # Closed form of the weighted least square # theta = (X^T W X)^(-1) * X^T W y W = np.diag(sample_weight) if intercept is False: X_aug = X else: dummy_column = np.ones(shape=(n_samples, 1)) X_aug = np.concatenate((dummy_column, X), axis=1) coefs2 = linalg.solve(X_aug.T.dot(W).dot(X_aug), X_aug.T.dot(W).dot(y)) if intercept is False: assert_array_almost_equal(coefs1, coefs2) else: assert_array_almost_equal(coefs1, coefs2[1:]) assert_almost_equal(inter1, coefs2[0]) def test_raises_value_error_if_sample_weights_greater_than_1d(): # Sample weights must be either scalar or 1D n_sampless = [2, 3] n_featuress = [3, 2] for n_samples, n_features in zip(n_sampless, n_featuress): X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) sample_weights_OK = rng.randn(n_samples) ** 2 + 1 sample_weights_OK_1 = 1. sample_weights_OK_2 = 2. reg = LinearRegression() # make sure the "OK" sample weights actually work reg.fit(X, y, sample_weights_OK) reg.fit(X, y, sample_weights_OK_1) reg.fit(X, y, sample_weights_OK_2) def test_fit_intercept(): # Test assertions on betas shape. X2 = np.array([[0.38349978, 0.61650022], [0.58853682, 0.41146318]]) X3 = np.array([[0.27677969, 0.70693172, 0.01628859], [0.08385139, 0.20692515, 0.70922346]]) y = np.array([1, 1]) lr2_without_intercept = LinearRegression(fit_intercept=False).fit(X2, y) lr2_with_intercept = LinearRegression(fit_intercept=True).fit(X2, y) lr3_without_intercept = LinearRegression(fit_intercept=False).fit(X3, y) lr3_with_intercept = LinearRegression(fit_intercept=True).fit(X3, y) assert_equal(lr2_with_intercept.coef_.shape, lr2_without_intercept.coef_.shape) assert_equal(lr3_with_intercept.coef_.shape, lr3_without_intercept.coef_.shape) assert_equal(lr2_without_intercept.coef_.ndim, lr3_without_intercept.coef_.ndim) def test_linear_regression_sparse(random_state=0): # Test that linear regression also works with sparse data random_state = check_random_state(random_state) for i in range(10): n = 100 X = sparse.eye(n, n) beta = random_state.rand(n) y = X * beta[:, np.newaxis] ols = LinearRegression() ols.fit(X, y.ravel()) assert_array_almost_equal(beta, ols.coef_ + ols.intercept_) assert_array_almost_equal(ols.predict(X) - y.ravel(), 0) def test_linear_regression_multiple_outcome(random_state=0): # Test multiple-outcome linear regressions X, y = make_regression(random_state=random_state) Y = np.vstack((y, y)).T n_features = X.shape[1] reg = LinearRegression(fit_intercept=True) reg.fit((X), Y) assert_equal(reg.coef_.shape, (2, n_features)) Y_pred = reg.predict(X) reg.fit(X, y) y_pred = reg.predict(X) assert_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3) def test_linear_regression_sparse_multiple_outcome(random_state=0): # Test multiple-outcome linear regressions with sparse data random_state = check_random_state(random_state) X, y = make_sparse_uncorrelated(random_state=random_state) X = sparse.coo_matrix(X) Y = np.vstack((y, y)).T n_features = X.shape[1] ols = LinearRegression() ols.fit(X, Y) assert_equal(ols.coef_.shape, (2, n_features)) Y_pred = ols.predict(X) ols.fit(X, y.ravel()) y_pred = ols.predict(X) assert_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3) def test_preprocess_data(): n_samples = 200 n_features = 2 X = rng.rand(n_samples, n_features) y = rng.rand(n_samples) expected_X_mean = np.mean(X, axis=0) expected_X_norm = np.std(X, axis=0) * np.sqrt(X.shape[0]) expected_y_mean = np.mean(y, axis=0) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=False, normalize=False) assert_array_almost_equal(X_mean, np.zeros(n_features)) assert_array_almost_equal(y_mean, 0) assert_array_almost_equal(X_norm, np.ones(n_features)) assert_array_almost_equal(Xt, X) assert_array_almost_equal(yt, y) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=True, normalize=False) assert_array_almost_equal(X_mean, expected_X_mean) assert_array_almost_equal(y_mean, expected_y_mean) assert_array_almost_equal(X_norm, np.ones(n_features)) assert_array_almost_equal(Xt, X - expected_X_mean) assert_array_almost_equal(yt, y - expected_y_mean) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=True, normalize=True) assert_array_almost_equal(X_mean, expected_X_mean) assert_array_almost_equal(y_mean, expected_y_mean) assert_array_almost_equal(X_norm, expected_X_norm) assert_array_almost_equal(Xt, (X - expected_X_mean) / expected_X_norm) assert_array_almost_equal(yt, y - expected_y_mean) def test_preprocess_data_multioutput(): n_samples = 200 n_features = 3 n_outputs = 2 X = rng.rand(n_samples, n_features) y = rng.rand(n_samples, n_outputs) expected_y_mean = np.mean(y, axis=0) args = [X, sparse.csc_matrix(X)] for X in args: _, yt, _, y_mean, _ = _preprocess_data(X, y, fit_intercept=False, normalize=False) assert_array_almost_equal(y_mean, np.zeros(n_outputs)) assert_array_almost_equal(yt, y) _, yt, _, y_mean, _ = _preprocess_data(X, y, fit_intercept=True, normalize=False) assert_array_almost_equal(y_mean, expected_y_mean) assert_array_almost_equal(yt, y - y_mean) _, yt, _, y_mean, _ = _preprocess_data(X, y, fit_intercept=True, normalize=True) assert_array_almost_equal(y_mean, expected_y_mean) assert_array_almost_equal(yt, y - y_mean) def test_preprocess_data_weighted(): n_samples = 200 n_features = 2 X = rng.rand(n_samples, n_features) y = rng.rand(n_samples) sample_weight = rng.rand(n_samples) expected_X_mean = np.average(X, axis=0, weights=sample_weight) expected_y_mean = np.average(y, axis=0, weights=sample_weight) # XXX: if normalize=True, should we expect a weighted standard deviation? # Currently not weighted, but calculated with respect to weighted mean expected_X_norm = (np.sqrt(X.shape[0]) * np.mean((X - expected_X_mean) ** 2, axis=0) ** .5) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=True, normalize=False, sample_weight=sample_weight) assert_array_almost_equal(X_mean, expected_X_mean) assert_array_almost_equal(y_mean, expected_y_mean) assert_array_almost_equal(X_norm, np.ones(n_features)) assert_array_almost_equal(Xt, X - expected_X_mean) assert_array_almost_equal(yt, y - expected_y_mean) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=True, normalize=True, sample_weight=sample_weight) assert_array_almost_equal(X_mean, expected_X_mean) assert_array_almost_equal(y_mean, expected_y_mean) assert_array_almost_equal(X_norm, expected_X_norm) assert_array_almost_equal(Xt, (X - expected_X_mean) / expected_X_norm) assert_array_almost_equal(yt, y - expected_y_mean) def test_sparse_preprocess_data_with_return_mean(): n_samples = 200 n_features = 2 # random_state not supported yet in sparse.rand X = sparse.rand(n_samples, n_features, density=.5) # , random_state=rng X = X.tolil() y = rng.rand(n_samples) XA = X.toarray() expected_X_norm = np.std(XA, axis=0) * np.sqrt(X.shape[0]) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=False, normalize=False, return_mean=True) assert_array_almost_equal(X_mean, np.zeros(n_features)) assert_array_almost_equal(y_mean, 0) assert_array_almost_equal(X_norm, np.ones(n_features)) assert_array_almost_equal(Xt.A, XA) assert_array_almost_equal(yt, y) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=True, normalize=False, return_mean=True) assert_array_almost_equal(X_mean, np.mean(XA, axis=0)) assert_array_almost_equal(y_mean, np.mean(y, axis=0)) assert_array_almost_equal(X_norm, np.ones(n_features)) assert_array_almost_equal(Xt.A, XA) assert_array_almost_equal(yt, y - np.mean(y, axis=0)) Xt, yt, X_mean, y_mean, X_norm = \ _preprocess_data(X, y, fit_intercept=True, normalize=True, return_mean=True) assert_array_almost_equal(X_mean, np.mean(XA, axis=0)) assert_array_almost_equal(y_mean, np.mean(y, axis=0)) assert_array_almost_equal(X_norm, expected_X_norm) assert_array_almost_equal(Xt.A, XA / expected_X_norm) assert_array_almost_equal(yt, y - np.mean(y, axis=0)) def test_csr_preprocess_data(): # Test output format of _preprocess_data, when input is csr X, y = make_regression() X[X < 2.5] = 0.0 csr = sparse.csr_matrix(X) csr_, y, _, _, _ = _preprocess_data(csr, y, True) assert_equal(csr_.getformat(), 'csr') def test_rescale_data(): n_samples = 200 n_features = 2 sample_weight = 1.0 + rng.rand(n_samples) X = rng.rand(n_samples, n_features) y = rng.rand(n_samples) rescaled_X, rescaled_y = _rescale_data(X, y, sample_weight) rescaled_X2 = X * np.sqrt(sample_weight)[:, np.newaxis] rescaled_y2 = y * np.sqrt(sample_weight) assert_array_almost_equal(rescaled_X, rescaled_X2) assert_array_almost_equal(rescaled_y, rescaled_y2) @ignore_warnings # all deprecation warnings def test_deprecation_center_data(): n_samples = 200 n_features = 2 w = 1.0 + rng.rand(n_samples) X = rng.rand(n_samples, n_features) y = rng.rand(n_samples) param_grid = product([True, False], [True, False], [True, False], [None, w]) for (fit_intercept, normalize, copy, sample_weight) in param_grid: XX = X.copy() # such that we can try copy=False as well X1, y1, X1_mean, X1_var, y1_mean = \ center_data(XX, y, fit_intercept=fit_intercept, normalize=normalize, copy=copy, sample_weight=sample_weight) XX = X.copy() X2, y2, X2_mean, X2_var, y2_mean = \ _preprocess_data(XX, y, fit_intercept=fit_intercept, normalize=normalize, copy=copy, sample_weight=sample_weight) assert_array_almost_equal(X1, X2) assert_array_almost_equal(y1, y2) assert_array_almost_equal(X1_mean, X2_mean) assert_array_almost_equal(X1_var, X2_var) assert_array_almost_equal(y1_mean, y2_mean) # Sparse cases X = sparse.csr_matrix(X) for (fit_intercept, normalize, copy, sample_weight) in param_grid: X1, y1, X1_mean, X1_var, y1_mean = \ center_data(X, y, fit_intercept=fit_intercept, normalize=normalize, copy=copy, sample_weight=sample_weight) X2, y2, X2_mean, X2_var, y2_mean = \ _preprocess_data(X, y, fit_intercept=fit_intercept, normalize=normalize, copy=copy, sample_weight=sample_weight, return_mean=False) assert_array_almost_equal(X1.toarray(), X2.toarray()) assert_array_almost_equal(y1, y2) assert_array_almost_equal(X1_mean, X2_mean) assert_array_almost_equal(X1_var, X2_var) assert_array_almost_equal(y1_mean, y2_mean) for (fit_intercept, normalize) in product([True, False], [True, False]): X1, y1, X1_mean, X1_var, y1_mean = \ sparse_center_data(X, y, fit_intercept=fit_intercept, normalize=normalize) X2, y2, X2_mean, X2_var, y2_mean = \ _preprocess_data(X, y, fit_intercept=fit_intercept, normalize=normalize, return_mean=True) assert_array_almost_equal(X1.toarray(), X2.toarray()) assert_array_almost_equal(y1, y2) assert_array_almost_equal(X1_mean, X2_mean) assert_array_almost_equal(X1_var, X2_var) assert_array_almost_equal(y1_mean, y2_mean)

unlicense

ndingwall/scikit-learn

sklearn/manifold/tests/test_mds.py

2

2566

import numpy as np from numpy.testing import assert_array_almost_equal import pytest from sklearn.manifold import _mds as mds from sklearn.utils._testing import ignore_warnings def test_smacof(): # test metric smacof using the data of "Modern Multidimensional Scaling", # Borg & Groenen, p 154 sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]]) Z = np.array([[-.266, -.539], [.451, .252], [.016, -.238], [-.200, .524]]) X, _ = mds.smacof(sim, init=Z, n_components=2, max_iter=1, n_init=1) X_true = np.array([[-1.415, -2.471], [1.633, 1.107], [.249, -.067], [-.468, 1.431]]) assert_array_almost_equal(X, X_true, decimal=3) def test_smacof_error(): # Not symmetric similarity matrix: sim = np.array([[0, 5, 9, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]]) with pytest.raises(ValueError): mds.smacof(sim) # Not squared similarity matrix: sim = np.array([[0, 5, 9, 4], [5, 0, 2, 2], [4, 2, 1, 0]]) with pytest.raises(ValueError): mds.smacof(sim) # init not None and not correct format: sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]]) Z = np.array([[-.266, -.539], [.016, -.238], [-.200, .524]]) with pytest.raises(ValueError): mds.smacof(sim, init=Z, n_init=1) def test_MDS(): sim = np.array([[0, 5, 3, 4], [5, 0, 2, 2], [3, 2, 0, 1], [4, 2, 1, 0]]) mds_clf = mds.MDS(metric=False, n_jobs=3, dissimilarity="precomputed") mds_clf.fit(sim) # TODO: Remove in 0.26 def test_MDS_pairwise_deprecated(): mds_clf = mds.MDS(metric='precomputed') msg = r"Attribute _pairwise was deprecated in version 0\.24" with pytest.warns(FutureWarning, match=msg): mds_clf._pairwise # TODO: Remove in 0.26 @ignore_warnings(category=FutureWarning) @pytest.mark.parametrize("dissimilarity, expected_pairwise", [ ("precomputed", True), ("euclidean", False), ]) def test_MDS_pairwise(dissimilarity, expected_pairwise): # _pairwise attribute is set correctly mds_clf = mds.MDS(dissimilarity=dissimilarity) assert mds_clf._pairwise == expected_pairwise

bsd-3-clause

akuefler/fovea

examples/HH_neuron/HH_simple_demo.py

1

20688

""" This is the main run script for simple demo involving Hodgkin-Huxley analysis. """ from PyDSTool.Toolbox.dssrt import * from PyDSTool.Toolbox.phaseplane import * import PyDSTool as dst import matplotlib.pyplot as plt import sys from fovea.graphics import gui from model_config import man as modelManager from model_config import name, header from HH_neuron import getHH_DSSRT, computePPlaneObjects, do_traj from fovea.common import castNull, castNullArray from math import * from scipy.optimize import fsolve ## ----- ----- ----- ----- ----- ----- ## ## BUILD GENERATOR OBJECT ## ## ----- ----- ----- ----- ----- ----- ## model = modelManager.instances[name] gen = list(model.registry.values())[0] # global define for convenience plotter = gui.plotter # ------------------------ def clip_to_pt(): """Extract clipboard point from gui to a dictionary""" pt = dst.filteredDict(gui.capturedPts['Master'], ['V', 'm', 'n']) return {'V': pt['V'], 'Na.m': pt['m'], 'K.n': pt['n']} class PPcallback(object): """ Dynamic figure axes class to support state-dependent user-interactive callbacks """ def __init__(self, xvar, num_x_points=30, num_y_points=30, nullcX_style=None, nullcY_style=None, vel_arrow_scale=1): self.nully = None self.nullx = None self.num_x_points = num_x_points self.num_y_points = num_y_points if nullcX_style is None: self.nullcX_style = 'b-' else: self.nullcX_style = nullcX_style if nullcY_style is None: self.nullcY_style = 'r-' else: self.nullcY_style = nullcY_style self.last_scale = None self.vel_arrow_scale = vel_arrow_scale self.first_call = True # is reset by __call__ def dQ_dt(self, Qstr, ix, points): """ Utility to find finite difference of any named quantity in given points at index ix """ if ix == 0: ix = 1 pt1 = points[ix] pt0 = points[ix-1] t1 = points.indepvararray[ix] t0 = points.indepvararray[ix-1] return (pt1[Qstr]-pt0[Qstr])/(t1-t0) class PPcallback_m(PPcallback): def __call__(self, time, hard_reset=False): """Callback 'function' to take care of refreshing and re-computing phase plane sub-plot when time is changed interactively. """ #print("\n\npplane call back, mouseUp =", gui._mouseUp) fig_struct = plotter.figs['Master'] # combine all layer information dynamicData = fig_struct.layers['nullclines_mV'].data.copy() dynamicData.update(fig_struct.layers['horiz_PP'].data) ax = gui.dynamicPlots['nullclines_mV'] #sc = fig_struct.layers['nullclines'].scale sc = [ax.get_xlim(), ax.get_ylim()] if hard_reset: force = True preComputed = False print("\n HARD REFRESH for phase plane") else: preComputed = False force = False # REPLACE WITH A PROPER CACHE STRUCTURE for key, val in dynamicData.items(): # dynamicData.keys are 'yNull_<time>' or 'xNull' or keys from horiz_PP etc. # re-use computed nullclines if time is in "cache", i.e. it shows up in the keys if key[6:] == str(time): # cache hit! val['display'] = True # not clear if this updates original data structure after copy # also check to see whether has been rescaled if self.last_scale == sc: preComputed = True else: force = True elif key[:5] != 'xNull': # Use != to clean up collision lines and ohter nullcline that are not for # this time value. # switch off y-nullcline (V_inf) display for the other times # yNull stays constant so keep that display=True val['display'] = False pt = gui.points[gui.ix] p = fig_struct.layers['points_mV'] p.display = True dV_dt = (pt['vinf']-pt['V'])/pt['tauv'] dm_dt = (pt['Na.minf']-pt['Na.m'])/pt['Na.taum'] dn_dt = (pt['K.ninf']-pt['K.n'])/pt['K.taun'] gui.addDataPoints([[pt['Na.m'], pt['Na.m']+dm_dt*self.vel_arrow_scale], [pt['V'], pt['V']+dV_dt*self.vel_arrow_scale]], layer='state_vel_mV', name='state', style=vel_vec_style, force=True) if self.first_call: gui.addDataPoints([gui.points['Na.m'], gui.points['V']], layer='vfp_mV', name='traj', style='y') # Virtual fixed point and linearized nullclines if 'fast_m' in model.name: with_jac = False do_fps = False fast_vars = ['Na.m'] else: with_jac = False do_fps = False fast_vars = None # update (or create) points try: gui.setPoint('state_pt', Point2D(pt['Na.m'], pt['V']), 'points_mV') gui.setPoint('vinf_pt', Point2D(pt['Na.m'], pt['vinf']), 'points_mV') except KeyError: gui.addDataPoints(Point2D(pt['Na.m'], pt['V']), coorddict = {'x': {'y':'y', 'style':'ko', 'layer':'points_mV', 'name':'state_pt'}}) gui.addDataPoints(Point2D(pt['Na.m'], pt['vinf']),coorddict = {'x': {'y':'y', 'style':'bx', 'layer':'points_mV', 'name':'vinf_pt'}}) d = fig_struct.layers['nullclines_mV'].data if not preComputed and gui._mouseUp: ## print("\nComputing phase plane...") ## print(" Current time = %.4f" % (time)) if self.nullx is None or force: # compute m nullcline this once only_var = None else: only_var = 'V' # refresh wait notification ax.text(0.05, 0.95, 'wait', transform=ax.transAxes, fontsize=22, color='r', fontweight='bold', va='top') gui.masterWin.canvas.draw() # comment out for testing - use surrogate below nulls = computePPlaneObjects(gen, 'Na.m', 'V', state=pt, num_x_points=self.num_x_points, num_y_points=self.num_y_points, only_var=only_var, with_jac=with_jac, do_fps=do_fps, fast_vars=fast_vars, subdomain={'V': sc[1], 'Na.m': sc[0]}) # Surrogate data - much faster to test with #self.nully = [[-100+time, -50+time/10., 0], [0.1, 0.4, 0.8]] #self.nullx = [[-130, -80, 50], [0.2, 0.3, 0.4]] self.nully = castNullArray(nulls['nullcY']) gui.addDataPoints(self.nully, layer='nullclines_mV', style=self.nullcY_style, name='yNull_'+str(time), force=force) # delete update 'wait' notice ax.texts = [] #ax.clear() gui.clearAxes(ax) if only_var is None: # nullx is added second so will be the second line self.nullx = castNullArray(nulls['nullcX']) gui.addDataPoints(self.nullx, layer='nullclines_mV', style=self.nullcX_style, name='xNull', force=force) #if force: # rescale = sc #else: # rescale = None gui.buildLayers(['nullclines_mV', 'horiz_PP', 'points_mV', 'state_vel_mV', 'vfp_mV'], ax, rescale=sc, figure='Master') self.last_scale = sc ## print(" Phase plane rebuild completed.\n") else: # just refresh display with the current selected data gui.clearAxes(ax) #if force: # rescale = sc #else: # rescale = None gui.buildLayers(['nullclines_mV', 'horiz_PP', 'points_mV', 'state_vel_mV', 'vfp_mV'], ax, rescale=sc, figure='Master') self.last_scale = sc gui.masterWin.canvas.draw() self.first_call = False class PPcallback_n(PPcallback): def __call__(self, time, hard_reset=False): """Callback 'function' to take care of refreshing and re-computing phase plane sub-plot when time is changed interactively. """ #print("\n\npplane call back, mouseUp =", gui._mouseUp) fig_struct = plotter.figs['Master'] # combine all layer information dynamicData = fig_struct.layers['nullclines_nV'].data.copy() ax = gui.dynamicPlots['nullclines_nV'] #sc = fig_struct.layers['nullclines'].scale sc = [ax.get_xlim(), ax.get_ylim()] if hard_reset: force = True preComputed = False print("\n HARD REFRESH for phase plane") else: preComputed = False force = False # REPLACE WITH A PROPER CACHE STRUCTURE for key, val in dynamicData.items(): # dynamicData.keys are 'yNull_<time>' or 'xNull' or keys from horiz_PP etc. # re-use computed nullclines if time is in "cache", i.e. it shows up in the keys if key[6:] == str(time): # cache hit! val['display'] = True # not clear if this updates original data structure after copy # also check to see whether has been rescaled if self.last_scale == sc: preComputed = True else: force = True elif key[:5] != 'xNull': # Use != to clean up collision lines and ohter nullcline that are not for # this time value. # switch off y-nullcline (V_inf) display for the other times # yNull stays constant so keep that display=True val['display'] = False pt = gui.points[gui.ix] p = fig_struct.layers['points_nV'] p.display = True dV_dt = (pt['vinf']-pt['V'])/pt['tauv'] dm_dt = (pt['Na.minf']-pt['Na.m'])/pt['Na.taum'] dn_dt = (pt['K.ninf']-pt['K.n'])/pt['K.taun'] gui.addDataPoints([[pt['K.n'], pt['K.n']+dn_dt*self.vel_arrow_scale], [pt['V'], pt['V']+dV_dt*self.vel_arrow_scale]], layer='state_vel_nV', name='state', style=vel_vec_style, force=True) if self.first_call: gui.addDataPoints([gui.points['K.n'], gui.points['V']], layer='vfp_nV', name='traj', style='y') gui.addDataPoints([gui.points['K.n'], gui.points['vinf']], layer='vfp_nV', name='quasiVnull', style='m--') ## vs = np.linspace(sc[1][0], sc[1][1], 50) ## x = dict(pt).copy() ## ## def vinf(n, v): ## x['K.n'] = n ## x['V'] = v ## x['Na.m'] = gen.auxfns.Na_dssrt_fn_minf(v) ## # assume autonomous system ## return model.Rhs(0, x, asarray=False)['V'] ## ## vinfs_inv_n = [fsolve(vinf, gen.auxfns.K_dssrt_fn_ninf(v), args=(v,)) for v in vs] ## plotter.addData([vinfs_inv_n, vs], layer='vfp_nV', name='vinf_fastm', style='b--') # Virtual fixed point and linearized nullclines if 'fast_m' in model.name: with_jac = False do_fps = False fast_vars = ['Na.m'] else: with_jac = False do_fps = False fast_vars = None # update (or create) points try: gui.setPoint('state_pt', Point2D(pt['K.n'], pt['V']), 'points_nV') gui.setPoint('vinf_pt', Point2D(pt['K.n'], pt['vinf']), 'points_nV') except KeyError: gui.addDataPoints(Point2D(pt['K.n'], pt['V']),coorddict = {'x': {'y':'y', 'style':'ko', 'layer':'points_nV', 'name':'state_pt'}}) gui.addDataPoints(Point2D(pt['K.n'], pt['vinf']), coorddict = {'x': {'y':'y', 'style':'bx', 'layer':'points_nV', 'name':'vinf_pt'}}) d = fig_struct.layers['nullclines_nV'].data if not preComputed and gui._mouseUp: ## print("\nComputing phase plane...") ## print(" Current time = %.4f" % (time)) if self.nullx is None or force: # compute m nullcline this once only_var = None else: only_var = 'V' # refresh wait notification ax.text(0.05, 0.95, 'wait', transform=ax.transAxes, fontsize=22, color='r', fontweight='bold', va='top') gui.masterWin.canvas.draw() # comment out for testing - use surrogate below nulls = computePPlaneObjects(gen, 'K.n', 'V', state=pt, num_x_points=self.num_x_points, num_y_points=self.num_y_points, only_var=only_var, with_jac=with_jac, do_fps=do_fps, fast_vars=fast_vars, subdomain={'V': sc[1], 'K.n': sc[0]}) # Surrogate data - much faster to test with #self.nully = [[-100+time, -50+time/10., 0], [0.1, 0.4, 0.8]] #self.nullx = [[-130, -80, 50], [0.2, 0.3, 0.4]] self.nully = castNullArray(nulls['nullcY']) gui.addDataPoints(self.nully, layer='nullclines_nV', style=self.nullcY_style, name='yNull_'+str(time), force=force) # delete update 'wait' notice ax.texts = [] #ax.clear() gui.clearAxes(ax) if only_var is None: # nullx is added second so will be the second line self.nullx = castNullArray(nulls['nullcX']) gui.addDataPoints(self.nullx, layer='nullclines_nV', style=self.nullcX_style, name='xNull', force=force) #if force: # rescale = sc #else: # rescale = None gui.buildLayers(['nullclines_nV', 'points_nV', 'state_vel_nV', 'vfp_nV'], ax, rescale=sc, figure='Master') self.last_scale = sc ## print(" Phase plane rebuild completed.\n") else: # just refresh display with the current selected data gui.clearAxes(ax) #if force: # rescale = sc #else: # rescale = None gui.buildLayers(['nullclines_nV', 'points_nV', 'state_vel_nV', 'vfp_nV'], ax, rescale=sc, figure='Master') self.last_scale = sc gui.masterWin.canvas.draw() self.first_call = False # ------------------------------------------------------------------------ ## Set dssrt_name to be for saved DSSRT data file ## Change for different parameter sets or just default to model name dssrt_name = name if 'typeI' in name and 'typeII' not in name: ### FOR TYPE I H-H ONLY Kgmax = 100 # 100 # 80 original dssrt_name = name+'_gK%i' % Kgmax model.set(pars={'K.g': Kgmax, 'Na.g': 50}) else: ### FOR TYPE II H-H ONLY Kgmax = 36 #39 or 42 with fast m # 36 original dssrt_name = name+'_gK%i' % Kgmax model.set(pars={'K.g': Kgmax, 'Ib.Ibias': 8.}) dV = 0.2 ##test_ic = {'K.n': 0.37220277852490802, ## 'Na.m': 0.080387043479386036, ## 'V': -59.5} ##model.set(ics=test_ic) ## ----- ----- ----- ----- ----- ----- ## ## GET GENERATOR TRAJECTORY ## ## ----- ----- ----- ----- ----- ----- ## orig_ics = model.query('ics') if 'no_h' in name: # no periodic orbit, just simulate for 12 ms if 'typeI' in name and 'typeII' not in name: t_end = 9 else: t_end = 12 model.set(tdata=[0, t_end]) model.compute('ref') ref_traj = model['ref'] else: # get periodic orbit t_end = 20 ref_traj, ref_pts, ref_tmin, ref_tmax = do_traj(model, t_end, do_plot=False) # re-sample traj at constant dt and declare to GUI #trajPts = ref_traj.sample(dt=0.01)[:-40] # cheap way to avoid overlap from pts not being periodic trajPts = ref_traj.sample(dt=0.01)[:len(ref_traj.sample(dt=0.01))-40] #[:-40] syntax not working in python 3 gui.addTimeFromPoints(trajPts) ## ----- ----- ----- ----- ----- ----- ## ## CREATE DIAGNOSTIC OBJECT ## ## ----- ----- ----- ----- ----- ----- ## gui.clean() gui.addFig('Master', title='Geometric Dynamic Analysis: '+dssrt_name, tdom=[0, t_end], domain=[(-100,50), (0,1)]) coorddict = {'V': {'x':'t', 'layer':'V','name':'V', 'style':'k-'}, 'vinf': {'x':'t', 'layer':'V','name':'Vinf', 'style':'k:'}, 'Na.m': {'x':'t', 'layer':'activs', 'name':'m', 'style':'g--'}, 'Na.minf': {'x':'t', 'layer':'activs', 'name':'minf', 'style':'g--'}, 'K.n': {'x':'t', 'layer':'activs', 'name':'n', 'style':'r-'}, 'K.ninf': {'x':'t', 'layer':'activs', 'name':'ninf', 'style':'r--'}, 'tauv': {'x':'t','layer':'activs','name':'tauv', 'style':'b:'}, 'Na.taum': {'x':'t', 'layer':'activs','name':'taum', 'style':'g:'}, 'K.taun': {'x':'t', 'layer':'activs','name':'taun', 'style':'r:'} } gui.addDataPoints(trajPts, coorddict = coorddict) print("Key for activations / time scales window") print(" Activations: line=activation, dashed=asymptotic") print(" Time scales: dots") print("Na: green") print("K: red") print("V: blue") ## ----- ----- ----- ----- ----- ----- ## ## COMPUTE V-m PHASE PLANE ## ## ----- ----- ----- ----- ----- ----- ## # start at t = 2ms gui.set_time(2) # global style defs vel_vec_style = {'color': 'k', 'linewidth': 2, 'linestyle': '-'} vinf_vec_style = {'color': 'b', 'linewidth': 2, 'linestyle': '-'} horiz_style = {'color': 'k', 'linestyle': '--', 'linewidth': 2} def make_layer(xvar): if xvar == 'Na.m': suffix = 'mV' else: suffix = 'nV' PP_layer_name = 'nullclines_'+suffix gui.addLayer(PP_layer_name, dynamic=True) if xvar == 'Na.m': gui.addLayer('horiz_PP') nullcX_style = 'g-' PPclass = PPcallback_m else: # no horizon layer for K.n nullcX_style = 'r-' PPclass = PPcallback_n PPplot = PPclass(xvar, nullcY_style = {'color': 'b', 'linestyle': '-', 'linewidth': 1}, nullcX_style=nullcX_style) gui.dynamicPlotFns[PP_layer_name] = PPplot gui.addLayer('points_'+suffix) gui.addLayer('state_vel_'+suffix) gui.addLayer('vfp_'+suffix) make_layer('Na.m') make_layer('K.n') # sub-plot specs: (row, col) integer coords start at top left dPlot11 = {'name': 'Trajectory', 'layers': ['V'], 'scale': [None, [-85, 45]], 'axes_vars': ['t', 'V'] } dPlot12 = {'name': 'Activations, Time scales', 'layers': ['activs'], 'scale': [None, [0,1]], 'axes_vars': ['t', 'no units, ms'] } pp1_name = 'Na-V Phaseplane' pp1_dom = [[0,1], [-75,50]] pp1_vars = ['m', 'V'] pp2_name = 'K-V Phaseplane' pp2_dom = [[0.,1], [-75,50]] pp2_vars = ['n', 'V'] # ISSUE: Rename 'scale' to 'domain' or 'extent' dPlot21 = {'name': pp1_name, 'scale': pp1_dom, 'layers': ['nullclines_mV', 'horiz_PP', 'points_mV', 'state_vel_mV', 'vfp_mV'], 'axes_vars': pp1_vars} dPlot22 = {'name': pp2_name, 'scale': pp2_dom, 'layers': ['nullclines_nV', 'points_nV', 'state_vel_nV', 'vfp_nV'], 'axes_vars': pp2_vars} dPlot_dict = {'11': dPlot11, '12': dPlot12, '21': dPlot21, '22': dPlot22} gui.setup(dPlot_dict, size=(14, 8)) gui.show_legends(subplot='Times') gui.show() gui.plus_dt(0) halt = True

bsd-3-clause

fzalkow/scikit-learn

examples/neural_networks/plot_rbm_logistic_classification.py

258

4609

""" ============================================================== Restricted Boltzmann Machine features for digit classification ============================================================== For greyscale image data where pixel values can be interpreted as degrees of blackness on a white background, like handwritten digit recognition, the Bernoulli Restricted Boltzmann machine model (:class:`BernoulliRBM <sklearn.neural_network.BernoulliRBM>`) can perform effective non-linear feature extraction. In order to learn good latent representations from a small dataset, we artificially generate more labeled data by perturbing the training data with linear shifts of 1 pixel in each direction. This example shows how to build a classification pipeline with a BernoulliRBM feature extractor and a :class:`LogisticRegression <sklearn.linear_model.LogisticRegression>` classifier. The hyperparameters of the entire model (learning rate, hidden layer size, regularization) were optimized by grid search, but the search is not reproduced here because of runtime constraints. Logistic regression on raw pixel values is presented for comparison. The example shows that the features extracted by the BernoulliRBM help improve the classification accuracy. """ from __future__ import print_function print(__doc__) # Authors: Yann N. Dauphin, Vlad Niculae, Gabriel Synnaeve # License: BSD import numpy as np import matplotlib.pyplot as plt from scipy.ndimage import convolve from sklearn import linear_model, datasets, metrics from sklearn.cross_validation import train_test_split from sklearn.neural_network import BernoulliRBM from sklearn.pipeline import Pipeline ############################################################################### # Setting up def nudge_dataset(X, Y): """ This produces a dataset 5 times bigger than the original one, by moving the 8x8 images in X around by 1px to left, right, down, up """ direction_vectors = [ [[0, 1, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [1, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 1], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 1, 0]]] shift = lambda x, w: convolve(x.reshape((8, 8)), mode='constant', weights=w).ravel() X = np.concatenate([X] + [np.apply_along_axis(shift, 1, X, vector) for vector in direction_vectors]) Y = np.concatenate([Y for _ in range(5)], axis=0) return X, Y # Load Data digits = datasets.load_digits() X = np.asarray(digits.data, 'float32') X, Y = nudge_dataset(X, digits.target) X = (X - np.min(X, 0)) / (np.max(X, 0) + 0.0001) # 0-1 scaling X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0) # Models we will use logistic = linear_model.LogisticRegression() rbm = BernoulliRBM(random_state=0, verbose=True) classifier = Pipeline(steps=[('rbm', rbm), ('logistic', logistic)]) ############################################################################### # Training # Hyper-parameters. These were set by cross-validation, # using a GridSearchCV. Here we are not performing cross-validation to # save time. rbm.learning_rate = 0.06 rbm.n_iter = 20 # More components tend to give better prediction performance, but larger # fitting time rbm.n_components = 100 logistic.C = 6000.0 # Training RBM-Logistic Pipeline classifier.fit(X_train, Y_train) # Training Logistic regression logistic_classifier = linear_model.LogisticRegression(C=100.0) logistic_classifier.fit(X_train, Y_train) ############################################################################### # Evaluation print() print("Logistic regression using RBM features:\n%s\n" % ( metrics.classification_report( Y_test, classifier.predict(X_test)))) print("Logistic regression using raw pixel features:\n%s\n" % ( metrics.classification_report( Y_test, logistic_classifier.predict(X_test)))) ############################################################################### # Plotting plt.figure(figsize=(4.2, 4)) for i, comp in enumerate(rbm.components_): plt.subplot(10, 10, i + 1) plt.imshow(comp.reshape((8, 8)), cmap=plt.cm.gray_r, interpolation='nearest') plt.xticks(()) plt.yticks(()) plt.suptitle('100 components extracted by RBM', fontsize=16) plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23) plt.show()

bsd-3-clause