content: """
  
  Scikit-learn utilities for input validation.
  """
  
  # Authors: Olivier Grisel
  #          Gael Varoquaux
  #          Andreas Mueller
  #          Lars Buitinck
  #          Alexandre Gramfort
  #          Nicolas Tresegnie
  # License: BSD 3 clause
  
  
  import warnings
  import numbers
  
  import numpy as np
  import scipy.sparse as sp
  
  from .. import six
  from ...utils.exceptions import NotFittedError, NonBLASDotWarning, \
      DataConversionWarning
  
  try:
      from inspect import signature
  except ImportError:
      from mlens.externals.funcsigs import signature
  
  FLOAT_DTYPES = (np.float64, np.float32, np.float16)
  
  # Silenced by default to reduce verbosity. Turn on at runtime for
  # performance profiling.
  warnings.simplefilter('ignore', NonBLASDotWarning)
  
  
  def _assert_all_finite(X):
      """Like assert_all_finite, but only for ndarray."""
      X = np.asanyarray(X)
      # First try an O(n) time, O(1) space solution for the common case that
      # everything is finite; fall back to O(n) space np.isfinite to prevent
      # false positives from overflow in sum method.
      if (X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum())
              and not np.isfinite(X).all()):
          raise ValueError("Input contains NaN, infinity"
                           " or a value too large for %r." % X.dtype)
  
  
  def assert_all_finite(X):
      """Throw a ValueError if X contains NaN or infinity.
      Parameters
      ----------
      X : array or sparse matrix
      """
      _assert_all_finite(X.data if sp.issparse(X) else X)
  
  
  def as_float_array(X, copy=True, force_all_finite=True):
      """Converts an array-like to an array of floats.
      The new dtype will be np.float32 or np.float64, depending on the original
      type. The function can create a copy or modify the argument depending
      on the argument copy.
      Parameters
      ----------
      X : {array-like, sparse matrix}
      copy : bool, optional
          If True, a copy of X will be created. If False, a copy may still be
          returned if X's dtype is not a floating point type.
      force_all_finite : boolean (default=True)
          Whether to raise an error on np.inf and np.nan in X.
      Returns
      -------
      XT : {array, sparse matrix}
          An array of type np.float
      """
      if isinstance(X, np.matrix) or (not isinstance(X, np.ndarray)
                                      and not sp.issparse(X)):
          return check_array(X, ['csr', 'csc', 'coo'], dtype=np.float64,
                             copy=copy, force_all_finite=force_all_finite,
                             ensure_2d=False)
      elif sp.issparse(X) and X.dtype in [np.float32, np.float64]:
          return X.copy() if copy else X
      elif X.dtype in [np.float32, np.float64]:  # is numpy array
          return X.copy('F' if X.flags['F_CONTIGUOUS'] else 'C') if copy else X
      else:
          if X.dtype.kind in 'uib' and X.dtype.itemsize <= 4:
              return_dtype = np.float32
          else:
              return_dtype = np.float64
          return X.astype(return_dtype)
  
  
  def _is_arraylike(x):
      """Returns whether the input is array-like"""
      return (hasattr(x, '__len__') or
              hasattr(x, 'shape') or
              hasattr(x, '__array__'))
  
  
  def _num_samples(x):
      """Return number of samples in array-like x."""
      if hasattr(x, 'fit') and callable(x.fit):
          # Don't get num_samples from an ensembles length!
          raise TypeError('Expected sequence or array-like, got '
                          'estimator %s' % x)
      if not hasattr(x, '__len__') and not hasattr(x, 'shape'):
          if hasattr(x, '__array__'):
              x = np.asarray(x)
          else:
              raise TypeError("Expected sequence or array-like, got %s" %
                              type(x))
      if hasattr(x, 'shape'):
          if len(x.shape) == 0:
              raise TypeError("Singleton array %r cannot be considered"
                              " a valid collection." % x)
          return x.shape[0]
      else:
          return len(x)
  
  
  def _shape_repr(shape):
      """Return a platform independent representation of an array shape
      Under Python 2, the `long` type introduces an 'L' suffix when using the
      default %r format for tuples of integers (typically used to store the shape
      of an array).
      Under Windows 64 bit (and Python 2), the `long` type is used by default
      in numpy shapes even when the integer dimensions are well below 32 bit.
      The platform specific type causes string messages or doctests to change
      from one platform to another which is not desirable.
      Under Python 3, there is no more `long` type so the `L` suffix is never
      introduced in string representation.
      >>> _shape_repr((1, 2))
      '(1, 2)'
      >>> one = 2 ** 64 / 2 ** 64  # force an upcast to `long` under Python 2
      >>> _shape_repr((one, 2 * one))
      '(1, 2)'
      >>> _shape_repr((1,))
      '(1,)'
      >>> _shape_repr(())
      '()'
      """
      if len(shape) == 0:
          return "()"
      joined = ", ".join("%d" % e for e in shape)
      if len(shape) == 1:
          # special notation for singleton tuples
          joined += ','
      return "(%s)" % joined
  
  
  def check_consistent_length(*arrays):
      """Check that all arrays have consistent first dimensions.
      Checks whether all objects in arrays have the same shape or length.
      Parameters
      ----------
      *arrays : list or tuple of input objects.
          Objects that will be checked for consistent length.
      """
  
      lengths = [_num_samples(X) for X in arrays if X is not None]
      uniques = np.unique(lengths)
      if len(uniques) > 1:
          raise ValueError("Found input variables with inconsistent numbers of"
                           " samples: %r" % [int(l) for l in lengths])
  
  
  def indexable(*iterables):
      """Make arrays indexable for cross-validation.
      Checks consistent length, passes through None, and ensures that everything
      can be indexed by converting sparse matrices to csr and converting
      non-interable objects to arrays.
      Parameters
      ----------
      *iterables : lists, dataframes, arrays, sparse matrices
          List of objects to ensure sliceability.
      """
      result = []
      for X in iterables:
          if sp.issparse(X):
              result.append(X.tocsr())
          elif hasattr(X, "__getitem__") or hasattr(X, "iloc"):
              result.append(X)
          elif X is None:
              result.append(X)
          else:
              result.append(np.array(X))
      check_consistent_length(*result)
      return result
  
  
  def _ensure_sparse_format(spmatrix, accept_sparse, dtype, copy,
                            force_all_finite):
      """Convert a sparse matrix to a given format.
      Checks the sparse format of spmatrix and converts if necessary.
      Parameters
      ----------
      spmatrix : scipy sparse matrix
          Input to validate and convert.
      accept_sparse : string, boolean or list/tuple of strings
          String[s] representing allowed sparse matrix formats ('csc',
          'csr', 'coo', 'dok', 'bsr', 'lil', 'dia'). If the input is sparse but
          not in the allowed format, it will be converted to the first listed
          format. True allows the input to be any format. False means
          that a sparse matrix input will raise an error.
      dtype : string, type or None
          Data type of result. If None, the dtype of the input is preserved.
      copy : boolean
          Whether a forced copy will be triggered. If copy=False, a copy might
          be triggered by a conversion.
      force_all_finite : boolean
          Whether to raise an error on np.inf and np.nan in X.
      Returns
      -------
      spmatrix_converted : scipy sparse matrix.
          Matrix that is ensured to have an allowed type.
      """
      if dtype is None:
          dtype = spmatrix.dtype
  
      changed_format = False
  
      if isinstance(accept_sparse, six.string_types):
          accept_sparse = [accept_sparse]
  
      if accept_sparse is False:
          raise TypeError('A sparse matrix was passed, but dense '
                          'data is required. Use X.toarray() to '
                          'convert to a dense numpy array.')
      elif isinstance(accept_sparse, (list, tuple)):
          if len(accept_sparse) == 0:
              raise ValueError("When providing 'accept_sparse' "
                               "as a tuple or list, it must contain at "
                               "least one string value.")
          # ensure correct sparse format
          if spmatrix.format not in accept_sparse:
              # create new with correct sparse
              spmatrix = spmatrix.asformat(accept_sparse[0])
              changed_format = True
      elif accept_sparse is not True:
          # any other type
          raise ValueError("Parameter 'accept_sparse' should be a string, "
                           "boolean or list of strings. You provided "
                           "'accept_sparse={}'.".format(accept_sparse))
  
      if dtype != spmatrix.dtype:
          # convert dtype
          spmatrix = spmatrix.astype(dtype)
      elif copy and not changed_format:
          # force copy
          spmatrix = spmatrix.copy()
  
      if force_all_finite:
          if not hasattr(spmatrix, "data"):
              warnings.warn("Can't check %s sparse matrix for nan or inf."
                            % spmatrix.format)
          else:
              _assert_all_finite(spmatrix.data)
      return spmatrix
  
  
  def check_array(array, accept_sparse=False, dtype="numeric", order=None,
                  copy=False, force_all_finite=True, ensure_2d=True,
                  allow_nd=False, ensure_min_samples=1, ensure_min_features=1,
                  warn_on_dtype=False, estimator=None):
      """Input validation on an array, list, sparse matrix or similar.
      By default, the input is converted to an at least 2D numpy array.
      If the dtype of the array is object, attempt converting to float,
      raising on failure.
      Parameters
      ----------
      array : object
          Input object to check / convert.
      accept_sparse : string, boolean or list/tuple of strings (default=False)
          String[s] representing allowed sparse matrix formats, such as 'csc',
          'csr', etc. If the input is sparse but not in the allowed format,
          it will be converted to the first listed format. True allows the input
          to be any format. False means that a sparse matrix input will
          raise an error.
          .. deprecated:: 0.19
             Passing 'None' to parameter ``accept_sparse`` in methods is
             deprecated in version 0.19 "and will be removed in 0.21. Use
             ``accept_sparse=False`` instead.
      dtype : string, type, list of types or None (default="numeric")
          Data type of result. If None, the dtype of the input is preserved.
          If "numeric", dtype is preserved unless array.dtype is object.
          If dtype is a list of types, conversion on the first type is only
          performed if the dtype of the input is not in the list.
      order : 'F', 'C' or None (default=None)
          Whether an array will be forced to be fortran or c-style.
          When order is None (default), then if copy=False, nothing is ensured
          about the memory layout of the output array; otherwise (copy=True)
          the memory layout of the returned array is kept as close as possible
          to the original array.
      copy : boolean (default=False)
          Whether a forced copy will be triggered. If copy=False, a copy might
          be triggered by a conversion.
      force_all_finite : boolean (default=True)
          Whether to raise an error on np.inf and np.nan in X.
      ensure_2d : boolean (default=True)
          Whether to raise a value error if X is not 2d.
      allow_nd : boolean (default=False)
          Whether to allow X.ndim > 2.
      ensure_min_samples : int (default=1)
          Make sure that the array has a minimum number of samples in its first
          axis (rows for a 2D array). Setting to 0 disables this check.
      ensure_min_features : int (default=1)
          Make sure that the 2D array has some minimum number of features
          (columns). The default value of 1 rejects empty datasets.
          This check is only enforced when the input data has effectively 2
          dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
          disables this check.
      warn_on_dtype : boolean (default=False)
          Raise DataConversionWarning if the dtype of the input data structure
          does not match the requested dtype, causing a memory copy.
      estimator : str or estimator instance (default=None)
          If passed, include the name of the estimator in warning messages.
      Returns
      -------
      X_converted : object
          The converted and validated X.
      """
      # accept_sparse 'None' deprecation check
      if accept_sparse is None:
          warnings.warn(
              "Passing 'None' to parameter 'accept_sparse' in methods "
              "check_array and check_X_y is deprecated in version 0.19 "
              "and will be removed in 0.21. Use 'accept_sparse=False' "
              " instead.", DeprecationWarning)
          accept_sparse = False
  
      # store whether originally we wanted numeric dtype
      dtype_numeric = isinstance(dtype, six.string_types) and dtype == "numeric"
  
      dtype_orig = getattr(array, "dtype", None)
      if not hasattr(dtype_orig, 'kind'):
          # not a data type (e.g. a column named dtype in a pandas DataFrame)
          dtype_orig = None
  
      if dtype_numeric:
          if dtype_orig is not None and dtype_orig.kind == "O":
              # if input is object, convert to float.
              dtype = np.float64
          else:
              dtype = None
  
      if isinstance(dtype, (list, tuple)):
          if dtype_orig is not None and dtype_orig in dtype:
              # no dtype conversion required
              dtype = None
          else:
              # dtype conversion required. Let's select the first element of the
              # list of accepted types.
              dtype = dtype[0]
  
      if estimator is not None:
          if isinstance(estimator, six.string_types):
              estimator_name = estimator
          else:
              estimator_name = estimator.__class__.__name__
      else:
          estimator_name = "Estimator"
      context = " by %s" % estimator_name if estimator is not None else ""
  
      if sp.issparse(array):
          array = _ensure_sparse_format(array, accept_sparse, dtype, copy,
                                        force_all_finite)
      else:
          array = np.array(array, dtype=dtype, order=order, copy=copy)
  
          if ensure_2d:
              if array.ndim == 1:
                  raise ValueError(
                      "Expected 2D array, got 1D array instead:\narray={}.\n"
                      "Reshape your data either using array.reshape(-1, 1) if "
                      "your data has a single feature or array.reshape(1, -1) "
                      "if it contains a single sample.".format(array))
              array = np.atleast_2d(array)
              # To ensure that array flags are maintained
              array = np.array(array, dtype=dtype, order=order, copy=copy)
  
          # make sure we actually converted to numeric:
          if dtype_numeric and array.dtype.kind == "O":
              array = array.astype(np.float64)
          if not allow_nd and array.ndim >= 3:
              raise ValueError("Found array with dim %d. %s expected <= 2."
                               % (array.ndim, estimator_name))
          if force_all_finite:
              _assert_all_finite(array)
  
      shape_repr = _shape_repr(array.shape)
      if ensure_min_samples > 0:
          n_samples = _num_samples(array)
          if n_samples < ensure_min_samples:
              raise ValueError("Found array with %d sample(s) (shape=%s) while a"
                               " minimum of %d is required%s."
                               % (n_samples, shape_repr, ensure_min_samples,
                                  context))
  
      if ensure_min_features > 0 and array.ndim == 2:
          n_features = array.shape[1]
          if n_features < ensure_min_features:
              raise ValueError("Found array with %d feature(s) (shape=%s) while"
                               " a minimum of %d is required%s."
                               % (n_features, shape_repr, ensure_min_features,
                                  context))
  
      if warn_on_dtype and dtype_orig is not None and array.dtype != dtype_orig:
          msg = ("Data with input dtype %s was converted to %s%s."
                 % (dtype_orig, array.dtype, context))
          warnings.warn(msg, DataConversionWarning)
      return array
  
  
  def check_X_y(X, y, accept_sparse=False, dtype="numeric", order=None,
                copy=False, force_all_finite=True, ensure_2d=True,
                allow_nd=False, multi_output=False, ensure_min_samples=1,
                ensure_min_features=1, y_numeric=False,
                warn_on_dtype=False, estimator=None):
      """Input validation for standard estimators.
      Checks X and y for consistent length, enforces X 2d and y 1d.
      Standard input checks are only applied to y, such as checking that y
      does not have np.nan or np.inf targets. For multi-label y, set
      multi_output=True to allow 2d and sparse y.  If the dtype of X is
      object, attempt converting to float, raising on failure.
      Parameters
      ----------
      X : nd-array, list or sparse matrix
          Input data.
      y : nd-array, list or sparse matrix
          Labels.
      accept_sparse : string, boolean or list of string (default=False)
          String[s] representing allowed sparse matrix formats, such as 'csc',
          'csr', etc. If the input is sparse but not in the allowed format,
          it will be converted to the first listed format. True allows the input
          to be any format. False means that a sparse matrix input will
          raise an error.
          .. deprecated:: 0.19
             Passing 'None' to parameter ``accept_sparse`` in methods is
             deprecated in version 0.19 "and will be removed in 0.21. Use
             ``accept_sparse=False`` instead.
      dtype : string, type, list of types or None (default="numeric")
          Data type of result. If None, the dtype of the input is preserved.
          If "numeric", dtype is preserved unless array.dtype is object.
          If dtype is a list of types, conversion on the first type is only
          performed if the dtype of the input is not in the list.
      order : 'F', 'C' or None (default=None)
          Whether an array will be forced to be fortran or c-style.
      copy : boolean (default=False)
          Whether a forced copy will be triggered. If copy=False, a copy might
          be triggered by a conversion.
      force_all_finite : boolean (default=True)
          Whether to raise an error on np.inf and np.nan in X. This parameter
          does not influence whether y can have np.inf or np.nan values.
      ensure_2d : boolean (default=True)
          Whether to make X at least 2d.
      allow_nd : boolean (default=False)
          Whether to allow X.ndim > 2.
      multi_output : boolean (default=False)
          Whether to allow 2-d y (array or sparse matrix). If false, y will be
          validated as a vector. y cannot have np.nan or np.inf values if
          multi_output=True.
      ensure_min_samples : int (default=1)
          Make sure that X has a minimum number of samples in its first
          axis (rows for a 2D array).
      ensure_min_features : int (default=1)
          Make sure that the 2D array has some minimum number of features
          (columns). The default value of 1 rejects empty datasets.
          This check is only enforced when X has effectively 2 dimensions or
          is originally 1D and ``ensure_2d`` is True. Setting to 0 disables
          this check.
      y_numeric : boolean (default=False)
          Whether to ensure that y has a numeric type. If dtype of y is object,
          it is converted to float64. Should only be used for regression
          algorithms.
      warn_on_dtype : boolean (default=False)
          Raise DataConversionWarning if the dtype of the input data structure
          does not match the requested dtype, causing a memory copy.
      estimator : str or estimator instance (default=None)
          If passed, include the name of the estimator in warning messages.
      Returns
      -------
      X_converted : object
          The converted and validated X.
      y_converted : object
          The converted and validated y.
      """
      X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,
                      ensure_2d, allow_nd, ensure_min_samples,
                      ensure_min_features, warn_on_dtype, estimator)
      if multi_output:
          y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,
                          dtype=None)
      else:
          y = column_or_1d(y, warn=True)
          _assert_all_finite(y)
      if y_numeric and y.dtype.kind == 'O':
          y = y.astype(np.float64)
  
      check_consistent_length(X, y)
  
      return X, y
  
  
  def column_or_1d(y, warn=False):
      """ Ravel column or 1d numpy array, else raises an error
      Parameters
      ----------
      y : array-like
      warn : boolean, default False
         To control display of warnings.
      Returns
      -------
      y : array
      """
      shape = np.shape(y)
      if len(shape) == 1:
          return np.ravel(y)
      if len(shape) == 2 and shape[1] == 1:
          if warn:
              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, stacklevel=2)
          return np.ravel(y)
  
      raise ValueError("bad input shape {0}".format(shape))
  
  
  def check_random_state(seed):
      """Turn seed into a np.random.RandomState instance
      Parameters
      ----------
      seed : None | int | instance of RandomState
          If seed is None, return the RandomState singleton used by np.random.
          If seed is an int, return a new RandomState instance seeded with seed.
          If seed is already a RandomState instance, return it.
          Otherwise raise ValueError.
      """
      if seed is None or seed is np.random:
          return np.random.mtrand._rand
      if isinstance(seed, (numbers.Integral, np.integer)):
          return np.random.RandomState(seed)
      if isinstance(seed, np.random.RandomState):
          return seed
      raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
                       ' instance' % seed)
  
  
  def has_fit_parameter(estimator, parameter):
      """Checks whether the estimator's fit method supports the given parameter.
      Parameters
      ----------
      estimator : object
          An estimator to inspect.
      parameter: str
          The searched parameter.
      Returns
      -------
      is_parameter: bool
          Whether the parameter was found to be a named parameter of the
          estimator's fit method.
      Examples
      --------
      >>> from sklearn.svm import SVC
      >>> has_fit_parameter(SVC(), "sample_weight")
      True
      """
      return parameter in signature(estimator.fit).parameters
  
  
  def check_symmetric(array, tol=1E-10, raise_warning=True,
                      raise_exception=False):
      """Make sure that array is 2D, square and symmetric.
      If the array is not symmetric, then a symmetrized version is returned.
      Optionally, a warning or exception is raised if the matrix is not
      symmetric.
      Parameters
      ----------
      array : nd-array or sparse matrix
          Input object to check / convert. Must be two-dimensional and square,
          otherwise a ValueError will be raised.
      tol : float
          Absolute tolerance for equivalence of arrays. Default = 1E-10.
      raise_warning : boolean (default=True)
          If True then raise a warning if conversion is required.
      raise_exception : boolean (default=False)
          If True then raise an exception if array is not symmetric.
      Returns
      -------
      array_sym : ndarray or sparse matrix
          Symmetrized version of the input array, i.e. the average of array
          and array.transpose(). If sparse, then duplicate entries are first
          summed and zeros are eliminated.
      """
      if (array.ndim != 2) or (array.shape[0] != array.shape[1]):
          raise ValueError("array must be 2-dimensional and square. "
                           "shape = {0}".format(array.shape))
  
      if sp.issparse(array):
          diff = array - array.T
          # only csr, csc, and coo have `data` attribute
          if diff.format not in ['csr', 'csc', 'coo']:
              diff = diff.tocsr()
          symmetric = np.all(abs(diff.data) < tol)
      else:
          symmetric = np.allclose(array, array.T, atol=tol)
  
      if not symmetric:
          if raise_exception:
              raise ValueError("Array must be symmetric")
          if raise_warning:
              warnings.warn("Array is not symmetric, and will be converted "
                            "to symmetric by average with its transpose.")
          if sp.issparse(array):
              conversion = 'to' + array.format
              array = getattr(0.5 * (array + array.T), conversion)()
          else:
              array = 0.5 * (array + array.T)
  
      return array
  
  
  def check_is_fitted(estimator, attributes, msg=None, all_or_any=all):
      """Perform is_fitted validation for estimator.
      Checks if the estimator is fitted by verifying the presence of
      "all_or_any" of the passed attributes and raises a NotFittedError with the
      given message.
      Parameters
      ----------
      estimator : estimator instance.
          estimator instance for which the check is performed.
      attributes : attribute name(s) given as string or a list/tuple of strings
          Eg.:
              ``["coef_", "estimator_", ...], "coef_"``
      msg : string
          The default error message is, "This %(name)s instance is not fitted
          yet. Call 'fit' with appropriate arguments before using this method."
          For custom messages if "%(name)s" is present in the message string,
          it is substituted for the estimator name.
          Eg. : "Estimator, %(name)s, must be fitted before sparsifying".
      all_or_any : callable, {all, any}, default all
          Specify whether all or any of the given attributes must exist.
      Returns
      -------
      None
      Raises
      ------
      NotFittedError
          If the attributes are not found.
      """
      if msg is None:
          msg = ("This %(name)s instance is not fitted yet. Call 'fit' with "
                 "appropriate arguments before using this method.")
  
      if not hasattr(estimator, 'fit'):
          raise TypeError("%s is not an estimator instance." % (estimator))
  
      if not isinstance(attributes, (list, tuple)):
          attributes = [attributes]
  
      if not all_or_any([hasattr(estimator, attr) for attr in attributes]):
          raise NotFittedError(msg % {'name': type(estimator).__name__})
  
  
  def check_non_negative(X, whom):
      """
      Check if there is any negative value in an array.
      Parameters
      ----------
      X : array-like or sparse matrix
          Input data.
      whom : string
          Who passed X to this function.
      """
      X = X.data if sp.issparse(X) else X
      if (X < 0).any():
          raise ValueError("Negative values in data passed to %s" % whom)
  