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import numpy as np |
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from . import check_consistent_length |
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from ._optional_dependencies import check_matplotlib_support |
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from ._response import _get_response_values_binary |
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from .multiclass import type_of_target |
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from .validation import _check_pos_label_consistency |
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class _BinaryClassifierCurveDisplayMixin: |
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"""Mixin class to be used in Displays requiring a binary classifier. |
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The aim of this class is to centralize some validations regarding the estimator and |
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the target and gather the response of the estimator. |
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""" |
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def _validate_plot_params(self, *, ax=None, name=None): |
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check_matplotlib_support(f"{self.__class__.__name__}.plot") |
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import matplotlib.pyplot as plt |
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if ax is None: |
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_, ax = plt.subplots() |
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name = self.estimator_name if name is None else name |
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return ax, ax.figure, name |
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@classmethod |
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def _validate_and_get_response_values( |
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cls, estimator, X, y, *, response_method="auto", pos_label=None, name=None |
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): |
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check_matplotlib_support(f"{cls.__name__}.from_estimator") |
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name = estimator.__class__.__name__ if name is None else name |
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y_pred, pos_label = _get_response_values_binary( |
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estimator, |
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X, |
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response_method=response_method, |
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pos_label=pos_label, |
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) |
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return y_pred, pos_label, name |
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@classmethod |
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def _validate_from_predictions_params( |
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cls, y_true, y_pred, *, sample_weight=None, pos_label=None, name=None |
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): |
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check_matplotlib_support(f"{cls.__name__}.from_predictions") |
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if type_of_target(y_true) != "binary": |
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raise ValueError( |
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f"The target y is not binary. Got {type_of_target(y_true)} type of" |
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" target." |
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) |
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check_consistent_length(y_true, y_pred, sample_weight) |
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pos_label = _check_pos_label_consistency(pos_label, y_true) |
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name = name if name is not None else "Classifier" |
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return pos_label, name |
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def _validate_score_name(score_name, scoring, negate_score): |
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"""Validate the `score_name` parameter. |
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If `score_name` is provided, we just return it as-is. |
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If `score_name` is `None`, we use `Score` if `negate_score` is `False` and |
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`Negative score` otherwise. |
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If `score_name` is a string or a callable, we infer the name. We replace `_` by |
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spaces and capitalize the first letter. We remove `neg_` and replace it by |
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`"Negative"` if `negate_score` is `False` or just remove it otherwise. |
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""" |
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if score_name is not None: |
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return score_name |
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elif scoring is None: |
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return "Negative score" if negate_score else "Score" |
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else: |
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score_name = scoring.__name__ if callable(scoring) else scoring |
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if negate_score: |
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if score_name.startswith("neg_"): |
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score_name = score_name[4:] |
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else: |
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score_name = f"Negative {score_name}" |
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elif score_name.startswith("neg_"): |
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score_name = f"Negative {score_name[4:]}" |
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score_name = score_name.replace("_", " ") |
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return score_name.capitalize() |
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def _interval_max_min_ratio(data): |
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"""Compute the ratio between the largest and smallest inter-point distances. |
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A value larger than 5 typically indicates that the parameter range would |
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better be displayed with a log scale while a linear scale would be more |
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suitable otherwise. |
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""" |
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diff = np.diff(np.sort(data)) |
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return diff.max() / diff.min() |
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def _validate_style_kwargs(default_style_kwargs, user_style_kwargs): |
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"""Create valid style kwargs by avoiding Matplotlib alias errors. |
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Matplotlib raises an error when, for example, 'color' and 'c', or 'linestyle' and |
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'ls', are specified together. To avoid this, we automatically keep only the one |
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specified by the user and raise an error if the user specifies both. |
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Parameters |
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---------- |
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default_style_kwargs : dict |
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The Matplotlib style kwargs used by default in the scikit-learn display. |
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user_style_kwargs : dict |
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The user-defined Matplotlib style kwargs. |
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Returns |
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------- |
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valid_style_kwargs : dict |
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The validated style kwargs taking into account both default and user-defined |
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Matplotlib style kwargs. |
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""" |
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invalid_to_valid_kw = { |
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"ls": "linestyle", |
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"c": "color", |
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"ec": "edgecolor", |
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"fc": "facecolor", |
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"lw": "linewidth", |
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"mec": "markeredgecolor", |
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"mfcalt": "markerfacecoloralt", |
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"ms": "markersize", |
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"mew": "markeredgewidth", |
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"mfc": "markerfacecolor", |
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"aa": "antialiased", |
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"ds": "drawstyle", |
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"font": "fontproperties", |
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"family": "fontfamily", |
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"name": "fontname", |
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"size": "fontsize", |
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"stretch": "fontstretch", |
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"style": "fontstyle", |
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"variant": "fontvariant", |
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"weight": "fontweight", |
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"ha": "horizontalalignment", |
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"va": "verticalalignment", |
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"ma": "multialignment", |
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} |
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for invalid_key, valid_key in invalid_to_valid_kw.items(): |
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if invalid_key in user_style_kwargs and valid_key in user_style_kwargs: |
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raise TypeError( |
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f"Got both {invalid_key} and {valid_key}, which are aliases of one " |
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"another" |
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) |
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valid_style_kwargs = default_style_kwargs.copy() |
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for key in user_style_kwargs.keys(): |
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if key in invalid_to_valid_kw: |
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valid_style_kwargs[invalid_to_valid_kw[key]] = user_style_kwargs[key] |
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else: |
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valid_style_kwargs[key] = user_style_kwargs[key] |
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return valid_style_kwargs |
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def _despine(ax): |
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"""Remove the top and right spines of the plot. |
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Parameters |
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---------- |
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ax : matplotlib.axes.Axes |
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The axes of the plot to despine. |
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""" |
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for s in ["top", "right"]: |
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ax.spines[s].set_visible(False) |
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for s in ["bottom", "left"]: |
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ax.spines[s].set_bounds(0, 1) |
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