234 lines
8 KiB
Python
234 lines
8 KiB
Python
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from itertools import product
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import numpy as np
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from .. import confusion_matrix
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from ...utils import check_matplotlib_support
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from ...utils.validation import _deprecate_positional_args
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from ...base import is_classifier
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class ConfusionMatrixDisplay:
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"""Confusion Matrix visualization.
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It is recommend to use :func:`~sklearn.metrics.plot_confusion_matrix` to
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create a :class:`ConfusionMatrixDisplay`. All parameters are stored as
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attributes.
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Read more in the :ref:`User Guide <visualizations>`.
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Parameters
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----------
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confusion_matrix : ndarray of shape (n_classes, n_classes)
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Confusion matrix.
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display_labels : ndarray of shape (n_classes,), default=None
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Display labels for plot. If None, display labels are set from 0 to
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`n_classes - 1`.
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Attributes
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----------
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im_ : matplotlib AxesImage
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Image representing the confusion matrix.
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text_ : ndarray of shape (n_classes, n_classes), dtype=matplotlib Text, \
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or None
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Array of matplotlib axes. `None` if `include_values` is false.
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ax_ : matplotlib Axes
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Axes with confusion matrix.
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figure_ : matplotlib Figure
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Figure containing the confusion matrix.
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"""
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def __init__(self, confusion_matrix, *, display_labels=None):
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self.confusion_matrix = confusion_matrix
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self.display_labels = display_labels
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@_deprecate_positional_args
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def plot(self, *, include_values=True, cmap='viridis',
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xticks_rotation='horizontal', values_format=None, ax=None):
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"""Plot visualization.
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Parameters
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----------
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include_values : bool, default=True
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Includes values in confusion matrix.
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cmap : str or matplotlib Colormap, default='viridis'
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Colormap recognized by matplotlib.
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xticks_rotation : {'vertical', 'horizontal'} or float, \
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default='horizontal'
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Rotation of xtick labels.
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values_format : str, default=None
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Format specification for values in confusion matrix. If `None`,
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the format specification is 'd' or '.2g' whichever is shorter.
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ax : matplotlib axes, default=None
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Axes object to plot on. If `None`, a new figure and axes is
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created.
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Returns
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-------
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display : :class:`~sklearn.metrics.ConfusionMatrixDisplay`
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"""
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check_matplotlib_support("ConfusionMatrixDisplay.plot")
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import matplotlib.pyplot as plt
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if ax is None:
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fig, ax = plt.subplots()
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else:
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fig = ax.figure
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cm = self.confusion_matrix
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n_classes = cm.shape[0]
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self.im_ = ax.imshow(cm, interpolation='nearest', cmap=cmap)
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self.text_ = None
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cmap_min, cmap_max = self.im_.cmap(0), self.im_.cmap(256)
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if include_values:
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self.text_ = np.empty_like(cm, dtype=object)
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# print text with appropriate color depending on background
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thresh = (cm.max() + cm.min()) / 2.0
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for i, j in product(range(n_classes), range(n_classes)):
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color = cmap_max if cm[i, j] < thresh else cmap_min
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if values_format is None:
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text_cm = format(cm[i, j], '.2g')
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if cm.dtype.kind != 'f':
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text_d = format(cm[i, j], 'd')
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if len(text_d) < len(text_cm):
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text_cm = text_d
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else:
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text_cm = format(cm[i, j], values_format)
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self.text_[i, j] = ax.text(
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j, i, text_cm,
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ha="center", va="center",
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color=color)
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if self.display_labels is None:
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display_labels = np.arange(n_classes)
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else:
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display_labels = self.display_labels
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fig.colorbar(self.im_, ax=ax)
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ax.set(xticks=np.arange(n_classes),
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yticks=np.arange(n_classes),
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xticklabels=display_labels,
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yticklabels=display_labels,
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ylabel="True label",
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xlabel="Predicted label")
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ax.set_ylim((n_classes - 0.5, -0.5))
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plt.setp(ax.get_xticklabels(), rotation=xticks_rotation)
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self.figure_ = fig
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self.ax_ = ax
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return self
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@_deprecate_positional_args
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def plot_confusion_matrix(estimator, X, y_true, *, labels=None,
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sample_weight=None, normalize=None,
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display_labels=None, include_values=True,
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xticks_rotation='horizontal',
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values_format=None,
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cmap='viridis', ax=None):
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"""Plot Confusion Matrix.
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Read more in the :ref:`User Guide <confusion_matrix>`.
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Parameters
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----------
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estimator : estimator instance
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Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
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in which the last estimator is a classifier.
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Input values.
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y : array-like of shape (n_samples,)
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Target values.
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labels : array-like of shape (n_classes,), default=None
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List of labels to index the matrix. This may be used to reorder or
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select a subset of labels. If `None` is given, those that appear at
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least once in `y_true` or `y_pred` are used in sorted order.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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normalize : {'true', 'pred', 'all'}, default=None
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Normalizes confusion matrix over the true (rows), predicted (columns)
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conditions or all the population. If None, confusion matrix will not be
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normalized.
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display_labels : array-like of shape (n_classes,), default=None
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Target names used for plotting. By default, `labels` will be used if
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it is defined, otherwise the unique labels of `y_true` and `y_pred`
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will be used.
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include_values : bool, default=True
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Includes values in confusion matrix.
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xticks_rotation : {'vertical', 'horizontal'} or float, \
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default='horizontal'
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Rotation of xtick labels.
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values_format : str, default=None
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Format specification for values in confusion matrix. If `None`,
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the format specification is 'd' or '.2g' whichever is shorter.
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cmap : str or matplotlib Colormap, default='viridis'
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Colormap recognized by matplotlib.
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ax : matplotlib Axes, default=None
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Axes object to plot on. If `None`, a new figure and axes is
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created.
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Returns
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-------
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display : :class:`~sklearn.metrics.ConfusionMatrixDisplay`
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Examples
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--------
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>>> import matplotlib.pyplot as plt # doctest: +SKIP
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>>> from sklearn.datasets import make_classification
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>>> from sklearn.metrics import plot_confusion_matrix
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>>> from sklearn.model_selection import train_test_split
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>>> from sklearn.svm import SVC
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>>> X, y = make_classification(random_state=0)
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>>> X_train, X_test, y_train, y_test = train_test_split(
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... X, y, random_state=0)
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>>> clf = SVC(random_state=0)
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>>> clf.fit(X_train, y_train)
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SVC(random_state=0)
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>>> plot_confusion_matrix(clf, X_test, y_test) # doctest: +SKIP
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>>> plt.show() # doctest: +SKIP
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"""
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check_matplotlib_support("plot_confusion_matrix")
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if not is_classifier(estimator):
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raise ValueError("plot_confusion_matrix only supports classifiers")
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y_pred = estimator.predict(X)
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cm = confusion_matrix(y_true, y_pred, sample_weight=sample_weight,
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labels=labels, normalize=normalize)
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if display_labels is None:
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if labels is None:
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display_labels = estimator.classes_
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else:
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display_labels = labels
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disp = ConfusionMatrixDisplay(confusion_matrix=cm,
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display_labels=display_labels)
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return disp.plot(include_values=include_values,
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cmap=cmap, ax=ax, xticks_rotation=xticks_rotation,
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values_format=values_format)
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