483 lines
16 KiB
Python
483 lines
16 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.multiclass import unique_labels
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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
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:func:`~sklearn.metrics.ConfusionMatrixDisplay.from_estimator` or
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:func:`~sklearn.metrics.ConfusionMatrixDisplay.from_predictions` 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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See Also
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--------
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confusion_matrix : Compute Confusion Matrix to evaluate the accuracy of a
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classification.
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ConfusionMatrixDisplay.from_estimator : Plot the confusion matrix
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given an estimator, the data, and the label.
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ConfusionMatrixDisplay.from_predictions : Plot the confusion matrix
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given the true and predicted labels.
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Examples
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--------
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>>> import matplotlib.pyplot as plt
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>>> from sklearn.datasets import make_classification
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>>> from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
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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(X, y,
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... 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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>>> predictions = clf.predict(X_test)
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>>> cm = confusion_matrix(y_test, predictions, labels=clf.classes_)
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>>> disp = ConfusionMatrixDisplay(confusion_matrix=cm,
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... display_labels=clf.classes_)
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>>> disp.plot()
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<...>
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>>> plt.show()
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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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def plot(
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self,
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*,
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include_values=True,
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cmap="viridis",
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xticks_rotation="horizontal",
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values_format=None,
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ax=None,
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colorbar=True,
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im_kw=None,
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text_kw=None,
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):
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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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colorbar : bool, default=True
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Whether or not to add a colorbar to the plot.
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im_kw : dict, default=None
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Dict with keywords passed to `matplotlib.pyplot.imshow` call.
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text_kw : dict, default=None
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Dict with keywords passed to `matplotlib.pyplot.text` call.
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.. versionadded:: 1.2
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Returns
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-------
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display : :class:`~sklearn.metrics.ConfusionMatrixDisplay`
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Returns a :class:`~sklearn.metrics.ConfusionMatrixDisplay` instance
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that contains all the information to plot the confusion matrix.
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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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default_im_kw = dict(interpolation="nearest", cmap=cmap)
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im_kw = im_kw or {}
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im_kw = {**default_im_kw, **im_kw}
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text_kw = text_kw or {}
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self.im_ = ax.imshow(cm, **im_kw)
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self.text_ = None
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cmap_min, cmap_max = self.im_.cmap(0), self.im_.cmap(1.0)
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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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default_text_kwargs = dict(ha="center", va="center", color=color)
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text_kwargs = {**default_text_kwargs, **text_kw}
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self.text_[i, j] = ax.text(j, i, text_cm, **text_kwargs)
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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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if colorbar:
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fig.colorbar(self.im_, ax=ax)
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ax.set(
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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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)
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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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@classmethod
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def from_estimator(
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cls,
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estimator,
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X,
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y,
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*,
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labels=None,
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sample_weight=None,
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normalize=None,
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display_labels=None,
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include_values=True,
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xticks_rotation="horizontal",
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values_format=None,
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cmap="viridis",
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ax=None,
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colorbar=True,
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im_kw=None,
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text_kw=None,
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):
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"""Plot Confusion Matrix given an estimator and some data.
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Read more in the :ref:`User Guide <confusion_matrix>`.
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.. versionadded:: 1.0
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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 confusion matrix. This may be used to
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reorder or select a subset of labels. If `None` is given, those
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that appear at least once in `y_true` or `y_pred` are used in
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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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Either to normalize the counts display in the matrix:
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- if `'true'`, the confusion matrix is normalized over the true
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conditions (e.g. rows);
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- if `'pred'`, the confusion matrix is normalized over the
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predicted conditions (e.g. columns);
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- if `'all'`, the confusion matrix is normalized by the total
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number of samples;
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- if `None` (default), the confusion matrix will not be 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
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if it is defined, otherwise the unique labels of `y_true` and
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`y_pred` 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`, the
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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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colorbar : bool, default=True
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Whether or not to add a colorbar to the plot.
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im_kw : dict, default=None
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Dict with keywords passed to `matplotlib.pyplot.imshow` call.
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text_kw : dict, default=None
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Dict with keywords passed to `matplotlib.pyplot.text` call.
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.. versionadded:: 1.2
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Returns
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-------
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display : :class:`~sklearn.metrics.ConfusionMatrixDisplay`
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See Also
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--------
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ConfusionMatrixDisplay.from_predictions : Plot the confusion matrix
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given the true and predicted labels.
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Examples
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--------
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>>> import matplotlib.pyplot as plt
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>>> from sklearn.datasets import make_classification
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>>> from sklearn.metrics import ConfusionMatrixDisplay
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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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>>> ConfusionMatrixDisplay.from_estimator(
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... clf, X_test, y_test)
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<...>
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>>> plt.show()
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"""
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method_name = f"{cls.__name__}.from_estimator"
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check_matplotlib_support(method_name)
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if not is_classifier(estimator):
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raise ValueError(f"{method_name} only supports classifiers")
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y_pred = estimator.predict(X)
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return cls.from_predictions(
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y,
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y_pred,
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sample_weight=sample_weight,
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labels=labels,
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normalize=normalize,
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display_labels=display_labels,
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include_values=include_values,
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cmap=cmap,
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ax=ax,
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xticks_rotation=xticks_rotation,
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values_format=values_format,
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colorbar=colorbar,
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im_kw=im_kw,
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text_kw=text_kw,
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)
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@classmethod
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def from_predictions(
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cls,
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y_true,
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y_pred,
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*,
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labels=None,
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sample_weight=None,
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normalize=None,
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display_labels=None,
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include_values=True,
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xticks_rotation="horizontal",
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values_format=None,
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cmap="viridis",
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ax=None,
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colorbar=True,
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im_kw=None,
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text_kw=None,
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):
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"""Plot Confusion Matrix given true and predicted labels.
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Read more in the :ref:`User Guide <confusion_matrix>`.
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.. versionadded:: 1.0
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Parameters
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----------
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y_true : array-like of shape (n_samples,)
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True labels.
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y_pred : array-like of shape (n_samples,)
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The predicted labels given by the method `predict` of an
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classifier.
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labels : array-like of shape (n_classes,), default=None
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List of labels to index the confusion matrix. This may be used to
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reorder or select a subset of labels. If `None` is given, those
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that appear at least once in `y_true` or `y_pred` are used in
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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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Either to normalize the counts display in the matrix:
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- if `'true'`, the confusion matrix is normalized over the true
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conditions (e.g. rows);
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- if `'pred'`, the confusion matrix is normalized over the
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predicted conditions (e.g. columns);
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|
- if `'all'`, the confusion matrix is normalized by the total
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|
number of samples;
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|
- if `None` (default), the confusion matrix will not be normalized.
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|
|
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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
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|
if it is defined, otherwise the unique labels of `y_true` and
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|
`y_pred` will be used.
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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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|
|
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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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||
|
|
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||
|
|
values_format : str, default=None
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||
|
|
Format specification for values in confusion matrix. If `None`, the
|
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|
|
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
|
||
|
|
Axes object to plot on. If `None`, a new figure and axes is
|
||
|
|
created.
|
||
|
|
|
||
|
|
colorbar : bool, default=True
|
||
|
|
Whether or not to add a colorbar to the plot.
|
||
|
|
|
||
|
|
im_kw : dict, default=None
|
||
|
|
Dict with keywords passed to `matplotlib.pyplot.imshow` call.
|
||
|
|
|
||
|
|
text_kw : dict, default=None
|
||
|
|
Dict with keywords passed to `matplotlib.pyplot.text` call.
|
||
|
|
|
||
|
|
.. versionadded:: 1.2
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
display : :class:`~sklearn.metrics.ConfusionMatrixDisplay`
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
ConfusionMatrixDisplay.from_estimator : Plot the confusion matrix
|
||
|
|
given an estimator, the data, and the label.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> import matplotlib.pyplot as plt
|
||
|
|
>>> from sklearn.datasets import make_classification
|
||
|
|
>>> from sklearn.metrics import ConfusionMatrixDisplay
|
||
|
|
>>> from sklearn.model_selection import train_test_split
|
||
|
|
>>> from sklearn.svm import SVC
|
||
|
|
>>> X, y = make_classification(random_state=0)
|
||
|
|
>>> X_train, X_test, y_train, y_test = train_test_split(
|
||
|
|
... X, y, random_state=0)
|
||
|
|
>>> clf = SVC(random_state=0)
|
||
|
|
>>> clf.fit(X_train, y_train)
|
||
|
|
SVC(random_state=0)
|
||
|
|
>>> y_pred = clf.predict(X_test)
|
||
|
|
>>> ConfusionMatrixDisplay.from_predictions(
|
||
|
|
... y_test, y_pred)
|
||
|
|
<...>
|
||
|
|
>>> plt.show()
|
||
|
|
"""
|
||
|
|
check_matplotlib_support(f"{cls.__name__}.from_predictions")
|
||
|
|
|
||
|
|
if display_labels is None:
|
||
|
|
if labels is None:
|
||
|
|
display_labels = unique_labels(y_true, y_pred)
|
||
|
|
else:
|
||
|
|
display_labels = labels
|
||
|
|
|
||
|
|
cm = confusion_matrix(
|
||
|
|
y_true,
|
||
|
|
y_pred,
|
||
|
|
sample_weight=sample_weight,
|
||
|
|
labels=labels,
|
||
|
|
normalize=normalize,
|
||
|
|
)
|
||
|
|
|
||
|
|
disp = cls(confusion_matrix=cm, display_labels=display_labels)
|
||
|
|
|
||
|
|
return disp.plot(
|
||
|
|
include_values=include_values,
|
||
|
|
cmap=cmap,
|
||
|
|
ax=ax,
|
||
|
|
xticks_rotation=xticks_rotation,
|
||
|
|
values_format=values_format,
|
||
|
|
colorbar=colorbar,
|
||
|
|
im_kw=im_kw,
|
||
|
|
text_kw=text_kw,
|
||
|
|
)
|