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confusion_matrix.py
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confusion_matrix.py
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from itertools import product
import numpy as np
from .. import confusion_matrix
from ...utils import check_matplotlib_support
from ...base import is_classifier
class ConfusionMatrixDisplay:
"""Confusion Matrix visualization.
It is recommend to use :func:`~sklearn.metrics.plot_confusion_matrix` to
create a :class:`ConfusionMatrixDisplay`. All parameters are stored as
attributes.
Read more in the :ref:`User Guide <visualizations>`.
Parameters
----------
confusion_matrix : ndarray of shape (n_classes, n_classes)
Confusion matrix.
display_labels : ndarray of shape (n_classes,)
Display labels for plot.
Attributes
----------
im_ : matplotlib AxesImage
Image representing the confusion matrix.
text_ : ndarray of shape (n_classes, n_classes), dtype=matplotlib Text, \
or None
Array of matplotlib axes. `None` if `include_values` is false.
ax_ : matplotlib Axes
Axes with confusion matrix.
figure_ : matplotlib Figure
Figure containing the confusion matrix.
"""
def __init__(self, confusion_matrix, display_labels):
self.confusion_matrix = confusion_matrix
self.display_labels = display_labels
def plot(self, include_values=True, cmap='viridis',
xticks_rotation='horizontal', values_format=None, ax=None):
"""Plot visualization.
Parameters
----------
include_values : bool, default=True
Includes values in confusion matrix.
cmap : str or matplotlib Colormap, default='viridis'
Colormap recognized by matplotlib.
xticks_rotation : {'vertical', 'horizontal'} or float, \
default='horizontal'
Rotation of xtick labels.
values_format : str, default=None
Format specification for values in confusion matrix. If `None`,
the format specification is '.2g'.
ax : matplotlib axes, default=None
Axes object to plot on. If `None`, a new figure and axes is
created.
Returns
-------
display : :class:`~sklearn.metrics.ConfusionMatrixDisplay`
"""
check_matplotlib_support("ConfusionMatrixDisplay.plot")
import matplotlib.pyplot as plt
if ax is None:
fig, ax = plt.subplots()
else:
fig = ax.figure
cm = self.confusion_matrix
n_classes = cm.shape[0]
self.im_ = ax.imshow(cm, interpolation='nearest', cmap=cmap)
self.text_ = None
cmap_min, cmap_max = self.im_.cmap(0), self.im_.cmap(256)
if include_values:
self.text_ = np.empty_like(cm, dtype=object)
if values_format is None:
values_format = '.2g'
# print text with appropriate color depending on background
thresh = (cm.max() + cm.min()) / 2.0
for i, j in product(range(n_classes), range(n_classes)):
color = cmap_max if cm[i, j] < thresh else cmap_min
self.text_[i, j] = ax.text(j, i,
format(cm[i, j], values_format),
ha="center", va="center",
color=color)
fig.colorbar(self.im_, ax=ax)
ax.set(xticks=np.arange(n_classes),
yticks=np.arange(n_classes),
xticklabels=self.display_labels,
yticklabels=self.display_labels,
ylabel="True label",
xlabel="Predicted label")
ax.set_ylim((n_classes - 0.5, -0.5))
plt.setp(ax.get_xticklabels(), rotation=xticks_rotation)
self.figure_ = fig
self.ax_ = ax
return self
def plot_confusion_matrix(estimator, X, y_true, labels=None,
sample_weight=None, normalize=None,
display_labels=None, include_values=True,
xticks_rotation='horizontal',
values_format=None,
cmap='viridis', ax=None):
"""Plot Confusion Matrix.
Read more in the :ref:`User Guide <confusion_matrix>`.
Parameters
----------
estimator : estimator instance
Trained classifier.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input values.
y : array-like of shape (n_samples,)
Target values.
labels : array-like of shape (n_classes,), default=None
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,), default=None
Sample weights.
normalize : {'true', 'pred', 'all'}, default=None
Normalizes confusion matrix over the true (rows), predicted (columns)
conditions or all the population. If None, confusion matrix will not be
normalized.
display_labels : array-like of shape (n_classes,), default=None
Target names used for plotting. By default, `labels` will be used if
it is defined, otherwise the unique labels of `y_true` and `y_pred`
will be used.
include_values : bool, default=True
Includes values in confusion matrix.
xticks_rotation : {'vertical', 'horizontal'} or float, \
default='horizontal'
Rotation of xtick labels.
values_format : str, default=None
Format specification for values in confusion matrix. If `None`,
the format specification is '.2g'.
cmap : str or matplotlib Colormap, default='viridis'
Colormap recognized by matplotlib.
ax : matplotlib Axes, default=None
Axes object to plot on. If `None`, a new figure and axes is
created.
Returns
-------
display : :class:`~sklearn.metrics.ConfusionMatrixDisplay`
"""
check_matplotlib_support("plot_confusion_matrix")
if not is_classifier(estimator):
raise ValueError("plot_confusion_matrix only supports classifiers")
y_pred = estimator.predict(X)
cm = confusion_matrix(y_true, y_pred, sample_weight=sample_weight,
labels=labels, normalize=normalize)
if display_labels is None:
if labels is None:
display_labels = estimator.classes_
else:
display_labels = labels
disp = ConfusionMatrixDisplay(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)