*YHhChddlmZddlZddlmZddlmZddlm Z ddl m Z dd l m Z Gd d Zy) )productN) is_classifier)check_matplotlib_support)_validate_style_kwargs) unique_labels)confusion_matrixceZdZdZdddZdddddddddd Zeddddddddddddd d Zeddddddddddddd d Zy) ConfusionMatrixDisplaya Confusion Matrix visualization. It is recommended to use :func:`~sklearn.metrics.ConfusionMatrixDisplay.from_estimator` or :func:`~sklearn.metrics.ConfusionMatrixDisplay.from_predictions` to create a :class:`ConfusionMatrixDisplay`. All parameters are stored as attributes. For general information regarding `scikit-learn` visualization tools, see the :ref:`Visualization Guide `. For guidance on interpreting these plots, refer to the :ref:`Model Evaluation Guide `. Parameters ---------- confusion_matrix : ndarray of shape (n_classes, n_classes) Confusion matrix. display_labels : ndarray of shape (n_classes,), default=None Display labels for plot. If None, display labels are set from 0 to `n_classes - 1`. 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. See Also -------- confusion_matrix : Compute Confusion Matrix to evaluate the accuracy of a classification. ConfusionMatrixDisplay.from_estimator : Plot the confusion matrix given an estimator, the data, and the label. ConfusionMatrixDisplay.from_predictions : Plot the confusion matrix given the true and predicted labels. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.metrics import confusion_matrix, 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) >>> predictions = clf.predict(X_test) >>> cm = confusion_matrix(y_test, predictions, labels=clf.classes_) >>> disp = ConfusionMatrixDisplay(confusion_matrix=cm, ... display_labels=clf.classes_) >>> disp.plot() <...> >>> plt.show() N)display_labelsc ||_||_y)Nr r )selfr r s _/var/www/html/planif/env/lib/python3.12/site-packages/sklearn/metrics/_plot/confusion_matrix.py__init__zConfusionMatrixDisplay.__init__Ts 0,Tviridis horizontal)include_valuescmapxticks_rotation values_formataxcolorbarim_kwtext_kwcvtdddlm} || j\} }n |j} |j } | j d} td|} |xsi}t| |}|xsi}|j| fi||_ d|_ |jjd|jjd}}|rtj| t|_ | j!| j#zdz }t%t'| t'| D]\}}| ||f|kr|n|}|Ut)| ||fd }| j*j,d k7r?' :llnGC))C  " "HHQK 94@  &}e<-R299R)5) !XX]]1-txx}}S/A( r8DJffh)S0Fi 0% 2BC I1$&q!tHv$58 ($R1Xu5Gxx}}+!'1a4#!6v;W5&,G$R1X}=G&*h85&Q#45H'R #*2771a#HK#H 1a4  I"    &YYy1N!00N  LLbL ) 99Y'99Y'&&$   Y_d+, ##%@  r) labels sample_weight normalizer rrrrrrrrc |jd}t|t|st|d|j |}|j |||||||| | | | | ||S)a9Plot Confusion Matrix given an estimator and some data. For general information regarding `scikit-learn` visualization tools, see the :ref:`Visualization Guide `. For guidance on interpreting these plots, refer to the :ref:`Model Evaluation Guide `. .. versionadded:: 1.0 Parameters ---------- estimator : estimator instance Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline` in which the last estimator is a 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 confusion 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 Either to normalize the counts display in the matrix: - if `'true'`, the confusion matrix is normalized over the true conditions (e.g. rows); - if `'pred'`, the confusion matrix is normalized over the predicted conditions (e.g. columns); - if `'all'`, the confusion matrix is normalized by the total number of samples; - if `None` (default), the 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 'd' or '.2g' whichever is shorter. 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. 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_predictions : Plot the confusion matrix given the true and predicted labels. 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) >>> ConfusionMatrixDisplay.from_estimator( ... clf, X_test, y_test) <...> >>> plt.show() For a detailed example of using a confusion matrix to evaluate a Support Vector Classifier, please see :ref:`sphx_glr_auto_examples_model_selection_plot_confusion_matrix.py` z.from_estimatorz only supports classifiers) rYrXrZr rrrrrrrr)__name__rr ValueErrorpredictfrom_predictions)cls estimatorXyrXrYrZr rrrrrrrr method_namey_preds rfrom_estimatorz%ConfusionMatrixDisplay.from_estimators|o6  -Y' },FGH H""1%## '))+'$  rc t|jd|| t||}n|}t|||||}|||}|j || | || | | |S)aPlot Confusion Matrix given true and predicted labels. For general information regarding `scikit-learn` visualization tools, see the :ref:`Visualization Guide `. For guidance on interpreting these plots, refer to the :ref:`Model Evaluation Guide `. .. versionadded:: 1.0 Parameters ---------- y_true : array-like of shape (n_samples,) True labels. y_pred : array-like of shape (n_samples,) The predicted labels given by the method `predict` of an classifier. labels : array-like of shape (n_classes,), default=None List of labels to index the confusion 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 Either to normalize the counts display in the matrix: - if `'true'`, the confusion matrix is normalized over the true conditions (e.g. rows); - if `'pred'`, the confusion matrix is normalized over the predicted conditions (e.g. columns); - if `'all'`, the confusion matrix is normalized by the total number of samples; - if `None` (default), the 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 'd' or '.2g' whichever is shorter. 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. 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() z.from_predictions)rYrXrZr)rrrrrrrr)rr\rr rW)r`y_truererXrYrZr rrrrrrrrrKdisps rr_z'ConfusionMatrixDisplay.from_predictionsasn !CLL>1B!CD  !~!.vv!>!'   '  B~Fyy)+'  r) r\ __module__ __qualname____doc__rrW classmethodrfr_rrr r sBH<@- $ rh$  #R R h $  !Q Q rr ) itertoolsrnumpyr8baserutils._optional_dependenciesrutils._plottingrutils.multiclassrr r rnrrrvs(!D5-d d r