U 3d?@sVddlmZddlZddlmZddlmZddlm Z ddl m Z Gd d d Z dS) )productN)confusion_matrix)check_matplotlib_support) unique_labels) is_classifierc@seZdZdZddddZddddddddd d d Zeddddddddddddd d dZeddddddddddddd ddZdS)ConfusionMatrixDisplaya Confusion Matrix visualization. It is recommend 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. Read more in the :ref:`User 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_labelscCs||_||_dS)Nrr )selfrr r J/tmp/pip-unpacked-wheel-zrfo1fqw/sklearn/metrics/_plot/confusion_matrix.py__init__MszConfusionMatrixDisplay.__init__TZviridisZ horizontal)include_valuescmapxticks_rotation values_formataxcolorbarim_kwtext_kwcCstdddlm} |dkr*| \} }n|j} |j} | jd} td|d} |pRi}| |}|pbi}|j| f||_ d|_ |j d|j d}}|rt j | td|_ | | d}tt| t| D]\}}| ||f|kr|n|}|dkrDt| ||fd }| jjd krVt| ||fd }t|t|krV|}nt| ||f|}td d |d }||}|j|||f||j ||f<q|jdkrt | }n|j}|r| j|j |d|jt | t | ||ddd|| ddf| j||d| |_||_ |S)aLPlot 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 'd' or '.2g' whichever is shorter. 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` Returns a :class:`~sklearn.metrics.ConfusionMatrixDisplay` instance that contains all the information to plot the confusion matrix. ConfusionMatrixDisplay.plotrNZnearest) interpolationrg?)dtypeg@z.2gfdcenter)Zhavacolor)rz True labelzPredicted label)ZxticksZyticksZ xticklabelsZ yticklabelsZylabelZxlabelg?g)Zrotation)!rZmatplotlib.pyplotZpyplotZsubplotsfigurershapedictZimshowZim_Ztext_rnpZ empty_likeobjectmaxminrrangeformatrkindlentextr ZarangersetZset_ylimZsetpZget_xticklabelsZfigure_Zax_)r rrrrrrrrZpltZfigcmZ n_classesZ default_im_kwZcmap_minZcmap_maxZthreshijrZtext_cmZtext_dZdefault_text_kwargsZ text_kwargsr r r rplotQs^3       r) labels sample_weight normalizer rrrrrrrrc CsZ|jd}t|t|s*t|d||}|j|||||||| | | | | ||dS)aPlot Confusion Matrix given an estimator and some data. Read more in the :ref:`User 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() z.from_estimatorz only supports classifiers) r2r1r3r rrrrrrrr)__name__rr ValueErrorZpredictfrom_predictions)clsZ estimatorXyr1r2r3r rrrrrrrr method_namey_predr r rfrom_estimators*w  z%ConfusionMatrixDisplay.from_estimatorc  Csht|jd|dkr0|dkr,t||}n|}t|||||d}|||d}|j|| | || | | |dS)aWPlot Confusion Matrix given true and predicted labels. Read more in the :ref:`User 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_predictionsN)r2r1r3r )rrrrrrrr)rr4rrr0)r7Zy_truer;r1r2r3r rrrrrrrrr-Zdispr r rr6Ss.t  z'ConfusionMatrixDisplay.from_predictions) r4 __module__ __qualname____doc__rr0 classmethodr<r6r r r rr sPA tr ) itertoolsrZnumpyr#rutilsrZutils.multiclassrbaserr r r r rs