_ndu4~ddlmZddlZddlmZddlmZddlmZddl m Z ddl m Z m Z mZd ZGd d ZdS) )reduceN) LabelEncoder)check_matplotlib_support)_safe_indexing) is_regressor)check_is_fitted_is_arraylike_not_scalar _num_featuresctd}|r+tjdrd}t||r1t jdkr|dvrd}t|dg}n|dkrgd }n|g}fd |D}t d |}|2tjjd d|d|S)awReturn prediction method from the `response_method` for decision boundary. Parameters ---------- estimator : object Fitted estimator to check. response_method : {'auto', 'predict_proba', 'decision_function', 'predict'} Specifies whether to use :term:`predict_proba`, :term:`decision_function`, :term:`predict` as the target response. If set to 'auto', the response method is tried in the following order: :term:`decision_function`, :term:`predict_proba`, :term:`predict`. Returns ------- prediction_method: callable Prediction method of estimator. classes_rzFMulti-label and multi-output multi-class classifiers are not supported>autopredictzUMulticlass classifiers are only supported when response_method is 'predict' or 'auto'rr)decision_function predict_probarc2g|]}t|dSN)getattr).0method estimators Jlib/python3.11/site-packages/sklearn/inspection/_plot/decision_boundary.py z3_check_boundary_response_method..5s%UUUfFD99UUUc |p|Sr)xys rz1_check_boundary_response_method..6s AFrNz' has none of the following attributes: , .) hasattrr r ValueErrorlenr __class____name__join)rresponse_method has_classesmsg methods_listprediction_methods` r_check_boundary_response_methodr.s/&)Z00K/ 0B10EFFVoo )s9-..22 "5 5 5' S// !!{ F " "HHH '( UUUU UUU224EFF "+ * *yy&& * * *   rc PeZdZdZddddZd dZedddd dddd d ZdS) DecisionBoundaryDisplaya Decisions boundary visualization. It is recommended to use :func:`~sklearn.inspection.DecisionBoundaryDisplay.from_estimator` to create a :class:`DecisionBoundaryDisplay`. All parameters are stored as attributes. Read more in the :ref:`User Guide `. .. versionadded:: 1.1 Parameters ---------- xx0 : ndarray of shape (grid_resolution, grid_resolution) First output of :func:`meshgrid `. xx1 : ndarray of shape (grid_resolution, grid_resolution) Second output of :func:`meshgrid `. response : ndarray of shape (grid_resolution, grid_resolution) Values of the response function. xlabel : str, default=None Default label to place on x axis. ylabel : str, default=None Default label to place on y axis. Attributes ---------- surface_ : matplotlib `QuadContourSet` or `QuadMesh` If `plot_method` is 'contour' or 'contourf', `surface_` is a :class:`QuadContourSet `. If `plot_method` is 'pcolormesh', `surface_` is a :class:`QuadMesh `. ax_ : matplotlib Axes Axes with confusion matrix. figure_ : matplotlib Figure Figure containing the confusion matrix. See Also -------- DecisionBoundaryDisplay.from_estimator : Plot decision boundary given an estimator. Examples -------- >>> import matplotlib.pyplot as plt >>> import numpy as np >>> from sklearn.datasets import load_iris >>> from sklearn.inspection import DecisionBoundaryDisplay >>> from sklearn.tree import DecisionTreeClassifier >>> iris = load_iris() >>> feature_1, feature_2 = np.meshgrid( ... np.linspace(iris.data[:, 0].min(), iris.data[:, 0].max()), ... np.linspace(iris.data[:, 1].min(), iris.data[:, 1].max()) ... ) >>> grid = np.vstack([feature_1.ravel(), feature_2.ravel()]).T >>> tree = DecisionTreeClassifier().fit(iris.data[:, :2], iris.target) >>> y_pred = np.reshape(tree.predict(grid), feature_1.shape) >>> display = DecisionBoundaryDisplay( ... xx0=feature_1, xx1=feature_2, response=y_pred ... ) >>> display.plot() <...> >>> display.ax_.scatter( ... iris.data[:, 0], iris.data[:, 1], c=iris.target, edgecolor="black" ... ) <...> >>> plt.show() N)xlabelylabelcL||_||_||_||_||_dSrxx0xx1responser1r2)selfr5r6r7r1r2s r__init__z DecisionBoundaryDisplay.__init__s)    rcontourfc tdddlm}|dvrtd||\}}t ||}||j|j|jfi||_ || s ||j n|}| ||| s ||jn|}||||_|j|_|S)aPlot visualization. Parameters ---------- plot_method : {'contourf', 'contour', 'pcolormesh'}, default='contourf' Plotting method to call when plotting the response. Please refer to the following matplotlib documentation for details: :func:`contourf `, :func:`contour `, :func:`pcolormesh `. ax : Matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. xlabel : str, default=None Overwrite the x-axis label. ylabel : str, default=None Overwrite the y-axis label. **kwargs : dict Additional keyword arguments to be passed to the `plot_method`. Returns ------- display: :class:`~sklearn.inspection.DecisionBoundaryDisplay` Object that stores computed values. zDecisionBoundaryDisplay.plotrNr:contour pcolormeshz:plot_method must be 'contourf', 'contour', or 'pcolormesh')rmatplotlib.pyplotpyplotr$subplotsrr5r6r7surface_ get_xlabelr1 set_xlabel get_ylabelr2 set_ylabelax_figurefigure_) r8 plot_methodaxr1r2kwargsplt_ plot_funcs rplotzDecisionBoundaryDisplay.plots < !!?@@@'''''' C C CL  :LLNNEArB ,, ! $(DHdmNNvNN  R]]__ $*NT[[F MM& ! ! !  R]]__ $*NT[[F MM& ! ! !y  rdg?r)grid_resolutionepsrJr)r1r2rKc lt|jdt||dkstd|d|dkstd|dd} || vr+d| } td | d |dt |} | d krtd | dt |dd t |dd }}||z ||z}}||z ||z}}tj tj |||tj |||\}}t|drd|j gddf}||j dddf<||j dddf<n8tj||f}t#||}||}|jdkr?t|dr/t%}|j|_||}|jdkr*t-|rtd|dddf}|t|dr |jdnd}|t|dr |jdnd}t1||||j||}|jd| |d| S)aPlot decision boundary given an estimator. Read more in the :ref:`User Guide `. Parameters ---------- estimator : object Trained estimator used to plot the decision boundary. X : {array-like, sparse matrix, dataframe} of shape (n_samples, 2) Input data that should be only 2-dimensional. grid_resolution : int, default=100 Number of grid points to use for plotting decision boundary. Higher values will make the plot look nicer but be slower to render. eps : float, default=1.0 Extends the minimum and maximum values of X for evaluating the response function. plot_method : {'contourf', 'contour', 'pcolormesh'}, default='contourf' Plotting method to call when plotting the response. Please refer to the following matplotlib documentation for details: :func:`contourf `, :func:`contour `, :func:`pcolormesh `. response_method : {'auto', 'predict_proba', 'decision_function', 'predict'}, default='auto' Specifies whether to use :term:`predict_proba`, :term:`decision_function`, :term:`predict` as the target response. If set to 'auto', the response method is tried in the following order: :term:`decision_function`, :term:`predict_proba`, :term:`predict`. For multiclass problems, :term:`predict` is selected when `response_method="auto"`. xlabel : str, default=None The label used for the x-axis. If `None`, an attempt is made to extract a label from `X` if it is a dataframe, otherwise an empty string is used. ylabel : str, default=None The label used for the y-axis. If `None`, an attempt is made to extract a label from `X` if it is a dataframe, otherwise an empty string is used. ax : Matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. **kwargs : dict Additional keyword arguments to be passed to the `plot_method`. Returns ------- display : :class:`~sklearn.inspection.DecisionBoundaryDisplay` Object that stores the result. See Also -------- DecisionBoundaryDisplay : Decision boundary visualization. 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 load_iris >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.inspection import DecisionBoundaryDisplay >>> iris = load_iris() >>> X = iris.data[:, :2] >>> classifier = LogisticRegression().fit(X, iris.target) >>> disp = DecisionBoundaryDisplay.from_estimator( ... classifier, X, response_method="predict", ... xlabel=iris.feature_names[0], ylabel=iris.feature_names[1], ... alpha=0.5, ... ) >>> disp.ax_.scatter(X[:, 0], X[:, 1], c=iris.target, edgecolor="k") <...> >>> plt.show() z.from_estimatorz,grid_resolution must be greater than 1. Got z instead.rz,eps must be greater than or equal to 0. Got r<r!zplot_method must be one of z. Got rz#n_features must be equal to 2. Got )axisilocNrr z)Multi-output regressors are not supportedcolumnsr4)rKrJr)rr'r r$r(r rminmaxnpmeshgridlinspacer#rWcopyravelc_r.rr transformndimrrXr0reshapeshaperP)clsrXrRrSrJr)r1r2rKrLpossible_plot_methodsavailable_methods num_featuresx0x1x0_minx0_maxx1_minx1_maxr5r6X_grid pred_funcr7encoderdisplays rfrom_estimatorz&DecisionBoundaryDisplay.from_estimatorsJ !CL!A!A!ABBB """""/#///  axxMsMMM !F 3 3 3 $ *? @ @ ..?.."...  %Q'' 1  MlMMM  11---~a/K/K/KBCCCC; K 8 8 K 8 8  S 1f   5VBE]''))F # FK1  # FK1  U399;; 34F3IOO 9V$$   * *wy*/M/M *"nnG(1G ((22H =A  I&& N !LMMM 1~H >%,Q %:%:BQYq\\F >%,Q %:%:BQYq\\F)%%ci00    w|Er{EEfEEEr)r:NNN)r' __module__ __qualname____doc__r9rP classmethodrurrrr0r0@sGGR6:$5555n   nFnFnFnF[nFnFnFrr0) functoolsrnumpyr\ preprocessingrutilsrrbaserutils.validationr r r r.r0rrrrs))))))------###### ---`wFwFwFwFwFwFwFwFwFwFr