h$,f_;nddlZddlmZddlmZddlmZmZddl m Z ddl m Z m Z mZdZGd d Zy) N) is_regressor) LabelEncoder)_safe_indexingcheck_matplotlib_support)_get_response_values)_is_arraylike_not_scalar _num_featurescheck_is_fittedct|d}|r%t|jdr d}t||r8t |jdkDr |dvr| d}t||dk(rd}|S|}|S|dk(rt |rd}|Sgd }|S|}|S) aValidate the response methods to be used with the fitted estimator. 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`. class_of_interest : int, float, bool, str or None The class considered when plotting the decision. If the label is specified, it is then possible to plot the decision boundary in multiclass settings. .. versionadded:: 1.4 Returns ------- prediction_method : list of str or str The name or list of names of the response methods to use. classes_rzFMulti-label and multi-output multi-class classifiers are not supported>autopredictzMulticlass classifiers are only supported when `response_method` is 'predict' or 'auto'. Else you must provide `class_of_interest` to plot the decision boundary of a specific class.rr)decision_function predict_probar)hasattrr r ValueErrorlenr) estimatorresponse_methodclass_of_interest has_classesmsgprediction_methods Jlib/python3.12/site-packages/sklearn/inspection/_plot/decision_boundary.py_check_boundary_response_methodrs2)Z0K/ 0B0B10EFVos9--.2 "5 5:K:SB  S/ !)8F)BI IX  F "  " )   !R  , c JeZdZdZddddZd dZedddd ddddd d Zy) 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 decision boundary. figure_ : matplotlib Figure Figure containing the decision boundary. 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)xlabelylabelcJ||_||_||_||_||_y)Nxx0xx1responser!r")selfr%r&r'r!r"s r__init__z DecisionBoundaryDisplay.__init__s%    rcontourfc tdddlm}|dvr td||j \}}t ||}||j |j|jfi||_ ||js!| |jn|}|j|||js!| |jn|}|j|||_|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.pyplotpyplotrsubplotsgetattrr%r&r'surface_ get_xlabelr! set_xlabel get_ylabelr" set_ylabelax_figurefigure_) r( plot_methodaxr!r"kwargsplt_ plot_funcs rplotzDecisionBoundaryDisplay.plots< !!?@' C CL  :LLNEArB , !$((DHHdmmNvN  R]]_$*NT[[F MM& !  R]]_$*NT[[F MM& !yy  rdg?r)grid_resolutionepsr;rrr!r"r<c t|jdt||dkDstd|d|dk\std|dd} || vr#dj | } td | d |dt |}|d k7rtd |dt |dd t |dd }}|j|z |j|z}}|j|z |j|z}}tjtj|||tj|||\}}t|drd|jgddfj}|j|jdddf<|j|jdddf<n1tj |j|jf}t#|||} t%||||d\}}}|dk(r8t|dr,t+}|j(|_|j-|}|j.dk7rDt1|r tdtj2|j(|k(d}|dd|f}|t|dr|j4dnd}| t|dr|j4dnd} ||||j7|j8|| }|j:d| |d| S#t$r/}dt'|vrtd|d|j(|d}~wwxYw)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"`. class_of_interest : int, float, bool or str, default=None The class considered when plotting the decision. If None, `estimator.classes_[1]` is considered as the positive class for binary classifiers. For multiclass classifiers, passing an explicit value for `class_of_interest` is mandatory. .. versionadded:: 1.4 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. sklearn.metrics.ConfusionMatrixDisplay.from_estimator : Plot the confusion matrix given an estimator, the data, and the label. sklearn.metrics.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,z, zplot_method must be one of z. Got rz#n_features must be equal to 2. Got )axisilocNT)r pos_labelreturn_response_method_usedzis not a valid labelzclass_of_interest=z+ is not a valid label: It should be one of rr z)Multi-output regressors are not supportedcolumnsr$)r<r;)r__name__r rjoinr rminmaxnpmeshgridlinspacerrHcopyravelc_rrstrr r transformndimr flatnonzerorKreshapeshaperA) clsrXrCrDr;rrr!r"r<r=possible_plot_methodsavailable_methods num_featuresx0x1x0_minx0_maxx1_minx1_maxr%r&X_gridrr'r?response_method_usedexcencodercol_idxdisplays rfrom_estimatorz&DecisionBoundaryDisplay.from_estimatorst\ !CLL>!AB ""#$I/  ax>se9M !F 3 3 $ *? @ -.?-@A"m9.  %Q' 1 5l^9M  11-~a/KBCCCC;; KK 8 KK 8 S 1f VVBE]'')F # FKK1  # FKK1 UU399; 34F; (9  0D 1+,0 1 -Ha-& 9 ,J1O"nG(11G ((2H ==A I& !LMM nnY%7%7;L%LMaPG7 +H >%,Q %:QYYq\F >%,Q %:QYYq\F%%cii0  w||Er{EfEEQ %S1!():(;<((1(:(:';=  s.L M*L>>M)r*NNN)rN __module__ __qualname____doc__r)rA classmethodrorMrrr r @sMGR6:$5n   MFMFrr )numpyrRbaser preprocessingrutilsrrutils._responserutils.validationr r r rr rMrrrzs2 )=3/dVFVFr