0PhJ<ddlZddlmZddlmZddlmZddlm Z ddl m Z ddl m Z dd lmZmZmZmZmZd ZGd d ZdS) N) is_regressor) LabelEncoder)_safe_indexing)check_matplotlib_support)_get_response_values)_get_adapter_from_container)_is_arraylike_not_scalar _is_pandas_df _is_polars_df _num_featurescheck_is_fittedc6t|d}|r+t|jdrd}t||r:t |jdkr"|dvr|d}t||dkrd n|}n|dkrt |rd }ngd }n|}|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. Cannot be None if multiclass and `response_method` is 'predict_proba' or 'decision_function'. .. 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>autopredictNzMulticlass 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 j/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/inspection/_plot/decision_boundary.py_check_boundary_response_methodr s2)Z00K/ 0B10EFFVoo,s9-..22 "5 5 5:K:SB  S// !)8F)B)BII F " "  " " R )   Q Q Q  + c ReZdZdZddddZd dZedddd ddddd 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 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)xlabelylabelcL||_||_||_||_||_dS)Nxx0xx1responser$r%)selfr(r)r*r$r%s r__init__z DecisionBoundaryDisplay.__init__s)    r!contourfc 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.pyplotpyplotrsubplotsgetattrr(r)r*surface_ get_xlabelr$ set_xlabel get_ylabelr% set_ylabelax_figurefigure_) r+ plot_methodaxr$r%kwargsplt_ plot_funcs rplotzDecisionBoundaryDisplay.plots < !!?@@@'''''' C C CL  :LLNNEArB ,, ! $(DHdmNNvNN  R]]__ $*NT[[F MM& ! ! !  R]]__ $*NT[[F MM& ! ! !y  r!dg?r)grid_resolutionepsr>rrr$r%r?c t|jdt||dkstd|d|dkstd|dd} || vr+d| } td | d |dt |}|d krtd |dt |dd t |dd }}||z ||z}}||z ||z}}tj tj |||tj |||\}}tj | | f}t|st|r,t!|}||||j}t'|||} t)||||d\}}}n?#t$r2}dt+|vrtd|d|j|d}~wwxYw|dkr?t/|dr/t1}|j|_||}|jdkrMt7|rtdtj|j|kd}|dd|f}|t/|dr |jdnd}| t/|dr |jdnd} |||||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"`. 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. Must have an explicit value for multiclass classifiers when `response_method` is 'predict_proba' or 'decision_function'. .. 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 )axis)columnsT)r pos_labelreturn_response_method_usedzis not a valid labelzclass_of_interest=z+ is not a valid label: It should be one of Nrrz)Multi-output regressors are not supportedrKr')r?r>) r__name__rrjoinr rminmaxnpmeshgridlinspacec_ravelr r r create_containerrKr rstrrrr transformndimr flatnonzeroreshapeshaperD)!clsrXrFrGr>rrr$r%r?r@possible_plot_methodsavailable_methods num_featuresx0x1x0_minx0_maxx1_minx1_maxr(r)X_gridadapterrr*rBresponse_method_usedexcencodercol_idxdisplays! rfrom_estimatorz&DecisionBoundaryDisplay.from_estimators5^ !CL!A!A!ABBB """""/#///  axxMsMMM !F 3 3 3 $ *? @ @ ..?.."...  %Q'' 1  MlMMM  11---~a/K/K/KBCCCC; K 8 8 K 8 8  S syy{{CIIKK/0    }Q// 1!44G-- .F < (9   0D 1+,0 111 -Ha--   %S11!=):==(1(:==   9 , ,J1O1O ,"nnG(1G ((22H =A  I&& N !LMMM nY%7;L%LMMaPG7 +H >%,Q %:%:BQYq\\F >%,Q %:%:BQYq\\F#%%ci00    w|Er{EEfEEEsH I)-II)r-NNN)rP __module__ __qualname____doc__r,rD classmethodrrrOr!rr#r#GsGGR6:$5555n   PFPFPFPF[PFPFPFr!r#)numpyrTbaser preprocessingrutilsrutils._optional_dependenciesrutils._responserutils._set_outputr utils.validationr r r r rr r#rOr!rrs ))))))######DDDDDD333333<<<<<<///dYFYFYFYFYFYFYFYFYFYFr!