-i8VSSKrSSKrSSKJr SSKJr SSKJr SSK J r SSK J r SSK Jr SS KJrJrJrJrJr S r"S S 5rg) 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_fittedc[US5nU(a*[URS5(a Sn[U5eUS:Xa[ U5(aSnU$/SQnU$UnU$)aValidate the response methods to be used with the fitted estimator. Parameters ---------- estimator : object Fitted estimator to check. response_method : {'auto', 'decision_function', 'predict_proba', 'predict'} Specifies whether to use :term:`decision_function`, :term:`predict_proba`, :term:`predict` as the target response. If set to 'auto', the response method is tried in the before mentioned order. 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 supportedautopredict)decision_function predict_probar)hasattrr r ValueErrorr) estimatorresponse_methodclass_of_interest has_classesmsgprediction_methods ]/var/www/html/venv/lib/python3.13/site-packages/sklearn/inspection/_plot/decision_boundary.py_check_boundary_response_methodrsx0)Z0K/ 0B0B10EFFVo&  " " )   !R  , c \\rSrSrSrSSSS.SjrSSjr\SS SS SSSSSS . S j5rS r g)DecisionBoundaryDisplay?aY 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 `. For a detailed example comparing the decision boundaries of multinomial and one-vs-rest logistic regression, please see :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_multinomial.py`. .. 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) or (grid_resolution, grid_resolution, n_classes) Values of the response function. multiclass_colors : list of str or str, default=None Specifies how to color each class when plotting all classes of multiclass problem. Ignored for binary problems and multiclass problems when plotting a single prediction value per point. Possible inputs are: * list: list of Matplotlib `color `_ strings, of length `n_classes` * str: name of :class:`matplotlib.colors.Colormap` * None: 'viridis' colormap is used to sample colors Single color colormaps will be generated from the colors in the list or colors taken from the colormap and passed to the `cmap` parameter of the `plot_method`. .. versionadded:: 1.7 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` or list of such objects If `plot_method` is 'contour' or 'contourf', `surface_` is :class:`QuadContourSet `. If `plot_method` is 'pcolormesh', `surface_` is :class:`QuadMesh `. multiclass_colors_ : array of shape (n_classes, 4) Colors used to plot each class in multiclass problems. Only defined when `color_of_interest` is None. .. versionadded:: 1.7 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)multiclass_colorsxlabelylabelcLXlX lX0lX@lXPlX`lgNxx0xx1responser#r$r%)selfr)r*r+r#r$r%s r__init__ DecisionBoundaryDisplay.__init__s# !2  rcontourfc "[S5 SSKnSSKJn US;a[ SUS35eUcUR 5up[ X!5n URRS:Xa0U "URURUR40UD6Ul GOURRSn S H&n X;dM [R"S U S 35 X[ M( URb[!UR["5(aURc U S ::aS OSn O URn U S :Xa&U S ::a UR%S S 5R&SU n OU S:Xa&U S::a UR%SS5R&SU n OUR%X5n [)U S5(dU "[*R,"SSU 55n OlU R&n O_[!UR[.5(a5URVs/sHoR&R1U5PM n nO [ S5eXlUS:Xa@URR5SS9nU "URURU4SU 0UD6Ul O[7U 5VVVVVs/sH:ununnnnUR&R8R;SU3SUUUS4/5PM< nnnnnn/Ul [7U5Hunn [*R<R?URSS2SS2U4URR5SS9U:H)S9nURRAU "URURU4SU 0UD65 M UcURC5(d"Uc URDOUnURGU5 UcURI5(d"Uc URJOUnURMU5 X l'URPUl)U$s snfs snnnnnf)aCPlot 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'. Got instead.)cmapcolors'z' is ignored in favor of 'multiclass_colors' in the multiclass case when the response method is 'decision_function' or 'predict_proba'. tab10 gist_rainbowtab20r8z,'multiclass_colors' must be a list or a str.r2axis colormap_)?rCrCrCrC)maskr7)*r matplotlibmatplotlib.pyplotpyplotrsubplotsgetattrr+ndimr)r*surface_shapewarningswarnr# isinstancestrget_cmapr8rnplinspacelistto_rgbamulticlass_colors_argmax enumerateLinearSegmentedColormap from_listmaarrayappend get_xlabelr$ set_xlabel get_ylabelr% set_ylabelax_figurefigure_)r, plot_methodaxr$r%kwargsmplplt_ plot_func n_responseskwargr7r8color class_map class_idxrgbmulticlass_cmapsr+s rplotDecisionBoundaryDisplay.plots< !!?@ ' C C"m9.  :LLNEAB, ==   "%dhh$--R6RDM----b1K+?MME7#BB  ,%%-&&22))1&1R&77^D11D 7?{b'8 \\'26==l{KFW_): \\'26==l{KF<<:D"422!%bkk!Q &D!E!%D22D99AEAWAWXAW**,,U3AWX !OPP&, #i' MM00a08 )HHdhh !:@!DJ! 4=V3D $4E/ `. 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', 'decision_function', 'predict_proba', 'predict'}, default='auto' Specifies whether to use :term:`decision_function`, :term:`predict_proba` or :term:`predict` as the target response. If set to 'auto', the response method is tried in the order as listed above. .. versionchanged:: 1.6 For multiclass problems, 'auto' no longer defaults to 'predict'. class_of_interest : int, float, bool or str, default=None The class to be plotted when `response_method` is 'predict_proba' or 'decision_function'. If None, `estimator.classes_[1]` is considered the positive class for binary classifiers. For multiclass classifiers, if None, all classes will be represented in the decision boundary plot; the class with the highest response value at each point is plotted. The color of each class can be set via `multiclass_colors`. .. versionadded:: 1.4 multiclass_colors : list of str, or str, default=None Specifies how to color each class when plotting multiclass 'predict_proba' or 'decision_function' and `class_of_interest` is None. Ignored in all other cases. Possible inputs are: * list: list of Matplotlib `color `_ strings, of length `n_classes` * str: name of :class:`matplotlib.colors.Colormap` * None: 'tab10' colormap is used to sample colors if the number of classes is less than or equal to 10, otherwise 'gist_rainbow' colormap. Single color colormaps will be generated from the colors in the list or colors taken from the colormap, and passed to the `cmap` parameter of the `plot_method`. .. versionadded:: 1.7 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_estimatorrNr?z,grid_resolution must be greater than 1. Got r4z,eps must be greater than or equal to 0. Got r1z, zplot_method must be one of z. Got r5z#n_features must be equal to 2. Got )rrrrz[When 'multiclass_colors' is a list, it must be of the same length as 'estimator.classes_' (z), got: .c3d># UH%nTRRU5(+v M' g7fr')r8 is_color_like).0colrhs r 9DecisionBoundaryDisplay.from_estimator..s*AR# 00555ARs-0z[When 'multiclass_colors' is a list, it can only contain valid Matplotlib color names. Got: zSWhen 'multiclass_colors' is a string, it must be a valid Matplotlib colormap. Got: r@)columnsT)r pos_labelreturn_response_method_usedzis not a valid labelzclass_of_interest=z+ is not a valid label: It should be one of rz)Multi-output regressors are not supportedr6rr()rfre)'r__name__rrErjoinr rlenrrOrTanyrPrG colormapsrminmaxrRmeshgridrSc_ravelr r r create_containerrrrr transformrJreshaperLr flatnonzeroru)$clsrXrxryrerrr#r$r%rfrgpossible_plot_methodsavailable_methods num_features n_classesx0x1x0_minx0_maxx1_minx1_maxr)r*X_gridadapterrr+rjresponse_method_usedexcencodercol_idxdisplayrhs$ @rfrom_estimator&DecisionBoundaryDisplay.from_estimator(sP !CLL>!AB " "#$I/  ax>se9M !F  3 $ *? @ -.?-@A"m9.  %Q' 1 5l^9M  M M!- :..!)"4"455:+T22()Y6$;;D+X012!5 AR%99J8KM+S11$CJJ,@,@,BB$55F4GI  11-~a/KBCCCC;; KK 8 KK 8 S syy{CIIK/0   }Q//1!4G-- .F < (9  0D 1+,0 1 -Ha-& 9 ,J1O1O"nG(11G ((2H ==A ''3HI&& !LMM ,..););?P)PQRST#AwJ/77C#++KSYYKr8JK >%,Q %:%:QYYq\F >%,Q %:%:QYYq\F/  ||ErEfEE] %S1!():(;<((1(:(:';=  s&P Q',QQ) rbrdr#rVr+rKr$r)r*r%)r/NNN) r __module__ __qualname____firstlineno____doc__r-ru classmethodr__static_attributes__rrrr!r!?sTdN8rs= )#D3<%PuFuFr