PisVddlZddlZddlmZddlmZddlmZddl m Z m Z ddl m Z ddlmZddlmZdd lmZmZmZd ZGd d ZdS) N) is_regressor) LabelEncoder)_safe_indexing) is_pandas_df is_polars_df)check_matplotlib_support)_get_response_values)_get_adapter_from_container)_is_arraylike_not_scalar _num_featurescheck_is_fittedct|d}|r+t|jdrd}t||dkrt |rd}ngd}n|}|S)aFValidate 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 ~/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/sklearn/inspection/_plot/decision_boundary.py_check_boundary_response_methodrs0)Z00K/ 0B10EFFVoo&   " " R )   Q Q Q  + c VeZdZdZdddddZd dZedddd dddddd d ZdS) DecisionBoundaryDisplayaDecisions 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_colorsxlabelylabelcZ||_||_||_||_||_||_dSNxx0xx1responser!r"r#)selfr'r(r)r!r"r#s r__init__z DecisionBoundaryDisplay.__init__s3  !2  rcontourfc tdddlddlm}|dvrt d|d||\}}t ||}|jjdkr!||j |j |jfi||_ nW|jj d} d D]!} | |vrtjd | d || ="|jt!|jt"r|j | d krd nd} n|j} | d kr*| d kr$|d d jd| } n| dkr*| dkr$|ddjd| } n|| | } t)| ds | t+jdd| } nE| j} n=t!|jt.rfd|jD} nt d| |_|dkr8|jd} ||j |j | fd| i||_ nfdt5| D}g|_ t5|D]\}} t*j|jdddd|f|jd|k}|j ||j |j |fd| i|||s ||jn|}| |||!s ||j"n|}|#|||_$|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'. 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_rainbowtab20r5cDg|]}j|S)r5to_rgba).0colormpls r z0DecisionBoundaryDisplay.plot..s)XXX#*,,U33XXXrz,'multiclass_colors' must be a list or a str.r/axisc rg|]3\}\}}}}jjd|d|||dfg4S) colormap_)?rHrHrHrH)r5LinearSegmentedColormap from_list)r@ class_idxrgb_rBs rrCz0DecisionBoundaryDisplay.plot.. sh$$$0 `. 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 r1z,eps must be greater than or equal to 0. Got r.z, zplot_method must be one of z. Got r2z#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: .c3NK|]}j| V dSr%)r5 is_color_like)r@colrBs r z9DecisionBoundaryDisplay.from_estimator..sG:= 00555rz[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: rD)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 supportedr3rr&)rornr>)'r__name__r rQrjoinr rlenrr[r`anyr\rS colormapsrminmaxr^meshgridr_c_ravelrrr create_containerrrr r transformrVreshaperXr flatnonzerorw)$clsrXryrzrnrrr!r"r#rorppossible_plot_methodsavailable_methods num_features n_classesx0x1x0_minx0_maxx1_minx1_maxr'r(X_gridadapterrr)rOresponse_method_usedexcencodercol_idxdisplayrBs$ @rfrom_estimatorz&DecisionBoundaryDisplay.from_estimator'sP !CL!A!A!ABBB """    ""/#///  axxMsMMM !F 3 3 3 $ *? @ @ ..?.."...  %Q'' 1  MlMMM  M M M!- :...!)"4555::+T22 ())Y66$5;D55011555 AR%M9JMM+S11 $CJ,@,@,B,BBB$I5FII  11---~a/K/K/KBCCCC; K 8 8 K 8 8  S syy{{CIIKK/0 ?? l1oo 1!44G-- .F < (9   0D 1+,0 111 -Ha--   %S11!=):==(1(:==   9 , ,J1O1O ,"nnG(1G ((22H =A  'x'3HHI&& N !LMMM ,.);?P)PQQRST78AAAwJ/7C+8+KSYKr8JKKK >%,Q %:%:BQYq\\F >%,Q %:%:BQYq\\F#/    w|Er{EEfEEEs5L M -MM )r,NNN)r __module__ __qualname____doc__r+rw classmethodrr>rrr r >sddN8rrrs(%%%%%%......((((((????????IIIIII888888AAAAAA%%%PuFuFuFuFuFuFuFuFuFuFr