\hTddlZddlZddlmZddlmZddlmZddl m Z ddl m Z ddl mZdd lmZmZmZmZmZd ZGd d Zy) 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_fittedct|d}|r%t|jdr d}t||dk(rt |rd}|Sgd}|S|}|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 d/var/www/html/engine/venv/lib/python3.12/site-packages/sklearn/inspection/_plot/decision_boundary.py_check_boundary_response_methodrsr0)Z0K/ 0B0B10EFVo&  " )   !R  , c NeZdZdZdddddZd dZedddd dddddd d Zy) 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_colorsxlabelylabelcX||_||_||_||_||_||_yNxx0xx1responser"r#r$)selfr(r)r*r"r#r$s r__init__z DecisionBoundaryDisplay.__init__s/  !2  rcontourfc Ltdddl}ddlm}|dvrt d|d||j \}}t ||} |jjdk(r0| |j|j|jfi||_ n|jjd} t|jts |jt|jtr |j} n | d krd } nd } | d k(r%| d kr |j!d d j"d| } n| d k(r%| d kr |j!d d j"d| } n|j!| | j"} nut|jtr)|j!|j| j"x} } n2|jD cgc]} |j"j%| } } | |_t)| Dcgc]9\}\}}}}|j"j*j-d|d|||dfg;}}}}}}g|_ t)|D]\}} t.j0j3|jdddd|f|jj5d|k(}|j7}d|vr|d=t9j:d|jj=| |j|j|fd| i|||j?s!| |j@n|}|jC|||jEs!| |jFn|}|jI|||_%|jL|_'|Scc} wcc}}}}}w)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. tab10 gist_rainbowtab20 colormap_)?r;r;r;r;axis)maskcmapzPlotting max class of multiclass 'decision_function' or 'predict_proba', thus 'multiclass_colors' used and 'cmap' kwarg ignored.)(r matplotlibmatplotlib.pyplotpyplotrsubplotsgetattrr*ndimr(r)surface_shape isinstancer"strget_cmapcolorsto_rgbamulticlass_colors_ enumerateLinearSegmentedColormap from_listnpmaarrayargmaxcopywarningswarnappend get_xlabelr# set_xlabel get_ylabelr$ set_ylabelax_figurefigure_)r+ plot_methodaxr#r$kwargsmplplt_ plot_func n_responsesr?rKcolor class_idxrgbmulticlass_cmapsr* safe_kwargss rplotzDecisionBoundaryDisplay.plotsi< !!?@ ' C C"m9.  :LLNEArB , ==   "%dhh$--R6RDM----b1K41137))1d44c:11D"b(&- 7?{b'8 \\'26==l{KFW_): \\'26==l{KF \\$ <CCFD22C8"%,,**K#&BFAWAWX#**,,U3XX&,D # 09/@   ,I|1a 22<< {+.BQ1cN-S   DM#,-=#>  455;;MM!Q /2==//Q/79DE' %kkm [(#F+MM0  $$dhh(UUU $  R]]_$*NT[[F MM& !  R]]_$*NT[[F MM& !yy  MY s "N>N dr;r) grid_resolutionepsr`rrr"r#r$rac  D#t|jdt|ddl#|dkDst d|d|dk\st d|dd} || vr#d j | }t d |d |dt |}|d k7rt d |d|dvr|t|drt|jx}d kDrt|trKt||k7rt d|dt|dt#fd|Drt d|t|tr*|#jjvrt d|t!|ddt!|dd}}|j#|z |j%|z}}|j#|z |j%|z}}t'j(t'j*|||t'j*|||\}}t&j,|j/|j/f}t1|s t3|r)t5|}|j7|||j8}t;|||} t=||||d\}}}|dk(r8t|dr,t?} |j| _ | jA|}|jBdk(r|jD|jF}ntI|r t d|Ft'jJ|j|k(d}!|dd|!fjD|jF}n+|jDg|jF|jFd}| t|d r|j8dnd!} | t|d r|j8dnd!} |||||| | "}"|"jLd$| |d#| S#t$r/}dt|vrt d|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', '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_estimatorrNz,grid_resolution must be greater than 1. Got r2z,eps must be greater than or equal to 0. Got r/z, zplot_method must be one of z. Got r3z#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: .c3VK|] }jj| "ywr&)rK is_color_like).0colrcs r z9DecisionBoundaryDisplay.from_estimator..s(:= 0055s&)z[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 supportedr4r{r')rar`)'r__name__rr@rjoinr rlenrrHlistanyrIrB colormapsrminmaxrQmeshgridlinspacec_ravelr r r create_containerr{rrr transformrEreshaperGr flatnonzeroro)$clsrXrqrrr`rrr"r#r$rarbpossible_plot_methodsavailable_methods num_features n_classesx0x1x0_minx0_maxx1_minx1_maxr(r)X_gridadapterrr*reresponse_method_usedexcencodercol_idxdisplayrcs$ @rfrom_estimatorz&DecisionBoundaryDisplay.from_estimator"sP !CLL>!AB " "#$I/  ax>se9M !F 3 3 $ *? @ -.?-@A"m9.  %Q' 1 5l^9M  M M!- :.!)"4"455:+T2()Y6$;;D+X012!5 AR%99J8KM+S1$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 ,J1O"nG(11G ((2H ==A 'x''3HI& !LMM ,..););?P)PQRST78AwJ/77C+8++KSYYKr8JK >%,Q %:QYYq\F >%,Q %:QYYq\F/  w||Er{EfEE] %S1!():(;<((1(:(:';=  sO'' P0*PP)r-NNN)r __module__ __qualname____doc__r,ro classmethodrrrrr!r!?sTdN8rs= )#D3<%PoFoFr