
    Mpjt                         S SK r S SKrS SKJrJrJrJr  S SKJ	r	  S SK
Jr  S SKJrJr  S SKJr  S SKJr  S SKJr  S S	KJr  S S
KJr  S SKJrJrJr  S rS r " S S5      rg)    N)is_classifieris_clustereris_outlier_detectoris_regressor)LabelEncoder)_safe_indexing)is_pandas_dfis_polars_df)check_matplotlib_support)_get_response_values)_get_adapter_from_container)PETROFF_COLORS)type_of_target)_is_arraylike_not_scalar_num_featurescheck_is_fittedc                     [        U S5      nU(       a*  [        U R                  S   5      (       a  Sn[        U5      eUS:X  a  [	        U 5      (       a  SnU$ / SQn U$ UnU$ )a  Validate 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.

Returns
-------
prediction_method : list of str or str
    The name or list of names of the response methods to use.
classes_r   zFMulti-label and multi-output multi-class classifiers are not supportedautopredict)decision_functionpredict_probar   )hasattrr   r   
ValueErrorr   )	estimatorresponse_methodhas_classesmsgprediction_methods        f/var/www/html/pdf-tiff/venv/lib/python3.13/site-packages/sklearn/inspection/_plot/decision_boundary.py_check_boundary_response_methodr!      sx    $ )Z0K/	0B0B10EFFVo& 	"" ) 	 !R  ,    c           	        ^  Uc  US::  a
  [         SU nOSn[        U[        5      (       a  UT R                  R	                  5       ;  a  [        SU 35      eT R                  R                  U5      nUR                  U:  a  [        SU SUR                   SU S35      eU" [        R                  " S	S
U5      5      $ [        U[        5      (       am  [        U5      U:w  a  [        SU S[        U5       S35      e[        U 4S jU 5       5      (       a  [        SU 35      eT R                  R                  U5      $ [        S5      e)a&  Select colors for multiclass decision boundary display.

Parameters
----------
mpl : module
    Imported `matplotlib` module.

multiclass_colors : str or list of matplotlib colors, default=None
    The colormap or colors to select.

    Possible inputs are:

    * None: defaults to list of accessible `Petroff colors
      <https://github.com/matplotlib/matplotlib/issues/9460#issuecomment-875185352>`_
      if `n_classes <= 10`, otherwise 'gist_rainbow' colormap
    * str: name of :class:`matplotlib.colors.Colormap`
    * list: list of length `n_classes` of `matplotlib colors
      <https://matplotlib.org/stable/users/explain/colors/colors.html#colors-def>`_

n_classes : int
    Number of colors to select.

Returns
-------
colors : ndarray of shape (n_classes, 4)
    RGBA colors, one per class.

N
   gist_rainbowzSWhen 'multiclass_colors' is a string, it must be a valid Matplotlib colormap. Got: z
Colormap 'z' only has z colors, but zv classes are to be displayed. Please specify a different colormap or provide a list of colors via 'multiclass_colors'.r      zdWhen 'multiclass_colors' is a list, it must be of the same length as the classes or labels to plot (z), got: .c              3   d   >#    U  H%  nTR                   R                  U5      (       + v   M'     g 7fN)colorsis_color_like).0colmpls     r    	<genexpr>!_select_colors.<locals>.<genexpr>x   s(     P>OsSZZ--c222>Os   -0z[When 'multiclass_colors' is a list, it can only contain valid Matplotlib color names. Got: z,'multiclass_colors' must be a list or a str.)r   
isinstancestrpyplot	colormapsr   get_cmapNnplinspacelistlenanyr*   to_rgba_array	TypeError)r.   multiclass_colors	n_classescmaps   `   r    _select_colorsrA   :   sv   <   ? .z	 : .#S))CJJ$8$8$::-->,?A  zz""#4566I./{466(-+ ''  BKK1i011	%t	,	, !Y.<<E;h()*!- 
 P>OPPP11B0CE  zz''(9:: FGGr"   c                   \    \ rS rSrSrSSSS.S jrSS jr\SS	SS
SSSSSS.	S j5       rSr	g)DecisionBoundaryDisplay   a.  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 <visualizations>`.

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 <numpy.meshgrid>`.

xx1 : ndarray of shape (grid_resolution, grid_resolution)
    Second output of :func:`meshgrid <numpy.meshgrid>`.

n_classes : int
    Expected number of unique classes or labels if `response` was generated by a
    :term:`classifier` or a :term:`clusterer`.

    For :term:`outlier detectors`, `n_classes` should be set to 2 by definition
    (inlier or outlier).

    For :term:`regressors`, `n_classes` should also be set to 2 by convention
    (continuous responses are displayed the same way as unthresholded binary
    responses).

    .. versionadded:: 1.9

response : ndarray of shape (grid_resolution, grid_resolution) or             (grid_resolution, grid_resolution, n_classes)
    Values of the response function.

multiclass_colors : str or list of matplotlib colors, default=None
    Specifies how to color each class when plotting all classes of
    :term:`multiclass` problems.

    Possible inputs are:

    * None: defaults to list of accessible `Petroff colors
      <https://github.com/matplotlib/matplotlib/issues/9460#issuecomment-875185352>`_
      if `n_classes <= 10`, otherwise 'gist_rainbow' colormap
    * str: name of :class:`matplotlib.colors.Colormap`
    * list: list of length `n_classes` of `matplotlib colors
      <https://matplotlib.org/stable/users/explain/colors/colors.html#colors-def>`_

    Single color (fading to white) 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`.

    When `response_method='predict'` and `plot_method='contour'`,
    `multiclass_colors` is ignored and the class boundaries are plotted in black
    instead as the boundary lines may overlap and the colors don't necessarily
    correspond to the classes.

    For :term:`binary` problems, `multiclass_colors` is also ignored and `cmap` or
    `colors` can be passed as kwargs instead, otherwise, the default colormap
    ('viridis') is used.

    .. versionadded:: 1.7
    .. versionchanged:: 1.9
        `multiclass_colors` is now also used when `response_method="predict"`,
        except for when `plot_method='contour'`, where it is ignored and "black" is
        used instead.
        The default colors changed from 'tab10' to the more accessible `Petroff
        colors <https://github.com/matplotlib/matplotlib/issues/9460#issuecomment-875185352>`_.

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 <matplotlib.contour.QuadContourSet>`. If
    `plot_method` is 'pcolormesh', `surface_` is
    :class:`QuadMesh <matplotlib.collections.QuadMesh>`.

multiclass_colors_ : array of shape (n_classes, 4)
    Colors used to plot each class in multiclass problems.
    Only defined when `n_classes` > 2.

    .. 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 matplotlib as mpl
>>> import numpy as np
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.inspection import DecisionBoundaryDisplay
>>> data = np.array([[0, 0], [1, 1], [2, 1], [2, 2], [3, 2], [3, 3]])
>>> target = np.arange(data.shape[0])
>>> clf = LogisticRegression().fit(data, target)
>>> plot_methods = ["contourf", "contour", "pcolormesh"]
>>> response_methods = ["predict_proba", "decision_function", "predict"]
>>> _, axes = plt.subplots(
...     nrows=3,
...     ncols=3,
...     figsize=(12, 12),
...     constrained_layout=True
... )
>>> for plot_method_idx, plot_method in enumerate(plot_methods):
...     for response_method_idx, response_method in enumerate(response_methods):
...         ax = axes[plot_method_idx, response_method_idx]
...         display = DecisionBoundaryDisplay.from_estimator(
...             clf,
...             data,
...             grid_resolution=300,
...             response_method=response_method,
...             plot_method=plot_method,
...             ax=ax,
...             alpha=0.5,
...         )
...         cmap = mpl.colors.ListedColormap(display.multiclass_colors_)
...         ax.scatter(
...             data[:, 0],
...             data[:, 1],
...             c=target.astype(int),
...             edgecolors="black",
...             cmap=cmap,
...         )
...         ax.set_title(
...             f"plot_method={plot_method}\nresponse_method={response_method}"
...         )
>>> plt.show()
N)r>   xlabelylabelc                X    Xl         X l        X0l        X@l        XPl        X`l        Xpl        g r)   xx0xx1r?   responser>   rE   rF   )selfrI   rJ   r?   rK   r>   rE   rF   s           r    __init__ DecisionBoundaryDisplay.__init__  s(     " !2r"   contourfc                 &   [        S5        SSKnSSKJn  US;  a  [	        SU S35      eUc  UR                  5       u  p[        X!5      n	U R                  S:X  a0  U	" U R                  U R                  U R                  40 UD6U l        GOS H&  n
X;   d  M
  [        R                  " S	U
 S
35        XZ	 M(     [        X`R                  U R                  5      U l        SU;  a  SUS'   U R                  R"                  S:X  Gav  [%        U R                   5       VVVVVs/ s H7  u  nu  ppUR&                  R(                  R+                  SU 3SXUS4/5      PM9     nnnnnn/ U l        [%        U5       H  u  nn[,        R.                  R1                  U R                  SS2SS2U4   U R                  R3                  SS9U:g  S9nU R                  R5                  U	" U R                  U R                  U4SU0UD65        M     US:X  am  U R                  R5                  U	" U R                  U R                  U R                  R3                  SS9SS[,        R6                  " U R                  5      S95        GOU R                  R"                  S:X  a  SU;  aY  US:X  a$  [,        R6                  " U R                  5      US'   O/US:X  a)  [,        R6                  " U R                  S-   5      S-
  US'   US:X  a1  U	" U R                  U R                  U R                  4SS0UD6U l        OUUR&                  R9                  U R                   5      nU	" U R                  U R                  U R                  4SU0UD6U l        Uc  UR;                  5       (       d"  Uc  U R<                  OUnUR?                  U5        Uc  URA                  5       (       d"  Uc  U RB                  OUnURE                  U5        X l#        URH                  U l%        U $ s  snnnnnf )a	  Plot 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 <matplotlib.pyplot.contourf>`,
    :func:`contour <matplotlib.pyplot.contour>`,
    :func:`pcolormesh <matplotlib.pyplot.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`. For
    :term:`binary` problems, `cmap` or `colors` can be set here to specify the
    colormap or colors, otherwise the default colormap ('viridis') is used. If
    not specified by the user, `zorder` is set to -1 to ensure that the decision
    boundary is plotted in the background (in case a scatter plot is added on
    top).

Returns
-------
display: :class:`~sklearn.inspection.DecisionBoundaryDisplay`
    Object that stores computed values.

See Also
--------
DecisionBoundaryDisplay.from_estimator : Plot decision boundary given an
    estimator.

Examples
--------
>>> import matplotlib as mpl
>>> 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,
...     n_classes=len(tree.classes_),
...     response=y_pred
... )
>>> display.plot()
<...>
>>> display.ax_.scatter(
...     iris.data[:, 0],
...     iris.data[:, 1],
...     c=iris.target,
...     cmap=mpl.colors.ListedColormap(display.multiclass_colors_),
...     edgecolor="black"
... )
<...>
>>> plt.show()
zDecisionBoundaryDisplay.plotr   NrO   contour
pcolormeshz@plot_method must be 'contourf', 'contour', or 'pcolormesh'. Got 	 instead.   )r@   r*   'zD' is ignored in favor of 'multiclass_colors' in the multiclass case.zorder   	colormap_)      ?r[   r[   r[   r[   axis)maskr@   rR   black)r*   rW   levelsr`   rO   r&   g      ?r*   )&r   
matplotlibmatplotlib.pyplotr3   r   subplotsgetattrr?   rI   rJ   rK   surface_warningswarnrA   r>   multiclass_colors_ndim	enumerater*   LinearSegmentedColormap	from_listr7   maarrayargmaxappendarangeListedColormap
get_xlabelrE   
set_xlabel
get_ylabelrF   
set_ylabelax_figurefigure_)rL   plot_methodaxrE   rF   kwargsr.   plt_	plot_funckwarg	class_idxrgbmulticlass_cmapsr@   rK   s                     r    plotDecisionBoundaryDisplay.plot+  s   T 	!!?@ 'CC"m9. 
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2+-99T^^a5G+H3+Nx()+$-$((DMM%BI%MS%DM
 ::44T5L5LMD$-$((DMM%@D%HN%DM R]]__$*NT[[FMM&!R]]__$*NT[[FMM&!yyw$s   >P
d   r[   r   )	grid_resolutionepsrz   r   class_of_interestr>   rE   rF   r{   c       	   
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U " UUUUUU	U
S9n"U"RJ                  " S XS.UD6$ )!a  Plot decision boundary given an estimator.

Read more in the :ref:`User Guide <visualizations>`.

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 <matplotlib.pyplot.contourf>`,
    :func:`contour <matplotlib.pyplot.contour>`,
    :func:`pcolormesh <matplotlib.pyplot.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. For :term:`binary` classifiers, if None,
    `estimator.classes_[1]` is considered the positive class. For
    :term:`multiclass` classifiers, if None, all classes will be represented in
    the decision boundary plot; when `response_method` is :term:`predict_proba`
    or :term:`decision_function`, 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 : str or list of matplotlib colors, default=None
    Specifies how to color each class when plotting :term:`multiclass` problems
    and `class_of_interest` is None.

    Possible inputs are:

    * None: defaults to list of accessible `Petroff colors
      <https://github.com/matplotlib/matplotlib/issues/9460#issuecomment-875185352>`_
      if `n_classes <= 10`, otherwise 'gist_rainbow' colormap
    * str: name of :class:`matplotlib.colors.Colormap`
    * list: list of length `n_classes` of `matplotlib colors
      <https://matplotlib.org/stable/users/explain/colors/colors.html#colors-def>`_

    Single color (fading to white) 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`.

    When `response_method='predict'` and `plot_method='contour'`,
    `multiclass_colors` is ignored and the class boundaries are plotted in black
    instead as the boundary lines may overlap and the colors don't necessarily
    correspond to the classes.

    For :term:`binary` problems, `multiclass_colors` is also ignored and `cmap`
    or `colors` can be passed as kwargs instead, otherwise, the default colormap
    ('viridis') is used.

    .. versionadded:: 1.7
    .. versionchanged:: 1.9
        `multiclass_colors` is now also used when `response_method="predict"`,
        except for when `plot_method='contour'`, where it is ignored and "black"
        is used instead.
        The default colors changed from 'tab10' to the more accessible `Petroff
        colors <https://github.com/matplotlib/matplotlib/issues/9460#issuecomment-875185352>`_.

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 as mpl
>>> 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,
... )
>>> cmap = mpl.colors.ListedColormap(disp.multiclass_colors_)
>>> disp.ax_.scatter(X[:, 0], X[:, 1], c=iris.target, edgecolor="k", cmap=cmap)
<...>
>>> plt.show()
r&   z,grid_resolution must be greater than 1. Got rT   r   z,eps must be greater than or equal to 0. Got rQ   z, zplot_method must be one of z. Got rU   z#n_features must be equal to 2. Got r\   )columnsNr   zclass_of_interest=z+ is not a valid label: It should be one of T)r   	pos_labelreturn_response_method_usedr   labels_)binary
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   r   create_containerr   r!   r   r   r   r   	transformr   r   r   r:   r   uniquer   r   	__class____name__ri   reshapeshapeflatnonzeror   )#clsr   Xr   r   rz   r   r   r>   rE   rF   r{   r|   possible_plot_methodsavailable_methodsnum_featuresx0x1x0_minx0_maxx1_minx1_maxrI   rJ   X_gridadapterr   rK   r~   response_method_usedencoderr?   target_typecol_idxdisplays#                                      r    from_estimator&DecisionBoundaryDisplay.from_estimator  s]   l 		""#$I/ 
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   $sklearn.utils._optional_dependenciesr   sklearn.utils._responser   sklearn.utils._set_outputr   sklearn.utils.fixesr   sklearn.utils.multiclassr   sklearn.utils.validationr   r   r   r!   rA   rC   r   r"   r    <module>r      sQ      W W . ( ? I 8 A . 3 DFHRe	F e	Fr"   