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    2d!                     @   s   d Z ddlZddlmZmZ ddlmZ ddlZddl	m
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mZ ddlmZ ddlmZ dd	lmZmZmZ dd
lmZ ddlmZ G dd deedZdddZdS )zGeneric feature selection mixin    N)ABCMetaabstractmethod)
attrgetter)issparse
csc_matrix   )TransformerMixin)_PLS)check_array	safe_masksafe_sqr)
_safe_tags)_check_feature_names_inc                   @   sH   e Zd ZdZdddZedd Zdd Zd	d
 Zdd Z	dddZ
dS )SelectorMixinz
    Transformer mixin that performs feature selection given a support mask

    This mixin provides a feature selector implementation with `transform` and
    `inverse_transform` functionality given an implementation of
    `_get_support_mask`.
    Fc                 C   s   |   }|s|S t|d S )a  
        Get a mask, or integer index, of the features selected.

        Parameters
        ----------
        indices : bool, default=False
            If True, the return value will be an array of integers, rather
            than a boolean mask.

        Returns
        -------
        support : array
            An index that selects the retained features from a feature vector.
            If `indices` is False, this is a boolean array of shape
            [# input features], in which an element is True iff its
            corresponding feature is selected for retention. If `indices` is
            True, this is an integer array of shape [# output features] whose
            values are indices into the input feature vector.
        r   )_get_support_masknpwhere)selfindicesmask r   C/tmp/pip-unpacked-wheel-zrfo1fqw/sklearn/feature_selection/_base.pyget_support!   s    zSelectorMixin.get_supportc                 C   s   dS )a  
        Get the boolean mask indicating which features are selected

        Returns
        -------
        support : boolean array of shape [# input features]
            An element is True iff its corresponding feature is selected for
            retention.
        Nr   )r   r   r   r   r   8   s    zSelectorMixin._get_support_maskc                 C   s(   | j |ddt| dd dd}| |S )aB  Reduce X to the selected features.

        Parameters
        ----------
        X : array of shape [n_samples, n_features]
            The input samples.

        Returns
        -------
        X_r : array of shape [n_samples, n_selected_features]
            The input samples with only the selected features.
        NZcsr	allow_nan)keyF)dtypeZaccept_sparseZforce_all_finitereset)Z_validate_datar   
_transform)r   Xr   r   r   	transformD   s    zSelectorMixin.transformc                 C   sl   |   }| s<tdt tjd|jd|j	d dfS t
||j	d krVtd|ddt||f S )z"Reduce X to the selected features.zYNo features were selected: either the data is too noisy or the selection test too strict.r   r      ,X has a different shape than during fitting.N)r   anywarningswarnUserWarningr   emptyr   reshapeshapelen
ValueErrorr   )r   r   r   r   r   r   r   \   s     zSelectorMixin._transformc                 C   s   t |rx| }| t|jdd}| }tdgt	|g}t
|j|j|f|jd t|d f|jd}|S |  }t|dd}| |jd krtd|jdkr|dddf }tj|jd |jf|jd}||dd|f< |S )a  Reverse the transformation operation.

        Parameters
        ----------
        X : array of shape [n_samples, n_selected_features]
            The input samples.

        Returns
        -------
        X_r : array of shape [n_samples, n_original_features]
            `X` with columns of zeros inserted where features would have
            been removed by :meth:`transform`.
        r!   r   )r)   r   Nr    r"   )r   Ztocscinverse_transformr   Zdiffindptrr(   ZravelZconcatenateZcumsumr   datar   r)   r*   r   r   r
   sumr+   ndimzerossize)r   r   itZcol_nonzerosr.   ZXtZsupportr   r   r   r-   j   s(    
zSelectorMixin.inverse_transformNc                 C   s   t | |}||   S )a  Mask feature names according to selected features.

        Parameters
        ----------
        input_features : array-like of str or None, default=None
            Input features.

            - If `input_features` is `None`, then `feature_names_in_` is
              used as feature names in. If `feature_names_in_` is not defined,
              then the following input feature names are generated:
              `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
            - If `input_features` is an array-like, then `input_features` must
              match `feature_names_in_` if `feature_names_in_` is defined.

        Returns
        -------
        feature_names_out : ndarray of str objects
            Transformed feature names.
        )r   r   )r   Zinput_featuresr   r   r   get_feature_names_out   s    
z#SelectorMixin.get_feature_names_out)F)N)__name__
__module____qualname____doc__r   r   r   r   r   r-   r5   r   r   r   r   r      s   

(r   )	metaclassr!   c                 C   s   t |trn|dkrdt | tr&td}qlt| dr:td}qlt| drNtd}qltd| jj dq~t|}nt|s~td|| }|dkr|S |d	kr|j	d
krt
|}qt
jj|d|d}n6|dkr|j	d
krt|}qt|jdd}ntd|S )a  
    Retrieve and aggregate (ndim > 1)  the feature importances
    from an estimator. Also optionally applies transformation.

    Parameters
    ----------
    estimator : estimator
        A scikit-learn estimator from which we want to get the feature
        importances.

    getter : "auto", str or callable
        An attribute or a callable to get the feature importance. If `"auto"`,
        `estimator` is expected to expose `coef_` or `feature_importances`.

    transform_func : {"norm", "square"}, default=None
        The transform to apply to the feature importances. By default (`None`)
        no transformation is applied.

    norm_order : int, default=1
        The norm order to apply when `transform_func="norm"`. Only applied
        when `importances.ndim > 1`.

    Returns
    -------
    importances : ndarray of shape (n_features,)
        The features importances, optionally transformed.
    autoZ_coef_Zcoef_Zfeature_importances_z;when `importance_getter=='auto'`, the underlying estimator z should have `coef_` or `feature_importances_` attribute. Either pass a fitted estimator to feature selector or call fit before calling transform.z4`importance_getter` has to be a string or `callable`Nnormr!   r   )axisordZsquare)r=   zpValid values for `transform_func` are None, 'norm' and 'square'. Those two transformation are only supported now)
isinstancestrr	   r   hasattrr+   	__class__r6   callabler1   r   absZlinalgr<   r   r0   )Z	estimatorgetterZtransform_funcZ
norm_orderZimportancesr   r   r   _get_feature_importances   s:    
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
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rF   )Nr!   )r9   r$   abcr   r   operatorr   Znumpyr   Zscipy.sparser   r   baser   Zcross_decomposition._plsr	   utilsr
   r   r   Zutils._tagsr   Zutils.validationr   r   rF   r   r   r   r   <module>   s    