sklearn：sklearn.feature_selection的SelectFromModel函数的简介、使用方法之详细攻略

sklearn：sklearn.feature_selection的SelectFromModel函数的简介、使用方法之详细攻略

SelectFromModel函数的简介

SelectFromModel函数的使用方法

1、SelectFromModel的原生代码

class SelectFromModel Found at: sklearn.feature_selection.from_model

class SelectFromModel(BaseEstimator, SelectorMixin, MetaEstimatorMixin):
    """Meta-transformer for selecting features based on importance weights.
    
    .. versionadded:: 0.17
    
    Parameters
    ----------
    estimator : object
    The base estimator from which the transformer is built.
    This can be both a fitted (if ``prefit`` is set to True)
    or a non-fitted estimator. The estimator must have either a
    ``feature_importances_`` or ``coef_`` attribute after fitting.
    
    threshold : string, float, optional default None
    The threshold value to use for feature selection. Features whose
    importance is greater or equal are kept while the others are
    discarded. If "median" (resp. "mean"), then the ``threshold`` value is
    the median (resp. the mean) of the feature importances. A scaling
    factor (e.g., "1.25*mean") may also be used. If None and if the
    estimator has a parameter penalty set to l1, either explicitly
    or implicitly (e.g, Lasso), the threshold used is 1e-5.
    Otherwise, "mean" is used by default.
    
    prefit : bool, default False
    Whether a prefit model is expected to be passed into the constructor
    directly or not. If True, ``transform`` must be called directly
    and SelectFromModel cannot be used with ``cross_val_score``,
    ``GridSearchCV`` and similar utilities that clone the estimator.
    Otherwise train the model using ``fit`` and then ``transform`` to do
    feature selection.
    
    norm_order : non-zero int, inf, -inf, default 1
    Order of the norm used to filter the vectors of coefficients below
    ``threshold`` in the case where the ``coef_`` attribute of the
    estimator is of dimension 2.
    
    Attributes
    ----------
    estimator_ : an estimator
    The base estimator from which the transformer is built.
    This is stored only when a non-fitted estimator is passed to the
    ``SelectFromModel``, i.e when prefit is False.
    
    threshold_ : float
    The threshold value used for feature selection.
    """
    def __init__(self, estimator, threshold=None, prefit=False, 
     norm_order=1):
        self.estimator = estimator
        self.threshold = threshold
        self.prefit = prefit
        self.norm_order = norm_order
    
    def _get_support_mask(self):
    # SelectFromModel can directly call on transform.
        if self.prefit:
            estimator = self.estimator
        elif hasattr(self, 'estimator_'):
            estimator = self.estimator_
        else:
            raise ValueError(
                'Either fit SelectFromModel before transform or set "prefit='
                'True" and pass a fitted estimator to the constructor.')
        scores = _get_feature_importances(estimator, self.norm_order)
        threshold = _calculate_threshold(estimator, scores, self.threshold)
        return scores >= threshold
    
    def fit(self, X, y=None, **fit_params):
        """Fit the SelectFromModel meta-transformer.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            The training input samples.

        y : array-like, shape (n_samples,)
            The target values (integers that correspond to classes in
            classification, real numbers in regression).

        **fit_params : Other estimator specific parameters

        Returns
        -------
        self : object
            Returns self.
        """
        if self.prefit:
            raise NotFittedError(
                "Since 'prefit=True', call transform directly")
        self.estimator_ = clone(self.estimator)
        self.estimator_.fit(X, y, **fit_params)
        return self
    
    @property
    def threshold_(self):
        scores = _get_feature_importances(self.estimator_, self.norm_order)
        return _calculate_threshold(self.estimator, scores, self.threshold)
    
    @if_delegate_has_method('estimator')
    def partial_fit(self, X, y=None, **fit_params):
        """Fit the SelectFromModel meta-transformer only once.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            The training input samples.

        y : array-like, shape (n_samples,)
            The target values (integers that correspond to classes in
            classification, real numbers in regression).

        **fit_params : Other estimator specific parameters

        Returns
        -------
        self : object
            Returns self.
        """
        if self.prefit:
            raise NotFittedError(
                "Since 'prefit=True', call transform directly")
        if not hasattr(self, "estimator_"):
            self.estimator_ = clone(self.estimator)
        self.estimator_.partial_fit(X, y, **fit_params)
        return self