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

目录

SelectFromModel函数的简介

1、使用SelectFromModel和LassoCV进行特征选择

2、L1-based feature selection

3、Tree-based feature selection

SelectFromModel函数的使用方法

1、SelectFromModel的原生代码

SelectFromModel函数的简介

        SelectFromModel is a meta-transformer that can be used along with any estimator that has a coef_ or feature_importances_ attribute after fitting. The features are considered unimportant and removed, if the corresponding coef_ or feature_importances_ values are below the provided threshold parameter. Apart from specifying the threshold numerically, there are built-in heuristics for finding a threshold using a string argument. Available heuristics are “mean”, “median” and float multiples of these like “0.1*mean”.
        SelectFromModel是一个元转换器，可以与任何在拟合后具有coef_或feature_importances_属性的estimator 一起使用。如果相应的coef_或feature_importances_值低于提供的阈值参数，则认为这些特性不重要并将其删除。除了以数字方式指定阈值外，还有使用字符串参数查找阈值的内置启发式方法。可用的试探法是“平均数”、“中位数”和这些数的浮点倍数，如“0.1*平均数”。

官网API：https://scikit-learn.org/stable/modules/feature_selection.htmlfeature-selection-using-selectfrommodel

1、使用SelectFromModel和LassoCV进行特征选择

 Author: Manoj Kumar <mks542@nyu.edu>
 License: BSD 3 clause
 
print(__doc__)
 
import matplotlib.pyplot as plt
import numpy as np
 
from sklearn.datasets import load_boston
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LassoCV
 
 Load the boston dataset.
X, y = load_boston(return_X_y=True)
 
 We use the base estimator LassoCV since the L1 norm promotes sparsity of features.
clf = LassoCV()
 
 Set a minimum threshold of 0.25
sfm = SelectFromModel(clf, threshold=0.25)
sfm.fit(X, y)
n_features = sfm.transform(X).shape[1]
 
 Reset the threshold till the number of features equals two.
 Note that the attribute can be set directly instead of repeatedly
 fitting the metatransformer.
while n_features > 2:
    sfm.threshold += 0.1
    X_transform = sfm.transform(X)
    n_features = X_transform.shape[1]
 
 Plot the selected two features from X.
plt.title(
    "Features selected from Boston using SelectFromModel with "
    "threshold %0.3f." % sfm.threshold)
feature1 = X_transform[:, 0]
feature2 = X_transform[:, 1]
plt.plot(feature1, feature2, 'r.')
plt.xlabel("Feature number 1")
plt.ylabel("Feature number 2")
plt.ylim([np.min(feature2), np.max(feature2)])
plt.show()

2、L1-based feature selection

-meta">>>> from sklearn.svm import LinearSVC
-meta">>>> from sklearn.datasets import load_iris
-meta">>>> from sklearn.feature_selection import SelectFromModel
-meta">>>> X, y = load_iris(return_X_y=True)
-meta">>>> X.shape
(150, 4)
-meta">>>> lsvc = LinearSVC(C=0.01, penalty="l1", dual=False).fit(X, y)
-meta">>>> model = SelectFromModel(lsvc, prefit=True)
-meta">>>> X_new = model.transform(X)
-meta">>>> X_new.shape
(150, 3)

3、Tree-based feature selection

-meta">>>> from sklearn.ensemble import ExtraTreesClassifier
-meta">>>> from sklearn.datasets import load_iris
-meta">>>> from sklearn.feature_selection import SelectFromModel
-meta">>>> X, y = load_iris(return_X_y=True)
-meta">>>> X.shape
(150, 4)
-meta">>>> clf = ExtraTreesClassifier(n_estimators=50)
-meta">>>> clf = clf.fit(X, y)
-meta">>>> clf.feature_importances_  
array([ 0.04...,  0.05...,  0.4...,  0.4...])
-meta">>>> model = SelectFromModel(clf, prefit=True)
-meta">>>> X_new = model.transform(X)
-meta">>>> X_new.shape               
(150, 2)

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
    
-meta">    @property
    def threshold_(self):
        scores = _get_feature_importances(self.estimator_, self.norm_order)
        return _calculate_threshold(self.estimator, scores, self.threshold)
    
-meta">    @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