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
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.html#feature-selection-using-selectfrommodel
| """Meta-transformer for selecting features based on importance weights. .. versionadded:: 0.17 | 用于根据重要性权重来选择特征的元转换器。 . .加入在0.17版本:: |
| 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. | 参数
用于特征选择的阈值。重要性大于或等于的特征被保留，其他特征被丢弃。如果“中位数”(分别地。(“均值”)，则“阈值”为中位数(resp，特征重要性的平均值)。也可以使用比例因子(例如“1.25*平均值”)。如果没有，并且估计量有一个参数惩罚设置为l1，不管是显式的还是隐式的(例如Lasso)，阈值为1e-5。否则，默认使用“mean”。
prefit模型是否应直接传递给构造函数。如果为True，则必须直接调用“transform”，SelectFromModel不能与cross_val_score 、GridSearchCV以及类似的克隆估计器的实用程序一起使用。否则，使用' ' fit ' '和' ' transform ' '训练模型进行特征选择。
|
| 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. """ | 属性 建立转换器的基estimator，只有在将非拟合估计量传递给SelectFromModel 时，才会存储它。当prefit 为假时。
threshold_ ：浮点类型 |
1、使用SelectFromModel和LassoCV进行特征选择
# Author: Manoj Kumar <mks542@nyu.edu> # License: BSD 3 clauseprint(__doc__)import matplotlib.pyplot as plt import numpy as npfrom 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 += 0.1X_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
>>> from sklearn.svm import LinearSVC >>> from sklearn.datasets import load_iris >>> from sklearn.feature_selection import SelectFromModel >>> X, y = load_iris(return_X_y=True) >>> X.shape (150, 4) >>> lsvc = LinearSVC(C=0.01, penalty="l1", dual=False).fit(X, y) >>> model = SelectFromModel(lsvc, prefit=True) >>> X_new = model.transform(X) >>> X_new.shape (150, 3)
3、Tree-based feature selection
>>> from sklearn.ensemble import ExtraTreesClassifier >>> from sklearn.datasets import load_iris >>> from sklearn.feature_selection import SelectFromModel >>> X, y = load_iris(return_X_y=True) >>> X.shape (150, 4) >>> clf = ExtraTreesClassifier(n_estimators=50) >>> clf = clf.fit(X, y) >>> clf.feature_importances_ array([ 0.04..., 0.05..., 0.4..., 0.4...]) >>> model = SelectFromModel(clf, prefit=True) >>> X_new = model.transform(X) >>> 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_modelclass SelectFromModel(BaseEstimator, SelectorMixin, MetaEstimatorMixin):"""Meta-transformer for selecting features based on importance weights... versionadded:: 0.17Parameters----------estimator : objectThe 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 NoneThe threshold value to use for feature selection. Features whoseimportance is greater or equal are kept while the others arediscarded. If "median" (resp. "mean"), then the ``threshold`` value isthe median (resp. the mean) of the feature importances. A scalingfactor (e.g., "1.25*mean") may also be used. If None and if theestimator has a parameter penalty set to l1, either explicitlyor implicitly (e.g, Lasso), the threshold used is 1e-5.Otherwise, "mean" is used by default.prefit : bool, default FalseWhether a prefit model is expected to be passed into the constructordirectly or not. If True, ``transform`` must be called directlyand 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 dofeature selection.norm_order : non-zero int, inf, -inf, default 1Order of the norm used to filter the vectors of coefficients below``threshold`` in the case where the ``coef_`` attribute of theestimator is of dimension 2.Attributes----------estimator_ : an estimatorThe 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_ : floatThe threshold value used for feature selection."""def __init__(self, estimator, threshold=None, prefit=False, norm_order=1):self.estimator = estimatorself.threshold = thresholdself.prefit = prefitself.norm_order = norm_orderdef _get_support_mask(self):# SelectFromModel can directly call on transform.if self.prefit:estimator = self.estimatorelif 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 >= thresholddef 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 inclassification, real numbers in regression).**fit_params : Other estimator specific parametersReturns-------self : objectReturns 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@propertydef 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 inclassification, real numbers in regression).**fit_params : Other estimator specific parametersReturns-------self : objectReturns 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
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