基于惩罚项的特征选择法.py

# -*- coding: utf-8 -*-
"""
Created on Fri Jun 30 16:08:26 2017

@author: Lenovo-Y430p
"""
#基于惩罚项的特征选择法
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LogisticRegression
from numpy import array
from  sklearn.datasets import load_iris
iris=load_iris()
#带L1惩罚项的逻辑回归作为基模型的特征选择
print(SelectFromModel(LogisticRegression(penalty="l1", C=0.1)).fit_transform(iris.data, iris.target))

class LR(LogisticRegression):
    def __init__(self, threshold=0.01, dual=False, tol=1e-4, C=1.0,
                 fit_intercept=True, intercept_scaling=1, class_weight=None,
                 random_state=None, solver='liblinear', max_iter=100,
                 multi_class='ovr', verbose=0, warm_start=False, n_jobs=1):

        #权值相近的阈值
        self.threshold = threshold
        LogisticRegression.__init__(self, penalty='l1', dual=dual, tol=tol, C=C,
                 fit_intercept=fit_intercept, intercept_scaling=intercept_scaling, class_weight=class_weight,
                 random_state=random_state, solver=solver, max_iter=max_iter,
                 multi_class=multi_class, verbose=verbose, warm_start=warm_start, n_jobs=n_jobs)
        #使用同样的参数创建L2逻辑回归
        self.l2 = LogisticRegression(penalty='l2', dual=dual, tol=tol, C=C, fit_intercept=fit_intercept, intercept_scaling=intercept_scaling, class_weight = class_weight, random_state=random_state, solver=solver, max_iter=max_iter, multi_class=multi_class, verbose=verbose, warm_start=warm_start, n_jobs=n_jobs)

    def fit(self, X, y, sample_weight=None):
        #训练L1逻辑回归
        super(LR, self).fit(X, y, sample_weight=sample_weight)
        self.coef_old_ = self.coef_.copy()
        #训练L2逻辑回归
        self.l2.fit(X, y, sample_weight=sample_weight)

        cntOfRow, cntOfCol = self.coef_.shape
        #权值系数矩阵的行数对应目标值的种类数目
        for i in range(cntOfRow):
            for j in range(cntOfCol):
                coef = self.coef_[i][j]
                #L1逻辑回归的权值系数不为0
                if coef != 0:
                    idx = [j]
                    #对应在L2逻辑回归中的权值系数
                    coef1 = self.l2.coef_[i][j]
                    for k in range(cntOfCol):
                        coef2 = self.l2.coef_[i][k]
                        #在L2逻辑回归中，权值系数之差小于设定的阈值，且在L1中对应的权值为0
                        if abs(coef1-coef2) < self.threshold and j != k and self.coef_[i][k] == 0:
                            idx.append(k)
                    #计算这一类特征的权值系数均值
                    mean = coef / len(idx)
                    self.coef_[i][idx] = mean
        return self
#带L1和L2惩罚项的逻辑回归作为基模型的特征选择
#参数threshold为权值系数之差的阈值
print(SelectFromModel(LR(threshold=0.5, C=0.1)).fit_transform(iris.data, iris.target))