特征决定了最优效果的上限，算法与模型只是让效果更逼近这个上限，所以特征工程与选择什么样的特征很重要！

以下是一些特征筛选与降维技巧

# -*- coding:utf-8 -*-import scipy as scimport libsvm_file_process as data_processimport numpy as npfrom minepy import MINEfrom sklearn.feature_selection import VarianceThresholdfrom sklearn.feature_selection import SelectKBestfrom sklearn.feature_selection import chi2from sklearn.feature_selection import f_regressionfrom sklearn.feature_selection import RFEfrom sklearn.svm import SVRfrom sklearn.linear_model import LogisticRegressionfrom sklearn.decomposition import PCAfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysisclass feature_select:    """    特征筛选方式：        相关链接：http://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection        皮尔逊相关性        互信息        单因素 - 卡方判断，F值，假正率        方差过滤        递归特征消除法 - 每次消除一个特征，依据是特征前面的系数        基于模型（LR/GBDT等）的特征选择 SelectFromModel            模型（LR/GBDT）必须有feature_importances_ 或 coef_这个属性    降维：        PCA（unsurperised）：一般用于无监督情况下的降维，有监督的时候，也可以小幅降维 去除噪音，然后再使用LDA 降维        LDA（surperised）:本质上是一个分类器,在使用上，要求降低的维度要小于分类的维度    """    def __init__(self):        self.data_path = "/trainData/libsvm2/"        self.trainData = ["20180101"]        # 计算互信息        self.mine = MINE(alpha=0.6, c=15, est="mic_approx")        # 方差过滤 一般用于无监督学习        self.variance_filter = VarianceThreshold(threshold=0.1)        # chi2 - 卡方检验; f_regression - f值; SelectFpr-假正率;等        self.chi_squared = SelectKBest(f_regression, k=2)        # 递归特征消除        self.estimator = LogisticRegression()  # SVR(kernel="linear")        self.selector = RFE(self.estimator, 5, step=1)        # PCA 降维        self.pca = PCA(n_components=5)        # LDA 降维        self.lda = LinearDiscriminantAnalysis(n_components=2)    def select(self):        for i in range(len(self.trainData)):            generator = data_process.get_data_batch(self.data_path + self.trainData[i] + "/part-00000", 100000)            labels, features = generator.next()            # 方差过滤            filter1 = self.variance_filter.fit_transform(features)            print filter1.shape, features.shape            print self.variance_filter.get_support()            # 卡方检验            filter2 = self.chi_squared.fit_transform(features, labels)            print filter2.shape            print self.chi_squared.get_support()            # 递归特征消除（比较耗时 暂时先注释掉）            # self.selector.fit(features, labels)            # print self.selector.support_            # PCA 降维            transform1 = self.pca.fit_transform(features)            print 'transform1:', transform1            # LDA降维            self.lda.fit(features, labels)            transform2 = self.lda.transform(features)            print 'transform2:', transform2            for j in range(int(features.shape[1]) - 870):                features_j = features[0:, j + 870: j + 871]                self.mine.compute_score(features_j.flatten(), labels.flatten())                # 计算互信息                print self.mine.mic()                # 计算皮尔逊系数                print j, sc.stats.pearsonr(features_j.reshape(-1, 1), labels.reshape(-1, 1))if __name__ == '__main__':    feature_util = feature_select()    feature_util.select()