scikit_feature_selection实战

在进行李宏毅HW01作业时，需进行特征选择。
选用scikit-learn 中的feature_selection.
参考：
selectkbest
feature selection
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实战注意点：

chi 2 适用于非零的参数，如果报错，换用 f_classif

model = SelectKBest(f_classif, k=4)
X_new1 = model.fit_transform(x, y)
X_new1.shape

计算结果中可能出现inf，影响最后特征重要性的排序。建议把model.score_打印出来查看，如果有inf,用零填充。

scores = model.scores_
where_are_inf = np.isinf(scores)
scores[where_are_inf] = 0

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3. 将前面的特征索引提取出来

代码

import math
import numpy as npimport pandas as pd
import os
import csvfrom sklearn.feature_selection import SelectKBest, chi2, f_classif
from sklearn.preprocessing import MinMaxScalerdef feature_select(feature_data,label_data,k=4,column=None):model = SelectKBest(f_classif,k=k)X_new = model.fit(feature_data,label_data)scores = model.scores_where_are_inf = np.isinf(scores)scores[where_are_inf] = 0indices = np.argsort(scores)[::-1]if column:k_best_features = [column[i] for i in indices[0:k].tolist()]print('k best features are:',k_best_features)return X_new,indices[:k]+1 # add column id index  原来第一行是 id ,索引要加上if __name__ == '__main__':train_data = pd.read_csv('./covid.train.csv').valuesx = train_data[:, 1:-1]  # pass first column idy = train_data[:, -1]x_selected, indices_selected = feature_select(x, y, 4)print(indices_selected)  # array([101,  85,  69,  53])