我们如何使用scikit-learn了解所选和省略的特征(列)名称(标题)

Question

我用一段数据解释了这个场景:

防爆.数据集.

GA_ID   PN_ID   PC_ID   MBP_ID  GR_ID   AP_ID   class
0.033   6.652   6.681   0.194   0.874   3.177     0
0.034   9.039   6.224   0.194   1.137   0         0
0.035   10.936  10.304  1.015   0.911   4.9       1
0.022   10.11   9.603   1.374   0.848   4.566     1
0.035   2.963   17.156  0.599   0.823   9.406     1
0.033   10.872  10.244  1.015   0.574   4.871     1
0.035   21.694  22.389  1.015   0.859   9.259     1
0.035   10.936  10.304  1.015   0.911   4.9       1
0.035   10.936  10.304  1.015   0.911   4.9       1
0.035   10.936  10.304  1.015   0.911   4.9       0
0.036   1.373   12.034  0.35    0.259   5.723     0
0.033   9.831   9.338   0.35    0.919   4.44      0

特征选择步骤1及其出现:VarianceThreshol

     PN_ID  PC_ID   MBP_ID  GR_ID   AP_ID   class
    6.652   6.681   0.194   0.874   3.177     0
    9.039   6.224   0.194   1.137   0         0
    10.936  10.304  1.015   0.911   4.9       1
    10.11   9.603   1.374   0.848   4.566     1
    2.963   17.156  0.599   0.823   9.406     1
    10.872  10.244  1.015   0.574   4.871     1
    21.694  22.389  1.015   0.859   9.259     1
    10.936  10.304  1.015   0.911   4.9       1
    10.936  10.304  1.015   0.911   4.9       1
    10.936  10.304  1.015   0.911   4.9       0
    1.373   12.034  0.35    0.259   5.723     0
    9.831   9.338   0.35    0.919   4.44      0

特征选择步骤2及其出现:基于树的特征选择(例如来自klearn.ensemble import ExtraTreesClassifier)

PN_ID   MBP_ID  GR_ID   AP_ID   class
6.652   0.194   0.874   3.177     0
9.039   0.194   1.137   0         0
10.936  1.015   0.911   4.9       1
10.11   1.374   0.848   4.566     1
2.963   0.599   0.823   9.406     1
10.872  1.015   0.574   4.871     1
21.694  1.015   0.859   9.259     1
10.936  1.015   0.911   4.9       1
10.936  1.015   0.911   4.9       1
10.936  1.015   0.911   4.9       0
1.373   0.35    0.259   5.723     0
9.831   0.35    0.919   4.44      0

在这里,我们可以得出结论,我们从6列(特征)和一个类标签开始,最后一步将其缩减为4个特征和一个类标签.GA_ID和PC_ID列已被删除,而模型已使用PN_ID,MBP_ID,GR_ID和AP_ID功能构建.

但不幸的是,当我使用scikit-learn库中的可用方法执行特征选择时,我发现它只返回数据的形状和缩小的数据,而没有选择和省略的特征的名称.

我已经写下了许多愚蠢的python代码(因为我不是很有经验的程序员)来找到答案但没有成功.

请建议我一些方法来摆脱它谢谢

(注意:特别是对于这篇文章,我从未在给定的示例数据集上执行过任何特征选择方法,而是我已经随机删除了该列来解释这个案例)

Answer 1

也许这段代码和评论的解释将有所帮助(从这里改编).

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import make_classification
from sklearn.ensemble import ExtraTreesClassifier

# Build a classification task using 3 informative features
X, y = make_classification(n_samples=1000,
                           n_features=10,
                           n_informative=3,
                           n_redundant=0,
                           n_repeated=0,
                           n_classes=2,
                           random_state=0,
                           shuffle=False)

# Build a forest and compute the feature importances
forest = ExtraTreesClassifier(n_estimators=250,
                              random_state=0)


forest.fit(X, y)

importances = forest.feature_importances_ #array with importances of each feature

idx = np.arange(0, X.shape[1]) #create an index array, with the number of features

features_to_keep = idx[importances > np.mean(importances)] #only keep features whose importance is greater than the mean importance
#should be about an array of size 3 (about)
print features_to_keep.shape

x_feature_selected = X[:,features_to_keep] #pull X values corresponding to the most important features

print x_feature_selected