how can we get to know the selected and omitted fe

Question

I am explaining the scenario with a piece of data:

Ex. data set.

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

feature selection step 1 and its out come : 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

feature selection step 2 and its out come : Tree-based feature selection (Ex. from 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

Here we can conclude that we started with 6 columns(features) and one class label and at the final step reduced down it to 4 features and one class label. GA_ID and PC_ID columns has been removed, while model has been constructed using PN_ID, MBP_ID, GR_ID and AP_ID features.

But unfortunately when i performed feature selection with the available methods in scikit-learn library I found that it returns only shape of the data and reduced data without the name of the selected and omitted features.

I have write down many stupid python codes (as i m not very experience programmer) to find the answer but not succeeded.

kindly please suggest me some way to get out of it thanks

(Note: Particularly for this post i have never performed any feature selection method on the given example data set, rather i have deleted the column randomly to explain the case)

Answer 1

Perhaps this code and commented explanations will help (adapted from here).

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