问题
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)
回答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
来源：https://stackoverflow.com/questions/29230019/how-can-we-get-to-know-the-selected-and-omitted-features-columns-names-heade