特征选择后打印 column/variable 个名字

Question

我正在 Iris dateset 上尝试特征选择。

我引用自 Feature Selection with Univariate Statistical Tests

我正在使用以下几行，我想找出重要的功能：

import pandas
from pandas import read_csv
from numpy import set_printoptions
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif

dataframe = pandas.read_csv("C:\dateset\iris.csv"]))
array = dataframe.values
X = array[:,0:4]
Y = array[:,4]

test = SelectKBest(score_func=f_classif, k=2)
fit = test.fit(X, Y)

set_printoptions(precision=2)
arr = fit.scores_

print (arr)

# [ 119.26   47.36 1179.03  959.32]

为了按分数显示前2名的索引，我添加了：

idx = (-arr).argsort()[:2]
print (idx)

# [2 3]

此外，我怎样才能得到 column/variable 个名称（而不是它们的索引）？

Answer 1

使用索引，这里可以使用列名，因为选择了前 4 列：

#first 4 columns
X = array[:,0:4]

cols = dataframe.columns[idx]

如果 X 变量的选择不同，则还需要按位置过滤 DataFrame:

#e.g. selected 3. to 7. column
X = array[:,2:6]

cols = dataframe.iloc[:, 2:6].columns[idx]

Answer 2

import pandas
from pandas import read_csv
from numpy import set_printoptions
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif

dataframe = pandas.read_csv("iris.csv")
array = dataframe.values
X = array[:,0:4]
Y = array[:,4]

test = SelectKBest(score_func=f_classif, k=2)
fit = test.fit(X, Y)

set_printoptions(precision=2)
arr = fit.scores_

idx = (-arr).argsort()[:2]
print (idx)

print (arr)
#names=[dataframe.columns[j] for j in X]

names = dataframe.columns[idx]
print(names)

输出

[2 3]
[ 119.26   47.36 1179.03  959.32]
Index(['petal_length', 'petal_width'], dtype='object')

特征选择后打印 column/variable 个名字

Printing column/variable names after feature selection

python

dataframe

feature-selection

pandas