功能选择Scikit了解

Question

在scikit-learn中运行功能选择后，我想展示相关变量，向我展示从方法中选择的变量，这怎么可能？ X.shape命令仅显示变量的数量，我想在选择特征后查看变量的名称。

from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2

iris = load_iris()

X, y = iris.data, iris.target

X.shape

X_new = SelectKBest(chi2, k=2).fit_transform(X, y)

X_new.shape

Answer 1

from sklearn.datasets import load_iris

from sklearn.feature_selection import SelectKBest

from sklearn.feature_selection import chi2

iris = load_iris()

X, y = iris.data, iris.target

X.shape

skb = SelectKBest(chi2, k=2)
skb.fit(X, y)
X_new = skb.transform(X)

X_new.shape

print skb.get_support(indices=True)

这将为您提供所选功能的索引。

Answer 2

您可以获取名称，但是需要使用pandas并将numpy转换为数据框。

范例 ：

from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
import pandas as pd

iris = load_iris()
X = pd.DataFrame(iris.data, columns=iris.feature_names)
y = pd.DataFrame(iris.target)

selector = SelectKBest(chi2, k=2)
selector.fit(X, y)

X_new = selector.transform(X)
X_new.shape

#text format
X.columns[selector.get_support(indices=True)]
#vector format
vector_names = list(X.columns[selector.get_support(indices=True)])

print(vector_names)

#2nd way 
X.columns[selector.get_support(indices=True)].tolist()

结果 ：

Index([u'petal length (cm)', u'petal width (cm)'], dtype='object')
['petal length (cm)', 'petal width (cm)']
['petal length (cm)', 'petal width (cm)']

Answer 3

选择特征之后，如果您只想选择那些被选择为重要的（“ True”）特征来构建较新的模型，则可以执行以下操作：

feats = X.T.tolist()

optimised_feats = []
for i,j in zip(X_new.support_,feats):
    if i == True:
        optimised_feats.append(j)
optimised_feats=np.array(optimised_feats).T