Python 如何在sklearn管道中通过特征消除选择特征名称？

我在sklearn管道中使用递归功能消除，管道如下所示：

from sklearn.pipeline import FeatureUnion, Pipeline
from sklearn import feature_selection
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC

X = ['I am a sentence', 'an example']
Y = [1, 2]
X_dev = ['another sentence']

# classifier
LinearSVC1 = LinearSVC(tol=1e-4,  C = 0.10000000000000001)
f5 = feature_selection.RFE(estimator=LinearSVC1, n_features_to_select=500, step=1)

pipeline = Pipeline([
    ('features', FeatureUnion([
       ('tfidf', TfidfVectorizer(ngram_range=(1, 3), max_features= 4000)), 
       ('custom_features', CustomFeatures())])),
    ('rfe_feature_selection', f5),
    ('clf', LinearSVC1),
    ])

pipeline.fit(X, Y)
y_pred = pipeline.predict(X_dev)

support = pipeline.named_steps['rfe_feature_selection'].support_
feature_names = pipeline_age.named_steps['union_vectorizer'].get_feature_names()
print np.array(feature_names)[support]

如何获取RFE选择的要素的要素名称？RFE应该选择最好的500个功能，但我真的需要看看选择了哪些功能

编辑：

我有一个复杂的管道，它包括多个管道和特征联合、百分位特征选择以及最后的递归特征消除：

fs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=90)
fs_vect = feature_selection.SelectPercentile(feature_selection.chi2, percentile=80)
f5 = feature_selection.RFE(estimator=svc, n_features_to_select=600, step=3)

countVecWord = TfidfVectorizer(ngram_range=(1, 3), max_features=2000, analyzer=u'word', sublinear_tf=True, use_idf = True, min_df=2, max_df=0.85, lowercase = True)
countVecWord_tags = TfidfVectorizer(ngram_range=(1, 4), max_features= 1000, analyzer=u'word', min_df=2, max_df=0.85, sublinear_tf=True, use_idf = True, lowercase = False)

pipeline = Pipeline([
        ('union', FeatureUnion(
                transformer_list=[

                ('vectorized_pipeline', Pipeline([
                    ('union_vectorizer', FeatureUnion([

                        ('stem_text', Pipeline([
                            ('selector', ItemSelector(key='stem_text')),
                            ('stem_tfidf', countVecWord)
                        ])),

                        ('pos_text', Pipeline([
                            ('selector', ItemSelector(key='pos_text')),
                            ('pos_tfidf', countVecWord_tags)
                        ])),

                    ])),
                        ('percentile_feature_selection', fs_vect)
                    ])),


                ('custom_pipeline', Pipeline([
                    ('custom_features', FeatureUnion([

                        ('pos_cluster', Pipeline([
                            ('selector', ItemSelector(key='pos_text')),
                            ('pos_cluster_inner', pos_cluster)
                        ])),

                        ('stylistic_features', Pipeline([
                            ('selector', ItemSelector(key='raw_text')),
                            ('stylistic_features_inner', stylistic_features)
                        ])),


                    ])),
                        ('percentile_feature_selection', fs),
                        ('inner_scale', inner_scaler)
                ])),

                ],

                # weight components in FeatureUnion
                # n_jobs=6,

                transformer_weights={
                    'vectorized_pipeline': 0.8,  # 0.8,
                    'custom_pipeline': 1.0  # 1.0
                },
        )),

        ('rfe_feature_selection', f5),
        ('clf', classifier),
        ])

我将尝试解释这些步骤。第一个管道由向量器组成，称为“向量化的_管道”，所有这些管道都有一个函数“获取_特征_名称”。第二个管道由我自己的特性组成，我也用fit、transform和get_feature_names函数实现了它们。当我使用@Kevin的建议时，我得到一个错误，即“union”（管道中我的顶级元素的名称）没有get\u feature\u names函数：

support = pipeline.named_steps['rfe_feature_selection'].support_
feature_names = pipeline.named_steps['union'].get_feature_names()
print np.array(feature_names)[support]

另外，当我尝试从单个FeatureUnion获取要素名称时，如下所示：

from sklearn.pipeline import FeatureUnion, Pipeline
from sklearn import feature_selection
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC

X = ['I am a sentence', 'an example']
Y = [1, 2]
X_dev = ['another sentence']

# classifier
LinearSVC1 = LinearSVC(tol=1e-4,  C = 0.10000000000000001)
f5 = feature_selection.RFE(estimator=LinearSVC1, n_features_to_select=500, step=1)

pipeline = Pipeline([
    ('features', FeatureUnion([
       ('tfidf', TfidfVectorizer(ngram_range=(1, 3), max_features= 4000)), 
       ('custom_features', CustomFeatures())])),
    ('rfe_feature_selection', f5),
    ('clf', LinearSVC1),
    ])

pipeline.fit(X, Y)
y_pred = pipeline.predict(X_dev)

support = pipeline.named_steps['rfe_feature_selection'].support_
feature_names = pipeline_age.named_steps['union_vectorizer'].get_feature_names()
print np.array(feature_names)[support]

我得到一个关键错误：

feature_names = pipeline.named_steps['union_vectorizer'].get_feature_names()
KeyError: 'union_vectorizer'

---

您可以使用名为_steps的属性

访问的每个步骤，以下是iris数据集上的一个示例，该示例仅选择
2
功能，但解决方案将扩展

from sklearn import datasets
from sklearn import feature_selection
from sklearn.svm import LinearSVC

iris = datasets.load_iris()
X = iris.data
y = iris.target

# classifier
LinearSVC1 = LinearSVC(tol=1e-4,  C = 0.10000000000000001)
f5 = feature_selection.RFE(estimator=LinearSVC1, n_features_to_select=2, step=1)

pipeline = Pipeline([
    ('rfe_feature_selection', f5),
    ('clf', LinearSVC1)
    ])

pipeline.fit(X, y)

使用
named_steps
可以访问管道中变换对象的属性和方法。属性
支持
（或方法
获取支持（）
）将返回所选功能的布尔掩码：

support = pipeline.named_steps['rfe_feature_selection'].support_

现在
support
是一个数组，您可以使用它有效地提取所选功能（列）的名称。确保功能名称在一个列表中，而不是python列表中

import numpy as np
feature_names = np.array(iris.feature_names) # transformed list to array

feature_names[support]

array(['sepal width (cm)', 'petal width (cm)'], 
      dtype='|S17')

编辑
根据我上面的评论，下面是您删除CustomFeautures（）函数的示例：

from sklearn.pipeline import FeatureUnion, Pipeline
from sklearn import feature_selection
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
import numpy as np

X = ['I am a sentence', 'an example']
Y = [1, 2]
X_dev = ['another sentence']

# classifier
LinearSVC1 = LinearSVC(tol=1e-4,  C = 0.10000000000000001)
f5 = feature_selection.RFE(estimator=LinearSVC1, n_features_to_select=500, step=1)

pipeline = Pipeline([
    ('features', FeatureUnion([
       ('tfidf', TfidfVectorizer(ngram_range=(1, 3), max_features= 4000))])), 
    ('rfe_feature_selection', f5),
    ('clf', LinearSVC1),
    ])

pipeline.fit(X, Y)
y_pred = pipeline.predict(X_dev)

support = pipeline.named_steps['rfe_feature_selection'].support_
feature_names = pipeline.named_steps['features'].get_feature_names()
np.array(feature_names)[support]

我的答案并没有真正解决如何在您的特定示例中提取您的功能，但是，很抱歉，您正在管道中创建这些功能。我不知道CustomFeatures（）是什么，但您可以访问管道中的其他步骤，类似地使用命名的\u步骤来提取功能名称列表。您好<代码>管道。命名的\u步骤

只是一个字典，它有3个键：“联合”、“rfe\u功能\u选择”和“clf”。您能否发布使用

pipeline.named_steps['union']获得的确切错误。获取\u feature_names（）

？您提到“我得到了一个错误，即‘union’（管道中我的顶级元素的名称）没有get_feature_names函数”，但我不相信这是正确的；）。我认为问题在于，

get\u feature\u names

只是

FeatureUnion

上的一个方法（而不是

Pipeline

），而

FeatureUnion

需要它的所有转换器来拥有这样一个方法。@ivan\u bilan你能提供一个上面的CustomFeatures（）函数的例子吗？我正在从事一个情绪分析项目，我试图使用sklearn pipeline添加一个数据帧功能，您的代码可以帮助您了解如何进行此操作。@StamTiniakos当然，您可以在上找到完整的代码示例，我已经为我的问题添加了更多信息，您的建议在我的管道上似乎不起作用。此解决方案在嵌套管道的情况下似乎不起作用，因为get\U feature\U名称似乎没有定义。