从Scikit(Python)中的管道检索中间特征 [英] retrieve intermediate features from a pipeline in Scikit (Python)
问题描述
我使用的管道与在此示例中:
>>> text_clf = Pipeline([('vect', CountVectorizer()),
... ('tfidf', TfidfTransformer()),
... ('clf', MultinomialNB()),
... ])
我使用GridSearchCV
在参数网格上找到最佳估计量.
over which I use GridSearchCV
to find the best estimators over a parameter grid.
但是,我想通过CountVectorizer()
的get_feature_names()
方法获取训练集的列名.如果不在管道外部实现CountVectorizer()
,这是否可能?
However, I would like to get the column names of my training set with the get_feature_names()
method from CountVectorizer()
. Is this possible without implementing CountVectorizer()
outside the pipeline?
推荐答案
使用get_params()
函数,您可以访问管道的各个部分及其各自的内部参数.这是访问'vect'
Using the get_params()
function, you can get access at the various parts of the pipeline and their respective internal parameters. Here's an example of accessing 'vect'
text_clf = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', MultinomialNB())]
print text_clf.get_params()['vect']
收益(对我来说)
CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
lowercase=True, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), preprocessor=None, stop_words=None,
strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
tokenizer=None, vocabulary=None)
在此示例中,我没有为任何数据装配流水线,因此此时调用get_feature_names()
将返回错误.
I haven't fitted the pipeline to any data in this example, so calling get_feature_names()
at this point will return an error.
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