CODEWITHSUNDEEP
pythonscikit-learnpipeline
Tanguy
Tanguy

Reputation: 3314

retrieve intermediate features from a pipeline in Scikit (Python)

I am using a pipeline very similar to the one given in this example :

>>> text_clf = Pipeline([('vect', CountVectorizer()),
...                      ('tfidf', TfidfTransformer()),
...                      ('clf', MultinomialNB()),
... ])

over which I use GridSearchCV to find the best estimators over a parameter grid.

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?

Upvotes: 7

Views: 3745

Answers (2)

AbtPst
AbtPst

Reputation: 8018

just for reference

The estimators of a pipeline are stored as a list in the steps attribute:
>>>

>>> clf.steps[0]
('reduce_dim', PCA(copy=True, n_components=None, whiten=False))

and as a dict in named_steps:
>>>

>>> clf.named_steps['reduce_dim']
PCA(copy=True, n_components=None, whiten=False)

from http://scikit-learn.org/stable/modules/pipeline.html

Upvotes: 4

rabbit
rabbit

Reputation: 1476

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']

yields (for me)

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)

I haven't fitted the pipeline to any data in this example, so calling get_feature_names() at this point will return an error.

Upvotes: 10

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