DOC example for feature_extraction.text.TfidfTransformer #15199
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thomasjpfan
reviewed
Oct 12, 2019
| @@ -1342,6 +1342,34 @@ class TfidfTransformer(TransformerMixin, BaseEstimator): | |||
| The inverse document frequency (IDF) vector; only defined | |||
| if ``use_idf`` is True. | |||
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| Examples | |||
| -------- | |||
| >>> from sklearn.feature_extraction.text import TfidfTransformer | |||
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We can use a pipeline to with CountVectorizer to create the count matrix with a custom vocabulary:
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import Pipeline
import numpy as np
corpus = ['this is the first document',
'this document is the second document',
'and this is the third one',
'is this the first document']
vocabulary = ['this', 'document', 'first', 'is', 'second', 'the',
'and', 'one']
pipe = Pipeline([('count', CountVectorizer(vocabulary=vocabulary)),
('tfid', TfidfTransformer())])
pipe.fit(corpus)
pipe['count'].transform(corpus).toarray()
pipe['tfid'].idf_
pipe.transform(corpus).shapeThere was a problem hiding this comment.
Yeah, sure! I'll work on this.
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I think it's done!
|
Merging with an implicit +1 from Thomas above. |
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Reference Issue
Addresses #3846
What does this implement/fix? Explain your changes.
Adds examples for feature_extraction.text.TfidfTransformer.
Any other comments?