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DOC example for feature_extraction.text.TfidfTransformer #15199

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Oct 13, 2019
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DOC example for feature_extraction.text.TfidfTransformer #15199

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e225eb6
added exaple for feature_extraction.text.TfidfTransformer
federicopisanu Oct 12, 2019
b30ab8a
fix for CircleCI test
federicopisanu Oct 12, 2019
a595bdc
Pipeline with CountVectorizer used for the TfidfTransfomer example
federicopisanu Oct 12, 2019
5c9b7e4
fix for CircleCI
federicopisanu Oct 12, 2019
d7b27b3
more fixes for CircleCI test
federicopisanu Oct 12, 2019
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more fixes for CircleCI test
federicopisanu Oct 12, 2019
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fixes for azure-pipepines test
federicopisanu Oct 12, 2019
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25 changes: 25 additions & 0 deletions sklearn/feature_extraction/text.py
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Expand Up @@ -1342,6 +1342,31 @@ class TfidfTransformer(TransformerMixin, BaseEstimator):
The inverse document frequency (IDF) vector; only defined
if ``use_idf`` is True.

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).shape

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Yeah, sure! I'll work on this.

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I think it's done!

>>> 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())]).fit(corpus)
>>> pipe['count'].transform(corpus).toarray()
array([[1, 1, 1, 1, 0, 1, 0, 0],
[1, 2, 0, 1, 1, 1, 0, 0],
[1, 0, 0, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 0, 1, 0, 0]])
>>> pipe['tfid'].idf_
array([1. , 1.22314355, 1.51082562, 1. , 1.91629073,
1. , 1.91629073, 1.91629073])
>>> pipe.transform(corpus).shape
(4, 8)

References
----------

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