DOC example for feature_extraction.text.TfidfTransformer
@@ -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
+ >>> 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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