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[MRG] Add docs for TfidfTransformer.idf_ (Fixes #8267) #8528

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[MRG] Add docs for TfidfTransformer.idf_ (Fixes #8267) #8528

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attribute documentation
Mar 4, 2017
864993a
attribute documentation
Mar 4, 2017
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11 changes: 8 additions & 3 deletions sklearn/feature_extraction/text.py
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Original file line number Diff line number Diff line change
Expand Up @@ -189,7 +189,7 @@ def build_preprocessor(self):
# hundreds of nanoseconds which is negligible when compared to the
# cost of tokenizing a string of 1000 chars for instance.
noop = lambda x: x

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please remove the whitespace

# accent stripping
if not self.strip_accents:
strip_accents = noop
Expand Down Expand Up @@ -996,6 +996,12 @@ class TfidfTransformer(BaseEstimator, TransformerMixin):
sublinear_tf : boolean, default=False
Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).

Attributes
----------
idf_ : numpy array of shape [n_features, 1]
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Copying @jmschrei's comment from #8532 -

This class doesn't seem to have or use a self.idf_ attribute. Do you mean self._idf_diag?

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There is a property.

returns None unless use_idf=True, then
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We would not usually describe an attribute with "returns".

returns 1-D matrix containing idf(d,t).
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I don't get what d is and why this 1-d vector is a function of two indices.


References
----------

Expand Down Expand Up @@ -1035,9 +1041,8 @@ def fit(self, X, y=None):
# log+1 instead of log makes sure terms with zero idf don't get
# suppressed entirely.
idf = np.log(float(n_samples) / df) + 1.0
self._idf_diag = sp.spdiags(idf, diags=0, m=n_features,
self._idf_diag = sp.spdiags(idf, diags=0, m=n_features,
n=n_features, format='csr')

return self

def transform(self, X, copy=True):
Expand Down