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[MRG +1] Fix text data tutorial #6923

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Merged
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Jun 23, 2016
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[MRG +1] Fix text data tutorial #6923

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6 changes: 3 additions & 3 deletions doc/tutorial/text_analytics/working_with_text_data.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -184,7 +184,7 @@ The most intuitive way to do so is the bags of words representation:

The bags of words representation implies that ``n_features`` is
the number of distinct words in the corpus: this number is typically
larger that 100,000.
larger than 100,000.

If ``n_samples == 10000``, storing ``X`` as a numpy array of type
float32 would require 10000 x 100000 x 4 bytes = **4GB in RAM** which
Expand Down Expand Up @@ -443,13 +443,13 @@ to speed up the computation::
The result of calling ``fit`` on a ``GridSearchCV`` object is a classifier
that we can use to ``predict``::

>>> twenty_train.target_names[gs_clf.predict(['God is love'])]
>>> twenty_train.target_names[gs_clf.predict(['God is love'])[0]]
'soc.religion.christian'

The object's ``best_score_`` and ``best_params_`` attributes store the best
mean score and the parameters setting corresponding to that score::

>>> gs_clf.best_score_
>>> gs_clf.best_score_ # doctest: +ELLIPSIS
0.900...
>>> for param_name in sorted(parameters.keys()):
... print("%s: %r" % (param_name, gs_clf.best_params_[param_name]))
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3 changes: 2 additions & 1 deletion sklearn/datasets/twenty_newsgroups.py
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Expand Up @@ -219,7 +219,8 @@ def fetch_20newsgroups(data_home=None, subset='train', categories=None,

if cache is None:
if download_if_missing:
print('Downloading 20news dataset. This may take a few minutes.')
logger.info("Downloading 20news dataset. "
"This may take a few minutes.")
cache = download_20newsgroups(target_dir=twenty_home,
cache_path=cache_path)
else:
Expand Down