10000 additional fixes to 20 newsgroups to prevent TypeError by BasilBeirouti · Pull Request #8204 · scikit-learn/scikit-learn · GitHub
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additional fixes to 20 newsgroups to prevent TypeError #8204

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Merged
merged 3 commits into from
Jan 15, 2017
Merged

additional fixes to 20 newsgroups to prevent TypeError #8204

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9a2b064
Merge remote-tracking branch 'scikit-learn/master' into bugfix
BasilBeirouti Jan 15, 2017
3b90190
removed stray space in '__main__ '
BasilBeirouti Jan 15, 2017
d30dccd
fixed pipeline bug causing TypeError and removed incorrect keyword ar…
BasilBeirouti Jan 15, 2017
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13 changes: 7 additions & 6 deletions examples/text/document_classification_20newsgroups.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,7 @@
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_selection import SelectFromModel
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.linear_model import RidgeClassifier
from sklearn.pipeline import Pipeline
Expand Down Expand Up @@ -85,7 +86,7 @@


def is_interactive():
return not hasattr(sys.modules['__main__ '], '__file__')
return not hasattr(sys.modules['__main__'], '__file__')

# work-around for Jupyter notebook and IPython console
argv = [] if is_interactive() else sys.argv[1:]
Expand Down Expand Up @@ -259,8 +260,8 @@ def benchmark(clf):
print('=' * 80)
print("%s penalty" % penalty.upper())
# Train Liblinear model
results.append(benchmark(LinearSVC(loss='l2', penalty=penalty,
dual=False, tol=1e-3)))
results.append(benchmark(LinearSVC(penalty=penalty, dual=False,
tol=1e-3)))

# Train SGD model
results.append(benchmark(SGDClassifier(alpha=.0001, n_iter=50,
Expand Down Expand Up @@ -288,9 +289,9 @@ def benchmark(clf):
# The smaller C, the stronger the regularization.
# The more regularization, the more sparsity.
results.append(benchmark(Pipeline([
('feature_selection', LinearSVC(penalty="l1", dual=False, tol=1e-3)),
('classification', LinearSVC())
])))
('feature_selection', SelectFromModel(LinearSVC(penalty="l1", dual=False,
tol=1e-3))),
('classification', LinearSVC(penalty="l2"))])))

# make some plots

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