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[MRG] DOC run three more examples using sys.argv #10301

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Dec 13, 2017
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[MRG] DOC run three more examples using sys.argv #10301

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d83472d
DOC run three more examples using sys.argv.
lesteve Dec 13, 2017
45d5f61
Fix flake8 in examples
lesteve Dec 13, 2017
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2 changes: 2 additions & 0 deletions ...t/document_classification_20newsgroups.py → ...t_document_classification_20newsgroups.py
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Expand Up @@ -88,6 +88,7 @@
def is_interactive():
return not hasattr(sys.modules['__main__'], '__file__')


# work-around for Jupyter notebook and IPython console
argv = [] if is_interactive() else sys.argv[1:]
(opts, args) = op.parse_args(argv)
Expand Down Expand Up @@ -136,6 +137,7 @@ def is_interactive():
def size_mb(docs):
return sum(len(s.encode('utf-8')) for s in docs) / 1e6


data_train_size_mb = size_mb(data_train.data)
data_test_size_mb = size_mb(data_test.data)

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8 changes: 5 additions & 3 deletions examples/text/document_clustering.py → examples/text/plot_document_clustering.py
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Expand Up @@ -27,8 +27,8 @@
Two algorithms are demoed: ordinary k-means and its more scalable cousin
minibatch k-means.

Additionally, latent semantic analysis can also be used to reduce dimensionality
and discover latent patterns in the data.
Additionally, latent semantic analysis can also be used to reduce
dimensionality and discover latent patterns in the data.

It can be noted that k-means (and minibatch k-means) are very sensitive to
feature scaling and that in this case the IDF weighting helps improve the
Expand Down Expand Up @@ -106,6 +106,7 @@
def is_interactive():
return not hasattr(sys.modules['__main__'], '__file__')


# work-around for Jupyter notebook and IPython console
argv = [] if is_interactive() else sys.argv[1:]
(opts, args) = op.parse_args(argv)
Expand Down Expand Up @@ -138,7 +139,8 @@ def is_interactive():
labels = dataset.target
true_k = np.unique(labels).shape[0]

print("Extracting features from the training dataset using a sparse vectorizer")
print("Extracting features from the training dataset "
"using a sparse vectorizer")
t0 = time()
if opts.use_hashing:
if opts.use_idf:
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2 changes: 1 addition & 1 deletion examples/text/hashing_vs_dict_vectorizer.py → ...s/text/plot_hashing_vs_dict_vectorizer.py
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Expand Up @@ -62,7 +62,7 @@ def token_freqs(doc):
'talk.religion.misc',
]
# Uncomment the following line to use a larger set (11k+ documents)
#categories = None
# categories = None

print(__doc__)
print("Usage: %s [n_features_for_hashing]" % sys.argv[0])
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