def test_20news():
try:
data = datasets.fetch_20newsgroups(subset='all',
download_if_missing=False,
shuffle=False)
except IOError:
raise SkipTest("Download 20 newsgroups to run this test")
# Extract a reduced dataset
data2cats = datasets.fetch_20newsgroups(subset='all',
categories=data.target_names[-1:-3:-1],
shuffle=False)
# Check that the ordering of the target_names is the same
# as the ordering in the full dataset
assert_equal(data2cats.target_names,
data.target_names[-2:])
# Assert that we have only 0 and 1 as labels
assert_equal(np.unique(data2cats.target).tolist(), [0, 1])
# Check that the first entry of the reduced dataset corresponds to
# the first entry of the corresponding category in the full dataset
entry1 = data2cats.data[0]
category = data2cats.target_names[data2cats.target[0]]
label = data.target_names.index(category)
entry2 = data.data[np.where(data.target == label)[0][0]]
assert_equal(entry1, entry2)
# check that the filenames are available too
assert_true(data.filenames[0].endswith(
"20news_home/20news-bydate-test/talk.politics.mideast/76560"))
def test_20news():
try:
data = datasets.fetch_20newsgroups(subset='all',
download_if_missing=False,
shuffle=False)
except IOError:
# Data not there
return
# Extract a reduced dataset
data2cats = datasets.fetch_20newsgroups(subset='all',
categories=data.target_names[-1:-3:-1],
shuffle=False)
# Check that the ordering of the target_names is the same
# as the ordering in the full dataset
nose.tools.assert_equal(data2cats.target_names,
data.target_names[-2:])
# Assert that we have only 0 and 1 as labels
nose.tools.assert_equal(np.unique(data2cats.target).tolist(), [0, 1])
# Check that the first entry of the reduced dataset corresponds to
# the first entry of the corresponding category in the full dataset
entry1 = data2cats.data[0]
category = data2cats.target_names[data2cats.target[0]]
label = data.target_names.index(category)
entry2 = data.data[np.where(data.target == label)[0][0]]
nose.tools.assert_true(entry1 == entry2)
def _load_docs(self, training_set):
if training_set == "newsgroup":
self.info("extract the 20 newsgroup dataset")
wide_dataset = fetch_20newsgroups()
docs = [open(f).read() for f in wide_dataset.filenames]
elif training_set == "docs":
docs = [res['content'] for res in
db.resources.find({'blacklisted': False,
'processed': True})]
return docs
###############################################################################
# Load some categories from the training set
categories = [
'alt.atheism',
'talk.religion.misc',
'comp.graphics',
'sci.space',
]
# Uncomment the following to do the analysis on all the categories
#categories = None
print "Loading 20 newsgroups dataset for categories:"
print categories if categories else "all"
data_train = fetch_20newsgroups(subset='train', categories=categories,
shuffle=True, random_state=42)
data_test = fetch_20newsgroups(subset='test', categories=categories,
shuffle=True, random_state=42)
print 'data loaded'
categories = data_train.target_names # for case categories == None
print "%d documents (training set)" % len(data_train.data)
print "%d documents (testing set)" % len(data_test.data)
print "%d categories" % len(categories)
print
# split a training set and a test set
y_train, y_test = data_train.target, data_test.target
################################################################################
# Load some categories from the training set
categories = [
'alt.atheism',
'talk.religion.misc',
'comp.graphics',
'sci.space',
]
# Uncomment the following to do the analysis on all the categories
#categories = None
print "Loading 20 newsgroups dataset for categories:"
print categories if categories else "all"
data_train = fetch_20newsgroups(subset='train', categories=categories,
shuffle=True, random_state=42)
data_test = fetch_20newsgroups(subset='test', categories=categories,
shuffle=True, random_state=42)
print "%d documents (training set)" % len(data_train.filenames)
print "%d documents (testing set)" % len(data_test.filenames)
print "%d categories" % len(data_train.target_names)
print
# split a training set and a test set
filenames_train, filenames_test = data_train.filenames, data_test.filenames
y_train, y_test = data_train.target, data_test.target
print "Extracting features from the training dataset using a sparse vectorizer"
t0 = time()
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
################################################################################
# Load some categories from the training set
categories = [
'alt.atheism',
'talk.religion.misc',
]
# Uncomment the following to do the analysis on all the categories
#categories = None
print "Loading 20 newsgroups dataset for categories:"
print categories
data = fetch_20newsgroups(subset='train', categories=categories)
print "%d documents" % len(data.filenames)
print "%d categories" % len(data.target_names)
print
################################################################################
# define a pipeline combining a text feature extractor with a simple
# classifier
pipeline = Pipeline([
('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', SGDClassifier()),
])
parameters = {
# uncommenting more parameters will give better exploring power but will
format='%(asctime)s %(levelname)s %(message)s')
################################################################################
# Load some categories from the training set
categories = [
'alt.atheism',
'talk.religion.misc',
]
# Uncomment the following to do the analysis on all the categories
#categories = None
print "Loading 20 newsgroups dataset for categories:"
print categories
data = fetch_20newsgroups(subset='train', categories=categories)
print "%d documents" % len(data.filenames)
print "%d categories" % len(data.target_names)
print
################################################################################
# define a pipeline combining a text feature extractor with a simple
# classifier
pipeline = Pipeline([
('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', SGDClassifier()),
])
parameters = {
# uncommenting more parameters will give better exploring power but will
