def test_word_analyzer_unigrams():
wa = WordNGramAnalyzer(min_n=1, max_n=1, stop_words=None)
text = u"J'ai mang\xe9 du kangourou ce midi, c'\xe9tait pas tr\xeas bon."
expected = [u'ai', u'mange', u'du', u'kangourou', u'ce', u'midi',
u'etait', u'pas', u'tres', u'bon']
assert_equal(wa.analyze(text), expected)
text = "This is a test, really.\n\n I met Harry yesterday."
expected = [u'this', u'is', u'test', u'really', u'met', u'harry',
u'yesterday']
assert_equal(wa.analyze(text), expected)
def test_dense_vectorizer():
wa = WordNGramAnalyzer()
train_data = [wa.analyze(d) for d in JUNK_FOOD_DOCS[:-1]]
test_data = [wa.analyze(JUNK_FOOD_DOCS[-1])]
# test without vocabulary
v1 = TermCountVectorizer()
counts_train = v1.transform(train_data)
assert_equal(counts_train[0, v1.vocabulary["pizza"]], 2)
v2 = TermCountVectorizer(vocabulary=v1.vocabulary)
# test with a pre-existing vocabulary
for v in (v1, v2):
counts_test = v.transform(test_data)
assert_equal(counts_test[0, v.vocabulary["coke"]], 1)
# test tf-idf
t1 = TfidfTransformer()
tfidf = t1.fit(counts_train).transform(counts_train)
assert_equal(len(t1.idf), len(v1.vocabulary))
assert_equal(tfidf.shape,
(len(train_data), len(v1.vocabulary)))
# test tf-idf with new data
tfidf_test = t1.transform(counts_test)
assert_equal(tfidf_test.shape,
(len(test_data), len(v1.vocabulary)))
# test tf alone
t2 = TfidfTransformer(use_idf=False)
tf = t2.fit(counts_train).transform(counts_train)
assert_equal(t2.idf, None)
assert_array_almost_equal(np.sum(tf, axis=1),
[1.0] * len(train_data))
# test the direct tfidf vectorizer
# (equivalent to term count vectorizer + tfidf transformer)
tv = TfidfVectorizer()
tfidf2 = tv.fit(train_data).transform(train_data)
assert_array_almost_equal(tfidf, tfidf2)
# test the direct tfidf vectorizer with new data
tfidf_test2 = tv.transform(test_data)
assert_array_almost_equal(tfidf_test, tfidf_test2)
def test_word_analyzer_unigrams_and_bigrams():
wa = WordNGramAnalyzer(min_n=1, max_n=2, stop_words=None)
text = u"J'ai mang\xe9 du kangourou ce midi, c'\xe9tait pas tr\xeas bon."
expected = [u'ai', u'mange', u'du', u'kangourou', u'ce', u'midi', u'etait',
u'pas', u'tres', u'bon', u'ai mange', u'mange du',
u'du kangourou', u'kangourou ce', u'ce midi', u'midi etait',
u'etait pas', u'pas tres', u'tres bon']
assert_equal(wa.analyze(text), expected)