def test_countvectorizer_custom_vocabulary():
vocab = {"pizza": 0, "beer": 1}
vocab_gpu = Series(vocab.keys())
ref = SkCountVect(vocabulary=vocab).fit_transform(DOCS)
X = CountVectorizer(vocabulary=vocab_gpu).fit_transform(DOCS_GPU)
cp.testing.assert_array_equal(X.todense(), ref.toarray())
def test_vectorizer_empty_token_case():
"""
We ignore empty tokens right now but sklearn treats them as a character
we might want to look into this more but
this should not be a concern for most piplines
"""
corpus = [
"a b ",
]
# we have extra null token here
# we slightly diverge from sklearn here as not treating it as a token
res = CountVectorizer(preprocessor=lambda s: s).\
fit_transform(Series(corpus))
ref = SkCountVect(
preprocessor=lambda s: s, tokenizer=lambda s: s.split(" ")
).fit_transform(corpus)
cp.testing.assert_array_equal(res.todense(), ref.toarray())
res = HashingVectorizer(preprocessor=lambda s: s).\
fit_transform(Series(corpus))
ref = SkHashVect(
preprocessor=lambda s: s, tokenizer=lambda s: s.split(" ")
).fit_transform(corpus)
assert_almost_equal_hash_matrices(res.todense().get(), ref.toarray())
def test_space_ngrams(ngram_range):
data = ['abc def. 123 456 789']
data_gpu = Series(data)
vec = CountVectorizer(ngram_range=ngram_range).fit(data_gpu)
ref = SkCountVect(ngram_range=ngram_range).fit(data)
assert (ref.get_feature_names()
) == vec.get_feature_names().to_arrow().to_pylist()
def test_only_delimiters():
data = ['abc def. 123',
' ',
'456 789']
data_gpu = Series(data)
res = CountVectorizer().fit_transform(data_gpu)
ref = SkCountVect().fit_transform(data)
cp.testing.assert_array_equal(res.todense(), ref.toarray())
def test_empty_doc_after_limit_features():
data = ['abc abc def',
'def abc',
'ghi']
data_gpu = Series(data)
count = CountVectorizer(min_df=2).fit_transform(data_gpu)
ref = SkCountVect(min_df=2).fit_transform(data)
cp.testing.assert_array_equal(count.todense(), ref.toarray())
def test_sngle_len():
single_token_ser = ['S I N G L E T 0 K E N Example', '1 2 3 4 5 eg']
single_token_gpu = Series(single_token_ser)
cv = CountVectorizer()
res = cv.fit_transform(single_token_gpu)
ref = SkCountVect().fit_transform(single_token_ser)
cp.testing.assert_array_equal(res.todense(), ref.toarray())
def test_character_ngrams(analyzer, ngram_range):
data = ['ab c', '' 'edf gh']
res = CountVectorizer(analyzer=analyzer, ngram_range=ngram_range)
res.fit(Series(data))
ref = SkCountVect(analyzer=analyzer, ngram_range=ngram_range).fit(data)
assert (ref.get_feature_names()
) == res.get_feature_names().to_arrow().to_pylist()
def test_non_ascii():
non_ascii = ('This is ascii,', 'but not this Αγγλικά.')
non_ascii_gpu = Series(non_ascii)
cv = CountVectorizer()
res = cv.fit_transform(non_ascii_gpu)
ref = SkCountVect().fit_transform(non_ascii)
assert 'αγγλικά' in set(cv.get_feature_names().to_arrow().to_pylist())
cp.testing.assert_array_equal(res.todense(), ref.toarray())
def test_count_vectorizer():
corpus = [
'This is the first document.',
'This document is the second document.',
'And this is the third one.',
'Is this the first document?',
]
res = CountVectorizer().fit_transform(Series(corpus))
ref = SkCountVect().fit_transform(corpus)
cp.testing.assert_array_equal(res.todense(), ref.toarray())
def test_vectorizer_inverse_transform():
vectorizer = CountVectorizer()
transformed_data = vectorizer.fit_transform(DOCS_GPU)
inversed_data = vectorizer.inverse_transform(transformed_data)
sk_vectorizer = SkCountVect()
sk_transformed_data = sk_vectorizer.fit_transform(DOCS)
sk_inversed_data = sk_vectorizer.inverse_transform(sk_transformed_data)
for doc, sk_doc in zip(inversed_data, sk_inversed_data):
doc = np.sort(doc.to_arrow().to_pylist())
sk_doc = np.sort(sk_doc)
if len(doc) + len(sk_doc) == 0:
continue
assert_array_equal(doc, sk_doc)
def test_countvectorizer_stop_words():
ref = SkCountVect(stop_words='english').fit_transform(DOCS)
X = CountVectorizer(stop_words='english').fit_transform(DOCS_GPU)
cp.testing.assert_array_equal(X.todense(), ref.toarray())
def test_word_analyzer(ngram_range):
v = CountVectorizer(ngram_range=ngram_range).fit(DOCS_GPU)
ref = SkCountVect(ngram_range=ngram_range).fit(DOCS)
assert (
ref.get_feature_names() == v.get_feature_names().to_arrow().to_pylist()
)
def test_countvectorizer_separate_fit_transform():
res = CountVectorizer().fit(DOCS_GPU).transform(DOCS_GPU)
ref = SkCountVect().fit(DOCS).transform(DOCS)
cp.testing.assert_array_equal(res.todense(), ref.toarray())
