def test_hashingvectorizer_norm(norm):
if norm not in ["l1", "l2", None]:
with pytest.raises(ValueError):
res = HashingVectorizer(norm=norm).fit_transform(DOCS_GPU)
else:
res = HashingVectorizer(norm=norm).fit_transform(DOCS_GPU)
ref = SkHashVect(norm=norm).fit_transform(DOCS)
assert_almost_equal_hash_matrices(res.todense().get(), ref.toarray())
def test_hashingvectorizer_lowercase(lowercase):
corpus = [
"This Is DoC",
"this DoC is the second DoC.",
"And this document is the third one.",
"and Is this the first document?",
]
res = HashingVectorizer(lowercase=lowercase).fit_transform(Series(corpus))
ref = SkHashVect(lowercase=lowercase).fit_transform(corpus)
assert_almost_equal_hash_matrices(res.todense().get(), ref.toarray())
def test_hashingvectorizer():
corpus = [
"This is the first document.",
"This document is the second document.",
"And this is the third one.",
"Is this the first document?",
]
res = HashingVectorizer().fit_transform(Series(corpus))
ref = SkHashVect().fit_transform(corpus)
assert_almost_equal_hash_matrices(res.todense().get(), ref.toarray())
def test_hashingvectorizer_delimiter():
corpus = ["a0b0c", "a 0 b0e", "c0d0f"]
res = HashingVectorizer(
delimiter="0", norm=None, preprocessor=lambda s: s
).fit_transform(Series(corpus))
# equivalent logic for sklearn
ref = SkHashVect(
tokenizer=lambda s: s.split("0"),
norm=None,
token_pattern=None,
preprocessor=lambda s: s,
).fit_transform(corpus)
assert_almost_equal_hash_matrices(res.todense().get(), ref.toarray())
def test_hashingvectorizer_stop_word():
ref = SkHashVect(stop_words="english").fit_transform(DOCS)
res = HashingVectorizer(stop_words="english").fit_transform(DOCS_GPU)
assert_almost_equal_hash_matrices(res.todense().get(), ref.toarray())
