def gpu_hashing_vectorizer(x):
vec = HashingVectorizer(n_features=N_FEATURES,
alternate_sign=alternate_sign,
ngram_range=ngram_range,
norm=norm,
preprocessor=preprocessor)
return vec.fit_transform(x)
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_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_hashingvectorizer_n_features():
n_features = 10
res = (
HashingVectorizer(n_features=n_features)
.fit_transform(DOCS_GPU).todense().get()
)
ref = SkHashVect(n_features=n_features).fit_transform(DOCS).toarray()
assert res.shape == ref.shape
def test_hashingvectorizer_alternate_sign():
# if alternate_sign = True
# we should have some negative and positive values
res = HashingVectorizer(alternate_sign=True).fit_transform(DOCS_GPU)
res_f_array = res.todense().get().flatten()
assert np.sum(res_f_array > 0, axis=0) > 0
assert np.sum(res_f_array < 0, axis=0) > 0
# if alternate_sign = False
# we should have no negative values and some positive values
res = HashingVectorizer(alternate_sign=False).fit_transform(DOCS_GPU)
res_f_array = res.todense().get().flatten()
assert np.sum(res_f_array > 0, axis=0) > 0
assert np.sum(res_f_array < 0, axis=0) == 0
def test_hashingvectorizer_dtype(dtype):
res = HashingVectorizer(dtype=dtype).fit_transform(DOCS_GPU)
assert res.dtype == dtype
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())