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import numpy as np
from numpy.testing import assert_array_equal
import pytest
from sklearn.feature_extraction import FeatureHasher
from sklearn.feature_extraction._hashing_fast import transform as _hashing_transform
def test_feature_hasher_dicts():
feature_hasher = FeatureHasher(n_features=16)
assert "dict" == feature_hasher.input_type
raw_X = [{"foo": "bar", "dada": 42, "tzara": 37}, {"foo": "baz", "gaga": "string1"}]
X1 = FeatureHasher(n_features=16).transform(raw_X)
gen = (iter(d.items()) for d in raw_X)
X2 = FeatureHasher(n_features=16, input_type="pair").transform(gen)
assert_array_equal(X1.toarray(), X2.toarray())
def test_feature_hasher_strings():
# mix byte and Unicode strings; note that "foo" is a duplicate in row 0
raw_X = [
["foo", "bar", "baz", "foo".encode("ascii")],
["bar".encode("ascii"), "baz", "quux"],
]
for lg_n_features in (7, 9, 11, 16, 22):
n_features = 2**lg_n_features
it = (x for x in raw_X) # iterable
feature_hasher = FeatureHasher(
n_features=n_features, input_type="string", alternate_sign=False
)
X = feature_hasher.transform(it)
assert X.shape[0] == len(raw_X)
assert X.shape[1] == n_features
assert X[0].sum() == 4
assert X[1].sum() == 3
assert X.nnz == 6
@pytest.mark.parametrize(
"raw_X",
[
["my_string", "another_string"],
(x for x in ["my_string", "another_string"]),
],
ids=["list", "generator"],
)
def test_feature_hasher_single_string(raw_X):
"""FeatureHasher raises error when a sample is a single string.
Non-regression test for gh-13199.
"""
msg = "Samples can not be a single string"
feature_hasher = FeatureHasher(n_features=10, input_type="string")
with pytest.raises(ValueError, match=msg):
feature_hasher.transform(raw_X)
def test_hashing_transform_seed():
# check the influence of the seed when computing the hashes
raw_X = [
["foo", "bar", "baz", "foo".encode("ascii")],
["bar".encode("ascii"), "baz", "quux"],
]
raw_X_ = (((f, 1) for f in x) for x in raw_X)
indices, indptr, _ = _hashing_transform(raw_X_, 2**7, str, False)
raw_X_ = (((f, 1) for f in x) for x in raw_X)
indices_0, indptr_0, _ = _hashing_transform(raw_X_, 2**7, str, False, seed=0)
assert_array_equal(indices, indices_0)
assert_array_equal(indptr, indptr_0)
raw_X_ = (((f, 1) for f in x) for x in raw_X)
indices_1, _, _ = _hashing_transform(raw_X_, 2**7, str, False, seed=1)
with pytest.raises(AssertionError):
assert_array_equal(indices, indices_1)
def test_feature_hasher_pairs():
raw_X = (
iter(d.items())
for d in [{"foo": 1, "bar": 2}, {"baz": 3, "quux": 4, "foo": -1}]
)
feature_hasher = FeatureHasher(n_features=16, input_type="pair")
x1, x2 = feature_hasher.transform(raw_X).toarray()
x1_nz = sorted(np.abs(x1[x1 != 0]))
x2_nz = sorted(np.abs(x2[x2 != 0]))
assert [1, 2] == x1_nz
assert [1, 3, 4] == x2_nz
def test_feature_hasher_pairs_with_string_values():
raw_X = (
iter(d.items())
for d in [{"foo": 1, "bar": "a"}, {"baz": "abc", "quux": 4, "foo": -1}]
)
feature_hasher = FeatureHasher(n_features=16, input_type="pair")
x1, x2 = feature_hasher.transform(raw_X).toarray()
x1_nz = sorted(np.abs(x1[x1 != 0]))
x2_nz = sorted(np.abs(x2[x2 != 0]))
assert [1, 1] == x1_nz
assert [1, 1, 4] == x2_nz
raw_X = (iter(d.items()) for d in [{"bax": "abc"}, {"bax": "abc"}])
x1, x2 = feature_hasher.transform(raw_X).toarray()
x1_nz = np.abs(x1[x1 != 0])
x2_nz = np.abs(x2[x2 != 0])
assert [1] == x1_nz
assert [1] == x2_nz
assert_array_equal(x1, x2)
def test_hash_empty_input():
n_features = 16
raw_X = [[], (), iter(range(0))]
feature_hasher = FeatureHasher(n_features=n_features, input_type="string")
X = feature_hasher.transform(raw_X)
assert_array_equal(X.A, np.zeros((len(raw_X), n_features)))
def test_hasher_zeros():
# Assert that no zeros are materialized in the output.
X = FeatureHasher().transform([{"foo": 0}])
assert X.data.shape == (0,)
def test_hasher_alternate_sign():
X = [list("Thequickbrownfoxjumped")]
Xt = FeatureHasher(alternate_sign=True, input_type="string").fit_transform(X)
assert Xt.data.min() < 0 and Xt.data.max() > 0
Xt = FeatureHasher(alternate_sign=False, input_type="string").fit_transform(X)
assert Xt.data.min() > 0
def test_hash_collisions():
X = [list("Thequickbrownfoxjumped")]
Xt = FeatureHasher(
alternate_sign=True, n_features=1, input_type="string"
).fit_transform(X)
# check that some of the hashed tokens are added
# with an opposite sign and cancel out
assert abs(Xt.data[0]) < len(X[0])
Xt = FeatureHasher(
alternate_sign=False, n_features=1, input_type="string"
).fit_transform(X)
assert Xt.data[0] == len(X[0])
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