def test_one_hot_encoder_feature_names():
enc = OneHotEncoder()
X = [['Male', 1, 'girl', 2, 3], ['Female', 41, 'girl', 1, 10],
['Male', 51, 'boy', 12, 3], ['Male', 91, 'girl', 21, 30]]
enc.fit(X)
feature_names = enc.get_feature_names()
assert isinstance(feature_names, np.ndarray)
assert_array_equal([
'x0_Female', 'x0_Male', 'x1_1', 'x1_41', 'x1_51', 'x1_91', 'x2_boy',
'x2_girl', 'x3_1', 'x3_2', 'x3_12', 'x3_21', 'x4_3', 'x4_10', 'x4_30'
], feature_names)
feature_names2 = enc.get_feature_names(
['one', 'two', 'three', 'four', 'five'])
assert_array_equal([
'one_Female', 'one_Male', 'two_1', 'two_41', 'two_51', 'two_91',
'three_boy', 'three_girl', 'four_1', 'four_2', 'four_12', 'four_21',
'five_3', 'five_10', 'five_30'
], feature_names2)
with pytest.raises(ValueError, match="input_features should have length"):
enc.get_feature_names(['one', 'two'])
def test_one_hot_encoder_feature_names_unicode():
enc = OneHotEncoder()
X = np.array([['c❤t1', 'dat2']], dtype=object).T
enc.fit(X)
feature_names = enc.get_feature_names()
assert_array_equal(['x0_c❤t1', 'x0_dat2'], feature_names)
feature_names = enc.get_feature_names(input_features=['n👍me'])
assert_array_equal(['n👍me_c❤t1', 'n👍me_dat2'], feature_names)
def test_one_hot_encoder_feature_names_drop(drop, expected_names):
X = [['c', 2, 'a'], ['b', 2, 'b']]
ohe = OneHotEncoder(drop=drop)
ohe.fit(X)
feature_names = ohe.get_feature_names()
assert isinstance(feature_names, np.ndarray)
assert_array_equal(expected_names, feature_names)
