def test_transform_feature_with_warning(self):
name = 'dpsec'
data = np.array([-1, 0, 2, 3])
# run the test but suppress the expected runtime warnings
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
assert_array_equal(FeatureTransformer.transform_feature(data, name,
'sqrt',
raise_error=False),
np.sqrt(data))
assert_array_equal(FeatureTransformer.transform_feature(data, name,
'inv',
raise_error=False),
1 / data)
assert_array_equal(FeatureTransformer.transform_feature(data, name,
'addOneInv',
raise_error=False),
1 / (data + 1))
assert_array_equal(FeatureTransformer.transform_feature(data, name,
'log',
raise_error=False),
np.log(data))
assert_array_equal(FeatureTransformer.transform_feature(data, name,
'addOneLn',
raise_error=False),
np.log(data + 1))
def test_apply_inverse_transform(self):
assert_raises(ValueError, FeatureTransformer.apply_inverse_transform,
'name', np.array([0, 1, 2]))
assert_raises(ValueError, FeatureTransformer.apply_inverse_transform,
'name', np.array([-2, -3, 1, 2]))
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
assert_array_equal(
FeatureTransformer.apply_inverse_transform('name',
np.array([0, 2, 4]),
raise_error=False),
np.array([np.inf, 0.5, 0.25]))
assert_array_equal(
FeatureTransformer.apply_inverse_transform('name',
np.array([-2, -4, 1]),
raise_error=False),
np.array([-0.5, -0.25, 1]))
assert_array_equal(
FeatureTransformer.apply_inverse_transform('name', np.array([2,
4])),
np.array([0.5, 0.25]))
assert_array_equal(
FeatureTransformer.apply_inverse_transform('name',
np.array([-2, -4])),
np.array([-0.5, -0.25]))
def test_apply_add_one_inverse_transform(self):
assert_raises(ValueError, FeatureTransformer.apply_add_one_inverse_transform, 'name',
np.array([-1, -2, 3, 5]))
assert_array_equal(FeatureTransformer.apply_add_one_inverse_transform('name',
np.array([-2, -3,
1, 4]),
raise_error=False),
np.array([-1, -1 / 2, 1 / 2, 1 / 5]))
assert_array_equal(FeatureTransformer.apply_add_one_inverse_transform('name',
np.array([1, 4])),
np.array([1 / 2, 1 / 5]))
assert_array_equal(FeatureTransformer.apply_add_one_inverse_transform('name',
np.array([0, 4])),
np.array([1, 1 / 5]))
def test_apply_add_one_inverse_transform(self):
assert_raises(ValueError,
FeatureTransformer.apply_add_one_inverse_transform,
'name', np.array([-1, -2, 3, 5]))
assert_array_equal(
FeatureTransformer.apply_add_one_inverse_transform(
'name', np.array([-2, -3, 1, 4]), raise_error=False),
np.array([-1, -1 / 2, 1 / 2, 1 / 5]))
assert_array_equal(
FeatureTransformer.apply_add_one_inverse_transform(
'name', np.array([1, 4])), np.array([1 / 2, 1 / 5]))
assert_array_equal(
FeatureTransformer.apply_add_one_inverse_transform(
'name', np.array([0, 4])), np.array([1, 1 / 5]))
def test_apply_add_one_log_transform(self):
assert_raises(ValueError, FeatureTransformer.apply_add_one_log_transform, 'name',
np.array([-2, -3, 2, 3]))
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
assert_array_equal(FeatureTransformer.apply_add_one_log_transform('name',
np.array([-2, -0.5,
2, 4]),
raise_error=False),
np.array([np.nan, np.log(0.5), np.log(3), np.log(5)]))
assert_array_equal(FeatureTransformer.apply_add_one_log_transform('name',
np.array([2, 4])),
np.array([np.log(3), np.log(5)]))
assert_array_equal(FeatureTransformer.apply_add_one_log_transform('name',
np.array([0, 4])),
np.array([0, np.log(5)]))
def test_apply_sqrt_transform(self):
assert_raises(ValueError, FeatureTransformer.apply_sqrt_transform, 'name',
np.array([-2, -3, 1, 2]))
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
assert_array_equal(FeatureTransformer.apply_sqrt_transform('name',
np.array([-1, 2, 4]),
raise_error=False),
np.array([np.nan, np.sqrt(2), 2]))
assert_array_equal(FeatureTransformer.apply_sqrt_transform('name', np.array([2, 4])),
np.array([np.sqrt(2), 2]))
assert_array_equal(FeatureTransformer.apply_sqrt_transform('name', np.array([0.5, 4])),
np.array([np.sqrt(0.5), 2]))
assert_array_equal(FeatureTransformer.apply_sqrt_transform('name',
np.array([0, 4])),
np.array([0, 2]))
def test_apply_log_transform(self):
assert_raises(ValueError, FeatureTransformer.apply_log_transform, 'name',
np.array([-1, 2, 3]))
assert_raises(ValueError, FeatureTransformer.apply_log_transform, 'name',
np.array([0, 2, 3]))
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
assert_array_equal(FeatureTransformer.apply_log_transform('name',
np.array([-1, 1, 4]),
raise_error=False),
np.array([np.nan, np.log(1), np.log(4)]))
assert_array_equal(FeatureTransformer.apply_log_transform('name',
np.array([0, 1, 4]),
raise_error=False),
np.array([-np.inf, np.log(1), np.log(4)]))
assert_array_equal(FeatureTransformer.apply_log_transform('name', np.array([1, 4])),
np.array([np.log(1), np.log(4)]))
def test_apply_add_one_log_transform(self):
assert_raises(ValueError,
FeatureTransformer.apply_add_one_log_transform, 'name',
np.array([-2, -3, 2, 3]))
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
assert_array_equal(
FeatureTransformer.apply_add_one_log_transform(
'name', np.array([-2, -0.5, 2, 4]), raise_error=False),
np.array([np.nan, np.log(0.5),
np.log(3), np.log(5)]))
assert_array_equal(
FeatureTransformer.apply_add_one_log_transform(
'name', np.array([2, 4])), np.array([np.log(3),
np.log(5)]))
assert_array_equal(
FeatureTransformer.apply_add_one_log_transform(
'name', np.array([0, 4])), np.array([0, np.log(5)]))
def test_apply_inverse_transform(self):
assert_raises(ValueError, FeatureTransformer.apply_inverse_transform, 'name',
np.array([0, 1, 2]))
assert_raises(ValueError, FeatureTransformer.apply_inverse_transform, 'name',
np.array([-2, -3, 1, 2]))
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
assert_array_equal(FeatureTransformer.apply_inverse_transform('name',
np.array([0, 2, 4]),
raise_error=False),
np.array([np.inf, 0.5, 0.25]))
assert_array_equal(FeatureTransformer.apply_inverse_transform('name',
np.array([-2, -4, 1]),
raise_error=False),
np.array([-0.5, -0.25, 1]))
assert_array_equal(FeatureTransformer.apply_inverse_transform('name', np.array([2, 4])),
np.array([0.5, 0.25]))
assert_array_equal(FeatureTransformer.apply_inverse_transform('name', np.array([-2, -4])),
np.array([-0.5, -0.25]))
def test_apply_log_transform(self):
assert_raises(ValueError, FeatureTransformer.apply_log_transform,
'name', np.array([-1, 2, 3]))
assert_raises(ValueError, FeatureTransformer.apply_log_transform,
'name', np.array([0, 2, 3]))
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
assert_array_equal(
FeatureTransformer.apply_log_transform('name',
np.array([-1, 1, 4]),
raise_error=False),
np.array([np.nan, np.log(1), np.log(4)]))
assert_array_equal(
FeatureTransformer.apply_log_transform('name',
np.array([0, 1, 4]),
raise_error=False),
np.array([-np.inf, np.log(1), np.log(4)]))
assert_array_equal(
FeatureTransformer.apply_log_transform('name', np.array([1, 4])),
np.array([np.log(1), np.log(4)]))
def test_apply_sqrt_transform(self):
assert_raises(ValueError, FeatureTransformer.apply_sqrt_transform,
'name', np.array([-2, -3, 1, 2]))
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
assert_array_equal(
FeatureTransformer.apply_sqrt_transform('name',
np.array([-1, 2, 4]),
raise_error=False),
np.array([np.nan, np.sqrt(2), 2]))
assert_array_equal(
FeatureTransformer.apply_sqrt_transform('name', np.array([2, 4])),
np.array([np.sqrt(2), 2]))
assert_array_equal(
FeatureTransformer.apply_sqrt_transform('name', np.array([0.5,
4])),
np.array([np.sqrt(0.5), 2]))
assert_array_equal(
FeatureTransformer.apply_sqrt_transform('name', np.array([0, 4])),
np.array([0, 2]))
def test_transform_feature(self):
name = 'dpsec'
data = np.array([1, 2, 3, 4])
# run the test but suppress the expected runtime warnings
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
assert_raises(ValueError, FeatureTransformer.transform_feature,
data, name, 'add_one_inverse')
assert_array_equal(
FeatureTransformer.transform_feature(data, name, 'inv'),
1 / data)
assert_array_equal(
FeatureTransformer.transform_feature(data, name, 'raw'), data)
assert_array_equal(
FeatureTransformer.transform_feature(data, name, 'org'), data)
assert_array_equal(
FeatureTransformer.transform_feature(data, name, 'log'),
np.log(data))
assert_array_equal(
FeatureTransformer.transform_feature(data, name, 'addOneInv'),
1 / (data + 1))
assert_array_equal(
FeatureTransformer.transform_feature(data, name, 'addOneLn'),
np.log(data + 1))
def test_transform_feature_with_warning(self):
name = 'dpsec'
data = np.array([-1, 0, 2, 3])
# run the test but suppress the expected runtime warnings
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
assert_array_equal(
FeatureTransformer.transform_feature(data,
name,
'sqrt',
raise_error=False),
np.sqrt(data))
assert_array_equal(
FeatureTransformer.transform_feature(data,
name,
'inv',
raise_error=False),
1 / data)
assert_array_equal(
FeatureTransformer.transform_feature(data,
name,
'addOneInv',
raise_error=False),
1 / (data + 1))
assert_array_equal(
FeatureTransformer.transform_feature(data,
name,
'log',
raise_error=False),
np.log(data))
assert_array_equal(
FeatureTransformer.transform_feature(data,
name,
'addOneLn',
raise_error=False),
np.log(data + 1))
def test_transform_feature(self):
name = 'dpsec'
data = np.array([1, 2, 3, 4])
# run the test but suppress the expected runtime warnings
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
assert_raises(ValueError, FeatureTransformer.transform_feature, data, name,
'add_one_inverse')
assert_array_equal(FeatureTransformer.transform_feature(data, name, 'inv'), 1 / data)
assert_array_equal(FeatureTransformer.transform_feature(data, name, 'raw'), data)
assert_array_equal(FeatureTransformer.transform_feature(data, name, 'org'), data)
assert_array_equal(FeatureTransformer.transform_feature(data, name, 'log'),
np.log(data))
assert_array_equal(FeatureTransformer.transform_feature(data, name, 'addOneInv'),
1 / (data + 1))
assert_array_equal(FeatureTransformer.transform_feature(data, name, 'addOneLn'),
np.log(data + 1))