def test_min_max_scaler_iris(self):
iris = load_iris()
X = iris.data
scaler = MinMaxScaler()
# default params
X_trans = scaler.fit_transform(X)
assert_array_almost_equal(X_trans.min(axis=0), 0)
assert_array_almost_equal(X_trans.min(axis=0), 0)
assert_array_almost_equal(X_trans.max(axis=0), 1)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
# not default params: min=1, max=2
scaler = MinMaxScaler(feature_range=(1, 2))
X_trans = scaler.fit_transform(X)
assert_array_almost_equal(X_trans.min(axis=0), 1)
assert_array_almost_equal(X_trans.max(axis=0), 2)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
# min=-.5, max=.6
scaler = MinMaxScaler(feature_range=(-.5, .6))
X_trans = scaler.fit_transform(X)
assert_array_almost_equal(X_trans.min(axis=0), -.5)
assert_array_almost_equal(X_trans.max(axis=0), .6)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
# raises on invalid range
scaler = MinMaxScaler(feature_range=(2, 1))
self.assertRaises(ValueError, scaler.fit, X)
def test_min_max_scaler_1d(self):
"""Test scaling of dataset along single axis"""
rng = np.random.RandomState(0)
X = rng.randn(5)
X_orig_copy = X.copy()
scaler = MinMaxScaler()
X_scaled = scaler.fit(X).transform(X)
assert_array_almost_equal(X_scaled.min(axis=0), 0.0)
assert_array_almost_equal(X_scaled.max(axis=0), 1.0)
# check inverse transform
X_scaled_back = scaler.inverse_transform(X_scaled)
assert_array_almost_equal(X_scaled_back, X_orig_copy)
# Test with 1D list
X = [0., 1., 2, 0.4, 1.]
scaler = MinMaxScaler()
X_scaled = scaler.fit(X).transform(X)
assert_array_almost_equal(X_scaled.min(axis=0), 0.0)
assert_array_almost_equal(X_scaled.max(axis=0), 1.0)
def test_min_max_scaler_zero_variance_features(self):
"""Check min max scaler on toy data with zero variance features"""
X = [[0., 1., +0.5],
[0., 1., -0.1],
[0., 1., +1.1]]
X_new = [[+0., 2., 0.5],
[-1., 1., 0.0],
[+0., 1., 1.5]]
# default params
scaler = MinMaxScaler()
X_trans = scaler.fit_transform(X)
X_expected_0_1 = [[0., 0., 0.5],
[0., 0., 0.0],
[0., 0., 1.0]]
assert_array_almost_equal(X_trans, X_expected_0_1)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
X_trans_new = scaler.transform(X_new)
X_expected_0_1_new = [[+0., 1., 0.500],
[-1., 0., 0.083],
[+0., 0., 1.333]]
assert_array_almost_equal(X_trans_new, X_expected_0_1_new, decimal=2)
# not default params
scaler = MinMaxScaler(feature_range=(1, 2))
X_trans = scaler.fit_transform(X)
X_expected_1_2 = [[1., 1., 1.5],
[1., 1., 1.0],
[1., 1., 2.0]]
assert_array_almost_equal(X_trans, X_expected_1_2)
def test_min_max_scaler_sparse_boston_data(self):
# Use the boston housing dataset, because column three is 1HotEncoded!
# This is important to test; because the normal sklearn rescaler
# would set all values of the 1Hot Encoded column to zero, while we
# keep the values at 1.
X_train, Y_train, X_test, Y_test = get_dataset('boston',
make_sparse=True)
num_data_points = len(X_train.data)
expected_max_values = [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
expected_max_values = np.array(expected_max_values).reshape((1, -1))
scaler = MinMaxScaler()
scaler.fit(X_train, Y_train)
transformation = scaler.transform(X_train)
assert_array_almost_equal(np.array(transformation.todense().min(axis=0)),
np.zeros((1, 13)))
assert_array_almost_equal(np.array(transformation.todense().max(axis=0)),
expected_max_values)
# Test that the matrix is still sparse
self.assertTrue(sparse.issparse(transformation))
self.assertEqual(num_data_points, len(transformation.data))
def test_min_max_scaler_sparse_boston_data(self):
# Use the boston housing dataset, because column three is 1HotEncoded!
# This is important to test; because the normal sklearn rescaler
# would set all values of the 1Hot Encoded column to zero, while we
# keep the values at 1.
X_train, Y_train, X_test, Y_test = get_dataset('boston',
make_sparse=True)
num_data_points = len(X_train.data)
expected_max_values = [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
expected_max_values = np.array(expected_max_values).reshape((1, -1))
scaler = MinMaxScaler()
scaler.fit(X_train, Y_train)
transformation = scaler.transform(X_train)
assert_array_almost_equal(
np.array(transformation.todense().min(axis=0)), np.zeros((1, 13)))
assert_array_almost_equal(
np.array(transformation.todense().max(axis=0)),
expected_max_values)
# Test that the matrix is still sparse
self.assertTrue(sparse.issparse(transformation))
self.assertEqual(num_data_points, len(transformation.data))
def test_min_max_scaler_zero_variance_features(self):
"""Check min max scaler on toy data with zero variance features"""
X = [[0., 1., +0.5], [0., 1., -0.1], [0., 1., +1.1]]
X_new = [[+0., 2., 0.5], [-1., 1., 0.0], [+0., 1., 1.5]]
# default params
scaler = MinMaxScaler()
X_trans = scaler.fit_transform(X)
X_expected_0_1 = [[0., 0., 0.5], [0., 0., 0.0], [0., 0., 1.0]]
assert_array_almost_equal(X_trans, X_expected_0_1)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
X_trans_new = scaler.transform(X_new)
X_expected_0_1_new = [[+0., 1., 0.500], [-1., 0., 0.083],
[+0., 0., 1.333]]
assert_array_almost_equal(X_trans_new, X_expected_0_1_new, decimal=2)
# not default params
scaler = MinMaxScaler(feature_range=(1, 2))
X_trans = scaler.fit_transform(X)
X_expected_1_2 = [[1., 1., 1.5], [1., 1., 1.0], [1., 1., 2.0]]
assert_array_almost_equal(X_trans, X_expected_1_2)
def test_min_max_scaler_1d(self):
"""Test scaling of dataset along single axis"""
rng = np.random.RandomState(0)
X = rng.randn(5)
X_orig_copy = X.copy()
scaler = MinMaxScaler()
X_scaled = scaler.fit(X).transform(X)
assert_array_almost_equal(X_scaled.min(axis=0), 0.0)
assert_array_almost_equal(X_scaled.max(axis=0), 1.0)
# check inverse transform
X_scaled_back = scaler.inverse_transform(X_scaled)
assert_array_almost_equal(X_scaled_back, X_orig_copy)
# Test with 1D list
X = [0., 1., 2, 0.4, 1.]
scaler = MinMaxScaler()
X_scaled = scaler.fit(X).transform(X)
assert_array_almost_equal(X_scaled.min(axis=0), 0.0)
assert_array_almost_equal(X_scaled.max(axis=0), 1.0)
def __init__(self, random_state):
from autosklearn.pipeline.implementations.MinMaxScaler import \
MinMaxScaler
self.preprocessor = MinMaxScaler()
def test_min_max_scaler_iris(self):
iris = load_iris()
X = iris.data
scaler = MinMaxScaler()
# default params
X_trans = scaler.fit_transform(X)
assert_array_almost_equal(X_trans.min(axis=0), 0)
assert_array_almost_equal(X_trans.min(axis=0), 0)
assert_array_almost_equal(X_trans.max(axis=0), 1)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
# not default params: min=1, max=2
scaler = MinMaxScaler(feature_range=(1, 2))
X_trans = scaler.fit_transform(X)
assert_array_almost_equal(X_trans.min(axis=0), 1)
assert_array_almost_equal(X_trans.max(axis=0), 2)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
# min=-.5, max=.6
scaler = MinMaxScaler(feature_range=(-.5, .6))
X_trans = scaler.fit_transform(X)
assert_array_almost_equal(X_trans.min(axis=0), -.5)
assert_array_almost_equal(X_trans.max(axis=0), .6)
X_trans_inv = scaler.inverse_transform(X_trans)
assert_array_almost_equal(X, X_trans_inv)
# raises on invalid range
scaler = MinMaxScaler(feature_range=(2, 1))
self.assertRaises(ValueError, scaler.fit, X)
