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_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_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))
