def test_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 = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=False)
assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
assert_array_almost_equal(X_scaled.std(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 = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=False)
assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
X_scaled = scale(X)
assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
# Test with sparse list
X = scipy.sparse.coo_matrix((np.random.random((10,)),
([i**2 for i in range(10)],
[0 for i in range(10)])))
X = X.tocsr()
scaler = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=False)
self.assertFalse(np.any(np.isnan(X_scaled.data)))
self.assertAlmostEqual(X_scaled.mean(), 0)
self.assertAlmostEqual(np.sqrt(X_scaled.data.var()), 1)
# Check that X has not been copied
# self.assertTrue(X_scaled is X)
# Check that the matrix is still sparse
self.assertEqual(len(X.indices), 10)
def test_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 = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=False)
assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
assert_array_almost_equal(X_scaled.std(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 = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=False)
assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
X_scaled = scale(X)
assert_array_almost_equal(X_scaled.mean(axis=0), 0.0)
assert_array_almost_equal(X_scaled.std(axis=0), 1.0)
# Test with sparse list
X = scipy.sparse.coo_matrix((np.random.random(
(10, )), ([i**2 for i in range(10)], [0 for i in range(10)])))
X = X.tocsr()
scaler = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=False)
self.assertFalse(np.any(np.isnan(X_scaled.data)))
self.assertAlmostEqual(X_scaled.mean(), 0)
self.assertAlmostEqual(np.sqrt(X_scaled.data.var()), 1)
# Check that X has not been copied
# self.assertTrue(X_scaled is X)
# Check that the matrix is still sparse
self.assertEqual(len(X.indices), 10)
def test_scaler_2d_arrays(self):
"""Test scaling of 2d array along first axis"""
rng = np.random.RandomState(0)
X = rng.randn(4, 5)
X[:, 0] = 0.0 # first feature is always of zero
scaler = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=True)
self.assertFalse(np.any(np.isnan(X_scaled)))
assert_array_almost_equal(X_scaled.mean(axis=0), 5 * [0.0])
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
# Check that X has been copied
self.assertTrue(X_scaled is not X)
# check inverse transform
X_scaled_back = scaler.inverse_transform(X_scaled)
self.assertTrue(X_scaled_back is not X)
self.assertTrue(X_scaled_back is not X_scaled)
assert_array_almost_equal(X_scaled_back, X)
X_scaled = scale(X, axis=1, with_std=False)
self.assertFalse(np.any(np.isnan(X_scaled)))
assert_array_almost_equal(X_scaled.mean(axis=1), 4 * [0.0])
X_scaled = scale(X, axis=1, with_std=True)
self.assertFalse(np.any(np.isnan(X_scaled)))
assert_array_almost_equal(X_scaled.mean(axis=1), 4 * [0.0])
assert_array_almost_equal(X_scaled.std(axis=1), 4 * [1.0])
# Check that the data hasn't been modified
self.assertTrue(X_scaled is not X)
X_scaled = scaler.fit(X).transform(X, copy=False)
self.assertFalse(np.any(np.isnan(X_scaled)))
assert_array_almost_equal(X_scaled.mean(axis=0), 5 * [0.0])
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
# Check that X has not been copied
self.assertTrue(X_scaled is X)
X = rng.randn(4, 5)
X[:, 0] = 1.0 # first feature is a constant, non zero feature
scaler = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=True)
self.assertFalse(np.any(np.isnan(X_scaled)))
assert_array_almost_equal(X_scaled.mean(axis=0), 5 * [0.0])
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
# Check that X has not been copied
self.assertTrue(X_scaled is not X)
# Same thing for sparse matrices...
X = scipy.sparse.coo_matrix((np.random.random((12,)),
([i for i in range(12)],
[int(i / 3) for i in range(12)])))
X = X.tocsr()
scaler = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=False)
self.assertFalse(np.any(np.isnan(X_scaled.data)))
assert_array_almost_equal(
[X_scaled.data[X_scaled.indptr[i]:X_scaled.indptr[i + 1]].mean()
for i in range(X_scaled.shape[1])],
np.zeros((4, ), dtype=np.float64))
assert_array_almost_equal(np.sqrt([
X_scaled.data[X_scaled.indptr[i]:X_scaled.indptr[i + 1]].var()
for i in range(X_scaled.shape[1])]),
np.ones((4, ), dtype=np.float64))
# Because we change the sparse format to csc, we cannot assert that
# the matrix did not change!
# self.assertTrue(X_scaled is X)
# Check that the matrix is still sparse
self.assertEqual(len(X.indices), 12)
def test_scaler_2d_arrays(self):
"""Test scaling of 2d array along first axis"""
rng = np.random.RandomState(0)
X = rng.randn(4, 5)
X[:, 0] = 0.0 # first feature is always of zero
scaler = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=True)
self.assertFalse(np.any(np.isnan(X_scaled)))
assert_array_almost_equal(X_scaled.mean(axis=0), 5 * [0.0])
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
# Check that X has been copied
self.assertTrue(X_scaled is not X)
# check inverse transform
X_scaled_back = scaler.inverse_transform(X_scaled)
self.assertTrue(X_scaled_back is not X)
self.assertTrue(X_scaled_back is not X_scaled)
assert_array_almost_equal(X_scaled_back, X)
X_scaled = scale(X, axis=1, with_std=False)
self.assertFalse(np.any(np.isnan(X_scaled)))
assert_array_almost_equal(X_scaled.mean(axis=1), 4 * [0.0])
X_scaled = scale(X, axis=1, with_std=True)
self.assertFalse(np.any(np.isnan(X_scaled)))
assert_array_almost_equal(X_scaled.mean(axis=1), 4 * [0.0])
assert_array_almost_equal(X_scaled.std(axis=1), 4 * [1.0])
# Check that the data hasn't been modified
self.assertTrue(X_scaled is not X)
X_scaled = scaler.fit(X).transform(X, copy=False)
self.assertFalse(np.any(np.isnan(X_scaled)))
assert_array_almost_equal(X_scaled.mean(axis=0), 5 * [0.0])
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
# Check that X has not been copied
self.assertTrue(X_scaled is X)
X = rng.randn(4, 5)
X[:, 0] = 1.0 # first feature is a constant, non zero feature
scaler = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=True)
self.assertFalse(np.any(np.isnan(X_scaled)))
assert_array_almost_equal(X_scaled.mean(axis=0), 5 * [0.0])
assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])
# Check that X has not been copied
self.assertTrue(X_scaled is not X)
# Same thing for sparse matrices...
X = scipy.sparse.coo_matrix((np.random.random(
(12, )), ([i for i in range(12)], [int(i / 3)
for i in range(12)])))
X = X.tocsr()
scaler = StandardScaler()
X_scaled = scaler.fit(X).transform(X, copy=False)
self.assertFalse(np.any(np.isnan(X_scaled.data)))
assert_array_almost_equal([
X_scaled.data[X_scaled.indptr[i]:X_scaled.indptr[i + 1]].mean()
for i in range(X_scaled.shape[1])
], np.zeros((4, ), dtype=np.float64))
assert_array_almost_equal(
np.sqrt([
X_scaled.data[X_scaled.indptr[i]:X_scaled.indptr[i + 1]].var()
for i in range(X_scaled.shape[1])
]), np.ones((4, ), dtype=np.float64))
# Because we change the sparse format to csc, we cannot assert that
# the matrix did not change!
# self.assertTrue(X_scaled is X)
# Check that the matrix is still sparse
self.assertEqual(len(X.indices), 12)
