def test_fitting_full(self, shared_datadir):
test_data = (shared_datadir / 'test_mfcc/train_features.npy').resolve()
features = np.load(test_data)
scaler = StandardScaler()
scaler.fit(features)
assert scaler.mean == pytest.approx(-12.315056)
assert scaler.std == pytest.approx(151.7704)
def test_transform_slice(self, shared_datadir):
test_data = (shared_datadir / 'test_mfcc/train_features.npy').resolve()
features = np.load(test_data)
scaler = StandardScaler()
scaler.fit(features)
scaled = scaler.transform(features[0])
assert scaled.mean() == pytest.approx(-0.036157943)
assert scaled.std() == pytest.approx(0.8281236)
def test_transform_per_feature_slice(self, shared_datadir):
test_data = (shared_datadir / 'test_mfcc/train_features.npy').resolve()
features = np.load(test_data)
scaler = StandardScaler()
scaler.fit(features, (0, 1))
scaled = scaler.transform(features[0])
assert scaled.shape == features[0].shape
assert scaled.mean() == pytest.approx(-0.2528363)
assert scaled.std() == pytest.approx(0.8721026)
def test_fit_transform_full(self, shared_datadir):
test_data = (shared_datadir / 'test_mfcc/train_features.npy').resolve()
features = np.load(test_data)
scaler = StandardScaler()
scaled = scaler.fit_transform(features)
assert scaler.mean == pytest.approx(-12.315056)
assert scaler.std == pytest.approx(151.7704)
assert scaled.mean() == pytest.approx(0.0, abs=1e-9)
assert scaled.std() == pytest.approx(1.0)
def test_transform_time_summary_per_feature(self, shared_datadir):
test_data = (shared_datadir / 'test_mfcc/train_features.npy').resolve()
features = np.load(test_data)
features = features.mean(1)
scaler = StandardScaler()
scaler.fit(features, 0)
scaled = scaler.transform(features)
assert scaled.shape == features.shape
assert scaled.mean() == pytest.approx(0.0, abs=1e-6)
assert scaled.std() == pytest.approx(1.0)
def test_fit_time_summary_per_feature(self, shared_datadir):
test_data = (shared_datadir / 'test_mfcc/train_features.npy').resolve()
features = np.load(test_data)
features = features.mean(1)
scaler = StandardScaler()
scaler.fit(features, (0, ))
expected_mean = features.mean(0)
expected_std = features.std(0)
np.testing.assert_array_almost_equal(scaler.mean, expected_mean)
np.testing.assert_array_almost_equal(scaler.std, expected_std)
def test_transform_time_summary_full_slice(self, shared_datadir):
test_data = (shared_datadir / 'test_mfcc/train_features.npy').resolve()
features = np.load(test_data)
features = features.mean(1)
scaler = StandardScaler()
scaler.fit(features)
scaled = scaler.transform(features[0])
assert scaled.shape == features[0].shape
assert scaled.mean() == pytest.approx(-0.03666329, abs=1e-6)
assert scaled.std() == pytest.approx(0.81805766)
def test_fit_time_summary_full(self, shared_datadir):
test_data = (shared_datadir / 'test_mfcc/train_features.npy').resolve()
features = np.load(test_data)
features = features.mean(1)
scaler = StandardScaler()
scaler.fit(features)
expected_mean = features.mean()
expected_std = features.std()
assert scaler.mean == pytest.approx(expected_mean)
assert scaler.std == pytest.approx(expected_std)
def test_fit_per_feature(self, shared_datadir):
test_data = (shared_datadir / 'test_mfcc/train_features.npy').resolve()
features = np.load(test_data)
scaler = StandardScaler()
scaler.fit(features, (0, 1))
expected_mean = np.load(
(shared_datadir / 'test_mfcc/per_feature_mean.npy').resolve())
np.testing.assert_array_almost_equal(scaler.mean, expected_mean)
expected_std = np.load(
(shared_datadir / 'test_mfcc/per_feature_std.npy').resolve())
np.testing.assert_array_almost_equal(scaler.std, expected_std)
def test_fit_and_transform_per_feature(self, shared_datadir):
test_data = (shared_datadir / 'test_mfcc/train_features.npy').resolve()
features = np.load(test_data)
scaler = StandardScaler()
scaler.fit(features, (0, 1))
scaled = scaler.transform(features)
expected_mean = np.zeros((features.shape[2], ))
np.testing.assert_array_almost_equal(scaled.mean((0, 1)),
expected_mean, 1e-5)
expected_std = np.full(features.shape[2], 1)
np.testing.assert_array_almost_equal(scaled.std((0, 1)), expected_std,
1e-5)
def test_transform_time_summary_per_feature_slice(self, shared_datadir):
test_data = (shared_datadir / 'test_mfcc/train_features.npy').resolve()
features = np.load(test_data)
features = features.mean(1)
scaler = StandardScaler()
scaler.fit(features, 0)
expected_mean = features.mean(0)
expected_std = features.std(0)
expected_scaled = (features[0] - expected_mean) / expected_std
scaled = scaler.transform(features[0])
assert scaled.shape == features[0].shape
np.testing.assert_array_almost_equal(scaled, expected_scaled)
def test_fit_and_transform_per_feature_non_time_major(
self, shared_datadir):
test_data = (shared_datadir / 'test_mfcc/train_features.npy').resolve()
features = np.load(test_data)
features = np.swapaxes(features, 1, 2)[0:13]
scaler = StandardScaler()
scaler.fit(features, (0, 2))
scaled = scaler.transform(features)
expected_mean = np.zeros((features.shape[1], ))
np.testing.assert_array_almost_equal(scaled.mean((0, 2)),
expected_mean)
expected_std = np.full(features.shape[1], 1)
np.testing.assert_array_almost_equal(scaled.std((0, 2)), expected_std)
def test_empty_construction(self):
scaler = StandardScaler()