def test_scaler():
"""Test methods of Scaler."""
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
scaler = Scaler(epochs.info)
y = epochs.events[:, -1]
X = scaler.fit_transform(epochs_data, y)
assert_true(X.shape == epochs_data.shape)
X2 = scaler.fit(epochs_data, y).transform(epochs_data)
assert_array_equal(X2, X)
# these should be across time
assert_allclose(X.std(axis=-2), 1.)
assert_allclose(X.mean(axis=-2), 0., atol=1e-12)
# Test inverse_transform
Xi = scaler.inverse_transform(X, y)
assert_array_almost_equal(epochs_data, Xi)
for kwargs in [{'with_mean': False}, {'with_std': False}]:
scaler = Scaler(epochs.info, **kwargs)
scaler.fit(epochs_data, y)
assert_array_almost_equal(
X, scaler.inverse_transform(scaler.transform(X)))
# Test init exception
assert_raises(ValueError, scaler.fit, epochs, y)
assert_raises(ValueError, scaler.transform, epochs, y)
def test_scaler():
"""Test methods of Scaler."""
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
scaler = Scaler(epochs.info)
y = epochs.events[:, -1]
# np invalid divide value warnings
with warnings.catch_warnings(record=True):
X = scaler.fit_transform(epochs_data, y)
assert_true(X.shape == epochs_data.shape)
X2 = scaler.fit(epochs_data, y).transform(epochs_data)
assert_array_equal(X2, X)
# Test inverse_transform
with warnings.catch_warnings(record=True): # invalid value in mult
Xi = scaler.inverse_transform(X, y)
assert_array_almost_equal(epochs_data, Xi)
for kwargs in [{'with_mean': False}, {'with_std': False}]:
scaler = Scaler(epochs.info, **kwargs)
scaler.fit(epochs_data, y)
assert_array_almost_equal(
X, scaler.inverse_transform(scaler.transform(X)))
# Test init exception
assert_raises(ValueError, scaler.fit, epochs, y)
assert_raises(ValueError, scaler.transform, epochs, y)
def test_scaler():
"""Test methods of Scaler
"""
raw = io.Raw(raw_fname, preload=False)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
scaler = Scaler(epochs.info)
y = epochs.events[:, -1]
# np invalid divide value warnings
with warnings.catch_warnings(record=True):
X = scaler.fit_transform(epochs_data, y)
assert_true(X.shape == epochs_data.shape)
X2 = scaler.fit(epochs_data, y).transform(epochs_data)
assert_array_equal(X2, X)
# Test inverse_transform
with warnings.catch_warnings(record=True): # invalid value in mult
Xi = scaler.inverse_transform(X, y)
assert_array_equal(epochs_data, Xi)
# Test init exception
assert_raises(ValueError, scaler.fit, epochs, y)
assert_raises(ValueError, scaler.transform, epochs, y)
def test_scaler():
"""Test methods of Scaler
"""
raw = io.Raw(raw_fname, preload=False)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude="bads")
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
scaler = Scaler(epochs.info)
y = epochs.events[:, -1]
# np invalid divide value warnings
with warnings.catch_warnings(record=True):
X = scaler.fit_transform(epochs_data, y)
assert_true(X.shape == epochs_data.shape)
X2 = scaler.fit(epochs_data, y).transform(epochs_data)
assert_array_equal(X2, X)
# Test inverse_transform
Xi = scaler.inverse_transform(X, y)
assert_array_equal(epochs_data, Xi)
# Test init exception
assert_raises(ValueError, scaler.fit, epochs, y)
assert_raises(ValueError, scaler.transform, epochs, y)
def test_scaler():
"""Test methods of Scaler."""
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
y = epochs.events[:, -1]
methods = (None, dict(mag=5, grad=10, eeg=20), 'mean', 'median')
infos = (epochs.info, epochs.info, None, None)
epochs_data_t = epochs_data.transpose([1, 0, 2])
for method, info in zip(methods, infos):
if method == 'median' and not check_version('sklearn', '0.17'):
assert_raises(ValueError, Scaler, info, method)
continue
if method == 'mean' and not check_version('sklearn', ''):
assert_raises(ImportError, Scaler, info, method)
continue
scaler = Scaler(info, method)
X = scaler.fit_transform(epochs_data, y)
assert_equal(X.shape, epochs_data.shape)
if method is None or isinstance(method, dict):
sd = DEFAULTS['scalings'] if method is None else method
stds = np.zeros(len(picks))
for key in ('mag', 'grad'):
stds[pick_types(epochs.info, meg=key)] = 1. / sd[key]
stds[pick_types(epochs.info, meg=False, eeg=True)] = 1. / sd['eeg']
means = np.zeros(len(epochs.ch_names))
elif method == 'mean':
stds = np.array([np.std(ch_data) for ch_data in epochs_data_t])
means = np.array([np.mean(ch_data) for ch_data in epochs_data_t])
else: # median
percs = np.array([np.percentile(ch_data, [25, 50, 75])
for ch_data in epochs_data_t])
stds = percs[:, 2] - percs[:, 0]
means = percs[:, 1]
assert_allclose(X * stds[:, np.newaxis] + means[:, np.newaxis],
epochs_data, rtol=1e-12, atol=1e-20, err_msg=method)
X2 = scaler.fit(epochs_data, y).transform(epochs_data)
assert_array_equal(X, X2)
# inverse_transform
Xi = scaler.inverse_transform(X)
assert_array_almost_equal(epochs_data, Xi)
# Test init exception
assert_raises(ValueError, Scaler, None, None)
assert_raises(ValueError, scaler.fit, epochs, y)
assert_raises(ValueError, scaler.transform, epochs)
epochs_bad = Epochs(raw, events, event_id, 0, 0.01,
picks=np.arange(len(raw.ch_names))) # non-data chs
scaler = Scaler(epochs_bad.info, None)
assert_raises(ValueError, scaler.fit, epochs_bad.get_data(), y)
def test_scaler(info, method):
"""Test methods of Scaler."""
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True)
epochs_data = epochs.get_data()
y = epochs.events[:, -1]
epochs_data_t = epochs_data.transpose([1, 0, 2])
if method in ('mean', 'median'):
if not check_version('sklearn'):
with pytest.raises(ImportError, match='No module'):
Scaler(info, method)
return
if check_version('sklearn', '1.0'):
# 1.0.dev0 is a problem pending
# https://github.com/scikit-learn/scikit-learn/issues/19726
pytest.skip('Bug on sklear main as of 2021/03/19')
if info:
info = epochs.info
scaler = Scaler(info, method)
X = scaler.fit_transform(epochs_data, y)
assert_equal(X.shape, epochs_data.shape)
if method is None or isinstance(method, dict):
sd = DEFAULTS['scalings'] if method is None else method
stds = np.zeros(len(picks))
for key in ('mag', 'grad'):
stds[pick_types(epochs.info, meg=key)] = 1. / sd[key]
stds[pick_types(epochs.info, meg=False, eeg=True)] = 1. / sd['eeg']
means = np.zeros(len(epochs.ch_names))
elif method == 'mean':
stds = np.array([np.std(ch_data) for ch_data in epochs_data_t])
means = np.array([np.mean(ch_data) for ch_data in epochs_data_t])
else: # median
percs = np.array([
np.percentile(ch_data, [25, 50, 75]) for ch_data in epochs_data_t
])
stds = percs[:, 2] - percs[:, 0]
means = percs[:, 1]
assert_allclose(X * stds[:, np.newaxis] + means[:, np.newaxis],
epochs_data,
rtol=1e-12,
atol=1e-20,
err_msg=method)
X2 = scaler.fit(epochs_data, y).transform(epochs_data)
assert_array_equal(X, X2)
# inverse_transform
Xi = scaler.inverse_transform(X)
assert_array_almost_equal(epochs_data, Xi)
# Test init exception
pytest.raises(ValueError, Scaler, None, None)
pytest.raises(TypeError, scaler.fit, epochs, y)
pytest.raises(TypeError, scaler.transform, epochs)
epochs_bad = Epochs(raw,
events,
event_id,
0,
0.01,
baseline=None,
picks=np.arange(len(raw.ch_names))) # non-data chs
scaler = Scaler(epochs_bad.info, None)
pytest.raises(ValueError, scaler.fit, epochs_bad.get_data(), y)
