def test_csp():
"""Test Common Spatial Patterns algorithm on epochs
"""
raw = io.read_raw_fif(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[2:9:3] # subselect channels -> disable proj!
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True, proj=False)
epochs_data = epochs.get_data()
n_channels = epochs_data.shape[1]
n_components = 3
csp = CSP(n_components=n_components)
csp.fit(epochs_data, epochs.events[:, -1])
y = epochs.events[:, -1]
X = csp.fit_transform(epochs_data, y)
assert_true(csp.filters_.shape == (n_channels, n_channels))
assert_true(csp.patterns_.shape == (n_channels, n_channels))
assert_array_almost_equal(csp.fit(epochs_data, y).transform(epochs_data),
X)
# test init exception
assert_raises(ValueError, csp.fit, epochs_data,
np.zeros_like(epochs.events))
assert_raises(ValueError, csp.fit, epochs, y)
assert_raises(ValueError, csp.transform, epochs, y)
csp.n_components = n_components
sources = csp.transform(epochs_data)
assert_true(sources.shape[1] == n_components)
epochs.pick_types(meg='mag', copy=False)
# test plot patterns
components = np.arange(n_components)
csp.plot_patterns(epochs.info, components=components, res=12,
show=False)
# test plot filters
csp.plot_filters(epochs.info, components=components, res=12,
show=False)
# test covariance estimation methods (results should be roughly equal)
csp_epochs = CSP(cov_est="epoch")
csp_epochs.fit(epochs_data, y)
for attr in ('filters_', 'patterns_'):
corr = np.corrcoef(getattr(csp, attr).ravel(),
getattr(csp_epochs, attr).ravel())[0, 1]
assert_true(corr >= 0.95, msg='%s < 0.95' % corr)
# make sure error is raised for undefined estimation method
csp_fail = CSP(cov_est="undefined")
assert_raises(ValueError, csp_fail.fit, epochs_data, y)
def test_csp_twoclass_symmetry():
"""Test that CSP is symmetric when swapping classes."""
x, y = deterministic_toy_data(['class_a', 'class_b'])
csp = CSP(norm_trace=False, transform_into='average_power', log=True)
log_power = csp.fit_transform(x, y)
log_power_ratio_ab = log_power[0] - log_power[1]
x, y = deterministic_toy_data(['class_b', 'class_a'])
csp = CSP(norm_trace=False, transform_into='average_power', log=True)
log_power = csp.fit_transform(x, y)
log_power_ratio_ba = log_power[0] - log_power[1]
assert_array_almost_equal(log_power_ratio_ab,
log_power_ratio_ba)
def test_regularized_csp():
"""Test Common Spatial Patterns algorithm using regularized covariance
"""
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()
n_channels = epochs_data.shape[1]
n_components = 3
reg_cov = [None, 0.05, 'lws', 'oas']
for reg in reg_cov:
csp = CSP(n_components=n_components, reg=reg)
csp.fit(epochs_data, epochs.events[:, -1])
y = epochs.events[:, -1]
X = csp.fit_transform(epochs_data, y)
assert_true(csp.filters_.shape == (n_channels, n_channels))
assert_true(csp.patterns_.shape == (n_channels, n_channels))
assert_array_almost_equal(csp.fit(epochs_data, y).
transform(epochs_data), X)
# test init exception
assert_raises(ValueError, csp.fit, epochs_data,
np.zeros_like(epochs.events))
assert_raises(ValueError, csp.fit, epochs, y)
assert_raises(ValueError, csp.transform, epochs, y)
csp.n_components = n_components
sources = csp.transform(epochs_data)
assert_true(sources.shape[1] == n_components)
def test_csp_pipeline():
"""Test if CSP works in a pipeline."""
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
csp = CSP(reg=1, norm_trace=False)
svc = SVC()
pipe = Pipeline([("CSP", csp), ("SVC", svc)])
pipe.set_params(CSP__reg=0.2)
assert (pipe.get_params()["CSP__reg"] == 0.2)
def test_csp_component_ordering():
"""Test that CSP component ordering works as expected."""
x, y = deterministic_toy_data(['class_a', 'class_b'])
pytest.raises(ValueError, CSP, component_order='invalid')
# component_order='alternate' only works with two classes
csp = CSP(component_order='alternate')
with pytest.raises(ValueError):
csp.fit(np.zeros((3, 0, 0)), ['a', 'b', 'c'])
p_alt = CSP(component_order='alternate').fit(x, y).patterns_
p_mut = CSP(component_order='mutual_info').fit(x, y).patterns_
# This permutation of p_alt and p_mut is explained by the particular
# eigenvalues of the toy data: [0.06, 0.1, 0.5, 0.8].
# p_alt arranges them to [0.8, 0.06, 0.5, 0.1]
# p_mut arranges them to [0.06, 0.1, 0.8, 0.5]
assert_array_almost_equal(p_alt, p_mut[[2, 0, 3, 1]])
def test_csp():
"""Test Common Spatial Patterns algorithm on epochs
"""
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[2:9:3]
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True)
epochs_data = epochs.get_data()
n_channels = epochs_data.shape[1]
n_components = 3
csp = CSP(n_components=n_components)
csp.fit(epochs_data, epochs.events[:, -1])
y = epochs.events[:, -1]
X = csp.fit_transform(epochs_data, y)
assert_true(csp.filters_.shape == (n_channels, n_channels))
assert_true(csp.patterns_.shape == (n_channels, n_channels))
assert_array_almost_equal(
csp.fit(epochs_data, y).transform(epochs_data), X)
# test init exception
assert_raises(ValueError, csp.fit, epochs_data,
np.zeros_like(epochs.events))
assert_raises(ValueError, csp.fit, epochs, y)
assert_raises(ValueError, csp.transform, epochs, y)
csp.n_components = n_components
sources = csp.transform(epochs_data)
assert_true(sources.shape[1] == n_components)
epochs.pick_types(meg='mag', copy=False)
# test plot patterns
components = np.arange(n_components)
csp.plot_patterns(epochs.info, components=components, res=12, show=False)
# test plot filters
csp.plot_filters(epochs.info, components=components, res=12, show=False)
def test_csp():
"""Test Common Spatial Patterns algorithm on epochs."""
raw = io.read_raw_fif(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[2:12:3] # subselect channels -> disable proj!
raw.add_proj([], remove_existing=True)
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True,
proj=False)
epochs_data = epochs.get_data()
n_channels = epochs_data.shape[1]
y = epochs.events[:, -1]
# Init
pytest.raises(ValueError, CSP, n_components='foo', norm_trace=False)
for reg in ['foo', -0.1, 1.1]:
csp = CSP(reg=reg, norm_trace=False)
pytest.raises(ValueError, csp.fit, epochs_data, epochs.events[:, -1])
for reg in ['oas', 'ledoit_wolf', 0, 0.5, 1.]:
CSP(reg=reg, norm_trace=False)
for cov_est in ['foo', None]:
pytest.raises(ValueError, CSP, cov_est=cov_est, norm_trace=False)
pytest.raises(ValueError, CSP, norm_trace='foo')
for cov_est in ['concat', 'epoch']:
CSP(cov_est=cov_est, norm_trace=False)
n_components = 3
# Fit
for norm_trace in [True, False]:
csp = CSP(n_components=n_components, norm_trace=norm_trace)
csp.fit(epochs_data, epochs.events[:, -1])
assert_equal(len(csp.mean_), n_components)
assert_equal(len(csp.std_), n_components)
# Transform
X = csp.fit_transform(epochs_data, y)
sources = csp.transform(epochs_data)
assert (sources.shape[1] == n_components)
assert (csp.filters_.shape == (n_channels, n_channels))
assert (csp.patterns_.shape == (n_channels, n_channels))
assert_array_almost_equal(sources, X)
# Test data exception
pytest.raises(ValueError, csp.fit, epochs_data,
np.zeros_like(epochs.events))
pytest.raises(ValueError, csp.fit, epochs, y)
pytest.raises(ValueError, csp.transform, epochs)
# Test plots
epochs.pick_types(meg='mag')
cmap = ('RdBu', True)
components = np.arange(n_components)
for plot in (csp.plot_patterns, csp.plot_filters):
plot(epochs.info, components=components, res=12, show=False, cmap=cmap)
# Test with more than 2 classes
epochs = Epochs(raw,
events,
tmin=tmin,
tmax=tmax,
picks=picks,
event_id=dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4),
baseline=(None, 0),
proj=False,
preload=True)
epochs_data = epochs.get_data()
n_channels = epochs_data.shape[1]
n_channels = epochs_data.shape[1]
for cov_est in ['concat', 'epoch']:
csp = CSP(n_components=n_components, cov_est=cov_est, norm_trace=False)
csp.fit(epochs_data, epochs.events[:, 2]).transform(epochs_data)
assert_equal(len(csp._classes), 4)
assert_array_equal(csp.filters_.shape, [n_channels, n_channels])
assert_array_equal(csp.patterns_.shape, [n_channels, n_channels])
# Test average power transform
n_components = 2
assert (csp.transform_into == 'average_power')
feature_shape = [len(epochs_data), n_components]
X_trans = dict()
for log in (None, True, False):
csp = CSP(n_components=n_components, log=log, norm_trace=False)
assert (csp.log is log)
Xt = csp.fit_transform(epochs_data, epochs.events[:, 2])
assert_array_equal(Xt.shape, feature_shape)
X_trans[str(log)] = Xt
# log=None => log=True
assert_array_almost_equal(X_trans['None'], X_trans['True'])
# Different normalization return different transform
assert (np.sum((X_trans['True'] - X_trans['False'])**2) > 1.)
# Check wrong inputs
pytest.raises(ValueError, CSP, transform_into='average_power', log='foo')
# Test csp space transform
csp = CSP(transform_into='csp_space', norm_trace=False)
assert (csp.transform_into == 'csp_space')
for log in ('foo', True, False):
pytest.raises(ValueError,
CSP,
transform_into='csp_space',
log=log,
norm_trace=False)
n_components = 2
csp = CSP(n_components=n_components,
transform_into='csp_space',
norm_trace=False)
Xt = csp.fit(epochs_data, epochs.events[:, 2]).transform(epochs_data)
feature_shape = [len(epochs_data), n_components, epochs_data.shape[2]]
assert_array_equal(Xt.shape, feature_shape)
# Check mixing matrix on simulated data
y = np.array([100] * 50 + [1] * 50)
X, A = simulate_data(y)
for cov_est in ['concat', 'epoch']:
# fit csp
csp = CSP(n_components=1, cov_est=cov_est, norm_trace=False)
csp.fit(X, y)
# check the first pattern match the mixing matrix
# the sign might change
corr = np.abs(np.corrcoef(csp.patterns_[0, :].T, A[:, 0])[0, 1])
assert np.abs(corr) > 0.99
# check output
out = csp.transform(X)
corr = np.abs(np.corrcoef(out[:, 0], y)[0, 1])
assert np.abs(corr) > 0.95
def test_csp():
"""Test Common Spatial Patterns algorithm on epochs
"""
raw = io.read_raw_fif(raw_fname, preload=False, add_eeg_ref=False)
events = read_events(event_name)
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')
picks = picks[2:12:3] # subselect channels -> disable proj!
raw.add_proj([], remove_existing=True)
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True,
proj=False,
add_eeg_ref=False)
epochs_data = epochs.get_data()
n_channels = epochs_data.shape[1]
y = epochs.events[:, -1]
# Init
assert_raises(ValueError, CSP, n_components='foo')
for reg in ['foo', -0.1, 1.1]:
assert_raises(ValueError, CSP, reg=reg)
for reg in ['oas', 'ledoit_wolf', 0, 0.5, 1.]:
CSP(reg=reg)
for cov_est in ['foo', None]:
assert_raises(ValueError, CSP, cov_est=cov_est)
for cov_est in ['concat', 'epoch']:
CSP(cov_est=cov_est)
n_components = 3
csp = CSP(n_components=n_components)
# Fit
csp.fit(epochs_data, epochs.events[:, -1])
assert_equal(len(csp.mean_), n_components)
assert_equal(len(csp.std_), n_components)
# Transform
X = csp.fit_transform(epochs_data, y)
sources = csp.transform(epochs_data)
assert_true(sources.shape[1] == n_components)
assert_true(csp.filters_.shape == (n_channels, n_channels))
assert_true(csp.patterns_.shape == (n_channels, n_channels))
assert_array_almost_equal(sources, X)
# Test data exception
assert_raises(ValueError, csp.fit, epochs_data,
np.zeros_like(epochs.events))
assert_raises(ValueError, csp.fit, epochs, y)
assert_raises(ValueError, csp.transform, epochs)
# Test plots
epochs.pick_types(meg='mag')
cmap = ('RdBu', True)
components = np.arange(n_components)
for plot in (csp.plot_patterns, csp.plot_filters):
plot(epochs.info, components=components, res=12, show=False, cmap=cmap)
# Test covariance estimation methods (results should be roughly equal)
np.random.seed(0)
csp_epochs = CSP(cov_est="epoch")
csp_epochs.fit(epochs_data, y)
for attr in ('filters_', 'patterns_'):
corr = np.corrcoef(
getattr(csp, attr).ravel(),
getattr(csp_epochs, attr).ravel())[0, 1]
assert_true(corr >= 0.94)
# Test with more than 2 classes
epochs = Epochs(raw,
events,
tmin=tmin,
tmax=tmax,
picks=picks,
event_id=dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4),
baseline=(None, 0),
proj=False,
preload=True,
add_eeg_ref=False)
epochs_data = epochs.get_data()
n_channels = epochs_data.shape[1]
n_channels = epochs_data.shape[1]
for cov_est in ['concat', 'epoch']:
csp = CSP(n_components=n_components, cov_est=cov_est)
csp.fit(epochs_data, epochs.events[:, 2]).transform(epochs_data)
assert_equal(len(csp._classes), 4)
assert_array_equal(csp.filters_.shape, [n_channels, n_channels])
assert_array_equal(csp.patterns_.shape, [n_channels, n_channels])
# Test average power transform
n_components = 2
assert_true(csp.transform_into == 'average_power')
feature_shape = [len(epochs_data), n_components]
X_trans = dict()
for log in (None, True, False):
csp = CSP(n_components=n_components, log=log)
assert_true(csp.log is log)
Xt = csp.fit_transform(epochs_data, epochs.events[:, 2])
assert_array_equal(Xt.shape, feature_shape)
X_trans[str(log)] = Xt
# log=None => log=True
assert_array_almost_equal(X_trans['None'], X_trans['True'])
# Different normalization return different transform
assert_true(np.sum((X_trans['True'] - X_trans['False'])**2) > 1.)
# Check wrong inputs
assert_raises(ValueError, CSP, transform_into='average_power', log='foo')
# Test csp space transform
csp = CSP(transform_into='csp_space')
assert_true(csp.transform_into == 'csp_space')
for log in ('foo', True, False):
assert_raises(ValueError, CSP, transform_into='csp_space', log=log)
n_components = 2
csp = CSP(n_components=n_components, transform_into='csp_space')
Xt = csp.fit(epochs_data, epochs.events[:, 2]).transform(epochs_data)
feature_shape = [len(epochs_data), n_components, epochs_data.shape[2]]
assert_array_equal(Xt.shape, feature_shape)