def _compute_cospectral_covs(epochs, n_fft, n_overlap, fmin, fmax, fs):
X = epochs.get_data()
cospectral_covs = CospCovariances(window=n_fft,
overlap=n_overlap / n_fft,
fmin=fmin,
fmax=fmax,
fs=fs)
return cospectral_covs.transform(X).mean(axis=0).transpose((2, 0, 1))
def test_Cospcovariances():
"""Test fit CospCovariances"""
x = np.random.randn(2, 3, 1000)
cov = CospCovariances()
cov.fit(x)
cov.fit_transform(x)
assert_equal(cov.get_params(),
dict(window=128, overlap=0.75, fmin=None, fmax=None, fs=None))
def test_Cospcovariances():
"""Test fit CospCovariances"""
x = np.random.randn(2, 3, 1000)
cov = CospCovariances()
cov.fit(x)
cov.fit_transform(x)
assert_equal(cov.get_params(), dict(window=128, overlap=0.75, fmin=None,
fmax=None, fs=None))
def test_cosp_covariances(rndstate):
"""Test fit CospCovariances"""
n_matrices, n_channels, n_times = 2, 3, 1000
x = rndstate.randn(n_matrices, n_channels, n_times)
cov = CospCovariances()
cov.fit(x)
covmats = cov.transform(x)
assert cov.get_params() == dict(window=128,
overlap=0.75,
fmin=None,
fmax=None,
fs=None)
n_freqs = 65
assert covmats.shape == (n_matrices, n_channels, n_channels, n_freqs)
assert is_spsd(covmats.transpose(0, 3, 1, 2))
array_clfs['XdawnCov'] = make_pipeline(XdawnCovariances(6, estimator='oas'),
TangentSpace('riemann'),
LogisticRegression('l2'))
array_clfs['Xdawn'] = make_pipeline(Xdawn(12, estimator='oas'), DownSampler(5),
EpochsVectorizer(),
LogisticRegression('l2'))
# Induced activity models
baseclf = make_pipeline(
ElectrodeSelection(10, metric=dict(mean='logeuclid', distance='riemann')),
TangentSpace('riemann'), LogisticRegression('l1'))
array_clfs['Cosp'] = make_pipeline(
CospCovariances(fs=1000, window=32, overlap=0.95, fmax=300, fmin=1),
CospBoostingClassifier(baseclf))
array_clfs['HankelCov'] = make_pipeline(
DownSampler(2), HankelCovariances(delays=[2, 4, 8, 12, 16],
estimator='oas'),
TangentSpace('logeuclid'), LogisticRegression('l1'))
array_clfs['CSSP'] = make_pipeline(
HankelCovariances(delays=[2, 4, 8, 12, 16], estimator='oas'), CSP(30),
LogisticRegression('l1'))
patients = dataframe1.PatientID.values
index = array_clfs.keys() + ['Ensemble']
columns = ['ca', 'de', 'fp', 'ja', 'mv', 'wc', 'zt']
picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
exclude='bads')
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
epochs = Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
baseline=None, preload=True, add_eeg_ref=False, verbose=False)
labels = epochs.events[:, -1] - 2
# get epochs
epochs_data = epochs.get_data()
# compute cospectral covariance matrices
fmin = 2.0
fmax = 40.0
cosp = CospCovariances(window=128, overlap=0.98, fmin=fmin, fmax=fmax,
fs=160.0)
covmats = cosp.fit_transform(epochs_data[:, ::4, :])
fr = np.fft.fftfreq(128)[0:64]*160
fr = fr[(fr >= fmin) & (fr <= fmax)]
pv = []
Fv = []
# For each frequency bin, estimate the stats
for i in range(covmats.shape[3]):
p_test = PermutationTest(5000)
p, F = p_test.test(covmats[:, :, :, i], labels)
print(p_test.summary())
pv.append(p)
Fv.append(F[0])
# y = np.concatenate([y_s1, y_s2, y_s3, y_s4])
# X, y = get_data('/media/dmalt/SSD500/aut_gamma/Moscow_baseline_results_new/', 'ec')
# }}} test on 4 subjects #
# X, y = get_data('/home/dmalt/Data/aut_gamma/Moscow_baseline_results_new/', 'ec')
baseclf = make_pipeline(
ElectrodeSelection(2,
metric=dict(mean='logeuclid', distance='riemann')),
TangentSpace(metric='riemann'), LogisticRegression(penalty='l1'))
cosp_cov = CospCovariances(fs=500,
window=32,
overlap=0.95,
fmax=20,
fmin=1)
# # cosp_cov.fit_transform = ShrinkCovMat(cosp_cov.fit_transform)
# # cosp_cov.transform = shrink_cov_mat(cosp_cov.transform)
clf = make_pipeline(cosp_cov, ShrinkCovMat(),
CospBoostingClassifier(baseclf))
# clf = make_pipeline(DownSampler(2),
# HankelCovariances(delays=[2, 4, 8, 12, 16],
# estimator='oas'),
# TangentSpace(metric='logeuclid'),
# LogisticRegression(penalty='l1'))
# # # # clf = CospBoostingClassifier(baseclf)
def test_Cospcovariances():
"""Test fit CospCovariances"""
x = np.random.randn(2, 3, 1000)
cov = CospCovariances()
cov.fit(x)
cov.fit_transform(x)
def test_Cospcovariances():
"""Test fit CospCovariances"""
x = np.random.randn(2,3,1000)
cov = CospCovariances()
cov.fit(x)
cov.fit_transform(x)
