def test_epochs_vectorizer():
"""Test methods of EpochsVectorizer
"""
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]
with warnings.catch_warnings(record=True):
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
vector = EpochsVectorizer(epochs.info)
y = epochs.events[:, -1]
X = vector.fit_transform(epochs_data, y)
# Check data dimensions
assert_true(X.shape[0] == epochs_data.shape[0])
assert_true(X.shape[1] == epochs_data.shape[1] * epochs_data.shape[2])
assert_array_equal(vector.fit(epochs_data, y).transform(epochs_data), X)
# Check if data is preserved
n_times = epochs_data.shape[2]
assert_array_equal(epochs_data[0, 0, 0:n_times], X[0, 0:n_times])
# Check inverse transform
Xi = vector.inverse_transform(X, y)
assert_true(Xi.shape[0] == epochs_data.shape[0])
assert_true(Xi.shape[1] == epochs_data.shape[1])
assert_array_equal(epochs_data[0, 0, 0:n_times], Xi[0, 0, 0:n_times])
# Test init exception
assert_raises(ValueError, vector.fit, epochs, y)
assert_raises(ValueError, vector.transform, epochs, y)
def test_epochs_vectorizer():
"""Test methods of EpochsVectorizer
"""
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]
with warnings.catch_warnings(record=True):
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
vector = EpochsVectorizer(epochs.info)
y = epochs.events[:, -1]
X = vector.fit_transform(epochs_data, y)
# Check data dimensions
assert_true(X.shape[0] == epochs_data.shape[0])
assert_true(X.shape[1] == epochs_data.shape[1] * epochs_data.shape[2])
assert_array_equal(vector.fit(epochs_data, y).transform(epochs_data), X)
# Check if data is preserved
n_times = epochs_data.shape[2]
assert_array_equal(epochs_data[0, 0, 0:n_times], X[0, 0:n_times])
# Check inverse transform
Xi = vector.inverse_transform(X, y)
assert_true(Xi.shape[0] == epochs_data.shape[0])
assert_true(Xi.shape[1] == epochs_data.shape[1])
assert_array_equal(epochs_data[0, 0, 0:n_times], Xi[0, 0, 0:n_times])
# Test init exception
assert_raises(ValueError, vector.fit, epochs, y)
assert_raises(ValueError, vector.transform, epochs, y)
def _epochs_to_rawarray(epochs):
"""Concatenate the epochs in a raw object."""
from mne.io import RawArray
from mne.decoding import EpochsVectorizer
# bring channels to the first dimension
data = epochs.get_data().transpose((1, 0, 2))
raw_data = EpochsVectorizer().transform(data)
raw_array = RawArray(raw_data, epochs.info.copy())
return raw_array
exclude='bads')
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
proj=False,
picks=picks,
baseline=None,
preload=True,
add_eeg_ref=False,
verbose=False)
# Create classification pipeline
clf = make_pipeline(Xdawn(n_components=3), EpochsVectorizer(), MinMaxScaler(),
LogisticRegression(penalty='l1'))
# Get the labels
labels = epochs.events[:, -1]
# Cross validator
cv = StratifiedKFold(y=labels, n_folds=10, shuffle=True, random_state=42)
# Do cross-validation
preds = np.empty(len(labels))
for train, test in cv:
clf.fit(epochs[train], labels[train])
preds[test] = clf.predict(epochs[test])
# Classification report
rt_client.send_data(rt_epochs, picks, tmin=0, tmax=90, buffer_size=1000)
# Decoding in sensor space using a linear SVM
n_times = len(rt_epochs.times)
from sklearn import preprocessing # noqa
from sklearn.svm import SVC # noqa
from sklearn.pipeline import Pipeline # noqa
from sklearn.cross_validation import cross_val_score, ShuffleSplit # noqa
from mne.decoding import EpochsVectorizer, FilterEstimator # noqa
scores_x, scores, std_scores = [], [], []
filt = FilterEstimator(rt_epochs.info, 1, 40)
scaler = preprocessing.StandardScaler()
vectorizer = EpochsVectorizer()
clf = SVC(C=1, kernel='linear')
concat_classifier = Pipeline([('filter', filt), ('vector', vectorizer),
('scaler', scaler), ('svm', clf)])
data_picks = mne.pick_types(rt_epochs.info,
meg='grad',
eeg=False,
eog=True,
stim=False,
exclude=raw.info['bads'])
for ev_num, ev in enumerate(rt_epochs.iter_evoked()):
print("Just got epoch %d" % (ev_num + 1))
# Setup for reading the raw data
raw = io.Raw(raw_fname, preload=True)
raw.filter(1, 20, method='iir')
events = read_events(event_fname)
picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
exclude='bads')
epochs = Epochs(raw, events, event_id, tmin, tmax, proj=False,
picks=picks, baseline=None, preload=True,
add_eeg_ref=False, verbose=False)
# Create classification pipeline
clf = make_pipeline(Xdawn(n_components=3),
EpochsVectorizer(),
MinMaxScaler(),
LogisticRegression(penalty='l1'))
# Get the labels
labels = epochs.events[:, -1]
# Cross validator
cv = StratifiedKFold(y=labels, n_folds=10, shuffle=True, random_state=42)
# Do cross-validation
preds = np.empty(len(labels))
for train, test in cv:
clf.fit(epochs[train], labels[train])
preds[test] = clf.predict(epochs[test])