def test_time_generalization():
"""Test time generalization decoding
"""
raw = io.Raw(raw_fname, preload=False)
events = read_events(event_name)
picks = pick_types(raw.info,
meg='mag',
stim=False,
ecg=False,
eog=False,
exclude='bads')
picks = picks[1:13:3]
decim = 30
with warnings.catch_warnings(record=True):
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True,
decim=decim)
epochs_list = [epochs[k] for k in event_id.keys()]
scores = time_generalization(epochs_list, cv=2, random_state=42)
n_times = len(epochs.times)
assert_true(scores.shape == (n_times, n_times))
assert_true(scores.max() <= 1.)
assert_true(scores.min() >= 0.)
def test_time_generalization():
"""Test time generalization decoding
"""
raw = io.Raw(raw_fname, preload=False)
events = read_events(event_name)
picks = pick_types(raw.info, meg='mag', stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
decim = 30
with warnings.catch_warnings(record=True):
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True, decim=decim)
epochs_list = [epochs[k] for k in event_id.keys()]
scores = time_generalization(epochs_list, cv=2, random_state=42)
n_times = len(epochs.times)
assert_true(scores.shape == (n_times, n_times))
assert_true(scores.max() <= 1.)
assert_true(scores.min() >= 0.)
decim = 4 # decimate to make the example faster to run
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks, baseline=None, preload=True,
reject=dict(mag=1.5e-12), decim=decim)
epochs_list = [epochs[k] for k in event_id]
mne.epochs.equalize_epoch_counts(epochs_list)
###############################################################################
# Run decoding
# Compute Area Under the Curver (AUC) Receiver Operator Curve (ROC) score
# of time generalization. A perfect decoding would lead to AUCs of 1.
# Chance level is at 0.5.
# The default classifier is a linear SVM (C=1) after feature scaling.
scores = time_generalization(epochs_list, clf=None, cv=5, scoring="roc_auc",
shuffle=True, n_jobs=2)
###############################################################################
# Now visualize
times = 1e3 * epochs.times # convert times to ms
plt.figure()
plt.imshow(scores, interpolation='nearest', origin='lower',
extent=[times[0], times[-1], times[0], times[-1]],
vmin=0., vmax=1.)
plt.xlabel('Times Test (ms)')
plt.ylabel('Times Train (ms)')
plt.title('Time generalization (%s vs. %s)' % tuple(event_id.keys()))
plt.axvline(0, color='k')
plt.axhline(0, color='k')
plt.colorbar()
decim = 4 # decimate to make the example faster to run
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks, baseline=None, preload=True,
reject=dict(mag=1.5e-12), decim=decim)
epochs_list = [epochs[k] for k in event_id]
mne.epochs.equalize_epoch_counts(epochs_list)
###############################################################################
# Run decoding
# Compute Area Under the Curver (AUC) Receiver Operator Curve (ROC) score
# of time generalization. A perfect decoding would lead to AUCs of 1.
# Chance level is at 0.5.
# The default classifier is a linear SVM (C=1) after feature scaling.
scores = time_generalization(epochs_list, clf=None, cv=5, scoring="roc_auc",
shuffle=True, n_jobs=2)
###############################################################################
# Now visualize
times = 1e3 * epochs.times # convert times to ms
plt.figure()
plt.imshow(scores, interpolation='nearest', origin='lower',
extent=[times[0], times[-1], times[0], times[-1]],
vmin=0., vmax=1.)
plt.xlabel('Times Test (ms)')
plt.ylabel('Times Train (ms)')
plt.title('Time generalization (%s vs. %s)' % tuple(event_id.keys()))
plt.axvline(0, color='k')
plt.axhline(0, color='k')
plt.colorbar()
