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, 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[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True, add_eeg_ref=False)
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
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.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 standard_scaling(data, scalings="mean", log=False):
if log:
data = np.log(data + np.finfo(np.float32).eps)
if scalings in ["mean", "median"]:
scaler = Scaler(scalings=scalings)
data = scaler.fit_transform(data)
else:
raise ValueError("scalings should be mean or median")
return data
# downsample if necessary
if epo.info['sfreq'] != param['testresampfreq']:
epo = epo.resample(param['testresampfreq'])
# Drop bad trials and get indices
goodtrials = np.where(df['badtrial'] == 0)[0]
# Get external data for this part
df = df.iloc[goodtrials]
epo = epo[goodtrials]
# Standardize data before regression
scale = Scaler(scalings='mean') # Says mean but is z score, see docs
epo_z = mne.EpochsArray(scale.fit_transform(epo.get_data()), epo.info)
betasnp = []
for idx, regvar in enumerate(regvars):
# Standardize data
df[regvar + '_z'] = scipy.stats.zscore(df[regvar])
epo.metadata = df.assign(Intercept=1) # Add an intercept for later
# Perform regression
names = ["Intercept"] + [regvar + '_z']
res = mne.stats.linear_regression(epo_z,
epo.metadata[names],
names=names)
# Collect betas
def decoding_withKfold(X, Y_speech, Y_lips, n_fold, train_index, test_index,
examples, feature):
predictions_speech = np.zeros((Y_speech.shape))
speech = np.zeros((Y_speech.shape))
predictions_lips = np.zeros((Y_lips.shape))
lips = np.zeros((Y_lips.shape))
scores_speech = np.zeros((n_fold, ))
for k in range(0, n_fold):
eegScaler = MultiChannelScaler(scalings='mean')
speechScaler = MultiChannelScaler(scalings='mean')
lipsScaler = MultiChannelScaler(scalings='mean')
speechModel = LReg()
lipsModel = LReg()
#####COPY X AND Y VARIABLES
X_standard = np.zeros((X.shape))
Y_lips_standard = np.zeros((Y_lips.shape))
Y_speech_standard = np.zeros((Y_speech.shape))
# standardazing data
X_standard[train_index[k], :, :] = eegScaler.fit_transform(
X[train_index[k], :, :])
X_standard[test_index[k], :, :] = eegScaler.transform(
X[test_index[k], :, :])
Y_lips_standard[train_index[k], :] = lipsScaler.fit_transform(
Y_lips[train_index[k], :]).squeeze()
Y_lips_standard[test_index[k], :] = lipsScaler.transform(
Y_lips[test_index[k], :]).squeeze()
Y_speech_standard[train_index[k], :] = speechScaler.fit_transform(
Y_speech[train_index[k], :]).squeeze()
Y_speech_standard[test_index[k], :] = speechScaler.transform(
Y_speech[test_index[k], :]).squeeze()
X_TRAIN = X_standard[train_index[k], :, :]
X_TEST = X_standard[test_index[k], :, :]
Y_envelope_sp_TRAIN = Y_speech_standard[train_index[k], :]
Y_envelope_sp_TEST = Y_speech_standard[test_index[k], :]
Y_lips_ap_TRAIN = Y_lips_standard[train_index[k], :]
Y_lips_ap_TEST = Y_lips_standard[test_index[k], :]
#X_train and test now are (#trials,#channnels,#timepoints)
n_trial = X_TRAIN.shape[0]
n_trial_test = X_TEST.shape[0]
n_ch = X_TRAIN.shape[1]
trial_length = X_TRAIN.shape[2]
if examples == 'are_Trials':
X_TRAIN_tmp = np.zeros((X_TRAIN.shape[0], n_ch * trial_length))
X_TEST_tmp = np.zeros((X_TEST.shape[0], n_ch * trial_length))
for i in range(0, n_ch):
X_TRAIN_tmp[:, i * trial_length:(i + 1) *
trial_length] = X_TRAIN[:, i, :]
X_TEST_tmp[:, i * trial_length:(i + 1) *
trial_length] = X_TEST[:, i, :]
X_TRAIN = X_TRAIN_tmp
X_TEST = X_TEST_tmp
elif examples == 'are_Time':
X_TRAIN_tmp = np.zeros((n_trial * trial_length, n_ch))
X_TEST_tmp = np.zeros((n_trial_test * trial_length, n_ch))
Y_envelope_sp_TRAIN_tmp = np.zeros((n_trial * trial_length, ))
Y_envelope_sp_TEST_tmp = np.zeros((n_trial_test * trial_length, ))
Y_lips_ap_TRAIN_tmp = np.zeros((n_trial * trial_length, ))
Y_lips_ap_TEST_tmp = np.zeros((n_trial_test * trial_length, ))
for i in range(0, n_trial):
X_TRAIN_tmp[i * trial_length:(i + 1) *
trial_length, :] = X_TRAIN[i, :, :].T
Y_envelope_sp_TRAIN_tmp[i * trial_length:(i + 1) *
trial_length] = Y_envelope_sp_TRAIN[
i, :]
Y_lips_ap_TRAIN_tmp[i * trial_length:(i + 1) *
trial_length] = Y_lips_ap_TRAIN[i, :]
if i < X_TEST.shape[0]: #test trials are less than train
X_TEST_tmp[i * trial_length:(i + 1) *
trial_length, :] = X_TEST[i, :, :].T
Y_envelope_sp_TEST_tmp[i * trial_length:(i + 1) *
trial_length] = Y_envelope_sp_TEST[
i, :]
Y_lips_ap_TEST_tmp[i * trial_length:(i + 1) *
trial_length] = Y_lips_ap_TEST[i, :]
X_TRAIN = X_TRAIN_tmp
X_TEST = X_TEST_tmp
Y_envelope_sp_TRAIN = Y_envelope_sp_TRAIN_tmp
Y_envelope_sp_TEST = Y_envelope_sp_TEST_tmp
Y_lips_ap_TRAIN = Y_lips_ap_TRAIN_tmp
Y_lips_ap_TEST = Y_lips_ap_TEST_tmp
if feature == 'pca':
[pca, n_comp] = pca_decomposition(X_TRAIN)
X_TRAIN = pca.transform(X_TRAIN)[:, :n_comp]
X_TEST = pca.transform(X_TEST)[:, :n_comp]
if feature == 'Kpca':
[pca, n_comp] = kernel_pca_decomposition(X_TRAIN)
X_TRAIN = pca.transform(X_TRAIN)[:, :n_comp]
X_TEST = pca.transform(X_TEST)[:, :n_comp]
if feature == 'ica':
ICA_decomposition
[ica, selected_comps] = ICA_decomposition(X_TRAIN)
X_TRAIN = ica.transform(X_TRAIN)[:,
selected_comps.astype('int')]
X_TEST = ica.transform(X_TEST)[:, selected_comps.astype('int')]
if feature == 'derivative1':
de1 = np.diff(X_TRAIN, axis=0) / 0.01
de1 = np.concatenate((np.zeros((1, de1.shape[1])), de1),
axis=0)
for i in range(0, de1.shape[0], trial_length):
de1[i, :] = np.zeros((1, de1.shape[1]))
X_TRAIN = np.concatenate((X_TRAIN, de1), 1)
de1 = np.diff(X_TEST, axis=0) / 0.01
de1 = np.concatenate((np.zeros((1, de1.shape[1])), de1),
axis=0)
for i in range(0, de1.shape[0], trial_length):
de1[i, :] = np.zeros((1, de1.shape[1]))
X_TEST = np.concatenate((X_TEST, de1), 1)
if feature == 'derivative2':
de1 = np.diff(X_TRAIN, axis=0) / 0.01
de1 = np.concatenate((np.zeros((1, de1.shape[1])), de1),
axis=0)
for i in range(0, de1.shape[0], trial_length):
de1[i, :] = np.zeros((1, de1.shape[1]))
de2 = np.diff(de1, axis=0)
de2 = np.concatenate((np.zeros((1, de2.shape[1])), de2),
axis=0)
for i in range(0, de2.shape[0], trial_length):
de2[i, :] = np.zeros((1, de2.shape[1]))
de2[i + 1, :] = np.zeros((1, de2.shape[1]))
X_TRAIN = np.concatenate((np.concatenate(
(X_TRAIN, de1), 1), de2), 1)
de1 = np.diff(X_TEST, axis=0) / 0.01
de1 = np.concatenate((np.zeros((1, de1.shape[1])), de1),
axis=0)
for i in range(0, de1.shape[0], trial_length):
de1[i, :] = np.zeros((1, de1.shape[1]))
de2 = np.diff(de1, axis=0)
de2 = np.concatenate((np.zeros((1, de2.shape[1])), de2),
axis=0)
for i in range(0, de2.shape[0], trial_length):
de2[i, :] = np.zeros((1, de2.shape[1]))
de2[i + 1, :] = np.zeros((1, de2.shape[1]))
X_TEST = np.concatenate((np.concatenate(
(X_TEST, de1), 1), de2), 1)
if feature == 'polynomial':
X_TRAIN = np.concatenate((X_TRAIN, np.power(X_TRAIN, 2)), 1)
X_TEST = np.concatenate((X_TEST, np.power(X_TEST, 2)), 1)
# training models and predict
speechModel.fit(X_TRAIN, Y_envelope_sp_TRAIN)
lipsModel.fit(X_TRAIN, Y_lips_ap_TRAIN)
reconstructed_speech = speechModel.predict(X_TEST)
reconstructed_lips = lipsModel.predict(X_TEST)
if examples == 'are_Time':
reconstructed_speech_tmp = np.zeros((n_trial_test, trial_length))
reconstructed_lips_tmp = np.zeros((n_trial_test, trial_length))
Y_envelope_sp_TEST_tmp = np.zeros((n_trial_test, trial_length))
Y_lips_ap_TEST_tmp = np.zeros((n_trial_test, trial_length))
t = 0
for i in range(0, len(reconstructed_speech), trial_length):
reconstructed_speech_tmp[
t, :] = reconstructed_speech[i:i + trial_length]
reconstructed_lips_tmp[t, :] = reconstructed_lips[i:i +
trial_length]
Y_envelope_sp_TEST_tmp[t, :] = Y_envelope_sp_TEST[i:i +
trial_length]
Y_lips_ap_TEST_tmp[t, :] = Y_lips_ap_TEST[i:i + trial_length]
t += 1
reconstructed_speech = reconstructed_speech_tmp
reconstructed_lips = reconstructed_lips_tmp
Y_envelope_sp_TEST = Y_envelope_sp_TEST_tmp
Y_lips_ap_TEST = Y_lips_ap_TEST_tmp
predictions_speech[test_index[k], :] = reconstructed_speech
speech[test_index[k], :] = Y_envelope_sp_TEST
predictions_lips[test_index[k], :] = reconstructed_lips
lips[test_index[k], :] = Y_lips_ap_TEST
# computing scores
speech_score = evaluate(speech.T, predictions_speech.T, 'corrcoeff')
lips_score = evaluate(lips.T, predictions_lips.T, 'corrcoeff')
return speech_score, lips_score, predictions_speech, predictions_lips, speech, lips
def decoding(band,regularization,tmin,tmax,n_fold,subject_name, savepath):
data_path = "./ProcessedData/Final_"
eeg="_processed-epo.fif"
features="_Features-epo.fif"
sfreq=100
n_delays = int((tmax - tmin) * sfreq) + 1
T= [51, 61, 71, 81, 91, 101, 111, 121, 131, 141, 151]
results_speech= np.zeros((len(regularization),len(T)))# each raw is the results' vector for one regularization parameter
results_lips= np.zeros((len(regularization),len(T)))# each raw is the results' vector for one regularization parameter
results_speech_all_sub={}
results_lips_all_sub={}
predictions_lips_all_sub={}
predictions_speech_all_sub={}
for s in subject_name:
print('subject '+str(s))
X_orig = use_FreqBand(mne.read_epochs(data_path+s+eeg),band)
Features_orig = use_FreqBand(mne.read_epochs(data_path + s + features),band)
if band=='original':
X_orig=X_orig.get_data() # 3d array (N_trial, N_channel, N_time)
Y_envelope_sp_orig=Features_orig.get_data()[:,0,:] # 2d array (N_trial, N_time)
Y_lips_ap_orig=Features_orig.get_data()[:,2,:] # 2d array (N_trial, N_time)
else:
X_orig= np.mean(X_orig.data,2) # 3d array (N_trial, N_channel, N_time) #averaging power across frequencies
Y_envelope_sp_orig=np.mean(Features_orig.data[:,0,:,:],1)
Y_lips_ap_orig=np.mean(Features_orig.data[:,2,:,:],1)
time = mne.read_epochs(data_path + s + features).times # 1d array (N_time)
channels = mne.read_epochs(data_path + s + eeg).ch_names
predictions_speech = np.zeros((Y_envelope_sp_orig.shape[0], 200, len(T),len(regularization)))
predictions_lips = np.zeros((Y_lips_ap_orig.shape[0],200,len(T),len(regularization)))
train_index, test_index = k_fold(Y_envelope_sp_orig,n_fold) # define index for train and test for each of the k folds
#data standardizers
eegScaler= Scaler(scalings='mean')
speechScaler= Scaler(scalings='mean')
lipsScaler = Scaler(scalings='mean')
scores_speech = np.zeros((n_fold,))
scores_lips = np.zeros((n_fold,))
coefs_speech = np.zeros((n_fold, X_orig.shape[1], n_delays))
patterns_speech = coefs_speech.copy()
coefs_lips = np.zeros((n_fold, X_orig.shape[1], n_delays))
patterns_lips = coefs_lips.copy()
for i, r in enumerate(regularization):
rf_speech = RField(tmin, tmax, sfreq, feature_names=channels, scoring='r2', patterns=True, estimator=r)
rf_lips = RField(tmin, tmax, sfreq, feature_names=channels, scoring='r2', patterns=True, estimator=r)
print('reg parameter #'+str(i))
for j, t_start in enumerate(T): ##estracting the temporal interval of interest
t_end= t_start+200
X = X_orig[:,:,t_start:t_end] #only the eeg window is shifting
Y_envelope_sp = Y_envelope_sp_orig[:,101:301]
Y_lips_ap = Y_lips_ap_orig[:,101:301]
for k in range(0,n_fold):
#####COPY X AND Y VARIABLES
X_standard=np.zeros((X.shape))
Y_lips_ap_standard=np.zeros((Y_lips_ap.shape))
Y_envelope_sp_standard = np.zeros((Y_envelope_sp.shape))
#standardazing data
X_standard[train_index[k], :, :] = eegScaler.fit_transform(X[train_index[k], :, :])
X_standard[test_index[k], :, :] = eegScaler.transform(X[test_index[k], :, :])
Y_lips_ap_standard[train_index[k], :] = lipsScaler.fit_transform(Y_lips_ap[train_index[k], :])[:,:,0]
Y_lips_ap_standard[test_index[k], :] = lipsScaler.transform(Y_lips_ap[test_index[k], :])[:,:,0]
Y_envelope_sp_standard[train_index[k], :] = speechScaler.fit_transform(Y_envelope_sp[train_index[k], :])[:,:,0]
Y_envelope_sp_standard[test_index[k], :] = speechScaler.transform(Y_envelope_sp[test_index[k], :])[:,:,0]
#shaping data as desired by the decoding model (receptive field function)
X_standard = np.rollaxis(X_standard, 2, 0)
Y_envelope_sp_standard = np.rollaxis(Y_envelope_sp_standard, 1, 0)
Y_lips_ap_standard = np.rollaxis(Y_lips_ap_standard, 1, 0)
X_TRAIN= X_standard[:,train_index[k],:]
X_TEST= X_standard[:,test_index[k],:]
Y_envelope_sp_TRAIN = Y_envelope_sp_standard[:,train_index[k]]
Y_envelope_sp_TEST = Y_envelope_sp_standard[:,test_index[k]]
Y_lips_ap_TRAIN = Y_lips_ap_standard[:,train_index[k]]
Y_lips_ap_TEST = Y_lips_ap_standard[:,test_index[k]]
#training models and predict
rf_speech.fit(X_TRAIN,Y_envelope_sp_TRAIN)
rf_lips.fit(X_TRAIN,Y_lips_ap_TRAIN)
reconstructed_speech = rf_speech.predict(X_TEST)
reconstructed_lips = rf_lips.predict(X_TEST)
predictions_speech[test_index[k],:,j,i]=reconstructed_speech.T
predictions_lips[test_index[k],:,j,i]=reconstructed_lips.T
#computing scores
tmp_score_speech=0
tmp_score_lips = 0
for n, rec in enumerate(reconstructed_speech[:,:,0].T):
tmp_score_speech = tmp_score_speech + mean_squared_error(Y_envelope_sp_TEST[:,n]/max(abs(Y_envelope_sp_TEST[:,n])), rec/max(abs(rec)))
scores_speech[k]= tmp_score_speech/(n+1)
for n, rec in enumerate(reconstructed_lips[:,:,0].T):
tmp_score_lips = tmp_score_lips + mean_squared_error(Y_lips_ap_TEST[:, n]/max(abs(Y_lips_ap_TEST[:, n])), rec/max(abs(rec)))
scores_lips[k] = tmp_score_lips / (n+1)
# scores_speech[k] = rf_speech.score(X_TEST,Y_envelope_sp_TEST)[0]
# scores_lips[k] = rf_speech.score(X_TEST,Y_lips_ap_TEST)[0]
##coef_ is shape (n_outputs, n_features, n_delays).
# coefs_speech[k] = rf_speech.coef_[0, :, :]
# patterns_speech[k] = rf_speech.patterns_[0, :, :]
# coefs_lips[k] = rf_lips.coef_[0, :, :]
# patterns_lips[k] = rf_lips.patterns_[0, :, :]
# mean_coefs_lips = coefs_lips.mean(axis=0)
# mean_patterns_lips = patterns_lips.mean(axis=0)
mean_scores_lips = scores_lips.mean(axis=0)
# mean_coefs_speech = coefs_speech.mean(axis=0)
# mean_patterns_speech = patterns_speech.mean(axis=0)
mean_scores_speech = scores_speech.mean(axis=0)
#saving results for the i-th reg parameter and j-th time lag
results_speech[i, j] = mean_scores_speech
results_lips[i, j] = mean_scores_lips
results_speech_all_sub[s]=results_speech.copy()
results_lips_all_sub[s]=results_lips.copy()
predictions_speech_all_sub[s]=predictions_speech.copy()
predictions_lips_all_sub[s]=predictions_lips.copy()
np.save(savepath+'/results_speech_all_sub',results_speech_all_sub)
np.save(savepath+'/results_lips_all_sub',results_lips_all_sub)
np.save(savepath+'/predictions_speech_all_sub',predictions_speech_all_sub)
np.save(savepath+'/predictions_lips_all_sub',predictions_lips_all_sub)
tmp_results_speech = []
tmp_results_lips = []
for N, s in enumerate(subject_name):
if N ==0:
tmp_results_speech= np.asarray(results_speech_all_sub[s])
tmp_results_lips= np.asarray(results_lips_all_sub[s])
tmp_results_speech=np.dstack((tmp_results_speech, np.asarray(results_speech_all_sub[s])))
tmp_results_lips=np.dstack((tmp_results_lips,np.asarray(results_lips_all_sub[s])))
# computing grand average and standard deviation for each time lag
GAVG_sp = np.reshape(np.mean(tmp_results_speech,2),(len(regularization),11))
GAVG_lip = np.reshape(np.mean(tmp_results_lips,2),(len(regularization),11))
GAVG_sp_std = np.reshape(np.std(tmp_results_speech,2),(len(regularization),11))
GAVG_lip_std = np.reshape(np.std(tmp_results_lips,2),(len(regularization),11))
np.save(savepath+'/GAVG_sp',GAVG_sp)
np.save(savepath+'/GAVG_lip',GAVG_lip)
np.save(savepath+'/GAVG_sp_std',GAVG_sp_std)
np.save(savepath+'/GAVG_lip_std',GAVG_lip_std)
####PLOTTING RESULTS#####
T = np.reshape(T, (1, len(T)))
pp.figure(0)
for n, r in enumerate(regularization):
pp.errorbar((T[0,:] - 100) * 10, GAVG_sp[n,:], yerr=GAVG_sp_std[n,:])
pp.legend(regularization)
pp.title('speech MSE')
sfig=savepath+'/GAVG_specch.png'
pp.savefig(fname=sfig)
pp.figure(1)
for n, r in enumerate(regularization):
pp.errorbar((T[0, :] - 100) * 10, GAVG_lip[n, :], yerr=GAVG_lip_std[n, :])
pp.legend(regularization)
pp.title('lips MSE')
sfig = savepath +'/GAVG_lips.png'
pp.savefig(fname=sfig)
#pp.show()
print('bla')
def decoding_withKfold(X,Y_speech,Y_lips,n_fold,train_index,test_index,polynomialReg):
predictions_speech= np.zeros((Y_speech.shape))
speech = np.zeros((Y_speech.shape))
predictions_lips= np.zeros((Y_lips.shape))
lips = np.zeros((Y_lips.shape))
scores_speech=np.zeros((n_fold,))
for k in range(0, n_fold):
eegScaler = Scaler()
speechScaler = Scaler()
lipsScaler = Scaler()
speechModel = LReg()
lipsModel = LReg()
#####COPY X AND Y VARIABLES
X_standard = np.zeros((X.shape))
Y_lips_standard = np.zeros((Y_lips.shape))
Y_speech_standard = np.zeros((Y_speech.shape))
# standardazing data
X_standard[train_index[k], :] = eegScaler.fit_transform(X[train_index[k], :])
X_standard[test_index[k], :] = eegScaler.transform(X[test_index[k], :])
Y_lips_standard[train_index[k], :] = lipsScaler.fit_transform(Y_lips[train_index[k], :])
Y_lips_standard[test_index[k], :] = lipsScaler.transform(Y_lips[test_index[k], :])
Y_speech_standard[train_index[k], :] = speechScaler.fit_transform(Y_speech[train_index[k], :])
Y_speech_standard[test_index[k], :] = speechScaler.transform(Y_speech[test_index[k], :])
X_TRAIN = X_standard[ train_index[k], :]
X_TEST = X_standard[ test_index[k], :]
Y_envelope_sp_TRAIN = Y_speech_standard[train_index[k], :]
Y_envelope_sp_TEST = Y_speech_standard[test_index[k], :]
Y_lips_ap_TRAIN = Y_lips_standard[train_index[k], :]
Y_lips_ap_TEST = Y_lips_standard[test_index[k], :]
if polynomialReg == True:
X_TRAIN= np.concatenate((X_TRAIN,np.power(X_TRAIN,2)),1)
X_TEST = np.concatenate((X_TEST, np.power(X_TEST, 2)), 1)
# training models and predict
speechModel.fit(X_TRAIN, Y_envelope_sp_TRAIN)
lipsModel.fit(X_TRAIN, Y_lips_ap_TRAIN)
reconstructed_speech = speechModel.predict(X_TEST)
reconstructed_lips = lipsModel.predict(X_TEST)
predictions_speech[test_index[k], :] = reconstructed_speech
speech[test_index[k], :] = Y_envelope_sp_TEST
predictions_lips[test_index[k], :] = reconstructed_lips
lips[test_index[k], :] = Y_lips_ap_TEST
# computing scores
speech_score = evaluate(speech.T, predictions_speech.T, 'corrcoeff')
lips_score = evaluate(lips.T, predictions_lips.T, 'corrcoeff')
return speech_score, lips_score, predictions_speech, predictions_lips, speech, lips
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)
