def score_ensemble_rot(settings, subject_target, ntop):
dataset = settings['dataset']
paradigm = settings['paradigm']
session = settings['session']
storage = settings['storage']
filepath = '../results/' + dataset + '/TL_intra-subject_scores.pkl'
acc_intra_dict = joblib.load(filepath)
scores = []
subject_sources = []
for subject in settings['subject_list']:
if subject == subject_target:
continue
else:
scores.append(acc_intra_dict[subject])
subject_sources.append(subject)
scores = np.array(scores)
subject_sources = np.array(subject_sources)
idx_sort = scores.argsort()[::-1]
scores = scores[idx_sort]
subject_sources = subject_sources[idx_sort]
subject_sources_ntop = subject_sources[:ntop]
# get the geometric means for each subject (each class and also the center)
filename = '../results/' + dataset + '/subject_means.pkl'
subj_means = joblib.load(filename)
# get the data for the target subject
target_org = GD.get_dataset(dataset, subject_target, session, storage)
if paradigm == 'MI':
# things here are only implemented for MI for now
target_org['covs'] = Covariances(estimator='oas').fit_transform(
target_org['signals'])
target_org['labels'] = target_org['labels']
ncovs = settings['ncovs_list'][0]
nrzt = 10
score_rzt = 0.0
for rzt in range(nrzt):
# split randomly the target dataset
target_org_train, target_org_test = get_target_split_motorimagery(
target_org, ncovs)
covs_train_target = target_org_train['covs']
labs_train_target = target_org_train['labels']
MC_target = mean_riemann(covs_train_target)
M1_target = mean_riemann(
covs_train_target[labs_train_target == 'left_hand'])
M2_target = mean_riemann(
covs_train_target[labs_train_target == 'right_hand'])
M1_target_rct = np.dot(invsqrtm(MC_target),
np.dot(M1_target, invsqrtm(MC_target)))
M2_target_rct = np.dot(invsqrtm(MC_target),
np.dot(M2_target, invsqrtm(MC_target)))
covs_train_target = np.stack([M1_target_rct, M2_target_rct])
labs_train_target = np.array(['left_hand', 'right_hand'])
clf = []
for subj_source in subject_sources_ntop:
MC_source = subj_means[subj_source]['center']
M1_source = subj_means[subj_source]['left_hand']
M2_source = subj_means[subj_source]['right_hand']
M1_source_rct = np.dot(invsqrtm(MC_source),
np.dot(M1_source, invsqrtm(MC_source)))
M2_source_rct = np.dot(invsqrtm(MC_source),
np.dot(M2_source, invsqrtm(MC_source)))
M = [M1_target_rct, M2_target_rct]
Mtilde = [M1_source_rct, M2_source_rct]
R = manifoptim.get_rotation_matrix(M, Mtilde)
M1_source_rot = np.dot(R, np.dot(M1_source_rct, R.T))
M2_source_rot = np.dot(R, np.dot(M2_source_rct, R.T))
covs_train_source = np.stack([M1_source_rot, M2_source_rot])
labs_train_source = np.array(['left_hand', 'right_hand'])
covs_train = np.concatenate([covs_train_source, covs_train_target])
labs_train = np.concatenate([labs_train_source, labs_train_target])
clfi = MDM()
# problems here when using integer instead of floats on the sample_weight
clfi.fit(covs_train,
labs_train,
sample_weight=np.array(
[200.0, 200.0, 2.0 * ncovs, 2.0 * ncovs]))
clf.append(clfi)
covs_test = target_org_test['covs']
labs_test = target_org_test['labels']
ypred = []
for clfi in clf:
yi = clfi.predict(covs_test)
ypred.append(yi)
ypred = np.array(ypred)
majorvoting = []
for j in range(ypred.shape[1]):
ypredj = ypred[:, j]
values_unique, values_count = np.unique(ypredj, return_counts=True)
majorvoting.append(values_unique[np.argmax(values_count)])
majorvoting = np.array(majorvoting)
score_rzt = score_rzt + np.mean(majorvoting == labs_test)
score = score_rzt / nrzt
return score
def score_pooling_rct(settings, subject_target, ntop):
dataset = settings['dataset']
paradigm = settings['paradigm']
session = settings['session']
storage = settings['storage']
filepath = '../results/' + dataset + '/TL_intra-subject_scores.pkl'
acc_intra_dict = joblib.load(filepath)
scores = []
subject_sources = []
for subject in settings['subject_list']:
if subject == subject_target:
continue
else:
scores.append(acc_intra_dict[subject])
subject_sources.append(subject)
scores = np.array(scores)
subject_sources = np.array(subject_sources)
idx_sort = scores.argsort()[::-1]
scores = scores[idx_sort]
subject_sources = subject_sources[idx_sort]
subject_sources_ntop = subject_sources[:ntop]
# get the geometric means for each subject (each class and also the center)
filename = '../results/' + dataset + '/subject_means.pkl'
subj_means = joblib.load(filename)
# get the data for the target subject
target_org = GD.get_dataset(dataset, subject_target, session, storage)
if paradigm == 'MI':
# things here are only implemented for MI for now
target_org['covs'] = Covariances(estimator='oas').fit_transform(
target_org['signals'])
target_org['labels'] = target_org['labels']
ncovs = settings['ncovs_list'][0]
score_rzt = 0.0
nrzt = 10
for rzt in range(nrzt):
# split randomly the target dataset
target_org_train, target_org_test = get_target_split_motorimagery(
target_org, ncovs)
# get the data from the sources and pool it all together
class_mean_1 = []
class_mean_2 = []
for subj_source in subject_sources_ntop:
MC_source = subj_means[subj_source]['center']
M1_source = subj_means[subj_source]['left_hand']
M2_source = subj_means[subj_source]['right_hand']
M1_source_rct = np.dot(invsqrtm(MC_source),
np.dot(M1_source, invsqrtm(MC_source)))
class_mean_1.append(M1_source_rct)
M2_source_rct = np.dot(invsqrtm(MC_source),
np.dot(M2_source, invsqrtm(MC_source)))
class_mean_2.append(M2_source_rct)
class_mean_1_source = np.stack(class_mean_1)
class_mean_2_source = np.stack(class_mean_2)
covs_train_source = np.concatenate(
[class_mean_1_source, class_mean_2_source])
labs_train_source = np.concatenate([
len(class_mean_1_source) * ['left_hand'],
len(class_mean_2_source) * ['right_hand']
])
# re-center data for the target
covs_train_target = target_org['covs']
MC_target = mean_riemann(covs_train_target)
labs_train_target = target_org['labels']
class_mean_1_target = mean_riemann(
covs_train_target[labs_train_target == 'left_hand'])
class_mean_1_target = np.dot(
invsqrtm(MC_target),
np.dot(class_mean_1_target, invsqrtm(MC_target)))
class_mean_2_target = mean_riemann(
covs_train_target[labs_train_target == 'right_hand'])
class_mean_2_target = np.dot(
invsqrtm(MC_target),
np.dot(class_mean_2_target, invsqrtm(MC_target)))
covs_train_target = np.stack(
[class_mean_1_target, class_mean_2_target])
labs_train_target = np.array(['left_hand', 'right_hand'])
covs_train = np.concatenate([covs_train_source, covs_train_target])
labs_train = np.concatenate([labs_train_source, labs_train_target])
covs_test = target_org_test['covs']
labs_test = target_org_test['labels']
# do the classification
clf = MDM()
clf.fit(covs_train, labs_train)
score_rzt = score_rzt + clf.score(covs_test, labs_test)
score = score_rzt / nrzt
return score