elif bn == 'sample':
data_tmp = copy.deepcopy(data)
for i in range(len(data_tmp)):
for j in range(len(data_tmp[0])):
data_tmp[i][j] = utils.norminx(data_tmp[i][j])
elif bn == 'global':
data_tmp = copy.deepcopy(data)
for i in range(len(data_tmp)):
for j in range(len(data_tmp[0])):
data_tmp[i][j] = utils.normalization(data_tmp[i][j])
elif bn == 'none':
data_tmp = copy.deepcopy(data)
else:
pass
trial_total, category_number, _ = utils.get_number_of_label_n_trial(
dataset_name)
# training settings
batch_size = 32
iteration = 15000
lr = 0.01
momentum = 0.9
log_interval = 10
# meernet = MEERNet(model=models.MEERN())
# store the results
csub = []
csesn = []
# cross-subject, for 3 sessions, 1-14 as sources, 15 as target
temp_c0 = []
temp_c0u = []
temp_foit = []
temp_time_baseline = []
temp_time_updated_based = []
temp_time_FOIT = []
#
# cd_count = 16
cd_count = 0
dataset_name = 'seed4'
if dataset_name == 'seed3':
cd_count = 9
elif dataset_name == 'seed4':
cd_count = 16
number_trial, number_label, labels = utils.get_number_of_label_n_trial(
dataset_name)
sub_data, sub_label = utils.load_by_session(dataset_name) # 3*14*(m*310)
for ses_number in range(3):
print("Session id: ", ses_number)
# cross-subject, 取sub15
cd_data, cd_label, ud_data, ud_label = utils.pick_one_data(dataset_name,
ses_number,
cd_count,
sub_id=14)
sub_data_ses, sub_label_ses = sub_data[ses_number], sub_label[
ses_number] # 14*(m*310)
sub_data_ses, sub_label_ses = shuffle(sub_data_ses,
sub_label_ses,
random_state=0)
def al(dataset_name='seed4', FOIT_type='cross-all', rounds=10, batch_size=50):
data, label = utils.load_source_data(dataset_name=dataset_name,
FOIT_type=FOIT_type)
_, number_label, _ = utils.get_number_of_label_n_trial(dataset_name)
# data, label = utils.load_session_data_label(dataset_name, 0) # as unlabelled data
cd_count = 16 if dataset_name == 'seed4' else 9 if dataset_name == 'seed3' else print(
'Wrong dataset_name')
iteration_number = 3 if FOIT_type == 'cross-subject' else 15
accs = [([]) for i in range(iteration_number)]
times = [([]) for i in range(iteration_number)]
for ite in range(iteration_number):
session_id = -1
sub_id = -1
if FOIT_type == 'cross-subject':
session_id = ite
sub_id = 14
elif FOIT_type == 'cross-session':
session_id = 2
sub_id = ite
elif FOIT_type == 'cross-all':
session_id = 1
sub_id = ite
else:
print('Wrong FOIT type!')
# print("Ite: ", ite)
cd_data, cd_label, ud_data, ud_label = utils.pick_one_data(
dataset_name,
session_id=session_id,
cd_count=cd_count,
sub_id=sub_id)
cd_data, cd_label = shuffle(cd_data, cd_label, random_state=0)
ud_data, ud_label = shuffle(ud_data, ud_label, random_state=0)
cd_data_min, cd_data_max = np.min(cd_data), np.max(cd_data)
cd_data = utils.normalization(cd_data) # labelled data
ud_data = utils.normalization(ud_data) # test data
if FOIT_type == 'cross-all':
data_ite, label_ite = data.copy(), label.copy()
for i in range(len(data)):
data_ite[i], label_ite[i] = shuffle(data_ite[i],
label_ite[i],
random_state=0)
# data_ite, label_ite = shuffle(data, label, random_state=0)
for i in range(len(data)):
data_ite[i] = utils.norm_with_range(data_ite[i], cd_data_min,
cd_data_max)
# data_ite = utils.normalization(data_ite)
elif FOIT_type == 'cross-session':
data_ite, label_ite = data[ite], label[ite]
for i in range(len(data_ite)):
data_ite[i], label_ite[i] = shuffle(data_ite[i],
label_ite[i],
random_state=0)
# data_ite[i] = utils.normalization(data_ite[i])
data_ite[i] = utils.norm_with_range(data_ite[i], cd_data_min,
cd_data_max)
# data_ite = utils.normalization(data_ite)
else:
data_ite, label_ite = data[ite], label[ite]
for i in range(len(data_ite)):
data_ite[i], label_ite[i] = shuffle(data_ite[i],
label_ite[i],
random_state=0)
# data_ite, label_ite = shuffle(data_ite, label_ite, random_state=0)
for i in range(len(data_ite)):
# data_ite[i] = utils.normalization(data_ite[i])
data_ite[i] = utils.norm_with_range(data_ite[i], cd_data_min,
cd_data_max)
# data_ite, label_ite = data.copy(), label.copy()
# for i in range(len(data)):
# data_ite[i], label_ite[i] = shuffle(data_ite[i], label_ite[i], random_state=0)
# for i in range(len(data)):
# data_ite[i] = utils.norm_with_range(data_ite[i], cd_data_min, cd_data_max)
# baseline
clf = svm.LinearSVC(max_iter=30000)
clf = CalibratedClassifierCV(clf, cv=5)
since = time.time()
clf.fit(cd_data, cd_label.squeeze())
time_baseline = time.time() - since
scoreA = utils.test(clf, ud_data, ud_label.squeeze())
accs[ite].append(scoreA)
times[ite].append(time_baseline)
# select the data from the reservoir iteratively
s_data_all, s_label_all = utils.stack_list(data_ite, label_ite)
L_S_data = None
L_S_label = None
for i in range(rounds):
# print("Rounds: ", i)
# print(type(s_data_all))
# print(s_data_all.shape)
s_data_all_predict_proba = clf.predict_proba(s_data_all)
s_label_all_proba = utils.get_one_hot(s_label_all.squeeze(),
number_label)
confidence = np.zeros((s_label_all_proba.shape[0], 1))
for i in range(s_label_all_proba.shape[0]):
confidence[i] = s_label_all_proba[i].dot(
s_data_all_predict_proba[i].T)
# confidence[i] = log_loss(s_label_all_proba[i], s_data_all_predict_proba[i])
indices = np.argsort(confidence,
axis=0) # take the minimum topK indices
topK_indices = indices[:batch_size]
S_data = None
S_label = None
for i in topK_indices:
one_data = s_data_all[i]
one_label = s_label_all[i]
if S_data is not None:
S_data = np.vstack((S_data, one_data))
S_label = np.vstack((S_label, one_label))
else:
S_data = one_data
S_label = one_label
for i in range(len(s_data_all) - 1, -1, -1):
if i in topK_indices:
s_data_all = np.delete(s_data_all, i, axis=0)
s_label_all = np.delete(s_label_all, i, axis=0)
if L_S_data is None:
L_S_data = cd_data.copy()
L_S_label = cd_label.copy()
else:
pass
L_S_data = np.vstack((L_S_data, S_data))
L_S_label = np.vstack((L_S_label, S_label))
L_S_data, L_S_label = shuffle(L_S_data, L_S_label, random_state=0)
clf.fit(L_S_data, L_S_label.squeeze())
time_updated_time = time.time() - since
times[ite].append(time_updated_time)
scoreTMP = utils.test(clf, ud_data, ud_label.squeeze())
accs[ite].append(scoreTMP)
ResultTime = []
ResultAcc = []
ResultStd = []
for i in range(rounds + 1):
tmpTime = []
tmpAcc = []
for j in range(iteration_number):
tmpTime.append(times[j][i])
tmpAcc.append(accs[j][i])
ResultTime.append(np.mean(tmpTime))
ResultAcc.append(np.mean(tmpAcc))
ResultStd.append(np.std(tmpAcc))
print("Time: ", ResultTime)
print("Accs: ", ResultAcc)
print("Stds: ", ResultStd)
def FOIT(dataset_name='seed4',
rho=1,
clf_name='lr',
threshold=0.6,
with_balance=True,
FOIT_type='cross-all'):
# time and accs
c0_acc = []
c0u_acc = []
foit_acc = []
time_c0 = []
time_c0u = []
time_foit = []
cd_count = 16 if dataset_name == 'seed4' else 9 if dataset_name == 'seed3' else print(
'Wrong dataset_name')
iteration_number = 3 if FOIT_type == 'cross-subject' else 15
number_trial, number_label, labels = utils.get_number_of_label_n_trial(
dataset_name)
data, label = utils.load_source_data(dataset_name=dataset_name,
FOIT_type=FOIT_type)
for ite in range(iteration_number):
# The data of 15th sub is taken as the cd and ud data in cross-subject for each session (iteration_number=3)
# The data of 3rd session is taken as the cd and ud data in cross-session for each subject (iteration_number=14)
# For each iteration, the data of one sub from session 2 is taken as the cd and ud data in cross-all situation
# print("Iteration: {}".format(ite))
'''
Parameters
'''
session_id = -1
sub_id = -1
accs_number = -1
if FOIT_type == 'cross-subject':
session_id = ite
sub_id = 14
accs_number = 14
elif FOIT_type == 'cross-session':
session_id = 2
sub_id = ite
accs_number = 2
elif FOIT_type == 'cross-all':
session_id = 1
sub_id = ite
accs_number = 15
else:
print('Wrong FOIT type!')
'''
Data
'''
# data
cd_data, cd_label, ud_data, ud_label = utils.pick_one_data(
dataset_name,
session_id=session_id,
cd_count=cd_count,
sub_id=sub_id)
cd_data, cd_label = shuffle(cd_data, cd_label, random_state=0)
ud_data, ud_label = shuffle(ud_data, ud_label, random_state=0)
cd_data_min, cd_data_max = np.min(cd_data), np.max(cd_data)
cd_data = utils.normalization(cd_data)
ud_data = utils.normalization(ud_data)
if FOIT_type == 'cross-all':
data_ite, label_ite = data.copy(), label.copy()
for i in range(len(data)):
data_ite[i], label_ite[i] = shuffle(data_ite[i],
label_ite[i],
random_state=0)
# data_ite, label_ite = shuffle(data, label, random_state=0)
for i in range(len(data)):
data_ite[i] = utils.norm_with_range(data_ite[i], cd_data_min,
cd_data_max)
# data_ite = utils.normalization(data_ite)
elif FOIT_type == 'cross-session':
data_ite, label_ite = data[ite], label[ite]
for i in range(len(data_ite)):
data_ite[i], label_ite[i] = shuffle(data_ite[i],
label_ite[i],
random_state=0)
# data_ite[i] = utils.normalization(data_ite[i])
data_ite[i] = utils.norm_with_range(data_ite[i], cd_data_min,
cd_data_max)
# data_ite = utils.normalization(data_ite)
else:
data_ite, label_ite = data[ite], label[ite]
for i in range(len(data_ite)):
data_ite[i], label_ite[i] = shuffle(data_ite[i],
label_ite[i],
random_state=0)
# data_ite, label_ite = shuffle(data_ite, label_ite, random_state=0)
for i in range(len(data_ite)):
# data_ite[i] = utils.normalization(data_ite[i])
data_ite[i] = utils.norm_with_range(data_ite[i], cd_data_min,
cd_data_max)
# data_ite = utils.normalization(data_ite)
# print(len(data_ite), len(label_ite[0]), len(label_ite[0][0]))
'''
A)
'''
if clf_name == 'svm':
clf = svm.LinearSVC(max_iter=30000)
# clf = svm.SVC(probability=True, max_iter=10000)
elif clf_name == 'lr':
clf = LogisticRegression(max_iter=30000)
else:
print('Unexcepted clf name, using LR as the baseline now.')
clf = svm.LinearSVC(max_iter=30000)
clf = CalibratedClassifierCV(clf, cv=5)
since = time.time()
clf.fit(cd_data, cd_label.squeeze())
time_baseline = time.time() - since
# print('Baseline training complete in {:.4f}'.format(time_baseline))
scoreA = utils.test(clf, ud_data, ud_label.squeeze())
# print('Baseline score: {}'.format(scoreA))
time_c0.append(time_baseline)
c0_acc.append(scoreA)
'''
B)
'''
accs = []
clf_sources = []
for i in range(accs_number):
if FOIT_type == 'cross-subject':
path = 'models/' + dataset_name + '/csu/sesn' + str(
ite) + '/lr' + str(i) + '.m'
elif FOIT_type == 'cross-session':
path = 'models/' + dataset_name + '/csn/sub' + str(
ite) + '/lr' + str(i) + '.m'
else:
path = 'models/' + dataset_name + '/csun/lr' + str(i) + '.m'
temp_clf = joblib.load(path)
clf_sources.append(temp_clf)
score = utils.test(temp_clf, ud_data, ud_label.squeeze())
accs.append(score)
if FOIT_type == 'cross-session':
pass
else:
accs = utils.normalization(accs)
# print('Accs of classifiers, normalized: {}'.format(accs))
'''
C)
'''
s_data_all, s_label_all = utils.stack_list(data_ite, label_ite)
s_data_all_predict_proba = clf.predict_proba(s_data_all)
s_label_all_proba = utils.get_one_hot(s_label_all.squeeze(),
number_label)
confidence = np.zeros((s_label_all_proba.shape[0], 1))
for i in range(s_label_all_proba.shape[0]):
confidence[i] = s_label_all_proba[i].dot(
s_data_all_predict_proba[i].T)
if with_balance:
## divide into 4 categories
data_ite_divided = []
label_ite_divided = []
conf_divided = []
for i in range(number_label):
data_ite_divided.append([])
label_ite_divided.append([])
conf_divided.append([])
for i in range(len(s_data_all)):
temp_label = s_label_all[i][0]
data_ite_divided[temp_label].append(s_data_all[i])
label_ite_divided[temp_label].append(s_label_all[i])
conf_divided[temp_label].append(confidence[i])
indices = []
for i in range(number_label):
indices.append(np.argsort(conf_divided[i], axis=0)[::-1])
topK_indices = [
indices[i][:int(rho * len(cd_label) / 4)]
for i in range(len(indices))
]
S_data = None
S_label = None
for i in range(len(topK_indices)):
for j in topK_indices[i]:
# temp_conf = conf_divided[i][j[0]]
one_data = data_ite_divided[i][j[0]]
one_label = label_ite_divided[i][j[0]]
if S_data is not None:
S_data = np.vstack((S_data, one_data))
S_label = np.vstack((S_label, one_label))
else:
S_data = one_data
S_label = one_label
elif with_balance is False:
indices = np.argsort(confidence, axis=0)[::-1]
topK_indices = indices[:int(rho * len(cd_label))]
S_data = None
S_label = None
for i in topK_indices:
one_data = s_data_all[i]
one_label = s_label_all[i]
if S_data is not None:
S_data = np.vstack((S_data, one_data))
S_label = np.vstack((S_label, one_label))
else:
S_data = one_data
S_label = one_label
else:
print('Unexcepted value of with balance!')
raise EnvironmentError
'''
D)
'''
L_S_data = cd_data.copy()
L_S_label = cd_label.copy()
L_S_data = np.vstack((L_S_data, S_data))
L_S_label = np.vstack((L_S_label, S_label))
L_S_data, L_S_label = shuffle(L_S_data, L_S_label, random_state=0)
# L_S_data = utils.normalization(L_S_data) # to decide
clf.fit(L_S_data, L_S_label.squeeze())
time_updated_baseline = time.time() - since
# print('Updated baseline training complete in {:.4f}s'.format(time_updated_baseline))
time_c0u.append(time_updated_baseline)
scoreD = utils.test(clf, ud_data, ud_label.squeeze())
# print('Updated model score: {}'.format(scoreD))
c0u_acc.append(scoreD)
'''
E)
'''
weight = (len(accs) + 1) / 2
proba_result_all = clf.predict_proba(ud_data) * weight
if FOIT_type == 'cross-session':
weight_for_clfs = utils.decide_which_clf_to_use(scoreD, accs)
for j in range(len(weight_for_clfs)):
proba_result_all += clf_sources[j].predict_proba(
utils.normalization(ud_data)) * weight_for_clfs[j]
else:
for i in range(len(clf_sources)):
if accs[i] > threshold:
proba_result_all += clf_sources[i].predict_proba(
ud_data) * accs[i]
corrects = np.sum(
np.argmax(proba_result_all, axis=1) == ud_label.squeeze())
time_ensemble = time.time() - since
time_foit.append(time_ensemble)
scoreE = corrects / len(ud_label)
# print('Ensembled model score: {}'.format(scoreE))
foit_acc.append(scoreE)
# print(c0_acc)
# print(c0u_acc)
# print(foit_acc)
print('Mean acc and std of A: {} {}'.format(np.mean(c0_acc),
np.std(c0_acc)))
print('Mean acc and std of D: {} {}'.format(np.mean(c0u_acc),
np.std(c0u_acc)))
print('Mean acc and std of E: {} {}'.format(np.mean(foit_acc),
np.std(foit_acc)))
print("Time cost for training baseline: ", np.mean(time_c0))
print("Time cost for training updated baseline: ", np.mean(time_c0u))
print("Time cost for training FOIT: ", np.mean(time_foit))
