n_batch = int((T - T % batch_size) / batch_size)
batch_Y = np.split(Y[:T - T % batch_size], n_batch)
batch_X = np.split(X[:T - T % batch_size], n_batch)
initial_batches = 50
label_lag = 3
### DDM in batch
if args.model == 'ddm':
train_X = X[:initial_batches * batch_size]
train_Y = Y[:initial_batches * batch_size]
first_undrift_index = initial_batches + 1
clf2 = classification_method(n_estimators=20)
clf2.fit(train_X, train_Y)
dd = DDM()
warning_index = []
drift_list = []
prequential_acc = []
retraining_time = 0
total_retraining_samples = 0
total_added_samples = 0
ret_ind = []
for i in range(initial_batches + label_lag + 50, n_batch):
prequential_acc.append(clf2.score(batch_X[i], batch_Y[i]))
if dd.set_input(1 - clf2.score(batch_X[i - 3], batch_Y[i - 3])):
start_time = time.time()
print('CHANGE DETECTED at ' + str(i))
for i in range(50):
data_s, target_s = next(dl_source)
train_xs.append(data_s)
train_ys.append(target_s)
batch_size = 64
T = len(source_dataset)
n_batch = int((T - T % batch_size) / batch_size)
initial_batches = 50
label_lag = 3
train_clf(model_f, model_c, train_xs, train_ys, train_xt, train_yt,
drift_num, optimizer_f, optimizer_c)
first_undrift_index = initial_batches + 1
dd = DDM()
warning_index = []
drift_list = []
prequential_acc = []
retraining_time = 0
total_retraining_samples = 0
total_added_samples = 0
ret_ind = []
previous_xs, previous_ys = [], []
previous_xt, previous_yt = [], []
no_drift_count = 0
if True:
if True:
drift_num = 10
_, target_dataset = get_dataset(task, drift_num)
for i in range(train_batch_num):
data_s, target_s = image_batches.pop(0)
train_xs.append(data_s)
train_ys.append(target_s)
train_xtogether.append(data_s)
train_ytogether.append(target_s)
previous_xtogether.append(data_s)
previous_ytogether.append(target_s)
train_clf(model_f, model_c, train_xs, train_ys)
previous_xs, previous_ys = [], []
q1_list,q2_list,q3_list,qAE_list,qspn_list,qFS_list = [],[],[],[],[],[]
dd = DDM(3,2)
warning_index = []
dd_2 = DDM(3,2)
warning_index_2 = []
dd_3 = DDM(3,2)
warning_index_3 = []
dd_AE = DDM(3,2)
warning_index_AE = []
dd_spn = DDM(3,2)
warning_index_spn = []
dd_FS = DDM(3,2)
warning_index_FS = []
q1_drift, q2_drift, q3_drift, qAE_drift,qspn_drift, qFS_drift = False, False, False, False, False, False
first_training_index = sys.maxsize
drift_1, drift_2, drift_3, drift_AE, drift_spn, drift_FS = [],[],[],[],[],[]