def sim_ddm(input_stream, start_point=0):
ddm = DDM()
change_point = []
detected_warning = []
for i in range(len(input_stream)):
ddm.add_element(input_stream[i])
if ddm.detected_warning_zone():
detected_warning.append(i + start_point)
if ddm.detected_change():
# plt.axvline(i, color='r', linestyle='dashed')
change_point.append(i + start_point)
# print('Change detected in data: ' + str(input_stream[i]) + ' - at index: ' + str(i)+'\n\n')
return detected_warning, change_point
def ddm_test():
ddm = DDM()
true_occur_position = 4443
data_stream = np.load("data/stream_acc.npy")
for i in tqdm(range(data_stream.shape[0])):
# print(data_stream[i])
# print(i)
ddm.add_element(data_stream[i])
if ddm.detected_warning_zone():
print('Warning zone has been detected in data: ' +
str(data_stream[i]) + ' - of index: ' + str(i))
if ddm.detected_change():
print('Change has been detected in data: ' + str(data_stream[i]) +
' - of index: ' + str(i))
if (n_global > detect_end):
if ht_p is not None:
drift_point = detect_end - 2 * grace
print("Accuracy of ht: " + str(np.mean(pred_grace_ht)))
print("Accuracy of ht_p: " + str(np.mean(pred_grace_ht_p)))
if (np.mean(pred_grace_ht_p) > np.mean(pred_grace_ht)):
print("TP detected at: " + str(drift_point))
TP_ddm.append(drift_point)
ht = ht_p
else:
print("FP detected at: " + str(drift_point))
FP_ddm.append(drift_point)
ht_p = None
pred_grace_ht = []
pred_grace_ht_p = []
if ddm.detected_warning_zone():
w_ddm += 1
if ddm.detected_change():
d_ddm += 1
ht_p = HoeffdingTreeClassifier()
grace_end = n_global + grace
detect_end = n_global + 2 * grace
else:
pred_grace_ht.append(y_test == y_predict)
pred_grace_ht_p.append(y_test == ht_p.predict(X_test))
if ht_p is not None:
ht_p.partial_fit(X_test, y_test)
ht.partial_fit(X_test, y_test)
if n_global % 10000 == 0:
# For Travis CI
