accuracy_1 = true_predictions_1 / total
accuracy_2 = true_predictions_2 / total
accuracy_3_mv = true_predictions_3_mv / total
accuracy_3_av = true_predictions_3_av / total
accuracy_3_goowe = true_predictions_3_goowe / total
print('\tSTREAM 1 :: Data instance: {} - Accuracy: {}'.format(int(total), round(accuracy_1*100.0, 3)))
print('\tSTREAM 2 :: Data instance: {} - Accuracy: {}'.format(int(total), round(accuracy_2*100.0, 3)))
print('\tSTREAM 3 :: Data instance: {} - Accuracies: MV: {} - AV: {} - Goowe: {}'.format(int(total),
round(accuracy_3_mv*100.0, 3), round(accuracy_3_av*100.0, 3), round(accuracy_3_goowe*100.0, 3)))
print('\t==========================================================================')
goowe_1.partial_fit(X_1, y_1)
goowe_2.partial_fit(X_2, y_2)
goowe_3.update(X_3, y_3, 1, 1)
# Now, for the remaining instances, do ITTT (Interleaved Test Then Train).
while(stream_1.has_more_samples() and stream_2.has_more_samples() and stream_3.has_more_samples() and instances_counter < instances_num):
if(instances_counter % CHUNK_SIZE == 0):
accuracy_1 = 0.0
total_1 = 0.0
true_predictions_1 = 0.0
accuracy_2 = 0.0
total_2 = 0.0
true_predictions_2 = 0.0
accuracy_3_mv = 0.0
accuracy_3_av = 0.0
accuracy_3_goowe = 0.0
total_3 = 0.0
true_predictions_3_mv = 0.0
true_predictions_3_av = 0.0
true_predictions_3_goowe = 0.0
from skmultiflow.data import SEAGenerator
from skmultiflow.meta import AdaptiveRandomForestClassifier
stream = SEAGenerator(random_state=1)
# Setup Adaptive Random Forest Classifier
arf = AdaptiveRandomForestClassifier()
# Setup variables to control loop and track performance
n_samples = 0
correct_cnt = 0
max_samples = 1200
# Train the estimator with the samples provided by the data stream
while n_samples < max_samples and stream.has_more_samples():
X, y = stream.next_sample()
y_pred = arf.predict(X)
if y[0] == y_pred[0]:
correct_cnt += 1
arf.partial_fit(X, y)
n_samples += 1
# Display results
print('Adaptive Random Forest ensemble classifier example')
print('{} samples analyzed.'.format(n_samples))
print('Accuracy: {}'.format(correct_cnt / n_samples))
