def parameter_q_and_t(self):
accuracy_of_combinations = []
combination = []
quantile_percent = [0.50, 0.75, 1.0]
threshold = [0.5, 0.6, 0.7]
test_X, test_y = get_data_batches(self.X_array, self.y_array)
ensemble_clf = DecisionTreeClassifier()
clf = HoeffdingTree()
bootstrap_count = 100
for q in quantile_percent:
for t in threshold:
Train_X = test_X[0]
Train_y = test_y[0].flatten()
clf = clf.fit(Train_X, Train_y)
MPD3_detector = MPD3(bootstrap_count, q, t)
ensemble = MPD3_detector.ensemble_bootstrap(Train_X, Train_y)
batch_accuracy = []
result = []
for i in range(len(test_X) - 1):
index = i + 1
prediction = clf.predict(test_X[index])
batch_accuracy.append(
accuracy_score(test_y[index], prediction))
mpd_value = MPD3_detector.MPD_score(
test_X[index], ensemble)
if MPD3_detector.drift_check(mpd_value):
Train_X = test_X[index]
Train_y = test_y[index].flatten()
clf = clf.partial_fit(Train_X, Train_y)
ensemble = MPD3_detector.ensemble_bootstrap(
Train_X, Train_y)
mean_accuracy = np.average(batch_accuracy)
accuracy_of_combinations.append(mean_accuracy)
combination.append([q, t])
index_of_max_acc = np.argmax(accuracy_of_combinations)
final_q, final_t = combination[index_of_max_acc]
return final_q, final_t
X_train_RBF70, X_test_RBF70, y_train_RBF70, y_test_RBF70 = train_test_split(RBF70_X, RBF70_Y, test_size=0.3) # In[292]: ### Hoeffding Tree Batch Classification ### # In[293]: ### RBF HT ### HT = HoeffdingTree() HT.fit(X_train_RBF, y_train_RBF) HT.score(X_test_RBF, y_test_RBF) # In[294]: ### RBF 10 HT ### HT = HoeffdingTree() HT.fit(X_train_RBF10, y_train_RBF10) HT.score(X_test_RBF10, y_test_RBF10)