algorithm='SAMME', n_estimators=200, learning_rate=0.8)
bdt.fit(Feature_train, Label_train.ravel())
Label_predict = bdt.predict(Feature_test)
elif m == 'SMOTE-AdaBoost-DT':
sm = SMOTE()
Feature_train_o, Label_train_o = sm.fit_sample(Feature_train, Label_train.ravel())
bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=2, min_samples_split=20, min_samples_leaf=5),
algorithm='SAMME', n_estimators=200, learning_rate=0.8)
bdt.fit(Feature_train_o, Label_train_o)
Label_predict = bdt.predict(Feature_test)
elif m == 'META-DES':
pool_classifiers = RandomForestClassifier(n_estimators=10)
pool_classifiers.fit(Feature_train, Label_train.ravel())
metades = METADES(pool_classifiers)
metades.fit(Feature_train, Label_train.ravel())
Label_predict = metades.predict(Feature_test)
elif m == 'MCB':
pool_classifiers = RandomForestClassifier(n_estimators=10)
pool_classifiers.fit(Feature_train, Label_train.ravel())
mcb = MCB(pool_classifiers)
mcb.fit(Feature_train, Label_train.ravel())
Label_predict = mcb.predict(Feature_test)
elif m == 'DES-MI':
pool_classifiers = RandomForestClassifier(n_estimators=10)
pool_classifiers.fit(Feature_train, Label_train.ravel())
dmi = DESMI(pool_classifiers)
dmi.fit(Feature_train, Label_train.ravel())
Label_predict = dmi.predict(Feature_test)
elif m == 'One_vs_Rest-SMOTE-XGBoost':
sm = SMOTE()
Feature_train_o, Label_train_o = sm.fit_sample(Feature_train, Label_train.ravel())
y_kne_pred = kne.predict(X_test)
accuracy_kne = accuracy_score(Y_test, y_kne_pred)
# print('accuracy = ', accuracy_kne)
micro_f1_kne = f1_score(Y_test - 1, y_kne_pred - 1, average='micro')
# print('micro_f1 =', micro_f1_kne)
macro_f1_kne = f1_score(Y_test - 1, y_kne_pred - 1, average='macro')
# print('macro_f1 =', macro_f1_kne)
test_time_kne_end = time.time()
#---------------------- Test META DES Pharse --------------------------
test_time_mdes_start = time.time()
y_mdes_pred = meta.predict(X_test)
accuracy_mdes = accuracy_score(Y_test, y_mdes_pred)
# print('accuracy = ', accuracy_mdes)
micro_f1_mdes = f1_score(Y_test - 1, y_mdes_pred - 1, average='micro')
# print('micro_f1 =', micro_f1_mdes)
# print('support_macro:',precision_recall_fscore_support(Y_test, y_mdes_pred, average='macro'))
macro_f1_mdes = f1_score(Y_test - 1, y_mdes_pred - 1, average='macro')
# print('macro_f1 =', macro_f1_mdes)
# print('micro:', precision_recall_fscore_support(Y_test, y_mdes_pred, average='micro'))
test_time_mdes_end = time.time()
#---------------------- Test KNU Pharse --------------------------
test_time_knu_start = time.time()
y_knu_pred = knu.predict(X_test)
