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())
clf = OneVsRestClassifier(xgboost.XGBClassifier())
clf.fit(Feature_train_o, Label_train_o)
Label_predict = clf.predict(Feature_test)
elif m == 'One_vs_Rest-XGBoost':
clf = OneVsRestClassifier(xgboost.XGBClassifier())
clf.fit(Feature_train, Label_train.ravel())
Label_predict = clf.predict(Feature_test)
ml_record.measure(i, Label_test, Label_predict, 'weighted')
i += 1
file_wirte = "Result_One_vs_All.txt"
ml_record.output(file_wirte, m, Dir)
#print("Data Set Folder: ", file, ", SMOTE folder id: ", str(k))
Feature_train_smote = np.concatenate(
(SMOTE_feature_train_list[k][0],
SMOTE_feature_valid_list[k][0]))
Label_train_smote = np.concatenate(
(SMOTE_label_train_list[k][0],
SMOTE_label_valid_list[k][0]))
pool_classifiers[k].fit(Feature_train_smote,
Label_train_smote.ravel())
if m == 'META-DES-XGBoost':
metades = METADES(pool_classifiers)
metades.fit(Feature_train, Label_train.ravel())
Label_predict = metades.predict(Feature_test)
elif m == 'MCB-XGBoost':
mcb = MCB(pool_classifiers)
mcb.fit(Feature_train, Label_train.ravel())
Label_predict = mcb.predict(Feature_test)
elif m == 'DES-MI-XGBoost':
dmi = DESMI(pool_classifiers)
dmi.fit(Feature_train, Label_train.ravel())
Label_predict = dmi.predict(Feature_test)
print(confusion_matrix(Label_test, Label_predict))
ml_record.measure(i, Label_test, Label_predict, 'weighted')
i += 1
file_wirte = "Result_Esemble_Tang.txt"
ml_record.output(file_wirte, m, 'Tang')