test_X, test_y = train[test_index], labels[test_index] ################################################################################ ## list of prediction results from different classifiers predictions = [] ############################# ## XGBoost Classifier ############################# xgb_param = param.xgboost_para() xgboost_clf = clf.xgboost_classifer(xgb_param, train_X, train_y); ## xgboost input data requires specifc format xg_test = xgb.DMatrix(test_X, label=test_y) ## prediction from xgboost y_prob = xgboost_clf.predict(xg_test) predictions.append(y_prob) ############################# ## Lasagne NN Classifier ############################# # standardize the data for NN
train_X, train_y = train[train_index], labels[train_index] test_X, test_y = train[test_index], labels[test_index] ################################################################################ ## list of prediction results from different classifiers predictions = [] ############################# ## XGBoost Classifier ############################# xgb_param = param.xgboost_para() xgboost_clf = clf.xgboost_classifer(xgb_param, train_X, train_y) ## xgboost input data requires specifc format xg_test = xgb.DMatrix(test_X, label=test_y) ## prediction from xgboost y_prob = xgboost_clf.predict(xg_test) predictions.append(y_prob) ############################# ## Lasagne NN Classifier ############################# # standardize the data for NN unlabeled_X = np.concatenate((train_X, test_X), axis=0) scaler = StandardScaler()