/* * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see <http://www.gnu.org/licenses/>. */ /** * TrainTestSet.java * Copyright (C) 2016 University of Waikato, Hamilton, NZ */ package mekaexamples.classifiers; import meka.classifiers.multilabel.BR; import meka.classifiers.multilabel.Evaluation; import meka.core.MLUtils; import meka.core.Result; import weka.core.Instances; import weka.core.converters.ConverterUtils.DataSource; /** * Builds and evaluates a BR Meka classifier on user supplied train/test datasets. * <br> * Expected parameters: <train> <test> * <br> * Note: The datasets must have been prepared for Meka already and compatible. * * @author FracPete (fracpete at waikato dot ac dot nz) * @version $Revision$ */ public class TrainTestSet { public static void main(String[] args) throws Exception { if (args.length != 2) throw new IllegalArgumentException("Required arguments: <train> <test>"); System.out.println("Loading train: " + args[0]); Instances train = DataSource.read(args[0]); MLUtils.prepareData(train); System.out.println("Loading test: " + args[1]); Instances test = DataSource.read(args[1]); MLUtils.prepareData(test); // compatible? String msg = train.equalHeadersMsg(test); if (msg != null) throw new IllegalStateException(msg); System.out.println("Build BR classifier on " + args[0]); BR classifier = new BR(); // further configuration of classifier classifier.buildClassifier(train); System.out.println("Evaluate BR classifier on " + args[1]); String top = "PCut1"; String vop = "3"; Result result = Evaluation.evaluateModel(classifier, train, test, top, vop); System.out.println(result); } }