/*
* 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 2 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, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* StoringAndLoadingModels.java
* Copyright (C) 2009-2010 Aristotle University of Thessaloniki, Thessaloniki, Greece
*/
package mulan.examples;
import java.util.logging.Level;
import java.util.logging.Logger;
import mulan.classifier.MultiLabelLearner;
import mulan.classifier.transformation.BinaryRelevance;
import mulan.data.MultiLabelInstances;
import mulan.evaluation.Evaluation;
import mulan.evaluation.Evaluator;
import weka.classifiers.trees.J48;
import weka.core.SerializationHelper;
import weka.core.Utils;
/**
* This example shows how you can store a learned model and load a stored model.
*
* @author Grigorios Tsoumakas
* @version 2010.12.15
*/
public class StoringAndLoadingModels {
public static void main(String[] args) {
try {
String trainingDataFilename = Utils.getOption("train", args);
String testingDataFilename = Utils.getOption("test", args);
String labelsFilename = Utils.getOption("labels", args);
System.out.println("Loading the training data set...");
MultiLabelInstances trainingData = new MultiLabelInstances(trainingDataFilename, labelsFilename);
System.out.println("Loading the testing data set...");
MultiLabelInstances testingData = new MultiLabelInstances(testingDataFilename, labelsFilename);
BinaryRelevance learner1 = new BinaryRelevance(new J48());
String modelFilename = Utils.getOption("model", args);
System.out.println("Building the model...");
learner1.build(trainingData);
System.out.println("Storing the model...");
SerializationHelper.write(modelFilename, learner1);
System.out.println("Loading the model...");
BinaryRelevance learner2;
learner2 = (BinaryRelevance) (MultiLabelLearner) SerializationHelper.read(modelFilename);
Evaluator evaluator = new Evaluator();
Evaluation evaluation;
evaluation = evaluator.evaluate(learner2, testingData);
System.out.println(evaluation);
} catch (Exception ex) {
Logger.getLogger(StoringAndLoadingModels.class.getName()).log(Level.SEVERE, null, ex);
}
}
}