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The MEKA project provides an open source implementation of methods for multi-label classification and evaluation. It is based on the WEKA Machine Learning Toolkit. Several benchmark methods are also included, as well as the pruned sets and classifier chains methods, other methods from the scientific literature, and a wrapper to the MULAN framework.

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/*
 *   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 .
 */

package meka.classifiers.multilabel.meta;

import meka.classifiers.multilabel.BR;
import meka.classifiers.multilabel.ProblemTransformationMethod;
import weka.classifiers.AbstractClassifier;
import weka.core.*;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;

import java.util.ArrayList;
import java.util.List;

/**
 * MBR.java - Meta BR: BR stacked with feature outputs into another BR.
 * Described in: Godbole and Sarawagi, Discriminative Methods for Multi-labeled Classification. 
 * 
 * @version	June 2009
 * @author 	Jesse Read ([email protected])
 */
public class MBR extends ProblemTransformationMethod implements TechnicalInformationHandler {

	/** for serialization. */
	private static final long serialVersionUID = 865889198021748917L;
	
	protected BR m_BASE = null;
	protected BR m_META = null;

	public MBR() {
		// default classifier for GUI
		this.m_Classifier = new BR();
	}

	/**
	 * Description to display in the GUI.
	 * 
	 * @return		the description
	 */
	@Override
	public String globalInfo() {
		return "BR stacked with feature outputs.\nFor more information see:\n" + getTechnicalInformation().toString();
	}

	@Override
	protected String defaultClassifierString() {
		return BR.class.getName();
	}

	@Override
	public TechnicalInformation getTechnicalInformation() {
		TechnicalInformation	result;
		
		result = new TechnicalInformation(Type.INPROCEEDINGS);
		result.setValue(Field.AUTHOR, "Shantanu Godbole, Sunita Sarawagi");
		result.setValue(Field.TITLE, "Discriminative Methods for Multi-labeled Classification");
		result.setValue(Field.BOOKTITLE, "Advances in Knowledge Discovery and Data Mining");
		result.setValue(Field.YEAR, "2004");
		result.setValue(Field.PAGES, "22-30");
		result.setValue(Field.SERIES, "LNCS");
		
		return result;
	}


	@Override
	public void buildClassifier(Instances data) throws Exception {
	  	testCapabilities(data);
	  	
		int c = data.classIndex();

		// Base BR

		if (getDebug()) System.out.println("Build BR Base ("+c+" models)");
		m_BASE = (BR)AbstractClassifier.forName(getClassifier().getClass().getName(),((AbstractClassifier)getClassifier()).getOptions());
		m_BASE.buildClassifier(data);

		// Meta BR

		if (getDebug()) System.out.println("Prepare Meta data           ");
		Instances meta_data = new Instances(data);

	  	List BinaryClass = new ArrayList<>(c);
		BinaryClass.add("0");
		BinaryClass.add("1");

		for(int i = 0; i < c; i++) {
			meta_data.insertAttributeAt(new Attribute("metaclass"+i,BinaryClass),c);
		}

		for(int i = 0; i < data.numInstances(); i++) {
			double cfn[] = m_BASE.distributionForInstance(data.instance(i));
			for(int a = 0; a < cfn.length; a++) {
				meta_data.instance(i).setValue(a+c,cfn[a]);
			}
		}

		meta_data.setClassIndex(c);
		m_InstancesTemplate = new Instances(meta_data, 0);

		if (getDebug()) System.out.println("Build BR Meta ("+c+" models)");

		m_META = (BR)AbstractClassifier.forName(getClassifier().getClass().getName(),((AbstractClassifier)getClassifier()).getOptions());
		m_META.buildClassifier(meta_data);
	}

	@Override
	public double[] distributionForInstance(Instance instance) throws Exception {

		int c = instance.classIndex();

		double result[] = m_BASE.distributionForInstance(instance);

		instance.setDataset(null);

		for (int i = 0; i < c; i++) {
			instance.insertAttributeAt(c);
		}

		instance.setDataset(m_InstancesTemplate);

		for (int i = 0; i < c; i++) {
			instance.setValue(c+i,result[i]);
		}

		return m_META.distributionForInstance(instance);
	}

	@Override
	public String getRevision() {
	    return RevisionUtils.extract("$Revision: 9117 $");
	}

	public static void main(String args[]) {
		ProblemTransformationMethod.evaluation(new MBR(), args);
	}
}