com.github.fracpete.multisearch.optimize.SMOKernels.java Source code

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Here is the source code for com.github.fracpete.multisearch.optimize.SMOKernels.java

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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 <http://www.gnu.org/licenses/>.
 */

/**
 * J48ConfidenceFactor.java
 * Copyright (C) 2016 University of Waikato, Hamilton, NZ
 */

package com.github.fracpete.multisearch.optimize;

import com.github.fracpete.multisearch.ExampleHelper;
import weka.classifiers.functions.SMO;
import weka.classifiers.functions.supportVector.PolyKernel;
import weka.classifiers.functions.supportVector.RBFKernel;
import weka.classifiers.meta.MultiSearch;
import weka.classifiers.meta.multisearch.DefaultEvaluationMetrics;
import weka.core.Instances;
import weka.core.SelectedTag;
import weka.core.Utils;
import weka.core.setupgenerator.AbstractParameter;
import weka.core.setupgenerator.ListParameter;
import weka.core.setupgenerator.MathParameter;
import weka.core.setupgenerator.ParameterGroup;

/**
 * Optimizes the RBFKernel and PolyKernel for SMO in separate search-spaces.
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision$
 */
public class SMOKernels {

    /**
     * The first parameter must be dataset,
     * the (optional) second the class index (1-based, 'first' and 'last'
     * also supported).
     *
     * @param args   the commandline options
     * @throws Exception   if optimization fails for some reason
     */
    public static void main(String[] args) throws Exception {
        if (args.length == 0) {
            System.err.println("\nUsage: SMOKernels <dataset> [classindex]\n");
            System.exit(1);
        }

        // load data
        Instances data = ExampleHelper.loadData(args[0], (args.length > 1) ? args[1] : null);

        // configure classifier we want to optimize
        SMO smo = new SMO();

        // configure multisearch
        // 1. RBFKernel
        ListParameter listRBF = new ListParameter();
        listRBF.setProperty("kernel");
        listRBF.setList(RBFKernel.class.getName());
        MathParameter gamma = new MathParameter();
        gamma.setProperty("kernel.gamma");
        gamma.setBase(10);
        gamma.setMin(-4);
        gamma.setMax(1);
        gamma.setStep(1);
        gamma.setExpression("pow(BASE,I)");
        ParameterGroup groupRBF = new ParameterGroup();
        groupRBF.setParameters(new AbstractParameter[] { listRBF, gamma });
        // 2. PolyKernel
        ListParameter listPoly = new ListParameter();
        listPoly.setProperty("kernel");
        listPoly.setList(PolyKernel.class.getName());
        MathParameter exp = new MathParameter();
        exp.setProperty("kernel.exponent");
        exp.setBase(10);
        exp.setMin(1);
        exp.setMax(5);
        exp.setStep(1);
        exp.setExpression("I");
        ParameterGroup groupPoly = new ParameterGroup();
        groupPoly.setParameters(new AbstractParameter[] { listPoly, exp });
        // assemble everything
        MultiSearch multi = new MultiSearch();
        multi.setClassifier(smo);
        multi.setSearchParameters(new AbstractParameter[] { groupRBF, groupPoly });
        SelectedTag tag = new SelectedTag(DefaultEvaluationMetrics.EVALUATION_ACC,
                new DefaultEvaluationMetrics().getTags());
        multi.setEvaluation(tag);

        // output configuration
        System.out.println("\nMultiSearch commandline:\n" + Utils.toCommandLine(multi));

        // optimize
        System.out.println("\nOptimizing...\n");
        multi.buildClassifier(data);
        System.out.println("Best setup:\n" + Utils.toCommandLine(multi.getBestClassifier()));
        System.out.println("Best parameters: " + multi.getGenerator().evaluate(multi.getBestValues()));
    }
}