adams.ml.model.clustering.WekaClusterer.java Source code

Introduction

Here is the source code for adams.ml.model.clustering.WekaClusterer.java

Source

/*
 *   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/>.
 */

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

package adams.ml.model.clustering;

import adams.core.option.OptionUtils;
import adams.ml.capabilities.Capabilities;
import adams.ml.data.Dataset;
import adams.ml.data.WekaConverter;
import weka.clusterers.SimpleKMeans;
import weka.core.Instances;

/**
 <!-- globalinfo-start -->
 * Wraps around a Weka clusterer.
 * <br><br>
 <!-- globalinfo-end -->
 *
 <!-- options-start -->
 * <pre>-logging-level &lt;OFF|SEVERE|WARNING|INFO|CONFIG|FINE|FINER|FINEST&gt; (property: loggingLevel)
 * &nbsp;&nbsp;&nbsp;The logging level for outputting errors and debugging output.
 * &nbsp;&nbsp;&nbsp;default: WARNING
 * </pre>
 * 
 * <pre>-strict-capabilities &lt;boolean&gt; (property: strictCapabilities)
 * &nbsp;&nbsp;&nbsp;If enabled, a strict capabilities test is performed; otherwise, it is attempted 
 * &nbsp;&nbsp;&nbsp;to adjust the data to fit the algorithm's capabilities.
 * &nbsp;&nbsp;&nbsp;default: false
 * </pre>
 * 
 * <pre>-clusterer &lt;weka.clusterers.Clusterer&gt; (property: clusterer)
 * &nbsp;&nbsp;&nbsp;The clusterer to use.
 * &nbsp;&nbsp;&nbsp;default: weka.clusterers.SimpleKMeans -init 0 -max-candidates 100 -periodic-pruning 10000 -min-density 2.0 -t1 -1.25 -t2 -1.0 -N 2 -A \"weka.core.EuclideanDistance -R first-last\" -I 500 -num-slots 1 -S 10
 * </pre>
 * 
 <!-- options-end -->
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision$
 */
public class WekaClusterer extends AbstractClusterer {

    private static final long serialVersionUID = -4086036132431888958L;

    /** the weka classifier to use. */
    protected weka.clusterers.Clusterer m_Clusterer;

    /**
     * Returns a string describing the object.
     *
     * @return          a description suitable for displaying in the gui
     */
    @Override
    public String globalInfo() {
        return "Wraps around a Weka clusterer.";
    }

    /**
     * Adds options to the internal list of options.
     */
    @Override
    public void defineOptions() {
        super.defineOptions();

        m_OptionManager.add("clusterer", "clusterer", new SimpleKMeans());
    }

    /**
     * Sets the clusterer to use.
     *
     * @param value   the clusterer
     */
    public void setClusterer(weka.clusterers.Clusterer value) {
        m_Clusterer = value;
        reset();
    }

    /**
     * Returns the clusterer to use.
     *
     * @return      the clusterer
     */
    public weka.clusterers.Clusterer getClusterer() {
        return m_Clusterer;
    }

    /**
     * Returns the tip text for this property.
     *
     * @return       tip text for this property suitable for
     *          displaying in the GUI or for listing the options.
     */
    public String clustererTipText() {
        return "The clusterer to use.";
    }

    /**
     * Returns the algorithm's capabilities in terms of data.
     *
     * @return      the algorithm's capabilities
     */
    @Override
    public Capabilities getCapabilities() {
        Capabilities result;

        result = super.getCapabilities();
        result.assign(WekaConverter.convertCapabilities(m_Clusterer.getCapabilities()));

        return result;
    }

    /**
     * Builds a model from the data.
     *
     * @param data   the data to use for building the model
     * @return      the generated model
     * @throws Exception   if the build fails
     */
    @Override
    protected ClusteringModel doBuildModel(Dataset data) throws Exception {
        Instances inst;
        weka.clusterers.Clusterer clusterer;

        inst = WekaConverter.toInstances(data);
        clusterer = (weka.clusterers.Clusterer) OptionUtils.shallowCopy(m_Clusterer);
        if (clusterer == null)
            throw new Exception(
                    "Failed to create shallow copy of classifier: " + OptionUtils.getCommandLine(m_Clusterer));

        clusterer.buildClusterer(inst);

        return new WekaClusteringModel(clusterer, data, inst);
    }
}