adams.ml.model.regression.WekaRegressor.java Source code

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Here is the source code for adams.ml.model.regression.WekaRegressor.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/>.
 */

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

package adams.ml.model.regression;

import adams.core.option.OptionUtils;
import adams.ml.capabilities.Capabilities;
import adams.ml.data.Dataset;
import adams.ml.data.WekaConverter;
import weka.classifiers.functions.LinearRegressionJ;
import weka.core.Instances;

/**
 <!-- globalinfo-start -->
 * Wraps around a Weka classifier that handles numeric classes (= regression).
 * <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>-classifier &lt;weka.classifiers.Classifier&gt; (property: classifier)
 * &nbsp;&nbsp;&nbsp;The classifier to use.
 * &nbsp;&nbsp;&nbsp;default: weka.classifiers.functions.LinearRegressionJ -S 0 -R 1.0E-8 -num-decimal-places 4
 * </pre>
 * 
 <!-- options-end -->
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision$
 */
public class WekaRegressor extends AbstractRegressor {

    private static final long serialVersionUID = -4086036132431888958L;

    /** the weka classifier to use. */
    protected weka.classifiers.Classifier m_Classifier;

    /**
     * Returns a string describing the object.
     *
     * @return          a description suitable for displaying in the gui
     */
    @Override
    public String globalInfo() {
        return "Wraps around a Weka classifier that handles numeric classes (= regression).";
    }

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

        m_OptionManager.add("classifier", "classifier", new LinearRegressionJ());
    }

    /**
     * Sets the classifier to use.
     *
     * @param value   the classifier
     */
    public void setClassifier(weka.classifiers.Classifier value) {
        m_Classifier = value;
        reset();
    }

    /**
     * Returns the classifier to use.
     *
     * @return      the classifier
     */
    public weka.classifiers.Classifier getClassifier() {
        return m_Classifier;
    }

    /**
     * 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 classifierTipText() {
        return "The classifier 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_Classifier.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 RegressionModel doBuildModel(Dataset data) throws Exception {
        Instances inst;
        weka.classifiers.Classifier classifier;

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

        classifier.buildClassifier(inst);

        return new WekaRegressionModel(classifier, data, inst);
    }
}