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

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

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

package adams.ml.model.regression;

import adams.core.logging.LoggingObject;
import adams.data.spreadsheet.Row;
import adams.ml.data.Dataset;
import adams.ml.data.DatasetInfo;
import adams.ml.data.WekaConverter;
import adams.ml.model.ModelHelper;
import weka.classifiers.Classifier;
import weka.core.Instance;
import weka.core.Instances;

/**
 * Regression model for Weka classifiers.
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision$
 */
public class WekaRegressionModel extends LoggingObject implements RegressionModel {

    private static final long serialVersionUID = 4557749254683230577L;

    /** the underlying model. */
    protected Classifier m_Model;

    /** the dataset info. */
    protected DatasetInfo m_DatasetInfo;

    /** the instances used. */
    protected Instances m_InstancesHeader;

    /**
     * Initializes the model.
     *
     * @param model   the built Weka classifier
     * @param data   the training data
     * @param inst   the Weka training data
     */
    public WekaRegressionModel(Classifier model, Dataset data, Instances inst) {
        m_Model = model;
        m_DatasetInfo = new DatasetInfo(data);
        m_InstancesHeader = new Instances(inst, 0);
    }

    /**
     * Returns the regression for the given row.
     *
     * @param row      the row to make predictions for
     * @return      the prediction
     * @throws Exception   if prediction fails
     */
    @Override
    public double classify(Row row) throws Exception {
        Instance inst;

        inst = WekaConverter.toInstance(m_InstancesHeader, row);
        if (inst == null)
            throw new Exception("Failed to convert data row into Weka instance: " + row);

        return m_Model.classifyInstance(inst);
    }

    /**
     * Returns information about the dataset used for building the model.
     *
     * @return      the information
     */
    @Override
    public DatasetInfo getDatasetInfo() {
        return m_DatasetInfo;
    }

    /**
     * Returns the Instances header used for building the model.
     *
     * @return      the header
     */
    public Instances getInstancesHeader() {
        return m_InstancesHeader;
    }

    /**
     * Gets a short string description of the model.
     *
     * @return      the description, null if none available
     */
    @Override
    public String getModelDescription() {
        return m_Model.toString();
    }

    /**
     * Checks whether the dataset is compatible with the model.
     *
     * @param data   the dataset to check
     * @return      null if compatible, otherwise error message why not
     */
    public String isCompatible(Dataset data) {
        return ModelHelper.isCompatible(this, data);
    }
}