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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 .
 */

/**
 * MeasurementEvaluationStatisticsTableModel.java
 * Copyright (C) 2015 University of Waikato, Hamilton, NZ
 */

package meka.gui.core;

import meka.experiment.evaluationstatistics.EvaluationStatistics;
import meka.experiment.evaluationstatistics.EvaluationStatisticsUtils;

import javax.swing.table.AbstractTableModel;
import java.util.ArrayList;
import java.util.List;

/**
 * Table model for displaying a Dataset X Classifier table for a specific measurement.
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision$
 */
public class MeasurementEvaluationStatisticsTableModel
		extends AbstractTableModel {

	private static final long serialVersionUID = 6418545317753165337L;

	/** the underlying statistics. */
	protected List m_Statistics;

	/** the measurement. */
	protected String m_Measurement;

	/** the datasets. */
	protected List m_Datasets;

	/** the classifiers. */
	protected List m_Classifiers;

	/** whether to show the classifier or an index in the column header. */
	protected boolean m_ShowIndex;

	/**
	 * Initializes the model with no statistics.
	 */
	public MeasurementEvaluationStatisticsTableModel() {
		this(new ArrayList(), null, true);
	}

	/**
	 * Initializes the model with the statistics.
	 *
	 * @param stats         the statistics to use
	 * @param measurement   the measurement to display
	 * @param showIndex     whether to show an index or the classifier in the header
	 */
	public MeasurementEvaluationStatisticsTableModel(List stats, String measurement, boolean showIndex) {
		m_Statistics  = stats;
		m_Measurement = measurement;
		m_ShowIndex   = showIndex;
		m_Classifiers = EvaluationStatisticsUtils.commandLines(stats, true);
		m_Datasets    = EvaluationStatisticsUtils.relations(stats, true);
	}

	/**
	 * Returns the name of the column.
	 *
	 * @param column    the column index
	 * @return          the name
	 */
	@Override
	public String getColumnName(int column) {
		if (column == 0)
			return "Dataset";
		else if (m_ShowIndex)
			return "[" + column + "]";
		else
			return m_Classifiers.get(column - 1);
	}

	/**
	 * Returns the number of datasets in this model.
	 *
	 * @return          the number of datasets
	 */
	@Override
	public int getRowCount() {
		return m_Datasets.size();
	}

	/**
	 * The number of columns in this model.
	 *
	 * @return          the number of columns
	 */
	@Override
	public int getColumnCount() {
		return m_Classifiers.size() + 1;
	}

	/**
	 * Returns the type of the column.
	 *
	 * @param columnIndex   the column index
	 * @return              the type
	 */
	@Override
	public Class getColumnClass(int columnIndex) {
		if (columnIndex == 0)
			return String.class;
		else
			return Double.class;
	}

	/**
	 * Returns the cell value at the specified location.
	 *
	 * @param rowIndex          the row
	 * @param columnIndex       the column
	 * @return                  the value, null if not available
	 */
	@Override
	public Object getValueAt(int rowIndex, int columnIndex) {
		List    stats;

		if (columnIndex == 0) {
			return m_Datasets.get(rowIndex);
		}
		else {
			if (m_Measurement == null)
				return null;
			stats = EvaluationStatisticsUtils.measurements(
					m_Statistics,
					m_Classifiers.get(columnIndex - 1),
					m_Datasets.get(rowIndex),
					m_Measurement);
			if (stats.size() > 1)
				System.err.println("Found multiple values for measurement '" + m_Measurement + "': " + stats.size());
			if (stats.size() > 0)
				return stats.get(0);
			else
				return null;
		}
	}

	/**
	 * Sets the measurement the model is for.
	 *
	 * @param value             the measurement
	 */
	public void setMeasurement(String value) {
		m_Measurement = value;
		fireTableDataChanged();
	}

	/**
	 * Returns the measurement the model is for.
	 *
	 * @return                  the measurement
	 */
	public String getMeasurement() {
		return m_Measurement;
	}

	/**
	 * Sets whether to show an index or the classifier in the header.
	 *
	 * @param value             true if to show index
	 */
	public void setShowIndex(boolean value) {
		m_ShowIndex = value;
		fireTableDataChanged();
	}

	/**
	 * Returns whethert o show an index or the classifier in the header.
	 *
	 * @return                  true if to show index
	 */
	public boolean getShowIndex() {
		return m_ShowIndex;
	}

	/**
	 * Returns the underlying statistics.
	 *
	 * @return                  the statistics
	 */
	public List getStatistics() {
		return m_Statistics;
	}
}