MeasuredCriterionTest.java

/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2008 by Rapid-I and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapid-i.com
 *
 *  This program is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU Affero 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 Affero General Public License for more details.
 *
 *  You should have received a copy of the GNU Affero General Public License
 *  along with this program.  If not, see http://www.gnu.org/licenses/.
 */
package com.rapidminer.operator.performance.test;

import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.MemoryExampleTable;
import com.rapidminer.example.test.ExampleTestTools;
import com.rapidminer.operator.performance.AbsoluteError;
import com.rapidminer.operator.performance.AbstractPerformanceEvaluator;
import com.rapidminer.operator.performance.PerformanceCriterion;
import com.rapidminer.operator.performance.PerformanceVector;
import com.rapidminer.operator.performance.SquaredError;
import com.rapidminer.tools.att.AttributeSet;


/**
 * Tests regression critetia.
 * 
 * @author Simon Fischer, Ingo Mierswa
 * @version $Id: MeasuredCriterionTest.java,v 1.16 2006/04/05 08:57:27
 *          ingomierswa Exp $
 */
public class MeasuredCriterionTest extends CriterionTestCase {

	private ExampleSet exampleSet1, exampleSet2;

	private ExampleSet createExampleSet(double[][] labelValues, double[] predictedValues) throws Exception {
		Attribute label = ExampleTestTools.attributeReal();
		label.setTableIndex(0);
		List<Attribute> attributeList = new LinkedList<Attribute>();
		attributeList.add(label);

		MemoryExampleTable exampleTable = new MemoryExampleTable(attributeList, ExampleTestTools.createDataRowReader(labelValues));
		AttributeSet attributeSet = new AttributeSet();
		attributeSet.setSpecialAttribute("label", label);
		ExampleSet exampleSet = exampleTable.createExampleSet(attributeSet);
		Attribute predictedLabel = ExampleTestTools.createPredictedLabel(exampleSet);
		Iterator<Example> r = exampleSet.iterator();
		for (int i = 0; i < predictedValues.length; i++)
			r.next().setValue(predictedLabel, predictedValues[i]);

		return exampleSet;
	}

	public void setUp() throws Exception {
		super.setUp();
		exampleSet1 = createExampleSet(new double[][] { { 5.0 }, { 3.0 }, { -1.0 }, { -4.0 }, { 0.0 }, { 2.0 } }, new double[] { 6.0, 1.0, 0.0, -1.0, 3.0, -2.0 });
		exampleSet2 = createExampleSet(new double[][] { { 3.0 }, { 6.0 }, { -1.0 } }, new double[] { 1.0, 8.0, -4.0 });
	}

	public void tearDown() throws Exception {
		exampleSet1 = exampleSet2 = null;
		super.tearDown();
	}

	/** Tests calculation, average, and clone. */
	private void criterionTest(PerformanceCriterion c1, PerformanceCriterion c2, double expected1, double expected2, double expectedOverall) throws Exception {
		PerformanceVector pv1 = new PerformanceVector();
		pv1.addCriterion(c1);
		AbstractPerformanceEvaluator.evaluate(null, exampleSet1, pv1, new LinkedList<PerformanceCriterion>(), false, true);
		assertEquals(c1.getName() + " 1", expected1, c1.getAverage(), 0.00000001);
		assertEquals(c1.getName() + " 1 clone", expected1, ((PerformanceCriterion) c1.clone()).getAverage(), 0.00000001);

		PerformanceVector pv2 = new PerformanceVector();
		pv2.addCriterion(c2);
		AbstractPerformanceEvaluator.evaluate(null, exampleSet2, pv2, new LinkedList<PerformanceCriterion>(), false, true);
		assertEquals(c2.getName() + " 2", expected2, c2.getAverage(), 0.00000001);
		assertEquals(c2.getName() + " 2 clone", expected2, ((PerformanceCriterion) c2.clone()).getAverage(), 0.00000001);

		c1.buildAverage(c2);
		assertEquals(c1.getName() + " average", expectedOverall, c1.getMikroAverage(), 0.00000001);
		assertEquals(c1.getName() + " makro average", (expected1 + expected2) / 2.0, c1.getMakroAverage(), 0.00000001);
	}

	public void testAbsoluteError() throws Exception {
		criterionTest(new AbsoluteError(), new AbsoluteError(), 14.0 / 6.0, 7.0 / 3.0, 21.0 / 9.0);
	}

	public void testSquaredError() throws Exception {
		criterionTest(new SquaredError(), new SquaredError(), 40.0 / 6.0, 17.0 / 3.0, 57.0 / 9.0);
	}

	// public void testScaledError() throws Exception {
	// criterionTest(new ScaledError(), new ScaledError(),
	// 14.0/6.0/9.0,
	// 7.0/3.0/7.0,
	// (14.0/9.0 + 7.0/7.0) / 9.0);
	// }
}