TrainingImplTest.java

/*
 * This file is part of ALOE.
 *
 * ALOE 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.

 * ALOE 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 ALOE.  If not, see <http://www.gnu.org/licenses/>.
 *
 * Copyright (c) 2012 SCCL, University of Washington (http://depts.washington.edu/sccl)
 */
package etc.aloe.cscw2013;

import etc.aloe.data.ExampleSet;
import etc.aloe.data.Predictions;
import java.util.ArrayList;
import java.util.List;
import org.junit.After;
import org.junit.AfterClass;
import org.junit.Before;
import org.junit.BeforeClass;
import org.junit.Test;
import static org.junit.Assert.*;
import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.Instances;

/**
 *
 * @author Michael Brooks <mjbrooks@uw.edu>
 */
public class TrainingImplTest {

    private Instances testInstances;
    private Instances instances;

    public TrainingImplTest() {
    }

    @BeforeClass
    public static void setUpClass() {
    }

    @AfterClass
    public static void tearDownClass() {
    }

    @Before
    public void setUp() {
        ArrayList<Attribute> attributes = new ArrayList<Attribute>();

        attributes.add(new Attribute("id"));

        ArrayList<String> classValues = new ArrayList<String>();
        classValues.add("false");
        classValues.add("true");
        attributes.add(new Attribute("class", classValues));

        this.instances = new Instances("TrainInstances", attributes, 12);
        this.instances.setClassIndex(1);

        this.instances.add(new DenseInstance(1.0, new double[]{1.0, 1.0}));
        this.instances.add(new DenseInstance(1.0, new double[]{2.0, 1.0}));
        this.instances.add(new DenseInstance(1.0, new double[]{3.0, 1.0}));
        this.instances.add(new DenseInstance(1.0, new double[]{4.0, 1.0}));
        this.instances.add(new DenseInstance(1.0, new double[]{5.0, 1.0}));
        this.instances.add(new DenseInstance(1.0, new double[]{6.0, 1.0}));
        this.instances.add(new DenseInstance(1.0, new double[]{7.0, 0.0}));
        this.instances.add(new DenseInstance(1.0, new double[]{8.0, 0.0}));
        this.instances.add(new DenseInstance(1.0, new double[]{9.0, 0.0}));
        this.instances.add(new DenseInstance(1.0, new double[]{10.0, 0.0}));
        this.instances.add(new DenseInstance(1.0, new double[]{11.0, 0.0}));
        this.instances.add(new DenseInstance(1.0, new double[]{12.0, 0.0}));

        this.testInstances = new Instances("TestInstances", attributes, 4);
        this.testInstances.setClassIndex(1);

        this.testInstances.add(new DenseInstance(1.0, new double[]{1.0, 1.0}));
        this.testInstances.add(new DenseInstance(1.0, new double[]{6.0, 1.0}));
        this.testInstances.add(new DenseInstance(1.0, new double[]{7.0, 0.0}));
        this.testInstances.add(new DenseInstance(1.0, new double[]{12.0, 0.0}));
    }

    @After
    public void tearDown() {
    }

    /**
     * Test of train method, of class TrainingImpl.
     */
    @Test
    public void testTrain() {
        System.out.println("train");

        TrainingImpl instance = new TrainingImpl();
        WekaModel model = instance.train(new ExampleSet(instances));

        //The test here is whether the model works
        Boolean[] expResult = new Boolean[]{true, true, false, false};
        Double[] expConfidence = new Double[]{1.0, 1.0, 0.0, 0.0};

        ExampleSet examples = new ExampleSet(testInstances);
        Predictions predictions = model.getPredictions(examples);

        assertEquals(expResult.length, predictions.size());

        for (int i = 0; i < expResult.length; i++) {
            assertEquals(expResult[i], predictions.getPredictedLabel(i));
            assertEquals(expConfidence[i], predictions.getPredictionConfidence(i));
            assertEquals(testInstances.get(i).classValue(), predictions.getTrueLabel(i) ? 1.0 : 0.0, 0.0);
        }
    }
}