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* (the "License"); you may not use this file except in compliance with
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* http://www.apache.org/licenses/LICENSE-2.0
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* Unless required by applicable law or agreed to in writing, software
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package org.apache.mahout.classifier.naivebayes;
import java.io.File;
import com.google.common.io.Closeables;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.SequenceFile;
import org.apache.hadoop.io.Text;
import org.apache.mahout.classifier.AbstractVectorClassifier;
import org.apache.mahout.classifier.naivebayes.training.TrainNaiveBayesJob;
import org.apache.mahout.common.MahoutTestCase;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.math.hadoop.MathHelper;
import org.junit.Before;
import org.junit.Test;
public class NaiveBayesTest extends MahoutTestCase {
private Configuration conf;
private File inputFile;
private File outputDir;
private File tempDir;
static final Text LABEL_STOLEN = new Text("/stolen/");
static final Text LABEL_NOT_STOLEN = new Text("/not_stolen/");
static final Vector.Element COLOR_RED = MathHelper.elem(0, 1);
static final Vector.Element COLOR_YELLOW = MathHelper.elem(1, 1);
static final Vector.Element TYPE_SPORTS = MathHelper.elem(2, 1);
static final Vector.Element TYPE_SUV = MathHelper.elem(3, 1);
static final Vector.Element ORIGIN_DOMESTIC = MathHelper.elem(4, 1);
static final Vector.Element ORIGIN_IMPORTED = MathHelper.elem(5, 1);
@Override
@Before
public void setUp() throws Exception {
super.setUp();
conf = getConfiguration();
inputFile = getTestTempFile("trainingInstances.seq");
outputDir = getTestTempDir("output");
outputDir.delete();
tempDir = getTestTempDir("tmp");
SequenceFile.Writer writer = new SequenceFile.Writer(FileSystem.get(conf), conf,
new Path(inputFile.getAbsolutePath()), Text.class, VectorWritable.class);
try {
writer.append(LABEL_STOLEN, trainingInstance(COLOR_RED, TYPE_SPORTS, ORIGIN_DOMESTIC));
writer.append(LABEL_NOT_STOLEN, trainingInstance(COLOR_RED, TYPE_SPORTS, ORIGIN_DOMESTIC));
writer.append(LABEL_STOLEN, trainingInstance(COLOR_RED, TYPE_SPORTS, ORIGIN_DOMESTIC));
writer.append(LABEL_NOT_STOLEN, trainingInstance(COLOR_YELLOW, TYPE_SPORTS, ORIGIN_DOMESTIC));
writer.append(LABEL_STOLEN, trainingInstance(COLOR_YELLOW, TYPE_SPORTS, ORIGIN_IMPORTED));
writer.append(LABEL_NOT_STOLEN, trainingInstance(COLOR_YELLOW, TYPE_SUV, ORIGIN_IMPORTED));
writer.append(LABEL_STOLEN, trainingInstance(COLOR_YELLOW, TYPE_SUV, ORIGIN_IMPORTED));
writer.append(LABEL_NOT_STOLEN, trainingInstance(COLOR_YELLOW, TYPE_SUV, ORIGIN_DOMESTIC));
writer.append(LABEL_NOT_STOLEN, trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_IMPORTED));
writer.append(LABEL_STOLEN, trainingInstance(COLOR_RED, TYPE_SPORTS, ORIGIN_IMPORTED));
} finally {
Closeables.close(writer, false);
}
}
@Test
public void toyData() throws Exception {
TrainNaiveBayesJob trainNaiveBayes = new TrainNaiveBayesJob();
trainNaiveBayes.setConf(conf);
trainNaiveBayes.run(new String[] { "--input", inputFile.getAbsolutePath(), "--output", outputDir.getAbsolutePath(),
"-el", "--tempDir", tempDir.getAbsolutePath() });
NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);
AbstractVectorClassifier classifier = new StandardNaiveBayesClassifier(naiveBayesModel);
assertEquals(2, classifier.numCategories());
Vector prediction = classifier.classifyFull(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());
// should be classified as not stolen
assertTrue(prediction.get(0) < prediction.get(1));
}
@Test
public void toyDataComplementary() throws Exception {
TrainNaiveBayesJob trainNaiveBayes = new TrainNaiveBayesJob();
trainNaiveBayes.setConf(conf);
trainNaiveBayes.run(new String[] { "--input", inputFile.getAbsolutePath(), "--output", outputDir.getAbsolutePath(),
"-el", "--trainComplementary",
"--tempDir", tempDir.getAbsolutePath() });
NaiveBayesModel naiveBayesModel = NaiveBayesModel.materialize(new Path(outputDir.getAbsolutePath()), conf);
AbstractVectorClassifier classifier = new ComplementaryNaiveBayesClassifier(naiveBayesModel);
assertEquals(2, classifier.numCategories());
Vector prediction = classifier.classifyFull(trainingInstance(COLOR_RED, TYPE_SUV, ORIGIN_DOMESTIC).get());
// should be classified as not stolen
assertTrue(prediction.get(0) < prediction.get(1));
}
static VectorWritable trainingInstance(Vector.Element... elems) {
DenseVector trainingInstance = new DenseVector(6);
for (Vector.Element elem : elems) {
trainingInstance.set(elem.index(), elem.get());
}
return new VectorWritable(trainingInstance);
}
}