/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.mllib.classification;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import java.io.Serializable;
import java.util.Arrays;
import java.util.List;
public class JavaNaiveBayesSuite implements Serializable {
private transient JavaSparkContext sc;
@Before
public void setUp() {
sc = new JavaSparkContext("local", "JavaNaiveBayesSuite");
}
@After
public void tearDown() {
sc.stop();
sc = null;
System.clearProperty("spark.driver.port");
}
private static final List<LabeledPoint> POINTS = Arrays.asList(
new LabeledPoint(0, Vectors.dense(1.0, 0.0, 0.0)),
new LabeledPoint(0, Vectors.dense(2.0, 0.0, 0.0)),
new LabeledPoint(1, Vectors.dense(0.0, 1.0, 0.0)),
new LabeledPoint(1, Vectors.dense(0.0, 2.0, 0.0)),
new LabeledPoint(2, Vectors.dense(0.0, 0.0, 1.0)),
new LabeledPoint(2, Vectors.dense(0.0, 0.0, 2.0))
);
private int validatePrediction(List<LabeledPoint> points, NaiveBayesModel model) {
int correct = 0;
for (LabeledPoint p: points) {
if (model.predict(p.features()) == p.label()) {
correct += 1;
}
}
return correct;
}
@Test
public void runUsingConstructor() {
JavaRDD<LabeledPoint> testRDD = sc.parallelize(POINTS, 2).cache();
NaiveBayes nb = new NaiveBayes().setLambda(1.0);
NaiveBayesModel model = nb.run(testRDD.rdd());
int numAccurate = validatePrediction(POINTS, model);
Assert.assertEquals(POINTS.size(), numAccurate);
}
@Test
public void runUsingStaticMethods() {
JavaRDD<LabeledPoint> testRDD = sc.parallelize(POINTS, 2).cache();
NaiveBayesModel model1 = NaiveBayes.train(testRDD.rdd());
int numAccurate1 = validatePrediction(POINTS, model1);
Assert.assertEquals(POINTS.size(), numAccurate1);
NaiveBayesModel model2 = NaiveBayes.train(testRDD.rdd(), 0.5);
int numAccurate2 = validatePrediction(POINTS, model2);
Assert.assertEquals(POINTS.size(), numAccurate2);
}
}