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* 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.
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package org.apache.spark.mllib.regression;
import java.io.Serializable;
import java.util.List;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.util.LinearDataGenerator;
public class JavaLinearRegressionSuite implements Serializable {
private transient JavaSparkContext sc;
@Before
public void setUp() {
sc = new JavaSparkContext("local", "JavaLinearRegressionSuite");
}
@After
public void tearDown() {
sc.stop();
sc = null;
}
int validatePrediction(List<LabeledPoint> validationData, LinearRegressionModel model) {
int numAccurate = 0;
for (LabeledPoint point: validationData) {
Double prediction = model.predict(point.features());
// A prediction is off if the prediction is more than 0.5 away from expected value.
if (Math.abs(prediction - point.label()) <= 0.5) {
numAccurate++;
}
}
return numAccurate;
}
@Test
public void runLinearRegressionUsingConstructor() {
int nPoints = 100;
double A = 3.0;
double[] weights = {10, 10};
JavaRDD<LabeledPoint> testRDD = sc.parallelize(
LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 42, 0.1), 2).cache();
List<LabeledPoint> validationData =
LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 17, 0.1);
LinearRegressionWithSGD linSGDImpl = new LinearRegressionWithSGD();
linSGDImpl.setIntercept(true);
LinearRegressionModel model = linSGDImpl.run(testRDD.rdd());
int numAccurate = validatePrediction(validationData, model);
Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
}
@Test
public void runLinearRegressionUsingStaticMethods() {
int nPoints = 100;
double A = 0.0;
double[] weights = {10, 10};
JavaRDD<LabeledPoint> testRDD = sc.parallelize(
LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 42, 0.1), 2).cache();
List<LabeledPoint> validationData =
LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 17, 0.1);
LinearRegressionModel model = LinearRegressionWithSGD.train(testRDD.rdd(), 100);
int numAccurate = validatePrediction(validationData, model);
Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
}
@Test
public void testPredictJavaRDD() {
int nPoints = 100;
double A = 0.0;
double[] weights = {10, 10};
JavaRDD<LabeledPoint> testRDD = sc.parallelize(
LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 42, 0.1), 2).cache();
LinearRegressionWithSGD linSGDImpl = new LinearRegressionWithSGD();
LinearRegressionModel model = linSGDImpl.run(testRDD.rdd());
JavaRDD<Vector> vectors = testRDD.map(new Function<LabeledPoint, Vector>() {
@Override
public Vector call(LabeledPoint v) throws Exception {
return v.features();
}
});
JavaRDD<Double> predictions = model.predict(vectors);
// Should be able to get the first prediction.
predictions.first();
}
}