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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.
*/
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.jblas.DoubleMatrix;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.util.LinearDataGenerator;
public class JavaRidgeRegressionSuite implements Serializable {
private transient JavaSparkContext sc;
@Before
public void setUp() {
sc = new JavaSparkContext("local", "JavaRidgeRegressionSuite");
}
@After
public void tearDown() {
sc.stop();
sc = null;
System.clearProperty("spark.driver.port");
}
double predictionError(List<LabeledPoint> validationData, RidgeRegressionModel model) {
double errorSum = 0;
for (LabeledPoint point: validationData) {
Double prediction = model.predict(point.features());
errorSum += (prediction - point.label()) * (prediction - point.label());
}
return errorSum / validationData.size();
}
List<LabeledPoint> generateRidgeData(int numPoints, int nfeatures, double eps) {
org.jblas.util.Random.seed(42);
// Pick weights as random values distributed uniformly in [-0.5, 0.5]
DoubleMatrix w = DoubleMatrix.rand(nfeatures, 1).subi(0.5);
// Set first two weights to eps
w.put(0, 0, eps);
w.put(1, 0, eps);
return LinearDataGenerator.generateLinearInputAsList(0.0, w.data, numPoints, 42, eps);
}
@Test
public void runRidgeRegressionUsingConstructor() {
int nexamples = 200;
int nfeatures = 20;
double eps = 10.0;
List<LabeledPoint> data = generateRidgeData(2*nexamples, nfeatures, eps);
JavaRDD<LabeledPoint> testRDD = sc.parallelize(data.subList(0, nexamples));
List<LabeledPoint> validationData = data.subList(nexamples, 2*nexamples);
RidgeRegressionWithSGD ridgeSGDImpl = new RidgeRegressionWithSGD();
ridgeSGDImpl.optimizer().setStepSize(1.0)
.setRegParam(0.0)
.setNumIterations(200);
RidgeRegressionModel model = ridgeSGDImpl.run(testRDD.rdd());
double unRegularizedErr = predictionError(validationData, model);
ridgeSGDImpl.optimizer().setRegParam(0.1);
model = ridgeSGDImpl.run(testRDD.rdd());
double regularizedErr = predictionError(validationData, model);
Assert.assertTrue(regularizedErr < unRegularizedErr);
}
@Test
public void runRidgeRegressionUsingStaticMethods() {
int nexamples = 200;
int nfeatures = 20;
double eps = 10.0;
List<LabeledPoint> data = generateRidgeData(2*nexamples, nfeatures, eps);
JavaRDD<LabeledPoint> testRDD = sc.parallelize(data.subList(0, nexamples));
List<LabeledPoint> validationData = data.subList(nexamples, 2*nexamples);
RidgeRegressionModel model = RidgeRegressionWithSGD.train(testRDD.rdd(), 200, 1.0, 0.0);
double unRegularizedErr = predictionError(validationData, model);
model = RidgeRegressionWithSGD.train(testRDD.rdd(), 200, 1.0, 0.1);
double regularizedErr = predictionError(validationData, model);
Assert.assertTrue(regularizedErr < unRegularizedErr);
}
}
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