/*
* 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.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.util.LinearDataGenerator;
public class JavaLassoSuite implements Serializable {
private transient JavaSparkContext sc;
@Before
public void setUp() {
sc = new JavaSparkContext("local", "JavaLassoSuite");
}
@After
public void tearDown() {
sc.stop();
sc = null;
System.clearProperty("spark.driver.port");
}
int validatePrediction(List<LabeledPoint> validationData, LassoModel 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 runLassoUsingConstructor() {
int nPoints = 10000;
double A = 0.0;
double[] weights = {-1.5, 1.0e-2};
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);
LassoWithSGD lassoSGDImpl = new LassoWithSGD();
lassoSGDImpl.optimizer().setStepSize(1.0)
.setRegParam(0.01)
.setNumIterations(20);
LassoModel model = lassoSGDImpl.run(testRDD.rdd());
int numAccurate = validatePrediction(validationData, model);
Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
}
@Test
public void runLassoUsingStaticMethods() {
int nPoints = 10000;
double A = 0.0;
double[] weights = {-1.5, 1.0e-2};
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);
LassoModel model = LassoWithSGD.train(testRDD.rdd(), 100, 1.0, 0.01, 1.0);
int numAccurate = validatePrediction(validationData, model);
Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
}
}