ml/tuning/JavaCrossValidatorSuite.java

0001 /*
0002  * Licensed to the Apache Software Foundation (ASF) under one or more
0003  * contributor license agreements.  See the NOTICE file distributed with
0004  * this work for additional information regarding copyright ownership.
0005  * The ASF licenses this file to You under the Apache License, Version 2.0
0006  * (the "License"); you may not use this file except in compliance with
0007  * the License.  You may obtain a copy of the License at
0008  *
0009  *    http://www.apache.org/licenses/LICENSE-2.0
0010  *
0011  * Unless required by applicable law or agreed to in writing, software
0012  * distributed under the License is distributed on an "AS IS" BASIS,
0013  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
0014  * See the License for the specific language governing permissions and
0015  * limitations under the License.
0016  */
0017
0018 package org.apache.spark.ml.tuning;
0019
0020 import java.io.IOException;
0021 import java.util.List;
0022
0023 import org.junit.Assert;
0024 import org.junit.Test;
0025
0026 import org.apache.spark.SharedSparkSession;
0027 import org.apache.spark.ml.classification.LogisticRegression;
0028 import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator;
0029 import org.apache.spark.ml.feature.LabeledPoint;
0030 import org.apache.spark.ml.param.ParamMap;
0031 import org.apache.spark.sql.Dataset;
0032 import org.apache.spark.sql.Row;
0033 import static org.apache.spark.ml.classification.LogisticRegressionSuite.generateLogisticInputAsList;
0034
0035
0036 public class JavaCrossValidatorSuite extends SharedSparkSession {
0037
0038   private transient Dataset<Row> dataset;
0039
0040   @Override
0041   public void setUp() throws IOException {
0042     super.setUp();
0043     List<LabeledPoint> points = generateLogisticInputAsList(1.0, 1.0, 100, 42);
0044     dataset = spark.createDataFrame(jsc.parallelize(points, 2), LabeledPoint.class);
0045   }
0046
0047   @Test
0048   public void crossValidationWithLogisticRegression() {
0049     LogisticRegression lr = new LogisticRegression();
0050     ParamMap[] lrParamMaps = new ParamGridBuilder()
0051       .addGrid(lr.regParam(), new double[]{0.001, 1000.0})
0052       .addGrid(lr.maxIter(), new int[]{0, 10})
0053       .build();
0054     BinaryClassificationEvaluator eval = new BinaryClassificationEvaluator();
0055     CrossValidator cv = new CrossValidator()
0056       .setEstimator(lr)
0057       .setEstimatorParamMaps(lrParamMaps)
0058       .setEvaluator(eval)
0059       .setNumFolds(3);
0060     CrossValidatorModel cvModel = cv.fit(dataset);
0061     LogisticRegression parent = (LogisticRegression) cvModel.bestModel().parent();
0062     Assert.assertEquals(0.001, parent.getRegParam(), 0.0);
0063     Assert.assertEquals(10, parent.getMaxIter());
0064   }
0065 }