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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
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   * See the License for the specific language governing permissions and
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  package org.apache.spark.ml.tuning;
  
  import java.io.IOException;
  import java.util.List;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.ml.classification.LogisticRegression;
  import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator;
  import org.apache.spark.ml.feature.LabeledPoint;
  import org.apache.spark.ml.param.ParamMap;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import static org.apache.spark.ml.classification.LogisticRegressionSuite.generateLogisticInputAsList;
  
  
  public class JavaCrossValidatorSuite extends SharedSparkSession {
  
    private transient Dataset<Row> dataset;
  
    @Override
    public void setUp() throws IOException {
      super.setUp();
      List<LabeledPoint> points = generateLogisticInputAsList(1.0, 1.0, 100, 42);
      dataset = spark.createDataFrame(jsc.parallelize(points, 2), LabeledPoint.class);
    }
  
    @Test
    public void crossValidationWithLogisticRegression() {
      LogisticRegression lr = new LogisticRegression();
      ParamMap[] lrParamMaps = new ParamGridBuilder()
        .addGrid(lr.regParam(), new double[]{0.001, 1000.0})
        .addGrid(lr.maxIter(), new int[]{0, 10})
        .build();
      BinaryClassificationEvaluator eval = new BinaryClassificationEvaluator();
      CrossValidator cv = new CrossValidator()
        .setEstimator(lr)
        .setEstimatorParamMaps(lrParamMaps)
        .setEvaluator(eval)
        .setNumFolds(3);
      CrossValidatorModel cvModel = cv.fit(dataset);
      LogisticRegression parent = (LogisticRegression) cvModel.bestModel().parent();
      Assert.assertEquals(0.001, parent.getRegParam(), 0.0);
      Assert.assertEquals(10, parent.getMaxIter());
    }
  }
  