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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.ml.classification;
  
  import java.io.IOException;
  import java.util.List;
  
  import scala.collection.JavaConverters;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.ml.feature.LabeledPoint;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import static org.apache.spark.ml.classification.LogisticRegressionSuite.generateMultinomialLogisticInput;
  
  public class JavaOneVsRestSuite extends SharedSparkSession {
  
    private transient Dataset<Row> dataset;
    private transient JavaRDD<LabeledPoint> datasetRDD;
  
    @Override
    public void setUp() throws IOException {
      super.setUp();
      int nPoints = 3;
  
      // The following coefficients and xMean/xVariance are computed from iris dataset with
      // lambda=0.2.
      // As a result, we are drawing samples from probability distribution of an actual model.
      double[] coefficients = {
        -0.57997, 0.912083, -0.371077, -0.819866, 2.688191,
        -0.16624, -0.84355, -0.048509, -0.301789, 4.170682};
  
      double[] xMean = {5.843, 3.057, 3.758, 1.199};
      double[] xVariance = {0.6856, 0.1899, 3.116, 0.581};
      List<LabeledPoint> points = JavaConverters.seqAsJavaListConverter(
        generateMultinomialLogisticInput(coefficients, xMean, xVariance, true, nPoints, 42)
      ).asJava();
      datasetRDD = jsc.parallelize(points, 2);
      dataset = spark.createDataFrame(datasetRDD, LabeledPoint.class);
    }
  
    @Test
    public void oneVsRestDefaultParams() {
      OneVsRest ova = new OneVsRest();
      ova.setClassifier(new LogisticRegression());
      Assert.assertEquals(ova.getLabelCol(), "label");
      Assert.assertEquals(ova.getPredictionCol(), "prediction");
      OneVsRestModel ovaModel = ova.fit(dataset);
      Dataset<Row> predictions = ovaModel.transform(dataset).select("label", "prediction");
      predictions.collectAsList();
      Assert.assertEquals(ovaModel.getLabelCol(), "label");
      Assert.assertEquals(ovaModel.getPredictionCol(), "prediction");
    }
  }
  