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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.ml.classification;
  
  import java.util.Arrays;
  import java.util.List;
  
  import org.junit.Test;
  import static org.junit.Assert.assertEquals;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.ml.linalg.VectorUDT;
  import org.apache.spark.ml.linalg.Vectors;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.RowFactory;
  import org.apache.spark.sql.types.DataTypes;
  import org.apache.spark.sql.types.Metadata;
  import org.apache.spark.sql.types.StructField;
  import org.apache.spark.sql.types.StructType;
  
  public class JavaNaiveBayesSuite extends SharedSparkSession {
  
    public void validatePrediction(Dataset<Row> predictionAndLabels) {
      for (Row r : predictionAndLabels.collectAsList()) {
        double prediction = r.getAs(0);
        double label = r.getAs(1);
        assertEquals(label, prediction, 1E-5);
      }
    }
  
    @Test
    public void naiveBayesDefaultParams() {
      NaiveBayes nb = new NaiveBayes();
      assertEquals("label", nb.getLabelCol());
      assertEquals("features", nb.getFeaturesCol());
      assertEquals("prediction", nb.getPredictionCol());
      assertEquals(1.0, nb.getSmoothing(), 1E-5);
      assertEquals("multinomial", nb.getModelType());
    }
  
    @Test
    public void testNaiveBayes() {
      List<Row> data = Arrays.asList(
        RowFactory.create(0.0, Vectors.dense(1.0, 0.0, 0.0)),
        RowFactory.create(0.0, Vectors.dense(2.0, 0.0, 0.0)),
        RowFactory.create(1.0, Vectors.dense(0.0, 1.0, 0.0)),
        RowFactory.create(1.0, Vectors.dense(0.0, 2.0, 0.0)),
        RowFactory.create(2.0, Vectors.dense(0.0, 0.0, 1.0)),
        RowFactory.create(2.0, Vectors.dense(0.0, 0.0, 2.0)));
  
      StructType schema = new StructType(new StructField[]{
        new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
        new StructField("features", new VectorUDT(), false, Metadata.empty())
      });
  
      Dataset<Row> dataset = spark.createDataFrame(data, schema);
      NaiveBayes nb = new NaiveBayes().setSmoothing(0.5).setModelType("multinomial");
      NaiveBayesModel model = nb.fit(dataset);
  
      Dataset<Row> predictionAndLabels = model.transform(dataset).select("prediction", "label");
      validatePrediction(predictionAndLabels);
    }
  }
  