content: /*
   * 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.feature;
  
  import java.util.Arrays;
  import java.util.List;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  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 JavaBucketizerSuite extends SharedSparkSession {
  
    @Test
    public void bucketizerTest() {
      double[] splits = {-0.5, 0.0, 0.5};
  
      StructType schema = new StructType(new StructField[]{
        new StructField("feature", DataTypes.DoubleType, false, Metadata.empty())
      });
      Dataset<Row> dataset = spark.createDataFrame(
        Arrays.asList(
          RowFactory.create(-0.5),
          RowFactory.create(-0.3),
          RowFactory.create(0.0),
          RowFactory.create(0.2)),
        schema);
  
      Bucketizer bucketizer = new Bucketizer()
        .setInputCol("feature")
        .setOutputCol("result")
        .setSplits(splits);
  
      List<Row> result = bucketizer.transform(dataset).select("result").collectAsList();
  
      for (Row r : result) {
        double index = r.getDouble(0);
        Assert.assertTrue((index >= 0) && (index <= 1));
      }
    }
  }
  