ml/feature/JavaBucketizerSuite.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.feature;
0019
0020 import java.util.Arrays;
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.sql.Dataset;
0028 import org.apache.spark.sql.Row;
0029 import org.apache.spark.sql.RowFactory;
0030 import org.apache.spark.sql.types.DataTypes;
0031 import org.apache.spark.sql.types.Metadata;
0032 import org.apache.spark.sql.types.StructField;
0033 import org.apache.spark.sql.types.StructType;
0034
0035 public class JavaBucketizerSuite extends SharedSparkSession {
0036
0037   @Test
0038   public void bucketizerTest() {
0039     double[] splits = {-0.5, 0.0, 0.5};
0040
0041     StructType schema = new StructType(new StructField[]{
0042       new StructField("feature", DataTypes.DoubleType, false, Metadata.empty())
0043     });
0044     Dataset<Row> dataset = spark.createDataFrame(
0045       Arrays.asList(
0046         RowFactory.create(-0.5),
0047         RowFactory.create(-0.3),
0048         RowFactory.create(0.0),
0049         RowFactory.create(0.2)),
0050       schema);
0051
0052     Bucketizer bucketizer = new Bucketizer()
0053       .setInputCol("feature")
0054       .setOutputCol("result")
0055       .setSplits(splits);
0056
0057     List<Row> result = bucketizer.transform(dataset).select("result").collectAsList();
0058
0059     for (Row r : result) {
0060       double index = r.getDouble(0);
0061       Assert.assertTrue((index >= 0) && (index <= 1));
0062     }
0063   }
0064
0065   @Test
0066   public void bucketizerMultipleColumnsTest() {
0067     double[][] splitsArray = {
0068       {-0.5, 0.0, 0.5},
0069       {-0.5, 0.0, 0.2, 0.5}
0070     };
0071
0072     StructType schema = new StructType(new StructField[]{
0073       new StructField("feature1", DataTypes.DoubleType, false, Metadata.empty()),
0074       new StructField("feature2", DataTypes.DoubleType, false, Metadata.empty()),
0075     });
0076     Dataset<Row> dataset = spark.createDataFrame(
0077       Arrays.asList(
0078         RowFactory.create(-0.5, -0.5),
0079         RowFactory.create(-0.3, -0.3),
0080         RowFactory.create(0.0, 0.0),
0081         RowFactory.create(0.2, 0.3)),
0082       schema);
0083
0084     Bucketizer bucketizer = new Bucketizer()
0085       .setInputCols(new String[] {"feature1", "feature2"})
0086       .setOutputCols(new String[] {"result1", "result2"})
0087       .setSplitsArray(splitsArray);
0088
0089     List<Row> result = bucketizer.transform(dataset).select("result1", "result2").collectAsList();
0090
0091     for (Row r : result) {
0092       double index1 = r.getDouble(0);
0093       Assert.assertTrue((index1 >= 0) && (index1 <= 1));
0094
0095       double index2 = r.getDouble(1);
0096       Assert.assertTrue((index2 >= 0) && (index2 <= 2));
0097     }
0098   }
0099 }