ml/stat/JavaKolmogorovSmirnovTestSuite.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.stat;
0019
0020 import java.io.IOException;
0021 import java.util.Arrays;
0022 import java.util.List;
0023
0024 import org.apache.commons.math3.distribution.NormalDistribution;
0025 import org.apache.spark.sql.Encoders;
0026 import org.junit.Assert;
0027 import org.junit.Test;
0028
0029 import org.apache.spark.SharedSparkSession;
0030 import org.apache.spark.api.java.function.Function;
0031 import org.apache.spark.sql.Dataset;
0032 import org.apache.spark.sql.Row;
0033
0034
0035 public class JavaKolmogorovSmirnovTestSuite extends SharedSparkSession {
0036
0037   private transient Dataset<Row> dataset;
0038
0039   @Override
0040   public void setUp() throws IOException {
0041     super.setUp();
0042     List<java.lang.Double> points = Arrays.asList(0.1, 1.1, 10.1, -1.1);
0043
0044     dataset = spark.createDataset(points, Encoders.DOUBLE()).toDF("sample");
0045   }
0046
0047   @Test
0048   public void testKSTestCDF() {
0049     // Create theoretical distributions
0050     NormalDistribution stdNormalDist = new NormalDistribution(0, 1);
0051
0052     // set seeds
0053     Long seed = 10L;
0054     stdNormalDist.reseedRandomGenerator(seed);
0055     Function<Double, Double> stdNormalCDF = (x) -> stdNormalDist.cumulativeProbability(x);
0056
0057     double pThreshold = 0.05;
0058
0059     // Comparing a standard normal sample to a standard normal distribution
0060     Row results = KolmogorovSmirnovTest
0061       .test(dataset, "sample", stdNormalCDF).head();
0062     double pValue1 = results.getDouble(0);
0063     // Cannot reject null hypothesis
0064     Assert.assertTrue(pValue1 > pThreshold);
0065   }
0066
0067   @Test
0068   public void testKSTestNamedDistribution() {
0069     double pThreshold = 0.05;
0070
0071     // Comparing a standard normal sample to a standard normal distribution
0072     Row results = KolmogorovSmirnovTest
0073             .test(dataset, "sample", "norm", 0.0, 1.0).head();
0074     double pValue1 = results.getDouble(0);
0075     // Cannot reject null hypothesis
0076     Assert.assertTrue(pValue1 > pThreshold);
0077   }
0078 }