Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/19108#discussion_r174977495
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/stat/KolmogorovSmirnovTestSuite.scala
---
@@ -0,0 +1,133 @@
+/*
+ * 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.stat
+
+import org.apache.commons.math3.distribution.{ExponentialDistribution,
NormalDistribution}
+import org.apache.commons.math3.stat.inference.{KolmogorovSmirnovTest =>
Math3KSTest}
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.util.DefaultReadWriteTest
+import org.apache.spark.ml.util.TestingUtils._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.Row
+
+class KolmogorovSmirnovTestSuite
+ extends SparkFunSuite with MLlibTestSparkContext with
DefaultReadWriteTest {
+
+ import testImplicits._
+
+ test("1 sample Kolmogorov-Smirnov test: apache commons math3
implementation equivalence") {
+ // Create theoretical distributions
+ val stdNormalDist = new NormalDistribution(0, 1)
+ val expDist = new ExponentialDistribution(0.6)
+
+ // set seeds
+ val seed = 10L
+ stdNormalDist.reseedRandomGenerator(seed)
+ expDist.reseedRandomGenerator(seed)
+
+ // Sample data from the distributions and parallelize it
+ val n = 100000
+ val sampledNormArray = stdNormalDist.sample(n)
+ val sampledNormDF = sc.parallelize(sampledNormArray, 10).toDF("sample")
+ val sampledExpArray = expDist.sample(n)
+ val sampledExpDF = sc.parallelize(sampledExpArray, 10).toDF("sample")
+
+ // Use a apache math commons local KS test to verify calculations
+ val ksTest = new Math3KSTest()
+ val pThreshold = 0.05
+
+ // Comparing a standard normal sample to a standard normal distribution
--- End diff --
Can this and the next set of code lines be combined into a single helper
method? That could help with adding in the test for the uniform distribution
as well.
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