Github user josepablocam commented on a diff in the pull request:
https://github.com/apache/spark/pull/6994#discussion_r33394801
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/stat/test/KSTest.scala ---
@@ -0,0 +1,191 @@
+/*
+ * 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.mllib.stat.test
+
+import org.apache.commons.math3.distribution.{NormalDistribution,
RealDistribution}
+import org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest
+
+import org.apache.spark.rdd.RDD
+
+/**
+ * Conduct the two-sided Kolmogorov Smirnov test for data sampled from a
+ * continuous distribution. By comparing the largest difference between
the empirical cumulative
+ * distribution of the sample data and the theoretical distribution we can
provide a test for the
+ * the null hypothesis that the sample data comes from that theoretical
distribution.
+ * For more information on KS Test:
https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test
+ *
+ * Implementation note: We seek to implement the KS test with a minimal
number of distributed
+ * passes. We sort the RDD, and then perform the following operations on a
per-partition basis:
+ * calculate an empirical cumulative distribution value for each
observation, and a theoretical
+ * cumulative distribution value. We know the latter to be correct, while
the former will be off by
+ * a constant (how large the constant is depends on how many values
precede it in other partitions).
+ * However, given that this constant simply shifts the ECDF upwards, but
doesn't change its shape,
+ * and furthermore, that constant is the same within a given partition, we
can pick 2 values
+ * in each partition that can potentially resolve to the largest global
distance. Namely, we
+ * pick the minimum distance and the maximum distance. Additionally, we
keep track of how many
+ * elements are in each partition. Once these three values have been
returned for every partition,
+ * we can collect and operate locally. Locally, we can now adjust each
distance by the appropriate
+ * constant (the cumulative sum of # of elements in the prior partitions
divided by the data set
+ * size). Finally, we take the maximum absolute value, and this is the
statistic.
+ */
+private[stat] object KSTest {
+
+ // Null hypothesis for the type of KS test to be included in the result.
+ object NullHypothesis extends Enumeration {
+ type NullHypothesis = Value
+ val oneSampleTwoSided = Value("Sample follows theoretical
distribution.")
+ }
+
+ /**
+ * Runs a KS test for 1 set of sample data, comparing it to a
theoretical distribution
+ * @param data `RDD[Double]` data on which to run test
+ * @param cdf `Double => Double` function to calculate the theoretical
CDF
+ * @return KSTestResult summarizing the test results (pval, statistic,
and null hypothesis)
+ */
+ def testOneSample(data: RDD[Double], cdf: Double => Double):
KSTestResult = {
+ val n = data.count().toDouble
+ val localData = data.sortBy(x => x).mapPartitions { part =>
+ val partDiffs = oneSampleDifferences(part, n, cdf) // local distances
+ searchOneSampleCandidates(partDiffs) // candidates: local extrema
+ }.collect()
+ val ksStat = searchOneSampleStatistic(localData, n) // result: global
extreme
+ evalOneSampleP(ksStat, n.toLong)
+ }
+
+ /**
+ * Runs a KS test for 1 set of sample data, comparing it to a
theoretical distribution
+ * @param data `RDD[Double]` data on which to run test
+ * @param createDist `Unit => RealDistribution` function to create a
theoretical distribution
+ * @return KSTestResult summarizing the test results (pval, statistic,
and null hypothesis)
+ */
+ def testOneSample(data: RDD[Double], createDist: () =>
RealDistribution): KSTestResult = {
+ val n = data.count().toDouble
+ val localData = data.sortBy(x => x).mapPartitions { part =>
+ val partDiffs = oneSampleDifferences(part, n, createDist) // local
distances
+ searchOneSampleCandidates(partDiffs) // candidates: local extrema
+ }.collect()
--- End diff --
got it, sorry, though that should be indented since it is in the
mapPartitions lambda. I'll indent back so that it is lined up with the val of
val localData
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
