Github user MrBago commented on a diff in the pull request:
https://github.com/apache/spark/pull/19108#discussion_r171438156
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/stat/KolmogorovSmirnovTest.scala ---
@@ -0,0 +1,104 @@
+/*
+ * 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 scala.annotation.varargs
+
+import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.mllib.stat.{Statistics => OldStatistics}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.functions.col
+
+/**
+ * :: Experimental ::
+ *
+ * Conduct the two-sided Kolmogorov Smirnov (KS) 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:
+ * @see <a
href="https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test";>
+ * Kolmogorov-Smirnov test (Wikipedia)</a>
+ */
+@Experimental
+@Since("2.3.0")
+object KolmogorovSmirnovTest {
+
+ /** Used to construct output schema of test */
+ private case class KolmogorovSmirnovTestResult(
+ pValues: Double,
+ statistics: Double)
+
+ private def getSampleRDD(dataset: DataFrame, sampleCol: String):
RDD[Double] = {
+ SchemaUtils.checkNumericType(dataset.schema, sampleCol)
+ dataset.select(col(sampleCol).cast("double")).rdd.map {
+ case Row(sample: Double) => sample
+ }
+ }
+
+ /**
+ * Conduct the two-sided Kolmogorov-Smirnov (KS) 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.
+ *
+ * @param dataset a `DataFrame` containing the sample of data to test
+ * @param sampleCol Name of sample column in dataset, of any numerical
type
+ * @param cdf a `Double => Double` function to calculate the theoretical
CDF at a given value
+ * @return DataFrame containing the test result for every feature
against the label.
+ * This DataFrame will contain a single Row with the following
fields:
+ * - `pValue: Double`
+ * - `statistic: Double`
+ */
+ @Since("2.3.0")
+ def test(dataset: DataFrame, sampleCol: String, cdf: Double => Double):
DataFrame = {
+ val spark = dataset.sparkSession
+
+ val rdd = getSampleRDD(dataset, sampleCol)
+ val testResult = OldStatistics.kolmogorovSmirnovTest(rdd, cdf)
+ spark.createDataFrame(Seq(KolmogorovSmirnovTestResult(
+ testResult.pValue, testResult.statistic)))
+ }
+
+ /**
+ * Convenience function to conduct a one-sample, two-sided
Kolmogorov-Smirnov test for probability
+ * distribution equality. Currently supports the normal distribution,
taking as parameters
+ * the mean and standard deviation.
+ *
+ * @param dataset a `DataFrame` containing the sample of data to test
+ * @param sampleCol Name of sample column in dataset, of any numerical
type
+ * @param distName a `String` name for a theoretical distribution,
currently only support "norm".
+ * @param params `Double*` specifying the parameters to be used for the
theoretical distribution
+ * @return DataFrame containing the test result for every feature
against the label.
--- End diff --
I believe the input is a scalar column and the output is a double, is that
right? We should update the language here to make that clear, it makes it sound
like the inputs and outputs are Vectors.
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