Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/17110#discussion_r104220081
--- Diff: mllib/src/main/scala/org/apache/spark/ml/stat/ChiSquare.scala ---
@@ -0,0 +1,81 @@
+/*
+ * 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.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.linalg.{Vector, Vectors, VectorUDT}
+import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
+import org.apache.spark.mllib.regression.{LabeledPoint => OldLabeledPoint}
+import org.apache.spark.mllib.stat.{Statistics => OldStatistics}
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions.col
+
+
+/**
+ * :: Experimental ::
+ *
+ * Chi-square hypothesis testing for categorical data.
+ *
+ * See <a
href="http://en.wikipedia.org/wiki/Chi-squared_test";>Wikipedia</a> for more
information
+ * on the Chi-squared test.
+ */
+@Experimental
+@Since("2.2.0")
+object ChiSquare {
+
+ /** Used to construct output schema of tests */
+ private case class ChiSquareResult(
+ pValues: Vector,
+ degreesOfFreedom: Array[Int],
+ statistics: Vector)
+
+ /**
+ * Conduct Pearson's independence test for every feature against the
label across the input RDD.
+ * For each feature, the (feature, label) pairs are converted into a
contingency matrix for which
+ * the Chi-squared statistic is computed. All label and feature values
must be categorical.
+ *
+ * The null hypothesis is that the occurrence of the outcomes is
statistically independent.
+ *
+ * @param dataset DataFrame of categorical labels and categorical
features.
+ * Real-valued features will be treated as categorical
for each distinct value.
+ * @param featuresCol Name of features column in dataset, of type
`Vector` (`VectorUDT`)
+ * @param labelCol Name of label column in dataset, of any numerical
type
+ * @return DataFrame containing the test result for every feature
against the label.
+ * This DataFrame will contain a single Row with the following
fields:
+ * - `pValues: Vector`
+ * - `degreesOfFreedom: Array[Int]`
+ * - `statistics: Vector`
+ * Each of these fields has one value per feature.
+ */
+ @Since("2.2.0")
+ def test(dataset: DataFrame, featuresCol: String, labelCol: String):
DataFrame = {
+ val spark = dataset.sparkSession
+ import spark.implicits._
+
+ SchemaUtils.checkColumnType(dataset.schema, featuresCol, new VectorUDT)
+ SchemaUtils.checkNumericType(dataset.schema, labelCol)
+ val rdd = dataset.select(col(labelCol).cast("double"),
col(featuresCol)).as[(Double, Vector)]
+ .rdd.map { case (label, features) => OldLabeledPoint(label,
OldVectors.fromML(features)) }
+ val testResults = OldStatistics.chiSqTest(rdd)
--- End diff --
Definitely; feel free to make a JIRA for it.
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