Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/17110#discussion_r103813679
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/stat/ChiSquareSuite.scala ---
@@ -0,0 +1,94 @@
+/*
+ * 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 java.util.Random
+
+import org.apache.spark.{SparkException, SparkFunSuite}
+import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.linalg.{Vector, Vectors}
+import org.apache.spark.ml.util.DefaultReadWriteTest
+import org.apache.spark.ml.util.TestingUtils._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+
+class ChiSquareSuite
+ extends SparkFunSuite with MLlibTestSparkContext with
DefaultReadWriteTest {
+
+ import testImplicits._
+
+ test("test DataFrame of labeled points") {
+ // labels: 1.0 (2 / 6), 0.0 (4 / 6)
+ // feature1: 0.5 (1 / 6), 1.5 (2 / 6), 3.5 (3 / 6)
+ // feature2: 10.0 (1 / 6), 20.0 (1 / 6), 30.0 (2 / 6), 40.0 (2 / 6)
+ val data = Seq(
+ LabeledPoint(0.0, Vectors.dense(0.5, 10.0)),
+ LabeledPoint(0.0, Vectors.dense(1.5, 20.0)),
+ LabeledPoint(1.0, Vectors.dense(1.5, 30.0)),
+ LabeledPoint(0.0, Vectors.dense(3.5, 30.0)),
+ LabeledPoint(0.0, Vectors.dense(3.5, 40.0)),
+ LabeledPoint(1.0, Vectors.dense(3.5, 40.0)))
+ for (numParts <- List(2, 4, 6, 8)) {
+ val df = spark.createDataFrame(sc.parallelize(data, numParts))
+ val chi = ChiSquare.test(df, "features", "label")
+ val (pValues: Vector, degreesOfFreedom: Array[Int], statistics:
Vector) =
+ chi.select("pValues", "degreesOfFreedom", "statistics")
+ .as[(Vector, Array[Int], Vector)].head()
+ assert(pValues ~== Vectors.dense(0.6873, 0.6823) relTol 1e-4)
+ assert(degreesOfFreedom === Array(2, 3))
+ assert(statistics ~== Vectors.dense(0.75, 1.5) relTol 1e-4)
+ }
+ }
+
+ test("large number of features (SPARK-3087)") {
+ // Test that the right number of results is returned
+ val numCols = 1001
+ val sparseData = Array(
+ LabeledPoint(0.0, Vectors.sparse(numCols, Seq((100, 2.0)))),
+ LabeledPoint(0.1, Vectors.sparse(numCols, Seq((200, 1.0)))))
+ val df = spark.createDataFrame(sparseData)
+ val chi = ChiSquare.test(df, "features", "label")
+ val (pValues: Vector, degreesOfFreedom: Array[Int], statistics:
Vector) =
+ chi.select("pValues", "degreesOfFreedom", "statistics")
+ .as[(Vector, Array[Int], Vector)].head()
+ assert(pValues.size === numCols)
+ assert(degreesOfFreedom.length === numCols)
+ assert(statistics.size === numCols)
+ assert(pValues(1000) !== null) // SPARK-3087
+ }
+
+ test("fail on continuous features or labels") {
+ // Detect continuous features or labels
+ val random = new Random(11L)
+ val continuousLabel =
+ Seq.fill(100000)(LabeledPoint(random.nextDouble(),
Vectors.dense(random.nextInt(2))))
--- End diff --
can the special value that is above the max categorical limit of 10000 be
refactored to a constant?
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