diff options
Diffstat (limited to 'mllib/src/test')
| -rw-r--r-- | mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala | 37 |
1 files changed, 28 insertions, 9 deletions
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala index 5bd0521298..6de3840b3f 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala @@ -17,8 +17,11 @@ package org.apache.spark.mllib.stat +import java.util.Random + import org.scalatest.FunSuite +import org.apache.spark.SparkException import org.apache.spark.mllib.linalg.{DenseVector, Matrices, Vectors} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.stat.test.ChiSqTest @@ -107,12 +110,13 @@ class HypothesisTestSuite extends FunSuite with LocalSparkContext { // 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 = Array(new LabeledPoint(0.0, Vectors.dense(0.5, 10.0)), - new LabeledPoint(0.0, Vectors.dense(1.5, 20.0)), - new LabeledPoint(1.0, Vectors.dense(1.5, 30.0)), - new LabeledPoint(0.0, Vectors.dense(3.5, 30.0)), - new LabeledPoint(0.0, Vectors.dense(3.5, 40.0)), - new LabeledPoint(1.0, Vectors.dense(3.5, 40.0))) + 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 chi = Statistics.chiSqTest(sc.parallelize(data, numParts)) val feature1 = chi(0) @@ -130,10 +134,25 @@ class HypothesisTestSuite extends FunSuite with LocalSparkContext { } // Test that the right number of results is returned - val numCols = 321 - val sparseData = Array(new LabeledPoint(0.0, Vectors.sparse(numCols, Seq((100, 2.0)))), - new LabeledPoint(0.0, Vectors.sparse(numCols, Seq((200, 1.0))))) + val numCols = 1001 + val sparseData = Array( + new LabeledPoint(0.0, Vectors.sparse(numCols, Seq((100, 2.0)))), + new LabeledPoint(0.1, Vectors.sparse(numCols, Seq((200, 1.0))))) val chi = Statistics.chiSqTest(sc.parallelize(sparseData)) assert(chi.size === numCols) + assert(chi(1000) != null) // SPARK-3087 + + // Detect continous features or labels + val random = new Random(11L) + val continuousLabel = + Seq.fill(100000)(LabeledPoint(random.nextDouble(), Vectors.dense(random.nextInt(2)))) + intercept[SparkException] { + Statistics.chiSqTest(sc.parallelize(continuousLabel, 2)) + } + val continuousFeature = + Seq.fill(100000)(LabeledPoint(random.nextInt(2), Vectors.dense(random.nextDouble()))) + intercept[SparkException] { + Statistics.chiSqTest(sc.parallelize(continuousFeature, 2)) + } } } |