[SPARK-3726] [MLlib] Allow sampling_rate not equal to 1.0 in RandomForests

I've added support for sampling_rate not equal to 1.0 . I have two major questions.

1. A Scala style test is failing, since the number of parameters now exceed 10.
2. I would like suggestions to understand how to test this.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #4073 from MechCoder/spark-3726 and squashes the following commits:

8012fb2 [MechCoder] Add test in Strategy
e0e0d9c [MechCoder] TST: Add better test
d1df1b2 [MechCoder] Add test to verify subsampling behavior
a7bfc70 [MechCoder] [SPARK-3726] Allow sampling_rate not equal to 1.0

1 files changed, 16 insertions, 0 deletions

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala
index f7f0f20c6c..55e963977b 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/RandomForestSuite.scala
@@ -196,6 +196,22 @@ class RandomForestSuite extends FunSuite with MLlibTestSparkContext {
       featureSubsetStrategy = "sqrt", seed = 12345)
     EnsembleTestHelper.validateClassifier(model, arr, 1.0)
   }
+
+  test("subsampling rate in RandomForest"){
+    val arr = EnsembleTestHelper.generateOrderedLabeledPoints(5, 20)
+    val rdd = sc.parallelize(arr)
+    val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 2,
+      numClasses = 2, categoricalFeaturesInfo = Map.empty[Int, Int],
+      useNodeIdCache = true)
+
+    val rf1 = RandomForest.trainClassifier(rdd, strategy, numTrees = 3,
+      featureSubsetStrategy = "auto", seed = 123)
+    strategy.subsamplingRate = 0.5
+    val rf2 = RandomForest.trainClassifier(rdd, strategy, numTrees = 3,
+      featureSubsetStrategy = "auto", seed = 123)
+    assert(rf1.toDebugString != rf2.toDebugString)
+  }
+
 }