[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

3 files changed, 24 insertions, 11 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
index e9304b5e5c..482dd4b272 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
@@ -140,6 +140,7 @@ private class RandomForest (
     logDebug("maxBins = " + metadata.maxBins)
     logDebug("featureSubsetStrategy = " + featureSubsetStrategy)
     logDebug("numFeaturesPerNode = " + metadata.numFeaturesPerNode)
+    logDebug("subsamplingRate = " + strategy.subsamplingRate)
 
     // Find the splits and the corresponding bins (interval between the splits) using a sample
     // of the input data.
@@ -155,19 +156,12 @@ private class RandomForest (
     // Cache input RDD for speedup during multiple passes.
     val treeInput = TreePoint.convertToTreeRDD(retaggedInput, bins, metadata)
 
-    val (subsample, withReplacement) = {
-      // TODO: Have a stricter check for RF in the strategy
-      val isRandomForest = numTrees > 1
-      if (isRandomForest) {
-        (1.0, true)
-      } else {
-        (strategy.subsamplingRate, false)
-      }
-    }
+    val withReplacement = if (numTrees > 1) true else false
 
     val baggedInput
-      = BaggedPoint.convertToBaggedRDD(treeInput, subsample, numTrees, withReplacement, seed)
-        .persist(StorageLevel.MEMORY_AND_DISK)
+      = BaggedPoint.convertToBaggedRDD(treeInput,
+          strategy.subsamplingRate, numTrees,
+          withReplacement, seed).persist(StorageLevel.MEMORY_AND_DISK)
 
     // depth of the decision tree
     val maxDepth = strategy.maxDepth
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index 972959885f..3308adb675 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -156,6 +156,9 @@ class Strategy (
       s"DecisionTree Strategy requires minInstancesPerNode >= 1 but was given $minInstancesPerNode")
     require(maxMemoryInMB <= 10240,
       s"DecisionTree Strategy requires maxMemoryInMB <= 10240, but was given $maxMemoryInMB")
+    require(subsamplingRate > 0 && subsamplingRate <= 1,
+      s"DecisionTree Strategy requires subsamplingRate <=1 and >0, but was given " +
+      s"$subsamplingRate")
   }
 
   /** Returns a shallow copy of this instance. */
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)
+  }
+
 }