change edge minInstancesPerNode to 2 and add one more test

mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala

@@ -683,8 +683,8 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     arr(2) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0))))
 
     val input = sc.parallelize(arr)
-    val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 2,
-      numClassesForClassification = 2, minInstancesPerNode = 4)
+    val strategy = new Strategy(algo = Classification, impurity = Gini,
+      maxDepth = 2, numClassesForClassification = 2, minInstancesPerNode = 2)
 
     val model = DecisionTree.train(input, strategy)
     assert(model.topNode.isLeaf)
@@ -701,11 +701,37 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
       new Array[Node](0), splits, bins, 10)
 
-    assert(bestSplits.length === 1)
+    assert(bestSplits.length == 1)
     val bestInfoStats = bestSplits(0)._2
     assert(bestInfoStats == InformationGainStats.invalidInformationGainStats)
   }
 
+  test("don't chose split that doesn't satify min instance per node requirements") {
+    // if a split doesn't satisfy min instances per node requirements,
+    // this split is invalid, even though the information gain of split is large.
+    val arr = new Array[LabeledPoint](4)
+    arr(0) = new LabeledPoint(0.0, Vectors.dense(0.0, 1.0))
+    arr(1) = new LabeledPoint(1.0, Vectors.dense(1.0, 1.0))
+    arr(2) = new LabeledPoint(0.0, Vectors.dense(0.0, 0.0))
+    arr(3) = new LabeledPoint(0.0, Vectors.dense(0.0, 0.0))
+
+    val input = sc.parallelize(arr)
+    val strategy = new Strategy(algo = Classification, impurity = Gini,
+      maxBins = 2, maxDepth = 2, categoricalFeaturesInfo = Map(0 -> 2, 1-> 2),
+      numClassesForClassification = 2, minInstancesPerNode = 2)
+    val metadata = DecisionTreeMetadata.buildMetadata(input, strategy)
+    val (splits, bins) = DecisionTree.findSplitsBins(input, metadata)
+    val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata)
+    val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
+      new Array[Node](0), splits, bins, 10)
+
+    assert(bestSplits.length == 1)
+    val bestSplit = bestSplits(0)._1
+    val bestSplitStats = bestSplits(0)._1
+    assert(bestSplit.feature == 1)
+    assert(bestSplitStats != InformationGainStats.invalidInformationGainStats)
+  }
+
   test("split must satisfy min info gain requirements") {
     val arr = new Array[LabeledPoint](3)
     arr(0) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0))))
@@ -731,7 +757,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
     val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
       new Array[Node](0), splits, bins, 10)
 
-    assert(bestSplits.length === 1)
+    assert(bestSplits.length == 1)
     val bestInfoStats = bestSplits(0)._2
     assert(bestInfoStats == InformationGainStats.invalidInformationGainStats)
   }