[SPARK-3381] [MLlib] Eliminate bins for unordered features in DecisionTrees

For unordered features, it is sufficient to use splits since the threshold of the split corresponds the threshold of the HighSplit of the bin and there is no use of the LowSplit.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #4231 from MechCoder/spark-3381 and squashes the following commits:

58c19a5 [MechCoder] COSMIT
c274b74 [MechCoder] Remove unordered feature calculation in labeledPointToTreePoint
b2b9b89 [MechCoder] COSMIT
d3ee042 [MechCoder] [SPARK-3381] [MLlib] Eliminate bins for unordered features

1 files changed, 1 insertions, 36 deletions

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
index 7b1aed5ffe..4c162df810 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
@@ -190,7 +190,7 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     assert(splits.length === 2)
     assert(bins.length === 2)
     assert(splits(0).length === 3)
-    assert(bins(0).length === 6)
+    assert(bins(0).length === 0)
 
     // Expecting 2^2 - 1 = 3 bins/splits
     assert(splits(0)(0).feature === 0)
@@ -228,41 +228,6 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext {
     assert(splits(1)(2).categories.contains(0.0))
     assert(splits(1)(2).categories.contains(1.0))
 
-    // Check bins.
-
-    assert(bins(0)(0).category === Double.MinValue)
-    assert(bins(0)(0).lowSplit.categories.length === 0)
-    assert(bins(0)(0).highSplit.categories.length === 1)
-    assert(bins(0)(0).highSplit.categories.contains(0.0))
-    assert(bins(1)(0).category === Double.MinValue)
-    assert(bins(1)(0).lowSplit.categories.length === 0)
-    assert(bins(1)(0).highSplit.categories.length === 1)
-    assert(bins(1)(0).highSplit.categories.contains(0.0))
-
-    assert(bins(0)(1).category === Double.MinValue)
-    assert(bins(0)(1).lowSplit.categories.length === 1)
-    assert(bins(0)(1).lowSplit.categories.contains(0.0))
-    assert(bins(0)(1).highSplit.categories.length === 1)
-    assert(bins(0)(1).highSplit.categories.contains(1.0))
-    assert(bins(1)(1).category === Double.MinValue)
-    assert(bins(1)(1).lowSplit.categories.length === 1)
-    assert(bins(1)(1).lowSplit.categories.contains(0.0))
-    assert(bins(1)(1).highSplit.categories.length === 1)
-    assert(bins(1)(1).highSplit.categories.contains(1.0))
-
-    assert(bins(0)(2).category === Double.MinValue)
-    assert(bins(0)(2).lowSplit.categories.length === 1)
-    assert(bins(0)(2).lowSplit.categories.contains(1.0))
-    assert(bins(0)(2).highSplit.categories.length === 2)
-    assert(bins(0)(2).highSplit.categories.contains(1.0))
-    assert(bins(0)(2).highSplit.categories.contains(0.0))
-    assert(bins(1)(2).category === Double.MinValue)
-    assert(bins(1)(2).lowSplit.categories.length === 1)
-    assert(bins(1)(2).lowSplit.categories.contains(1.0))
-    assert(bins(1)(2).highSplit.categories.length === 2)
-    assert(bins(1)(2).highSplit.categories.contains(1.0))
-    assert(bins(1)(2).highSplit.categories.contains(0.0))
-
   }
 
   test("Multiclass classification with ordered categorical features: split and bin calculations") {