Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/8734#discussion_r48616306
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala ---
@@ -337,12 +342,62 @@ class DecisionTreeSuite extends SparkFunSuite with
MLlibTestSparkContext {
assert(topNode.impurity !== -1.0)
// set impurity and predict for child nodes
- assert(topNode.leftNode.get.predict.predict === 0.0)
- assert(topNode.rightNode.get.predict.predict === 1.0)
+ if (topNode.leftNode.get.predict.predict === 0.0) {
+ assert(topNode.rightNode.get.predict.predict === 1.0)
+ } else {
+ assert(topNode.leftNode.get.predict.predict === 1.0)
+ assert(topNode.rightNode.get.predict.predict === 0.0)
+ }
assert(topNode.leftNode.get.impurity === 0.0)
assert(topNode.rightNode.get.impurity === 0.0)
}
+ test("Use soft prediction for binary classification with ordered
categorical features") {
+ val arr = Array(
+ LabeledPoint(0.0, Vectors.dense(1.0, 0.0, 0.0)), // left node
+ LabeledPoint(1.0, Vectors.dense(0.0, 1.0, 1.0)), // right node
+ LabeledPoint(0.0, Vectors.dense(2.0, 0.0, 0.0)), // left node
+ LabeledPoint(1.0, Vectors.dense(0.0, 2.0, 1.0)), // right node
+ LabeledPoint(1.0, Vectors.dense(1.0, 1.0, 0.0)), // left node
+ LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 2.0))) // left node
+ val input = sc.parallelize(arr)
+
+ val strategy = new Strategy(algo = Classification, impurity = Gini,
maxDepth = 1,
+ numClasses = 2, categoricalFeaturesInfo = Map(0 -> 3))
+ val metadata = DecisionTreeMetadata.buildMetadata(input, strategy)
+ val (splits, bins) = DecisionTree.findSplitsBins(input, metadata)
+
+ val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata)
+ val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, 1.0, 1,
false)
+
+ val topNode = Node.emptyNode(nodeIndex = 1)
+ assert(topNode.predict.predict === Double.MinValue)
+ assert(topNode.impurity === -1.0)
+ assert(topNode.isLeaf === false)
+
+ val nodesForGroup = Map((0, Array(topNode)))
+ val treeToNodeToIndexInfo = Map((0, Map(
+ (topNode.id, new RandomForest.NodeIndexInfo(0, None))
+ )))
+ val nodeQueue = new mutable.Queue[(Int, Node)]()
+ DecisionTree.findBestSplits(baggedInput, metadata, Array(topNode),
--- End diff --
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