Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/8734#discussion_r50617823
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala ---
@@ -331,12 +336,62 @@ 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") {
--- End diff --
Hmm, I just want a test case to show it actually order the bins by soft
prediction. Although @jkbradley suggested we should use directly
`binsToBestSplit`, but in order to do that, we also need to expose many details
of `findBestSplits` too, e.g., `binSeqOp`, `getNodeToFeatures` and
`partitionAggregates`...etc.
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