Github user chouqin commented on the pull request:
https://github.com/apache/spark/pull/2595#issuecomment-57423431
I don't know why unit test for pyspark has failed, I have tested in Scala
using the same test data, and it passes. For example here is code I wrote:
```scala
// this data is correspond to data defined at pyspark/mllib/tests.py:
line 125
val arr = new Array[LabeledPoint](4)
arr(0) = new LabeledPoint(0.0, Vectors.dense(1.0, 0.0, 0.0))
arr(1) = new LabeledPoint(1.0, Vectors.dense(0.0, 1.0, 1.0))
arr(2) = new LabeledPoint(0.0, Vectors.dense(2.0, 0.0, 0.0))
arr(3) = new LabeledPoint(1.0, Vectors.dense(0.0, 2.0, 1.0))
val input = sc.parallelize(arr)
// these parameters are correspond to DecisionTree.trainClassifier at
pyspark/mllib/tests.py: line 154
val strategy = new Strategy(algo = Classification, impurity = Gini,
maxDepth=5,
numClassesForClassification = 2, categoricalFeaturesInfo = Map(0 ->
3))
val model = DecisionTree.train(input, strategy)
// these asserts will pass
assert(model.predict(arr(0).features) == 0.0)
assert(model.predict(arr(1).features) == 1.0)
assert(model.predict(arr(2).features) == 0.0)
assert(model.predict(arr(3).features) == 1.0)
```
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