Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/12374#discussion_r70305428
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala ---
@@ -137,14 +137,47 @@ class RandomForestSuite extends SparkFunSuite with
MLlibTestSparkContext {
{
val fakeMetadata = new DecisionTreeMetadata(1, 0, 0, 0,
Map(), Set(),
- Array(3), Gini, QuantileStrategy.Sort,
+ Array(2), Gini, QuantileStrategy.Sort,
0, 0, 0.0, 0, 0
)
val featureSamples = Array(0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2).map(_.toDouble)
val splits =
RandomForest.findSplitsForContinuousFeature(featureSamples, fakeMetadata, 0)
assert(splits.length === 1)
assert(splits(0) === 1.0)
}
+
+ // find splits for constant feature
+ {
+ val fakeMetadata = new DecisionTreeMetadata(1, 0, 0, 0,
+ Map(), Set(),
+ Array(3), Gini, QuantileStrategy.Sort,
+ 0, 0, 0.0, 0, 0
+ )
+ val featureSamples = Array(0, 0, 0).map(_.toDouble)
+ val featureSamplesEmpty = Array.empty[Double]
+ val splits =
RandomForest.findSplitsForContinuousFeature(featureSamples, fakeMetadata, 0)
+ assert(splits === Array[Double]())
+ val splitsEmpty =
+ RandomForest.findSplitsForContinuousFeature(featureSamplesEmpty,
fakeMetadata, 0)
+ assert(splitsEmpty === Array[Double]())
+ }
+ }
+
+ test("train with constant features") {
+ val lp = LabeledPoint(1.0, Vectors.dense(0.0, 0.0, 0.0))
+ val data = Array.fill(5)(lp)
+ val rdd = sc.parallelize(data)
+ val strategy = new OldStrategy(
+ OldAlgo.Classification,
+ Gini,
+ maxDepth = 2,
+ numClasses = 100,
+ maxBins = 100,
+ categoricalFeaturesInfo = Map(0 -> 2, 1 -> 5))
--- End diff --
I am using this to not only test continuous features, but categorical ones
as well. I think we should leave it.
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