Github user BenFradet commented on a diff in the pull request:
https://github.com/apache/spark/pull/10355#discussion_r57219723
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
---
@@ -178,6 +179,34 @@ class RandomForestClassifierSuite extends
SparkFunSuite with MLlibTestSparkConte
assert(importances.toArray.forall(_ >= 0.0))
}
+ test("should support all NumericType labels") {
+ val dfs = MLTestingUtils.genClassifDFWithNumericLabelCol(sqlContext,
"label", "features")
+
+ val rf = new RandomForestClassifier().setFeaturesCol("features")
+
+ val expected = rf.setLabelCol("label")
+ .fit(TreeTests.setMetadata(dfs(DoubleType), 2, "label"))
+ dfs.keys.filter(_ != DoubleType).foreach { t =>
+ TreeTests.checkEqual(expected,
+ rf.setLabelCol("label").fit(TreeTests.setMetadata(dfs(t), 2,
"label")))
+ }
+ }
+
+ test("shouldn't support non NumericType labels") {
--- End diff --
I wanted to be able to do something like:
```scala
def checkNumericTypes[T <: Regressor, M <: Model](
regressor: T, sqlContext: SQLContext)(asserts: Seq[(M, M) => Unit]):
Unit = {
val dfs = MLTestingUtils.genRegressionDFWithNumericLabelCol(sqlContext,
"label", "features")
val expected = regressor.fit(dfs(DoubleType))
dfs.keys.filter(_ != DoubleType).foreach { t =>
val actual = regressor.fit(dfs(t))
asserts.foreach(f => f(expected, actual))
}
}
```
But unfortunately that doesn't work since I'm not able to capture which
model is being created by the regressor's `fit` method.
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