Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/11553#discussion_r56956545
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/feature/QuantileDiscretizerSuite.scala
---
@@ -91,6 +44,46 @@ class QuantileDiscretizerSuite
"Observed number of buckets does not equal expected number of
buckets.")
}
+ test("Test transform method on unseen data") {
+ val sqlCtx = SQLContext.getOrCreate(sc)
+ import sqlCtx.implicits._
+
+ val trainDF = sc.parallelize(1.0 to 100.0 by
1.0).map(Tuple1.apply).toDF("input")
+ val testDF = sc.parallelize(-10.0 to 110.0 by
1.0).map(Tuple1.apply).toDF("input")
+ val discretizer = new QuantileDiscretizer()
+ .setInputCol("input")
+ .setOutputCol("result")
+ .setNumBuckets(5)
+
+ val result = discretizer.fit(trainDF).transform(testDF)
+ val firstBucketSize = result.filter(result("result") === 0.0).count
+ val lastBucketSize = result.filter(result("result") === 4.0).count
+
+ assert(firstBucketSize === 30L,
+ "Size of first bucket did not equal expected value.")
+ assert(lastBucketSize === 31L,
+ "Size of last bucket did not equal expected value.")
+ }
+
+ test("Test default relativeError and setRelativeError") {
+ val discretizer = new QuantileDiscretizer()
+ val testValues = Array(0.1, 0.2, 0.3, 0.4)
--- End diff --
minor, but having these values is not really testing much. Perhaps
`Array(0.0, 0.005, 0.1)` will be better, to test that we can set an explicit
value below, between and above the default range.
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