Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/17819#discussion_r143724079
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala ---
@@ -187,6 +188,196 @@ class BucketizerSuite extends SparkFunSuite with
MLlibTestSparkContext with Defa
}
}
}
+
+ test("multiple columns: Bucket continuous features, without -inf,inf") {
+ // Check a set of valid feature values.
+ val splits = Array(Array(-0.5, 0.0, 0.5), Array(-0.1, 0.3, 0.5))
+ val validData1 = Array(-0.5, -0.3, 0.0, 0.2)
+ val validData2 = Array(0.5, 0.3, 0.0, -0.1)
+ val expectedBuckets1 = Array(0.0, 0.0, 1.0, 1.0)
+ val expectedBuckets2 = Array(1.0, 1.0, 0.0, 0.0)
+
+ val data = (0 until validData1.length).map { idx =>
+ (validData1(idx), validData2(idx), expectedBuckets1(idx),
expectedBuckets2(idx))
+ }
+ val dataFrame: DataFrame = data.toSeq.toDF("feature1", "feature2",
"expected1", "expected2")
+
+ val bucketizer1: Bucketizer = new Bucketizer()
+ .setInputCols(Array("feature1", "feature2"))
+ .setOutputCols(Array("result1", "result2"))
+ .setSplitsArray(splits)
+
+ assert(bucketizer1.isBucketizeMultipleColumns())
+
+ bucketizer1.transform(dataFrame).select("result1", "expected1",
"result2", "expected2")
+ .collect().foreach {
+ case Row(r1: Double, e1: Double, r2: Double, e2: Double) =>
+ assert(r1 === e1,
+ s"The feature value is not correct after bucketing. Expected
$e1 but found $r1")
+ assert(r2 === e2,
+ s"The feature value is not correct after bucketing. Expected
$e2 but found $r2")
+ }
+
+ // Check for exceptions when using a set of invalid feature values.
+ val invalidData1: Array[Double] = Array(-0.9) ++ validData1
--- End diff --
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