Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/11832#discussion_r56948423
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/feature/HashingTFSuite.scala ---
@@ -52,6 +52,27 @@ class HashingTFSuite extends SparkFunSuite with
MLlibTestSparkContext with Defau
assert(features ~== expected absTol 1e-14)
}
+ test("applying binary term freqs") {
+ val df = sqlContext.createDataFrame(Seq(
+ (0, "a a b c c c".split(" ").toSeq)
+ )).toDF("id", "words")
+ val n = 100
+ val hashingTF = new HashingTF()
+ .setInputCol("words")
+ .setOutputCol("features")
+ .setNumFeatures(n)
+ .setBinary(true)
+ val output = hashingTF.transform(df)
+ val attrGroup =
AttributeGroup.fromStructField(output.schema("features"))
+ require(attrGroup.numAttributes === Some(n))
+ val features = output.select("features").first().getAs[Vector](0)
+ // Assume perfect hash on "a", "b", "c".
+ def idx(any: Any): Int = Utils.nonNegativeMod(any.##, n)
+ val expected = Vectors.sparse(n,
+ Seq((idx("a"), 1.0), (idx("b"), 1.0), (idx("c"), 1.0)))
+ assert(features ~== expected absTol 1e-14)
+ }
--- End diff --
I think the idea is that you could test both normal mode and binary mode in
the same test - though I personally like separate test cases as it is more
obvious what went wrong when it fails. You can pare down this test though (see
my other comments).
I think the MLlib test is fine as is.
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