Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/16784#discussion_r103582543
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/LinearSVCSuite.scala ---
@@ -51,6 +54,12 @@ class LinearSVCSuite extends SparkFunSuite with
MLlibTestSparkContext with Defau
smallBinaryDataset = generateSVMInput(A, Array[Double](B, C), nPoints,
42).toDF()
smallValidationDataset = generateSVMInput(A, Array[Double](B, C),
nPoints, 17).toDF()
binaryDataset = generateSVMInput(1.0, Array[Double](1.0, 2.0, 3.0,
4.0), 10000, 42).toDF()
+
+ // Dataset for testing SparseVector
+ smallSparseBinaryDataset = generateSVMInput(A, Array[Double](B, C),
nPoints, 42, false).toDF()
--- End diff --
Why call generateSVMInput again? It seems brittle. I'd prefer to use a
UDF to convert the vectors to sparse vectors here.
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