Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/17078#discussion_r103342591
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
---
@@ -456,6 +456,32 @@ class LogisticRegressionSuite
assert(blrModel.intercept !== 0.0)
}
+ test("sparse coefficients in LogisticAggregator") {
+ val bcCoefficientsBinary =
spark.sparkContext.broadcast(Vectors.sparse(2, Array(0), Array(1.0)))
+ val bcFeaturesStd = spark.sparkContext.broadcast(Array(1.0))
+ val binaryAgg = new LogisticAggregator(bcCoefficientsBinary,
bcFeaturesStd, 2,
+ fitIntercept = true, multinomial = false)
+ val thrownBinary = withClue("binary logistic aggregator cannot handle
sparse coefficients") {
--- End diff --
I think we should handle sparse coefficients for further performance
improvement. But not in this PR.
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