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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