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From: dbtsai <git@git.apache.org>
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Subject: [GitHub] spark pull request #17746: [SPARK-20449][ML] Upgrade breeze version to 0.13....
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Message-Id: <20170424165635.DF262E0EE6@git1-us-west.apache.org>
Date: Mon, 24 Apr 2017 16:56:35 +0000 (UTC)
archived-at: Mon, 24 Apr 2017 16:56:37 -0000

Github user dbtsai commented on a diff in the pull request:

    https://github.com/apache/spark/pull/17746#discussion_r112999875
  
    --- Diff: R/pkg/inst/tests/testthat/test_mllib_classification.R ---
    @@ -288,18 +288,18 @@ test_that("spark.mlp", {
         c(0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 9, 9, 9, 9, 9))
       mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
       expect_equal(head(mlpPredictions$prediction, 10),
    -               c("1.0", "1.0", "1.0", "1.0", "2.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
    +               c("1.0", "1.0", "2.0", "1.0", "2.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
     
       model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2, initialWeights =
         c(0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 5.0, 5.0, 5.0, 5.0, 9.0, 9.0, 9.0, 9.0, 9.0))
       mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
       expect_equal(head(mlpPredictions$prediction, 10),
    -               c("1.0", "1.0", "1.0", "1.0", "2.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
    +               c("1.0", "1.0", "2.0", "1.0", "2.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
     
       model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2)
       mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
       expect_equal(head(mlpPredictions$prediction, 10),
    -               c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "0.0", "2.0", "1.0", "0.0"))
    +               c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "0.0", "0.0", "1.0", "0.0"))
    --- End diff --
    
    Why are the prediction results changed in R and Python test cases? The bugs being fixed are in LBFGS-B. In theory, the current optimizer only uses LBFGS, so the results should be the same. 


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