Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/17746#discussion_r113256277
--- 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 --
With small datasets, it can result an ill-conditioned problem, which is not
ideal for unit-tests since they can be very unstable.
I wonder with real bigger datasets we use in scala unittests, with breeze
0.13.1, whether the number of iterations required for convergency are the same
compared with breeze 0.13?
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