Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/15051#discussion_r78315909
--- Diff: R/pkg/R/mllib.R ---
@@ -694,8 +694,8 @@ setMethod("predict", signature(object = "KMeansModel"),
#' }
#' @note spark.mlp since 2.1.0
setMethod("spark.mlp", signature(data = "SparkDataFrame"),
- function(data, blockSize = 128, layers = c(3, 5, 2), solver =
"l-bfgs", maxIter = 100,
- tol = 0.5, stepSize = 1, seed = 1) {
+ function(data, blockSize = 128, layers, solver = "l-bfgs",
maxIter = 100,
+ tol = 1E-6, stepSize = 0.03, seed = -763139545) {
--- End diff --
yeah, it is a problem.
now I consider a better way:
we give the `seed` parameter default value `null`
`MultilayerPerceptronClassifierWrapper.fit` add a `null` check for `seed`
parameter,
if it is null, then do not call `MultilayerPerceptronClassifier.setSeed`
so it will automatically use the default seed.
how do you think about it ?
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