Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/15051#discussion_r79303689
--- Diff: R/pkg/R/mllib.R ---
@@ -694,8 +694,14 @@ 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, layers, blockSize = 128, solver = "l-bfgs",
maxIter = 100,
+ tol = 1E-6, stepSize = 0.03, seed = 0x7FFFFFFF) {
+ if (length(layers) <= 1) {
+ stop("layers vector require length > 0.")
+ }
+ if (any(sapply(layers, function(e) as.integer(e) != e))) {
--- End diff --
This way `layers = c(1.0, 2.0)` would pass the `as.integer(e) != e` test.
One possible issue is with how we are handling this on the Scala side.
Since we are passing it as `as.array(layers)`, this could end up as double in
JVM - would it handle that correctly?
There are other ways to do this but generally coercing to integer is a
reasonable approach.
One alternative implementation is this:
```
layers <- as.integer(na.omit(layers))
if (length(layers) <= 1) {
stop("layers vector must be integer values with length > 0.")
}
```
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