[SPARK-17499][SPARKR][ML][MLLIB] make the default params in sparkR spark.mlp consistent with MultilayerPerceptronClassifier

## What changes were proposed in this pull request?

update `MultilayerPerceptronClassifierWrapper.fit` paramter type:
`layers: Array[Int]`
`seed: String`

update several default params in sparkR `spark.mlp`:
`tol` --> 1e-6
`stepSize` --> 0.03
`seed` --> NULL ( when seed == NULL, the scala-side wrapper regard it as a `null` value and the seed will use the default one )
r-side `seed` only support 32bit integer.

remove `layers` default value, and move it in front of those parameters with default value.
add `layers` parameter validation check.

## How was this patch tested?

tests added.

Author: WeichenXu <WeichenXu123@outlook.com>

Closes #15051 from WeichenXu123/update_py_mlp_default.

1 files changed, 19 insertions, 0 deletions

diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R
index 24c40a8823..a1eaaf2091 100644
--- a/R/pkg/inst/tests/testthat/test_mllib.R
+++ b/R/pkg/inst/tests/testthat/test_mllib.R
@@ -391,6 +391,25 @@ test_that("spark.mlp", {
 
   unlink(modelPath)
 
+  # Test default parameter
+  model <- spark.mlp(df, layers = c(4, 5, 4, 3))
+  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
+  expect_equal(head(mlpPredictions$prediction, 10), c(1, 1, 1, 1, 0, 1, 2, 2, 1, 0))
+
+  # Test illegal parameter
+  expect_error(spark.mlp(df, layers = NULL), "layers must be a integer vector with length > 1.")
+  expect_error(spark.mlp(df, layers = c()), "layers must be a integer vector with length > 1.")
+  expect_error(spark.mlp(df, layers = c(3)), "layers must be a integer vector with length > 1.")
+
+  # Test random seed
+  # default seed
+  model <- spark.mlp(df, layers = c(4, 5, 4, 3), maxIter = 10)
+  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
+  expect_equal(head(mlpPredictions$prediction, 12), c(1, 1, 1, 1, 0, 1, 2, 2, 1, 2, 0, 1))
+  # seed equals 10
+  model <- spark.mlp(df, layers = c(4, 5, 4, 3), maxIter = 10, seed = 10)
+  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
+  expect_equal(head(mlpPredictions$prediction, 12), c(1, 1, 1, 1, 2, 1, 2, 2, 1, 0, 0, 1))
 })
 
 test_that("spark.naiveBayes", {