[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, 4 insertions, 4 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/MultilayerPerceptronClassifierWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/MultilayerPerceptronClassifierWrapper.scala
index be51e74187..1067300353 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/MultilayerPerceptronClassifierWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/MultilayerPerceptronClassifierWrapper.scala
@@ -53,26 +53,26 @@ private[r] object MultilayerPerceptronClassifierWrapper
   def fit(
       data: DataFrame,
       blockSize: Int,
-      layers: Array[Double],
+      layers: Array[Int],
       solver: String,
       maxIter: Int,
       tol: Double,
       stepSize: Double,
-      seed: Int
+      seed: String
      ): MultilayerPerceptronClassifierWrapper = {
     // get labels and feature names from output schema
     val schema = data.schema
 
     // assemble and fit the pipeline
     val mlp = new MultilayerPerceptronClassifier()
-      .setLayers(layers.map(_.toInt))
+      .setLayers(layers)
       .setBlockSize(blockSize)
       .setSolver(solver)
       .setMaxIter(maxIter)
       .setTol(tol)
       .setStepSize(stepSize)
-      .setSeed(seed)
       .setPredictionCol(PREDICTED_LABEL_COL)
+    if (seed != null && seed.length > 0) mlp.setSeed(seed.toInt)
     val pipeline = new Pipeline()
       .setStages(Array(mlp))
       .fit(data)