[SPARK-18438][SPARKR][ML] spark.mlp should support RFormula.

## What changes were proposed in this pull request?
```spark.mlp``` should support ```RFormula``` like other ML algorithm wrappers.
BTW, I did some cleanup and improvement for ```spark.mlp```.

## How was this patch tested?
Unit tests.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #15883 from yanboliang/spark-18438.

3 files changed, 62 insertions, 33 deletions

diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 7653ca7bcc..499c7b279e 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -1373,7 +1373,7 @@ setGeneric("spark.logit", function(data, formula, ...) { standardGeneric("spark.
 
 #' @rdname spark.mlp
 #' @export
-setGeneric("spark.mlp", function(data, ...) { standardGeneric("spark.mlp") })
+setGeneric("spark.mlp", function(data, formula, ...) { standardGeneric("spark.mlp") })
 
 #' @rdname spark.naiveBayes
 #' @export
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index 1065b4b37d..265e64e746 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -525,7 +525,7 @@ setMethod("write.ml", signature(object = "LDAModel", path = "character"),
 #' @note spark.isoreg since 2.1.0
 setMethod("spark.isoreg", signature(data = "SparkDataFrame", formula = "formula"),
           function(data, formula, isotonic = TRUE, featureIndex = 0, weightCol = NULL) {
-            formula <- paste0(deparse(formula), collapse = "")
+            formula <- paste(deparse(formula), collapse = "")
 
             if (is.null(weightCol)) {
               weightCol <- ""
@@ -775,7 +775,7 @@ setMethod("spark.logit", signature(data = "SparkDataFrame", formula = "formula")
                    tol = 1E-6, fitIntercept = TRUE, family = "auto", standardization = TRUE,
                    thresholds = 0.5, weightCol = NULL, aggregationDepth = 2,
                    probabilityCol = "probability") {
-            formula <- paste0(deparse(formula), collapse = "")
+            formula <- paste(deparse(formula), collapse = "")
 
             if (is.null(weightCol)) {
               weightCol <- ""
@@ -858,6 +858,8 @@ setMethod("summary", signature(object = "LogisticRegressionModel"),
 #'   Multilayer Perceptron}
 #'
 #' @param data a \code{SparkDataFrame} of observations and labels for model fitting.
+#' @param formula a symbolic description of the model to be fitted. Currently only a few formula
+#'                operators are supported, including '~', '.', ':', '+', and '-'.
 #' @param blockSize blockSize parameter.
 #' @param layers integer vector containing the number of nodes for each layer
 #' @param solver solver parameter, supported options: "gd" (minibatch gradient descent) or "l-bfgs".
@@ -870,7 +872,7 @@ setMethod("summary", signature(object = "LogisticRegressionModel"),
 #' @param ... additional arguments passed to the method.
 #' @return \code{spark.mlp} returns a fitted Multilayer Perceptron Classification Model.
 #' @rdname spark.mlp
-#' @aliases spark.mlp,SparkDataFrame-method
+#' @aliases spark.mlp,SparkDataFrame,formula-method
 #' @name spark.mlp
 #' @seealso \link{read.ml}
 #' @export
@@ -879,7 +881,7 @@ setMethod("summary", signature(object = "LogisticRegressionModel"),
 #' df <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm")
 #'
 #' # fit a Multilayer Perceptron Classification Model
-#' model <- spark.mlp(df, blockSize = 128, layers = c(4, 3), solver = "l-bfgs",
+#' model <- spark.mlp(df, label ~ features, blockSize = 128, layers = c(4, 3), solver = "l-bfgs",
 #'                    maxIter = 100, tol = 0.5, stepSize = 1, seed = 1,
 #'                    initialWeights = c(0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 9, 9, 9, 9, 9))
 #'
@@ -896,9 +898,10 @@ setMethod("summary", signature(object = "LogisticRegressionModel"),
 #' summary(savedModel)
 #' }
 #' @note spark.mlp since 2.1.0
-setMethod("spark.mlp", signature(data = "SparkDataFrame"),
-          function(data, layers, blockSize = 128, solver = "l-bfgs", maxIter = 100,
+setMethod("spark.mlp", signature(data = "SparkDataFrame", formula = "formula"),
+          function(data, formula, layers, blockSize = 128, solver = "l-bfgs", maxIter = 100,
                    tol = 1E-6, stepSize = 0.03, seed = NULL, initialWeights = NULL) {
+            formula <- paste(deparse(formula), collapse = "")
             if (is.null(layers)) {
               stop ("layers must be a integer vector with length > 1.")
             }
@@ -913,7 +916,7 @@ setMethod("spark.mlp", signature(data = "SparkDataFrame"),
               initialWeights <- as.array(as.numeric(na.omit(initialWeights)))
             }
             jobj <- callJStatic("org.apache.spark.ml.r.MultilayerPerceptronClassifierWrapper",
-                                "fit", data@sdf, as.integer(blockSize), as.array(layers),
+                                "fit", data@sdf, formula, as.integer(blockSize), as.array(layers),
                                 as.character(solver), as.integer(maxIter), as.numeric(tol),
                                 as.numeric(stepSize), seed, initialWeights)
             new("MultilayerPerceptronClassificationModel", jobj = jobj)
@@ -936,9 +939,10 @@ setMethod("predict", signature(object = "MultilayerPerceptronClassificationModel
 # Returns the summary of a Multilayer Perceptron Classification Model produced by \code{spark.mlp}
 
 #' @param object a Multilayer Perceptron Classification Model fitted by \code{spark.mlp}
-#' @return \code{summary} returns a list containing \code{labelCount}, \code{layers}, and
-#'         \code{weights}. For \code{weights}, it is a numeric vector with length equal to
-#'         the expected given the architecture (i.e., for 8-10-2 network, 100 connection weights).
+#' @return \code{summary} returns a list containing \code{numOfInputs}, \code{numOfOutputs},
+#'         \code{layers}, and \code{weights}. For \code{weights}, it is a numeric vector with
+#'         length equal to the expected given the architecture (i.e., for 8-10-2 network,
+#'         112 connection weights).
 #' @rdname spark.mlp
 #' @export
 #' @aliases summary,MultilayerPerceptronClassificationModel-method
@@ -946,10 +950,12 @@ setMethod("predict", signature(object = "MultilayerPerceptronClassificationModel
 setMethod("summary", signature(object = "MultilayerPerceptronClassificationModel"),
           function(object) {
             jobj <- object@jobj
-            labelCount <- callJMethod(jobj, "labelCount")
             layers <- unlist(callJMethod(jobj, "layers"))
+            numOfInputs <- head(layers, n = 1)
+            numOfOutputs <- tail(layers, n = 1)
             weights <- callJMethod(jobj, "weights")
-            list(labelCount = labelCount, layers = layers, weights = weights)
+            list(numOfInputs = numOfInputs, numOfOutputs = numOfOutputs,
+                 layers = layers, weights = weights)
           })
 
 #' Naive Bayes Models
diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R
index 07df4b6d6f..2a97a51cfa 100644
--- a/R/pkg/inst/tests/testthat/test_mllib.R
+++ b/R/pkg/inst/tests/testthat/test_mllib.R
@@ -371,12 +371,13 @@ test_that("spark.kmeans", {
 test_that("spark.mlp", {
   df <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
                 source = "libsvm")
-  model <- spark.mlp(df, blockSize = 128, layers = c(4, 5, 4, 3), solver = "l-bfgs", maxIter = 100,
-                     tol = 0.5, stepSize = 1, seed = 1)
+  model <- spark.mlp(df, label ~ features, blockSize = 128, layers = c(4, 5, 4, 3),
+                     solver = "l-bfgs", maxIter = 100, tol = 0.5, stepSize = 1, seed = 1)
 
   # Test summary method
   summary <- summary(model)
-  expect_equal(summary$labelCount, 3)
+  expect_equal(summary$numOfInputs, 4)
+  expect_equal(summary$numOfOutputs, 3)
   expect_equal(summary$layers, c(4, 5, 4, 3))
   expect_equal(length(summary$weights), 64)
   expect_equal(head(summary$weights, 5), list(-0.878743, 0.2154151, -1.16304, -0.6583214, 1.009825),
@@ -385,7 +386,7 @@ test_that("spark.mlp", {
   # Test predict method
   mlpTestDF <- df
   mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
-  expect_equal(head(mlpPredictions$prediction, 6), c(0, 1, 1, 1, 1, 1))
+  expect_equal(head(mlpPredictions$prediction, 6), c("1.0", "0.0", "0.0", "0.0", "0.0", "0.0"))
 
   # Test model save/load
   modelPath <- tempfile(pattern = "spark-mlp", fileext = ".tmp")
@@ -395,46 +396,68 @@ test_that("spark.mlp", {
   model2 <- read.ml(modelPath)
   summary2 <- summary(model2)
 
-  expect_equal(summary2$labelCount, 3)
+  expect_equal(summary2$numOfInputs, 4)
+  expect_equal(summary2$numOfOutputs, 3)
   expect_equal(summary2$layers, c(4, 5, 4, 3))
   expect_equal(length(summary2$weights), 64)
 
   unlink(modelPath)
 
   # Test default parameter
-  model <- spark.mlp(df, layers = c(4, 5, 4, 3))
+  model <- spark.mlp(df, label ~ features, layers = c(4, 5, 4, 3))
   mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
-  expect_equal(head(mlpPredictions$prediction, 10), c(1, 1, 1, 1, 0, 1, 2, 2, 1, 0))
+  expect_equal(head(mlpPredictions$prediction, 10),
+               c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
 
   # 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.")
+  expect_error(spark.mlp(df, label ~ features, layers = NULL),
+               "layers must be a integer vector with length > 1.")
+  expect_error(spark.mlp(df, label ~ features, layers = c()),
+               "layers must be a integer vector with length > 1.")
+  expect_error(spark.mlp(df, label ~ features, 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)
+  model <- spark.mlp(df, label ~ features, layers = c(4, 5, 4, 3), maxIter = 10)
   mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
-  expect_equal(head(mlpPredictions$prediction, 12), c(1, 1, 1, 1, 0, 1, 2, 2, 1, 2, 0, 1))
+  expect_equal(head(mlpPredictions$prediction, 10),
+               c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
   # seed equals 10
-  model <- spark.mlp(df, layers = c(4, 5, 4, 3), maxIter = 10, seed = 10)
+  model <- spark.mlp(df, label ~ features, layers = c(4, 5, 4, 3), maxIter = 10, seed = 10)
   mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
-  expect_equal(head(mlpPredictions$prediction, 12), c(1, 1, 1, 1, 2, 1, 2, 2, 1, 0, 0, 1))
+  expect_equal(head(mlpPredictions$prediction, 10),
+               c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
 
   # test initialWeights
-  model <- spark.mlp(df, layers = c(4, 3), maxIter = 2, initialWeights =
+  model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2, initialWeights =
     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$prediction, 12), c(1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1))
+  expect_equal(head(mlpPredictions$prediction, 10),
+               c("1.0", "1.0", "1.0", "1.0", "2.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
 
-  model <- spark.mlp(df, layers = c(4, 3), maxIter = 2, initialWeights =
+  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$prediction, 12), c(1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1))
+  expect_equal(head(mlpPredictions$prediction, 10),
+               c("1.0", "1.0", "1.0", "1.0", "2.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
 
-  model <- spark.mlp(df, layers = c(4, 3), maxIter = 2)
+  model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2)
   mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
-  expect_equal(head(mlpPredictions$prediction, 12), c(1, 1, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1))
+  expect_equal(head(mlpPredictions$prediction, 10),
+               c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "0.0", "2.0", "1.0", "0.0"))
+
+  # Test formula works well
+  df <- suppressWarnings(createDataFrame(iris))
+  model <- spark.mlp(df, Species ~ Sepal_Length + Sepal_Width + Petal_Length + Petal_Width,
+                     layers = c(4, 3))
+  summary <- summary(model)
+  expect_equal(summary$numOfInputs, 4)
+  expect_equal(summary$numOfOutputs, 3)
+  expect_equal(summary$layers, c(4, 3))
+  expect_equal(length(summary$weights), 15)
+  expect_equal(head(summary$weights, 5), list(-1.1957257, -5.2693685, 7.4489734, -6.3751413,
+               -10.2376130), tolerance = 1e-6)
 })
 
 test_that("spark.naiveBayes", {