[SPARK-16137][SPARKR] randomForest for R

## What changes were proposed in this pull request?

Random Forest Regression and Classification for R
Clean-up/reordering generics.R

## How was this patch tested?

manual tests, unit tests

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #15607 from felixcheung/rrandomforest.

4 files changed, 361 insertions, 34 deletions

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 7a89c01fee..9cd6269f9a 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -44,7 +44,8 @@ exportMethods("glm",
               "spark.gaussianMixture",
               "spark.als",
               "spark.kstest",
-              "spark.logit")
+              "spark.logit",
+              "spark.randomForest")
 
 # Job group lifecycle management methods
 export("setJobGroup",
@@ -350,7 +351,9 @@ export("as.DataFrame",
        "uncacheTable",
        "print.summary.GeneralizedLinearRegressionModel",
        "read.ml",
-       "print.summary.KSTest")
+       "print.summary.KSTest",
+       "print.summary.RandomForestRegressionModel",
+       "print.summary.RandomForestClassificationModel")
 
 export("structField",
        "structField.jobj",
@@ -375,6 +378,8 @@ S3method(print, structField)
 S3method(print, structType)
 S3method(print, summary.GeneralizedLinearRegressionModel)
 S3method(print, summary.KSTest)
+S3method(print, summary.RandomForestRegressionModel)
+S3method(print, summary.RandomForestClassificationModel)
 S3method(structField, character)
 S3method(structField, jobj)
 S3method(structType, jobj)
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 107e1c638b..0271b26a10 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -1310,9 +1310,11 @@ setGeneric("window", function(x, ...) { standardGeneric("window") })
 #' @export
 setGeneric("year", function(x) { standardGeneric("year") })
 
-#' @rdname spark.glm
+###################### Spark.ML Methods ##########################
+
+#' @rdname fitted
 #' @export
-setGeneric("spark.glm", function(data, formula, ...) { standardGeneric("spark.glm") })
+setGeneric("fitted")
 
 #' @param x,y For \code{glm}: logical values indicating whether the response vector
 #'          and model matrix used in the fitting process should be returned as
@@ -1332,13 +1334,38 @@ setGeneric("predict", function(object, ...) { standardGeneric("predict") })
 #' @export
 setGeneric("rbind", signature = "...")
 
+#' @rdname spark.als
+#' @export
+setGeneric("spark.als", function(data, ...) { standardGeneric("spark.als") })
+
+#' @rdname spark.gaussianMixture
+#' @export
+setGeneric("spark.gaussianMixture",
+           function(data, formula, ...) { standardGeneric("spark.gaussianMixture") })
+
+#' @rdname spark.glm
+#' @export
+setGeneric("spark.glm", function(data, formula, ...) { standardGeneric("spark.glm") })
+
+#' @rdname spark.isoreg
+#' @export
+setGeneric("spark.isoreg", function(data, formula, ...) { standardGeneric("spark.isoreg") })
+
 #' @rdname spark.kmeans
 #' @export
 setGeneric("spark.kmeans", function(data, formula, ...) { standardGeneric("spark.kmeans") })
 
-#' @rdname fitted
+#' @rdname spark.kstest
 #' @export
-setGeneric("fitted")
+setGeneric("spark.kstest", function(data, ...) { standardGeneric("spark.kstest") })
+
+#' @rdname spark.lda
+#' @export
+setGeneric("spark.lda", function(data, ...) { standardGeneric("spark.lda") })
+
+#' @rdname spark.logit
+#' @export
+setGeneric("spark.logit", function(data, formula, ...) { standardGeneric("spark.logit") })
 
 #' @rdname spark.mlp
 #' @export
@@ -1348,13 +1375,14 @@ setGeneric("spark.mlp", function(data, ...) { standardGeneric("spark.mlp") })
 #' @export
 setGeneric("spark.naiveBayes", function(data, formula, ...) { standardGeneric("spark.naiveBayes") })
 
-#' @rdname spark.survreg
+#' @rdname spark.randomForest
 #' @export
-setGeneric("spark.survreg", function(data, formula) { standardGeneric("spark.survreg") })
+setGeneric("spark.randomForest",
+           function(data, formula, ...) { standardGeneric("spark.randomForest") })
 
-#' @rdname spark.lda
+#' @rdname spark.survreg
 #' @export
-setGeneric("spark.lda", function(data, ...) { standardGeneric("spark.lda") })
+setGeneric("spark.survreg", function(data, formula) { standardGeneric("spark.survreg") })
 
 #' @rdname spark.lda
 #' @export
@@ -1364,20 +1392,6 @@ setGeneric("spark.posterior", function(object, newData) { standardGeneric("spark
 #' @export
 setGeneric("spark.perplexity", function(object, data) { standardGeneric("spark.perplexity") })
 
-#' @rdname spark.isoreg
-#' @export
-setGeneric("spark.isoreg", function(data, formula, ...) { standardGeneric("spark.isoreg") })
-
-#' @rdname spark.gaussianMixture
-#' @export
-setGeneric("spark.gaussianMixture",
-           function(data, formula, ...) {
-             standardGeneric("spark.gaussianMixture")
-           })
-
-#' @rdname spark.logit
-#' @export
-setGeneric("spark.logit", function(data, formula, ...) { standardGeneric("spark.logit") })
 
 #' @param object a fitted ML model object.
 #' @param path the directory where the model is saved.
@@ -1385,11 +1399,3 @@ setGeneric("spark.logit", function(data, formula, ...) { standardGeneric("spark.
 #' @rdname write.ml
 #' @export
 setGeneric("write.ml", function(object, path, ...) { standardGeneric("write.ml") })
-
-#' @rdname spark.als
-#' @export
-setGeneric("spark.als", function(data, ...) { standardGeneric("spark.als") })
-
-#' @rdname spark.kstest
-#' @export
-setGeneric("spark.kstest", function(data, ...) { standardGeneric("spark.kstest") })
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index 629f284b79..7a220b8d53 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -102,6 +102,20 @@ setClass("KSTest", representation(jobj = "jobj"))
 #' @note LogisticRegressionModel since 2.1.0
 setClass("LogisticRegressionModel", representation(jobj = "jobj"))
 
+#' S4 class that represents a RandomForestRegressionModel
+#'
+#' @param jobj a Java object reference to the backing Scala RandomForestRegressionModel
+#' @export
+#' @note RandomForestRegressionModel since 2.1.0
+setClass("RandomForestRegressionModel", representation(jobj = "jobj"))
+
+#' S4 class that represents a RandomForestClassificationModel
+#'
+#' @param jobj a Java object reference to the backing Scala RandomForestClassificationModel
+#' @export
+#' @note RandomForestClassificationModel since 2.1.0
+setClass("RandomForestClassificationModel", representation(jobj = "jobj"))
+
 #' Saves the MLlib model to the input path
 #'
 #' Saves the MLlib model to the input path. For more information, see the specific
@@ -112,7 +126,7 @@ setClass("LogisticRegressionModel", representation(jobj = "jobj"))
 #' @seealso \link{spark.glm}, \link{glm},
 #' @seealso \link{spark.als}, \link{spark.gaussianMixture}, \link{spark.isoreg}, \link{spark.kmeans},
 #' @seealso \link{spark.lda}, \link{spark.logit}, \link{spark.mlp}, \link{spark.naiveBayes},
-#' @seealso \link{spark.survreg}
+#' @seealso \link{spark.randomForest}, \link{spark.survreg},
 #' @seealso \link{read.ml}
 NULL
 
@@ -125,7 +139,8 @@ NULL
 #' @export
 #' @seealso \link{spark.glm}, \link{glm},
 #' @seealso \link{spark.als}, \link{spark.gaussianMixture}, \link{spark.isoreg}, \link{spark.kmeans},
-#' @seealso \link{spark.logit}, \link{spark.mlp}, \link{spark.naiveBayes}, \link{spark.survreg}
+#' @seealso \link{spark.logit}, \link{spark.mlp}, \link{spark.naiveBayes},
+#' @seealso \link{spark.randomForest}, \link{spark.survreg}
 NULL
 
 write_internal <- function(object, path, overwrite = FALSE) {
@@ -1122,6 +1137,10 @@ read.ml <- function(path) {
     new("ALSModel", jobj = jobj)
   } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.LogisticRegressionWrapper")) {
     new("LogisticRegressionModel", jobj = jobj)
+  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.RandomForestRegressorWrapper")) {
+    new("RandomForestRegressionModel", jobj = jobj)
+  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.RandomForestClassifierWrapper")) {
+    new("RandomForestClassificationModel", jobj = jobj)
   } else {
     stop("Unsupported model: ", jobj)
   }
@@ -1617,3 +1636,232 @@ print.summary.KSTest <- function(x, ...) {
   cat(summaryStr, "\n")
   invisible(x)
 }
+
+#' Random Forest Model for Regression and Classification
+#'
+#' \code{spark.randomForest} fits a Random Forest Regression model or Classification model on
+#' a SparkDataFrame. Users can call \code{summary} to get a summary of the fitted Random Forest
+#' model, \code{predict} to make predictions on new data, and \code{write.ml}/\code{read.ml} to
+#' save/load fitted models.
+#' For more details, see
+#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html}{Random Forest}
+#'
+#' @param data a SparkDataFrame for training.
+#' @param formula a symbolic description of the model to be fitted. Currently only a few formula
+#'                operators are supported, including '~', ':', '+', and '-'.
+#' @param type type of model, one of "regression" or "classification", to fit
+#' @param maxDepth Maximum depth of the tree (>= 0). (default = 5)
+#' @param maxBins Maximum number of bins used for discretizing continuous features and for choosing
+#'                how to split on features at each node. More bins give higher granularity. Must be
+#'                >= 2 and >= number of categories in any categorical feature. (default = 32)
+#' @param numTrees Number of trees to train (>= 1).
+#' @param impurity Criterion used for information gain calculation.
+#'                 For regression, must be "variance". For classification, must be one of
+#'                 "entropy" and "gini". (default = gini)
+#' @param minInstancesPerNode Minimum number of instances each child must have after split.
+#' @param minInfoGain Minimum information gain for a split to be considered at a tree node.
+#' @param checkpointInterval Param for set checkpoint interval (>= 1) or disable checkpoint (-1).
+#' @param featureSubsetStrategy The number of features to consider for splits at each tree node.
+#'        Supported options: "auto", "all", "onethird", "sqrt", "log2", (0.0-1.0], [1-n].
+#' @param seed integer seed for random number generation.
+#' @param subsamplingRate Fraction of the training data used for learning each decision tree, in
+#'                        range (0, 1]. (default = 1.0)
+#' @param probabilityCol column name for predicted class conditional probabilities, only for
+#'                       classification. (default = "probability")
+#' @param maxMemoryInMB Maximum memory in MB allocated to histogram aggregation.
+#' @param cacheNodeIds If FALSE, the algorithm will pass trees to executors to match instances with
+#'                     nodes.
+#' @param ... additional arguments passed to the method.
+#' @aliases spark.randomForest,SparkDataFrame,formula-method
+#' @return \code{spark.randomForest} returns a fitted Random Forest model.
+#' @rdname spark.randomForest
+#' @name spark.randomForest
+#' @export
+#' @examples
+#' \dontrun{
+#' # fit a Random Forest Regression Model
+#' df <- createDataFrame(longley)
+#' model <- spark.randomForest(df, Employed ~ ., type = "regression", maxDepth = 5, maxBins = 16)
+#'
+#' # get the summary of the model
+#' summary(model)
+#'
+#' # make predictions
+#' predictions <- predict(model, df)
+#'
+#' # save and load the model
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#'
+#' # fit a Random Forest Classification Model
+#' df <- createDataFrame(iris)
+#' model <- spark.randomForest(df, Species ~ Petal_Length + Petal_Width, "classification")
+#' }
+#' @note spark.randomForest since 2.1.0
+setMethod("spark.randomForest", signature(data = "SparkDataFrame", formula = "formula"),
+          function(data, formula, type = c("regression", "classification"),
+                   maxDepth = 5, maxBins = 32, numTrees = 20, impurity = NULL,
+                   minInstancesPerNode = 1, minInfoGain = 0.0, checkpointInterval = 10,
+                   featureSubsetStrategy = "auto", seed = NULL, subsamplingRate = 1.0,
+                   probabilityCol = "probability", maxMemoryInMB = 256, cacheNodeIds = FALSE) {
+            type <- match.arg(type)
+            formula <- paste(deparse(formula), collapse = "")
+            if (!is.null(seed)) {
+              seed <- as.character(as.integer(seed))
+            }
+            switch(type,
+                   regression = {
+                     if (is.null(impurity)) impurity <- "variance"
+                     impurity <- match.arg(impurity, "variance")
+                     jobj <- callJStatic("org.apache.spark.ml.r.RandomForestRegressorWrapper",
+                                         "fit", data@sdf, formula, as.integer(maxDepth),
+                                         as.integer(maxBins), as.integer(numTrees),
+                                         impurity, as.integer(minInstancesPerNode),
+                                         as.numeric(minInfoGain), as.integer(checkpointInterval),
+                                         as.character(featureSubsetStrategy), seed,
+                                         as.numeric(subsamplingRate),
+                                         as.integer(maxMemoryInMB), as.logical(cacheNodeIds))
+                     new("RandomForestRegressionModel", jobj = jobj)
+                   },
+                   classification = {
+                     if (is.null(impurity)) impurity <- "gini"
+                     impurity <- match.arg(impurity, c("gini", "entropy"))
+                     jobj <- callJStatic("org.apache.spark.ml.r.RandomForestClassifierWrapper",
+                                         "fit", data@sdf, formula, as.integer(maxDepth),
+                                         as.integer(maxBins), as.integer(numTrees),
+                                         impurity, as.integer(minInstancesPerNode),
+                                         as.numeric(minInfoGain), as.integer(checkpointInterval),
+                                         as.character(featureSubsetStrategy), seed,
+                                         as.numeric(subsamplingRate), as.character(probabilityCol),
+                                         as.integer(maxMemoryInMB), as.logical(cacheNodeIds))
+                     new("RandomForestClassificationModel", jobj = jobj)
+                   }
+            )
+          })
+
+# Makes predictions from a Random Forest Regression model or Classification model
+
+#' @param newData a SparkDataFrame for testing.
+#' @return \code{predict} returns a SparkDataFrame containing predicted labeled in a column named
+#' "prediction"
+#' @rdname spark.randomForest
+#' @aliases predict,RandomForestRegressionModel-method
+#' @export
+#' @note predict(randomForestRegressionModel) since 2.1.0
+setMethod("predict", signature(object = "RandomForestRegressionModel"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+#' @rdname spark.randomForest
+#' @aliases predict,RandomForestClassificationModel-method
+#' @export
+#' @note predict(randomForestClassificationModel) since 2.1.0
+setMethod("predict", signature(object = "RandomForestClassificationModel"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+# Save the Random Forest Regression or Classification model to the input path.
+
+#' @param object A fitted Random Forest regression model or classification model
+#' @param path The directory where the model is saved
+#' @param overwrite Overwrites or not if the output path already exists. Default is FALSE
+#'                  which means throw exception if the output path exists.
+#'
+#' @aliases write.ml,RandomForestRegressionModel,character-method
+#' @rdname spark.randomForest
+#' @export
+#' @note write.ml(RandomForestRegressionModel, character) since 2.1.0
+setMethod("write.ml", signature(object = "RandomForestRegressionModel", path = "character"),
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
+
+#' @aliases write.ml,RandomForestClassificationModel,character-method
+#' @rdname spark.randomForest
+#' @export
+#' @note write.ml(RandomForestClassificationModel, character) since 2.1.0
+setMethod("write.ml", signature(object = "RandomForestClassificationModel", path = "character"),
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
+
+#  Get the summary of an RandomForestRegressionModel model
+summary.randomForest <- function(model) {
+  jobj <- model@jobj
+  formula <- callJMethod(jobj, "formula")
+  numFeatures <- callJMethod(jobj, "numFeatures")
+  features <-  callJMethod(jobj, "features")
+  featureImportances <- callJMethod(callJMethod(jobj, "featureImportances"), "toString")
+  numTrees <- callJMethod(jobj, "numTrees")
+  treeWeights <- callJMethod(jobj, "treeWeights")
+  list(formula = formula,
+       numFeatures = numFeatures,
+       features = features,
+       featureImportances = featureImportances,
+       numTrees = numTrees,
+       treeWeights = treeWeights,
+       jobj = jobj)
+}
+
+#' @return \code{summary} returns the model's features as lists, depth and number of nodes
+#'                        or number of classes.
+#' @rdname spark.randomForest
+#' @aliases summary,RandomForestRegressionModel-method
+#' @export
+#' @note summary(RandomForestRegressionModel) since 2.1.0
+setMethod("summary", signature(object = "RandomForestRegressionModel"),
+          function(object) {
+            ans <- summary.randomForest(object)
+            class(ans) <- "summary.RandomForestRegressionModel"
+            ans
+          })
+
+#  Get the summary of an RandomForestClassificationModel model
+
+#' @rdname spark.randomForest
+#' @aliases summary,RandomForestClassificationModel-method
+#' @export
+#' @note summary(RandomForestClassificationModel) since 2.1.0
+setMethod("summary", signature(object = "RandomForestClassificationModel"),
+          function(object) {
+            ans <- summary.randomForest(object)
+            class(ans) <- "summary.RandomForestClassificationModel"
+            ans
+          })
+
+#  Prints the summary of Random Forest Regression Model
+print.summary.randomForest <- function(x) {
+  jobj <- x$jobj
+  cat("Formula: ", x$formula)
+  cat("\nNumber of features: ", x$numFeatures)
+  cat("\nFeatures: ", unlist(x$features))
+  cat("\nFeature importances: ", x$featureImportances)
+  cat("\nNumber of trees: ", x$numTrees)
+  cat("\nTree weights: ", unlist(x$treeWeights))
+
+  summaryStr <- callJMethod(jobj, "summary")
+  cat("\n", summaryStr, "\n")
+  invisible(x)
+}
+
+#' @param x summary object of Random Forest regression model or classification model
+#'          returned by \code{summary}.
+#' @rdname spark.randomForest
+#' @export
+#' @note print.summary.RandomForestRegressionModel since 2.1.0
+print.summary.RandomForestRegressionModel <- function(x, ...) {
+  print.summary.randomForest(x)
+}
+
+#  Prints the summary of Random Forest Classification Model
+
+#' @rdname spark.randomForest
+#' @export
+#' @note print.summary.RandomForestClassificationModel since 2.1.0
+print.summary.RandomForestClassificationModel <- function(x, ...) {
+  print.summary.randomForest(x)
+}
diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R
index 6d1fccc7c0..db98d0e455 100644
--- a/R/pkg/inst/tests/testthat/test_mllib.R
+++ b/R/pkg/inst/tests/testthat/test_mllib.R
@@ -871,4 +871,72 @@ test_that("spark.kstest", {
   expect_match(capture.output(stats)[1], "Kolmogorov-Smirnov test summary:")
 })
 
+test_that("spark.randomForest Regression", {
+  data <- suppressWarnings(createDataFrame(longley))
+  model <- spark.randomForest(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16,
+                              numTrees = 1)
+
+  predictions <- collect(predict(model, data))
+  expect_equal(predictions$prediction, c(60.323, 61.122, 60.171, 61.187,
+                                         63.221, 63.639, 64.989, 63.761,
+                                         66.019, 67.857, 68.169, 66.513,
+                                         68.655, 69.564, 69.331, 70.551),
+               tolerance = 1e-4)
+
+  stats <- summary(model)
+  expect_equal(stats$numTrees, 1)
+  expect_error(capture.output(stats), NA)
+  expect_true(length(capture.output(stats)) > 6)
+
+  model <- spark.randomForest(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16,
+                              numTrees = 20, seed = 123)
+  predictions <- collect(predict(model, data))
+  expect_equal(predictions$prediction, c(60.379, 61.096, 60.636, 62.258,
+                                         63.736, 64.296, 64.868, 64.300,
+                                         66.709, 67.697, 67.966, 67.252,
+                                         68.866, 69.593, 69.195, 69.658),
+               tolerance = 1e-4)
+  stats <- summary(model)
+  expect_equal(stats$numTrees, 20)
+
+  modelPath <- tempfile(pattern = "spark-randomForestRegression", fileext = ".tmp")
+  write.ml(model, modelPath)
+  expect_error(write.ml(model, modelPath))
+  write.ml(model, modelPath, overwrite = TRUE)
+  model2 <- read.ml(modelPath)
+  stats2 <- summary(model2)
+  expect_equal(stats$formula, stats2$formula)
+  expect_equal(stats$numFeatures, stats2$numFeatures)
+  expect_equal(stats$features, stats2$features)
+  expect_equal(stats$featureImportances, stats2$featureImportances)
+  expect_equal(stats$numTrees, stats2$numTrees)
+  expect_equal(stats$treeWeights, stats2$treeWeights)
+
+  unlink(modelPath)
+})
+
+test_that("spark.randomForest Classification", {
+  data <- suppressWarnings(createDataFrame(iris))
+  model <- spark.randomForest(data, Species ~ Petal_Length + Petal_Width, "classification",
+                              maxDepth = 5, maxBins = 16)
+
+  stats <- summary(model)
+  expect_equal(stats$numFeatures, 2)
+  expect_equal(stats$numTrees, 20)
+  expect_error(capture.output(stats), NA)
+  expect_true(length(capture.output(stats)) > 6)
+
+  modelPath <- tempfile(pattern = "spark-randomForestClassification", fileext = ".tmp")
+  write.ml(model, modelPath)
+  expect_error(write.ml(model, modelPath))
+  write.ml(model, modelPath, overwrite = TRUE)
+  model2 <- read.ml(modelPath)
+  stats2 <- summary(model2)
+  expect_equal(stats$depth, stats2$depth)
+  expect_equal(stats$numNodes, stats2$numNodes)
+  expect_equal(stats$numClasses, stats2$numClasses)
+
+  unlink(modelPath)
+})
+
 sparkR.session.stop()