[SPARK-14312][ML][SPARKR] NaiveBayes model persistence in SparkR

## What changes were proposed in this pull request?
SparkR ```NaiveBayesModel``` supports ```save/load``` by the following API:
```
df <- createDataFrame(sqlContext, infert)
model <- naiveBayes(education ~ ., df, laplace = 0)
ml.save(model, path)
model2 <- ml.load(path)
```

## How was this patch tested?
Add unit tests.

cc mengxr

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #12573 from yanboliang/spark-14312.

4 files changed, 68 insertions, 2 deletions

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 0f92b5e597..c0a63d6b3e 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -107,7 +107,8 @@ exportMethods("arrange",
               "write.jdbc",
               "write.json",
               "write.parquet",
-              "write.text")
+              "write.text",
+              "ml.save")
 
 exportClasses("Column")
 
@@ -299,7 +300,8 @@ export("as.DataFrame",
        "tableNames",
        "tables",
        "uncacheTable",
-       "print.summary.GeneralizedLinearRegressionModel")
+       "print.summary.GeneralizedLinearRegressionModel",
+       "ml.load")
 
 export("structField",
        "structField.jobj",
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 04274a12bc..f654d8330c 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -1200,3 +1200,7 @@ setGeneric("naiveBayes", function(formula, data, ...) { standardGeneric("naiveBa
 #' @rdname survreg
 #' @export
 setGeneric("survreg", function(formula, data, ...) { standardGeneric("survreg") })
+
+#' @rdname ml.save
+#' @export
+setGeneric("ml.save", function(object, path, ...) { standardGeneric("ml.save") })
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index 7dd82963a1..cda6100e79 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -338,6 +338,54 @@ setMethod("naiveBayes", signature(formula = "formula", data = "SparkDataFrame"),
             return(new("NaiveBayesModel", jobj = jobj))
           })
 
+#' Save the Bernoulli naive Bayes model to the input path.
+#'
+#' @param object A fitted Bernoulli naive Bayes 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.
+#'
+#' @rdname ml.save
+#' @name ml.save
+#' @export
+#' @examples
+#' \dontrun{
+#' df <- createDataFrame(sqlContext, infert)
+#' model <- naiveBayes(education ~ ., df, laplace = 0)
+#' path <- "path/to/model"
+#' ml.save(model, path)
+#' }
+setMethod("ml.save", signature(object = "NaiveBayesModel", path = "character"),
+          function(object, path, overwrite = FALSE) {
+            writer <- callJMethod(object@jobj, "write")
+            if (overwrite) {
+              writer <- callJMethod(writer, "overwrite")
+            }
+            invisible(callJMethod(writer, "save", path))
+          })
+
+#' Load a fitted MLlib model from the input path.
+#'
+#' @param path Path of the model to read.
+#' @return a fitted MLlib model
+#' @rdname ml.load
+#' @name ml.load
+#' @export
+#' @examples
+#' \dontrun{
+#' path <- "path/to/model"
+#' model <- ml.load(path)
+#' }
+ml.load <- function(path) {
+  path <- suppressWarnings(normalizePath(path))
+  jobj <- callJStatic("org.apache.spark.ml.r.RWrappers", "load", path)
+  if (isInstanceOf(jobj, "org.apache.spark.ml.r.NaiveBayesWrapper")) {
+    return(new("NaiveBayesModel", jobj = jobj))
+  } else {
+    stop(paste("Unsupported model: ", jobj))
+  }
+}
+
 #' Fit an accelerated failure time (AFT) survival regression model.
 #'
 #' Fit an accelerated failure time (AFT) survival regression model, similarly to R's survreg().
diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R
index 1597306bb6..63ec84e497 100644
--- a/R/pkg/inst/tests/testthat/test_mllib.R
+++ b/R/pkg/inst/tests/testthat/test_mllib.R
@@ -204,6 +204,18 @@ test_that("naiveBayes", {
                                "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "No",
                                "Yes", "Yes", "No", "No"))
 
+  # Test model save/load
+  modelPath <- tempfile(pattern = "naiveBayes", fileext = ".tmp")
+  ml.save(m, modelPath)
+  expect_error(ml.save(m, modelPath))
+  ml.save(m, modelPath, overwrite = TRUE)
+  m2 <- ml.load(modelPath)
+  s2 <- summary(m2)
+  expect_equal(s$apriori, s2$apriori)
+  expect_equal(s$tables, s2$tables)
+
+  unlink(modelPath)
+
   # Test e1071::naiveBayes
   if (requireNamespace("e1071", quietly = TRUE)) {
     expect_that(m <- e1071::naiveBayes(Survived ~ ., data = t1), not(throws_error()))