[SPARK-14831][.2][ML][R] rename ml.save/ml.load to write.ml/read.ml

## What changes were proposed in this pull request?

Continue the work of #12789 to rename ml.asve/ml.load to write.ml/read.ml, which are more consistent with read.df/write.df and other methods in SparkR.

I didn't rename `data` to `df` because we still use `predict` for prediction, which uses `newData` to match the signature in R.

## How was this patch tested?

Existing unit tests.

cc: yanboliang thunterdb

Author: Xiangrui Meng <meng@databricks.com>

Closes #12807 from mengxr/SPARK-14831.

R/pkg/NAMESPACE

@@ -110,7 +110,7 @@ exportMethods("arrange",
               "write.json",
               "write.parquet",
               "write.text",
-              "ml.save")
+              "write.ml")
 
 exportClasses("Column")
 
@@ -305,7 +305,7 @@ export("as.DataFrame",
        "tables",
        "uncacheTable",
        "print.summary.GeneralizedLinearRegressionModel",
-       "ml.load")
+       "read.ml")
 
 export("structField",
        "structField.jobj",

R/pkg/R/generics.R

@@ -1213,6 +1213,6 @@ setGeneric("spark.naiveBayes", function(data, formula, ...) { standardGeneric("s
 #' @export
 setGeneric("spark.survreg", function(data, formula, ...) { standardGeneric("spark.survreg") })
 
-#' @rdname ml.save
+#' @rdname write.ml
 #' @export
-setGeneric("ml.save", function(object, path, ...) { standardGeneric("ml.save") })
+setGeneric("write.ml", function(object, path, ...) { standardGeneric("write.ml") })

R/pkg/R/mllib.R

@@ -403,17 +403,17 @@ setMethod("spark.naiveBayes", signature(data = "SparkDataFrame", formula = "form
 #' @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
+#' @rdname write.ml
+#' @name write.ml
 #' @export
 #' @examples
 #' \dontrun{
 #' df <- createDataFrame(sqlContext, infert)
 #' model <- spark.naiveBayes(education ~ ., df, laplace = 0)
 #' path <- "path/to/model"
-#' ml.save(model, path)
+#' write.ml(model, path)
 #' }
-setMethod("ml.save", signature(object = "NaiveBayesModel", path = "character"),
+setMethod("write.ml", signature(object = "NaiveBayesModel", path = "character"),
           function(object, path, overwrite = FALSE) {
             writer <- callJMethod(object@jobj, "write")
             if (overwrite) {
@@ -429,16 +429,16 @@ setMethod("ml.save", signature(object = "NaiveBayesModel", path = "character"),
 #' @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
+#' @rdname write.ml
+#' @name write.ml
 #' @export
 #' @examples
 #' \dontrun{
 #' model <- spark.survreg(trainingData, Surv(futime, fustat) ~ ecog_ps + rx)
 #' path <- "path/to/model"
-#' ml.save(model, path)
+#' write.ml(model, path)
 #' }
-setMethod("ml.save", signature(object = "AFTSurvivalRegressionModel", path = "character"),
+setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "character"),
           function(object, path, overwrite = FALSE) {
             writer <- callJMethod(object@jobj, "write")
             if (overwrite) {
@@ -454,16 +454,16 @@ setMethod("ml.save", signature(object = "AFTSurvivalRegressionModel", path = "ch
 #' @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
+#' @rdname write.ml
+#' @name write.ml
 #' @export
 #' @examples
 #' \dontrun{
 #' model <- glm(y ~ x, trainingData)
 #' path <- "path/to/model"
-#' ml.save(model, path)
+#' write.ml(model, path)
 #' }
-setMethod("ml.save", signature(object = "GeneralizedLinearRegressionModel", path = "character"),
+setMethod("write.ml", signature(object = "GeneralizedLinearRegressionModel", path = "character"),
           function(object, path, overwrite = FALSE) {
             writer <- callJMethod(object@jobj, "write")
             if (overwrite) {
@@ -479,16 +479,16 @@ setMethod("ml.save", signature(object = "GeneralizedLinearRegressionModel", path
 #' @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
+#' @rdname write.ml
+#' @name write.ml
 #' @export
 #' @examples
 #' \dontrun{
 #' model <- spark.kmeans(x, k = 2, initializationMode="random")
 #' path <- "path/to/model"
-#' ml.save(model, path)
+#' write.ml(model, path)
 #' }
-setMethod("ml.save", signature(object = "KMeansModel", path = "character"),
+setMethod("write.ml", signature(object = "KMeansModel", path = "character"),
           function(object, path, overwrite = FALSE) {
             writer <- callJMethod(object@jobj, "write")
             if (overwrite) {
@@ -501,15 +501,15 @@ setMethod("ml.save", signature(object = "KMeansModel", path = "character"),
 #'
 #' @param path Path of the model to read.
 #' @return a fitted MLlib model
-#' @rdname ml.load
-#' @name ml.load
+#' @rdname read.ml
+#' @name read.ml
 #' @export
 #' @examples
 #' \dontrun{
 #' path <- "path/to/model"
-#' model <- ml.load(path)
+#' model <- read.ml(path)
 #' }
-ml.load <- function(path) {
+read.ml <- 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")) {

R/pkg/inst/tests/testthat/test_mllib.R

@@ -133,10 +133,10 @@ test_that("spark.glm save/load", {
   s <- summary(m)
 
   modelPath <- tempfile(pattern = "glm", fileext = ".tmp")
-  ml.save(m, modelPath)
-  expect_error(ml.save(m, modelPath))
-  ml.save(m, modelPath, overwrite = TRUE)
-  m2 <- ml.load(modelPath)
+  write.ml(m, modelPath)
+  expect_error(write.ml(m, modelPath))
+  write.ml(m, modelPath, overwrite = TRUE)
+  m2 <- read.ml(modelPath)
   s2 <- summary(m2)
 
   expect_equal(s$coefficients, s2$coefficients)
@@ -263,10 +263,10 @@ test_that("glm save/load", {
   s <- summary(m)
 
   modelPath <- tempfile(pattern = "glm", fileext = ".tmp")
-  ml.save(m, modelPath)
-  expect_error(ml.save(m, modelPath))
-  ml.save(m, modelPath, overwrite = TRUE)
-  m2 <- ml.load(modelPath)
+  write.ml(m, modelPath)
+  expect_error(write.ml(m, modelPath))
+  write.ml(m, modelPath, overwrite = TRUE)
+  m2 <- read.ml(modelPath)
   s2 <- summary(m2)
 
   expect_equal(s$coefficients, s2$coefficients)
@@ -311,10 +311,10 @@ test_that("spark.kmeans", {
 
   # Test model save/load
   modelPath <- tempfile(pattern = "kmeans", fileext = ".tmp")
-  ml.save(model, modelPath)
-  expect_error(ml.save(model, modelPath))
-  ml.save(model, modelPath, overwrite = TRUE)
-  model2 <- ml.load(modelPath)
+  write.ml(model, modelPath)
+  expect_error(write.ml(model, modelPath))
+  write.ml(model, modelPath, overwrite = TRUE)
+  model2 <- read.ml(modelPath)
   summary2 <- summary(model2)
   expect_equal(sort(unlist(summary.model$size)), sort(unlist(summary2$size)))
   expect_equal(summary.model$coefficients, summary2$coefficients)
@@ -378,10 +378,10 @@ test_that("naiveBayes", {
 
   # 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)
+  write.ml(m, modelPath)
+  expect_error(write.ml(m, modelPath))
+  write.ml(m, modelPath, overwrite = TRUE)
+  m2 <- read.ml(modelPath)
   s2 <- summary(m2)
   expect_equal(s$apriori, s2$apriori)
   expect_equal(s$tables, s2$tables)
@@ -435,10 +435,10 @@ test_that("spark.survreg", {
 
   # Test model save/load
   modelPath <- tempfile(pattern = "survreg", fileext = ".tmp")
-  ml.save(model, modelPath)
-  expect_error(ml.save(model, modelPath))
-  ml.save(model, modelPath, overwrite = TRUE)
-  model2 <- ml.load(modelPath)
+  write.ml(model, modelPath)
+  expect_error(write.ml(model, modelPath))
+  write.ml(model, modelPath, overwrite = TRUE)
+  model2 <- read.ml(modelPath)
   stats2 <- summary(model2)
   coefs2 <- as.vector(stats2$coefficients[, 1])
   expect_equal(coefs, coefs2)