[SPARK-17173][SPARKR] R MLlib refactor, cleanup, reformat, fix deprecation in test

## What changes were proposed in this pull request?

refactor, cleanup, reformat, fix deprecation in test

## How was this patch tested?

unit tests, manual tests

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #14735 from felixcheung/rmllibutil.

2 files changed, 98 insertions, 117 deletions

diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index 9a53c80aec..b36fbcee17 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -88,9 +88,9 @@ setClass("ALSModel", representation(jobj = "jobj"))
 #' @rdname write.ml
 #' @name write.ml
 #' @export
-#' @seealso \link{spark.glm}, \link{glm}, \link{spark.gaussianMixture}
-#' @seealso \link{spark.als}, \link{spark.kmeans}, \link{spark.lda}, \link{spark.naiveBayes}
-#' @seealso \link{spark.survreg}, \link{spark.isoreg}
+#' @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.naiveBayes}, \link{spark.survreg},
 #' @seealso \link{read.ml}
 NULL
 
@@ -101,11 +101,22 @@ NULL
 #' @rdname predict
 #' @name predict
 #' @export
-#' @seealso \link{spark.glm}, \link{glm}, \link{spark.gaussianMixture}
-#' @seealso \link{spark.als}, \link{spark.kmeans}, \link{spark.naiveBayes}, \link{spark.survreg}
-#' @seealso \link{spark.isoreg}
+#' @seealso \link{spark.glm}, \link{glm},
+#' @seealso \link{spark.als}, \link{spark.gaussianMixture}, \link{spark.isoreg}, \link{spark.kmeans},
+#' @seealso \link{spark.naiveBayes}, \link{spark.survreg},
 NULL
 
+write_internal <- function(object, path, overwrite = FALSE) {
+  writer <- callJMethod(object@jobj, "write")
+  if (overwrite) {
+    writer <- callJMethod(writer, "overwrite")
+  }
+  invisible(callJMethod(writer, "save", path))
+}
+
+predict_internal <- function(object, newData) {
+  dataFrame(callJMethod(object@jobj, "transform", newData@sdf))
+}
 
 #' Generalized Linear Models
 #'
@@ -173,7 +184,7 @@ setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"),
             jobj <- callJStatic("org.apache.spark.ml.r.GeneralizedLinearRegressionWrapper",
                                 "fit", formula, data@sdf, family$family, family$link,
                                 tol, as.integer(maxIter), as.character(weightCol))
-            return(new("GeneralizedLinearRegressionModel", jobj = jobj))
+            new("GeneralizedLinearRegressionModel", jobj = jobj)
           })
 
 #' Generalized Linear Models (R-compliant)
@@ -219,7 +230,7 @@ setMethod("glm", signature(formula = "formula", family = "ANY", data = "SparkDat
 #' @export
 #' @note summary(GeneralizedLinearRegressionModel) since 2.0.0
 setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"),
-          function(object, ...) {
+          function(object) {
             jobj <- object@jobj
             is.loaded <- callJMethod(jobj, "isLoaded")
             features <- callJMethod(jobj, "rFeatures")
@@ -245,7 +256,7 @@ setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"),
                         deviance = deviance, df.null = df.null, df.residual = df.residual,
                         aic = aic, iter = iter, family = family, is.loaded = is.loaded)
             class(ans) <- "summary.GeneralizedLinearRegressionModel"
-            return(ans)
+            ans
           })
 
 #  Prints the summary of GeneralizedLinearRegressionModel
@@ -275,8 +286,7 @@ print.summary.GeneralizedLinearRegressionModel <- function(x, ...) {
     " on", format(unlist(x[c("df.null", "df.residual")])), " degrees of freedom\n"),
     1L, paste, collapse = " "), sep = "")
   cat("AIC: ", format(x$aic, digits = 4L), "\n\n",
-    "Number of Fisher Scoring iterations: ", x$iter, "\n", sep = "")
-  cat("\n")
+    "Number of Fisher Scoring iterations: ", x$iter, "\n\n", sep = "")
   invisible(x)
   }
 
@@ -291,7 +301,7 @@ print.summary.GeneralizedLinearRegressionModel <- function(x, ...) {
 #' @note predict(GeneralizedLinearRegressionModel) since 1.5.0
 setMethod("predict", signature(object = "GeneralizedLinearRegressionModel"),
           function(object, newData) {
-            return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf)))
+            predict_internal(object, newData)
           })
 
 # Makes predictions from a naive Bayes model or a model produced by spark.naiveBayes(),
@@ -305,7 +315,7 @@ setMethod("predict", signature(object = "GeneralizedLinearRegressionModel"),
 #' @note predict(NaiveBayesModel) since 2.0.0
 setMethod("predict", signature(object = "NaiveBayesModel"),
           function(object, newData) {
-            return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf)))
+            predict_internal(object, newData)
           })
 
 # Returns the summary of a naive Bayes model produced by \code{spark.naiveBayes}
@@ -317,7 +327,7 @@ setMethod("predict", signature(object = "NaiveBayesModel"),
 #' @export
 #' @note summary(NaiveBayesModel) since 2.0.0
 setMethod("summary", signature(object = "NaiveBayesModel"),
-          function(object, ...) {
+          function(object) {
             jobj <- object@jobj
             features <- callJMethod(jobj, "features")
             labels <- callJMethod(jobj, "labels")
@@ -328,7 +338,7 @@ setMethod("summary", signature(object = "NaiveBayesModel"),
             tables <- matrix(tables, nrow = length(labels))
             rownames(tables) <- unlist(labels)
             colnames(tables) <- unlist(features)
-            return(list(apriori = apriori, tables = tables))
+            list(apriori = apriori, tables = tables)
           })
 
 # Returns posterior probabilities from a Latent Dirichlet Allocation model produced by spark.lda()
@@ -342,7 +352,7 @@ setMethod("summary", signature(object = "NaiveBayesModel"),
 #' @note spark.posterior(LDAModel) since 2.1.0
 setMethod("spark.posterior", signature(object = "LDAModel", newData = "SparkDataFrame"),
           function(object, newData) {
-            return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf)))
+            predict_internal(object, newData)
           })
 
 # Returns the summary of a Latent Dirichlet Allocation model produced by \code{spark.lda}
@@ -377,12 +387,11 @@ setMethod("summary", signature(object = "LDAModel"),
             vocabSize <- callJMethod(jobj, "vocabSize")
             topics <- dataFrame(callJMethod(jobj, "topics", maxTermsPerTopic))
             vocabulary <- callJMethod(jobj, "vocabulary")
-            return(list(docConcentration = unlist(docConcentration),
-                        topicConcentration = topicConcentration,
-                        logLikelihood = logLikelihood, logPerplexity = logPerplexity,
-                        isDistributed = isDistributed, vocabSize = vocabSize,
-                        topics = topics,
-                        vocabulary = unlist(vocabulary)))
+            list(docConcentration = unlist(docConcentration),
+                 topicConcentration = topicConcentration,
+                 logLikelihood = logLikelihood, logPerplexity = logPerplexity,
+                 isDistributed = isDistributed, vocabSize = vocabSize,
+                 topics = topics, vocabulary = unlist(vocabulary))
           })
 
 # Returns the log perplexity of a Latent Dirichlet Allocation model produced by \code{spark.lda}
@@ -395,8 +404,8 @@ setMethod("summary", signature(object = "LDAModel"),
 #' @note spark.perplexity(LDAModel) since 2.1.0
 setMethod("spark.perplexity", signature(object = "LDAModel", data = "SparkDataFrame"),
           function(object, data) {
-            return(ifelse(missing(data), callJMethod(object@jobj, "logPerplexity"),
-                   callJMethod(object@jobj, "computeLogPerplexity", data@sdf)))
+            ifelse(missing(data), callJMethod(object@jobj, "logPerplexity"),
+                   callJMethod(object@jobj, "computeLogPerplexity", data@sdf))
          })
 
 # Saves the Latent Dirichlet Allocation model to the input path.
@@ -412,11 +421,7 @@ setMethod("spark.perplexity", signature(object = "LDAModel", data = "SparkDataFr
 #' @note write.ml(LDAModel, character) since 2.1.0
 setMethod("write.ml", signature(object = "LDAModel", path = "character"),
           function(object, path, overwrite = FALSE) {
-            writer <- callJMethod(object@jobj, "write")
-            if (overwrite) {
-              writer <- callJMethod(writer, "overwrite")
-            }
-            invisible(callJMethod(writer, "save", path))
+            write_internal(object, path, overwrite)
           })
 
 #' Isotonic Regression Model
@@ -471,9 +476,9 @@ setMethod("spark.isoreg", signature(data = "SparkDataFrame", formula = "formula"
             }
 
             jobj <- callJStatic("org.apache.spark.ml.r.IsotonicRegressionWrapper", "fit",
-            data@sdf, formula, as.logical(isotonic), as.integer(featureIndex),
-              as.character(weightCol))
-            return(new("IsotonicRegressionModel", jobj = jobj))
+                                data@sdf, formula, as.logical(isotonic), as.integer(featureIndex),
+                                as.character(weightCol))
+            new("IsotonicRegressionModel", jobj = jobj)
           })
 
 #  Predicted values based on an isotonicRegression model
@@ -487,7 +492,7 @@ setMethod("spark.isoreg", signature(data = "SparkDataFrame", formula = "formula"
 #' @note predict(IsotonicRegressionModel) since 2.1.0
 setMethod("predict", signature(object = "IsotonicRegressionModel"),
           function(object, newData) {
-            return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf)))
+            predict_internal(object, newData)
           })
 
 #  Get the summary of an IsotonicRegressionModel model
@@ -499,11 +504,11 @@ setMethod("predict", signature(object = "IsotonicRegressionModel"),
 #' @export
 #' @note summary(IsotonicRegressionModel) since 2.1.0
 setMethod("summary", signature(object = "IsotonicRegressionModel"),
-          function(object, ...) {
+          function(object) {
             jobj <- object@jobj
             boundaries <- callJMethod(jobj, "boundaries")
             predictions <- callJMethod(jobj, "predictions")
-            return(list(boundaries = boundaries, predictions = predictions))
+            list(boundaries = boundaries, predictions = predictions)
           })
 
 #' K-Means Clustering Model
@@ -553,7 +558,7 @@ setMethod("spark.kmeans", signature(data = "SparkDataFrame", formula = "formula"
             initMode <- match.arg(initMode)
             jobj <- callJStatic("org.apache.spark.ml.r.KMeansWrapper", "fit", data@sdf, formula,
                                 as.integer(k), as.integer(maxIter), initMode)
-            return(new("KMeansModel", jobj = jobj))
+            new("KMeansModel", jobj = jobj)
           })
 
 #' Get fitted result from a k-means model
@@ -576,14 +581,14 @@ setMethod("spark.kmeans", signature(data = "SparkDataFrame", formula = "formula"
 #'}
 #' @note fitted since 2.0.0
 setMethod("fitted", signature(object = "KMeansModel"),
-          function(object, method = c("centers", "classes"), ...) {
+          function(object, method = c("centers", "classes")) {
             method <- match.arg(method)
             jobj <- object@jobj
             is.loaded <- callJMethod(jobj, "isLoaded")
             if (is.loaded) {
-              stop(paste("Saved-loaded k-means model does not support 'fitted' method"))
+              stop("Saved-loaded k-means model does not support 'fitted' method")
             } else {
-              return(dataFrame(callJMethod(jobj, "fitted", method)))
+              dataFrame(callJMethod(jobj, "fitted", method))
             }
           })
 
@@ -595,7 +600,7 @@ setMethod("fitted", signature(object = "KMeansModel"),
 #' @export
 #' @note summary(KMeansModel) since 2.0.0
 setMethod("summary", signature(object = "KMeansModel"),
-          function(object, ...) {
+          function(object) {
             jobj <- object@jobj
             is.loaded <- callJMethod(jobj, "isLoaded")
             features <- callJMethod(jobj, "features")
@@ -610,8 +615,8 @@ setMethod("summary", signature(object = "KMeansModel"),
             } else {
               dataFrame(callJMethod(jobj, "cluster"))
             }
-            return(list(coefficients = coefficients, size = size,
-                   cluster = cluster, is.loaded = is.loaded))
+            list(coefficients = coefficients, size = size,
+                 cluster = cluster, is.loaded = is.loaded)
           })
 
 #  Predicted values based on a k-means model
@@ -623,7 +628,7 @@ setMethod("summary", signature(object = "KMeansModel"),
 #' @note predict(KMeansModel) since 2.0.0
 setMethod("predict", signature(object = "KMeansModel"),
           function(object, newData) {
-            return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf)))
+            predict_internal(object, newData)
           })
 
 #' Naive Bayes Models
@@ -665,11 +670,11 @@ setMethod("predict", signature(object = "KMeansModel"),
 #' }
 #' @note spark.naiveBayes since 2.0.0
 setMethod("spark.naiveBayes", signature(data = "SparkDataFrame", formula = "formula"),
-          function(data, formula, smoothing = 1.0, ...) {
+          function(data, formula, smoothing = 1.0) {
             formula <- paste(deparse(formula), collapse = "")
             jobj <- callJStatic("org.apache.spark.ml.r.NaiveBayesWrapper", "fit",
             formula, data@sdf, smoothing)
-            return(new("NaiveBayesModel", jobj = jobj))
+            new("NaiveBayesModel", jobj = jobj)
           })
 
 # Saves the Bernoulli naive Bayes model to the input path.
@@ -684,11 +689,7 @@ setMethod("spark.naiveBayes", signature(data = "SparkDataFrame", formula = "form
 #' @note write.ml(NaiveBayesModel, character) since 2.0.0
 setMethod("write.ml", 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))
+            write_internal(object, path, overwrite)
           })
 
 # Saves the AFT survival regression model to the input path.
@@ -702,11 +703,7 @@ setMethod("write.ml", signature(object = "NaiveBayesModel", path = "character"),
 #' @seealso \link{read.ml}
 setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "character"),
           function(object, path, overwrite = FALSE) {
-            writer <- callJMethod(object@jobj, "write")
-            if (overwrite) {
-              writer <- callJMethod(writer, "overwrite")
-            }
-            invisible(callJMethod(writer, "save", path))
+            write_internal(object, path, overwrite)
           })
 
 #  Saves the generalized linear model to the input path.
@@ -720,11 +717,7 @@ setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "c
 #' @note write.ml(GeneralizedLinearRegressionModel, character) since 2.0.0
 setMethod("write.ml", signature(object = "GeneralizedLinearRegressionModel", path = "character"),
           function(object, path, overwrite = FALSE) {
-            writer <- callJMethod(object@jobj, "write")
-            if (overwrite) {
-              writer <- callJMethod(writer, "overwrite")
-            }
-            invisible(callJMethod(writer, "save", path))
+            write_internal(object, path, overwrite)
           })
 
 #  Save fitted MLlib model to the input path
@@ -738,11 +731,7 @@ setMethod("write.ml", signature(object = "GeneralizedLinearRegressionModel", pat
 #' @note write.ml(KMeansModel, character) since 2.0.0
 setMethod("write.ml", signature(object = "KMeansModel", path = "character"),
           function(object, path, overwrite = FALSE) {
-            writer <- callJMethod(object@jobj, "write")
-            if (overwrite) {
-              writer <- callJMethod(writer, "overwrite")
-            }
-            invisible(callJMethod(writer, "save", path))
+            write_internal(object, path, overwrite)
           })
 
 #  Save fitted IsotonicRegressionModel to the input path
@@ -757,11 +746,7 @@ setMethod("write.ml", signature(object = "KMeansModel", path = "character"),
 #' @note write.ml(IsotonicRegression, character) since 2.1.0
 setMethod("write.ml", signature(object = "IsotonicRegressionModel", path = "character"),
           function(object, path, overwrite = FALSE) {
-            writer <- callJMethod(object@jobj, "write")
-            if (overwrite) {
-              writer <- callJMethod(writer, "overwrite")
-            }
-           invisible(callJMethod(writer, "save", path))
+            write_internal(object, path, overwrite)
           })
 
 #  Save fitted MLlib model to the input path
@@ -776,11 +761,7 @@ setMethod("write.ml", signature(object = "IsotonicRegressionModel", path = "char
 #' @note write.ml(GaussianMixtureModel, character) since 2.1.0
 setMethod("write.ml", signature(object = "GaussianMixtureModel", path = "character"),
           function(object, path, overwrite = FALSE) {
-            writer <- callJMethod(object@jobj, "write")
-            if (overwrite) {
-              writer <- callJMethod(writer, "overwrite")
-            }
-            invisible(callJMethod(writer, "save", path))
+            write_internal(object, path, overwrite)
           })
 
 #' Load a fitted MLlib model from the input path.
@@ -801,21 +782,21 @@ 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")) {
-    return(new("NaiveBayesModel", jobj = jobj))
+    new("NaiveBayesModel", jobj = jobj)
   } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.AFTSurvivalRegressionWrapper")) {
-    return(new("AFTSurvivalRegressionModel", jobj = jobj))
+    new("AFTSurvivalRegressionModel", jobj = jobj)
   } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.GeneralizedLinearRegressionWrapper")) {
-      return(new("GeneralizedLinearRegressionModel", jobj = jobj))
+    new("GeneralizedLinearRegressionModel", jobj = jobj)
   } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.KMeansWrapper")) {
-      return(new("KMeansModel", jobj = jobj))
+    new("KMeansModel", jobj = jobj)
   } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.LDAWrapper")) {
-      return(new("LDAModel", jobj = jobj))
+    new("LDAModel", jobj = jobj)
   } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.IsotonicRegressionWrapper")) {
-      return(new("IsotonicRegressionModel", jobj = jobj))
+    new("IsotonicRegressionModel", jobj = jobj)
   } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.GaussianMixtureWrapper")) {
-      return(new("GaussianMixtureModel", jobj = jobj))
+    new("GaussianMixtureModel", jobj = jobj)
   } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.ALSWrapper")) {
-      return(new("ALSModel", jobj = jobj))
+    new("ALSModel", jobj = jobj)
   } else {
     stop(paste("Unsupported model: ", jobj))
   }
@@ -860,7 +841,7 @@ setMethod("spark.survreg", signature(data = "SparkDataFrame", formula = "formula
             formula <- paste(deparse(formula), collapse = "")
             jobj <- callJStatic("org.apache.spark.ml.r.AFTSurvivalRegressionWrapper",
                                 "fit", formula, data@sdf)
-            return(new("AFTSurvivalRegressionModel", jobj = jobj))
+            new("AFTSurvivalRegressionModel", jobj = jobj)
           })
 
 #' Latent Dirichlet Allocation
@@ -926,7 +907,7 @@ setMethod("spark.lda", signature(data = "SparkDataFrame"),
                                 as.numeric(subsamplingRate), topicConcentration,
                                 as.array(docConcentration), as.array(customizedStopWords),
                                 maxVocabSize)
-            return(new("LDAModel", jobj = jobj))
+            new("LDAModel", jobj = jobj)
           })
 
 # Returns a summary of the AFT survival regression model produced by spark.survreg,
@@ -946,7 +927,7 @@ setMethod("summary", signature(object = "AFTSurvivalRegressionModel"),
             coefficients <- as.matrix(unlist(coefficients))
             colnames(coefficients) <- c("Value")
             rownames(coefficients) <- unlist(features)
-            return(list(coefficients = coefficients))
+            list(coefficients = coefficients)
           })
 
 # Makes predictions from an AFT survival regression model or a model produced by
@@ -960,7 +941,7 @@ setMethod("summary", signature(object = "AFTSurvivalRegressionModel"),
 #' @note predict(AFTSurvivalRegressionModel) since 2.0.0
 setMethod("predict", signature(object = "AFTSurvivalRegressionModel"),
           function(object, newData) {
-            return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf)))
+            predict_internal(object, newData)
           })
 
 #' Multivariate Gaussian Mixture Model (GMM)
@@ -1014,7 +995,7 @@ setMethod("spark.gaussianMixture", signature(data = "SparkDataFrame", formula =
             formula <- paste(deparse(formula), collapse = "")
             jobj <- callJStatic("org.apache.spark.ml.r.GaussianMixtureWrapper", "fit", data@sdf,
                                 formula, as.integer(k), as.integer(maxIter), as.numeric(tol))
-            return(new("GaussianMixtureModel", jobj = jobj))
+            new("GaussianMixtureModel", jobj = jobj)
           })
 
 #  Get the summary of a multivariate gaussian mixture model
@@ -1027,7 +1008,7 @@ setMethod("spark.gaussianMixture", signature(data = "SparkDataFrame", formula =
 #' @export
 #' @note summary(GaussianMixtureModel) since 2.1.0
 setMethod("summary", signature(object = "GaussianMixtureModel"),
-          function(object, ...) {
+          function(object) {
             jobj <- object@jobj
             is.loaded <- callJMethod(jobj, "isLoaded")
             lambda <- unlist(callJMethod(jobj, "lambda"))
@@ -1052,8 +1033,8 @@ setMethod("summary", signature(object = "GaussianMixtureModel"),
             } else {
               dataFrame(callJMethod(jobj, "posterior"))
             }
-            return(list(lambda = lambda, mu = mu, sigma = sigma,
-                   posterior = posterior, is.loaded = is.loaded))
+            list(lambda = lambda, mu = mu, sigma = sigma,
+                 posterior = posterior, is.loaded = is.loaded)
           })
 
 #  Predicted values based on a gaussian mixture model
@@ -1067,7 +1048,7 @@ setMethod("summary", signature(object = "GaussianMixtureModel"),
 #' @note predict(GaussianMixtureModel) since 2.1.0
 setMethod("predict", signature(object = "GaussianMixtureModel"),
           function(object, newData) {
-            return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf)))
+            predict_internal(object, newData)
           })
 
 #' Alternating Least Squares (ALS) for Collaborative Filtering
@@ -1149,7 +1130,7 @@ setMethod("spark.als", signature(data = "SparkDataFrame"),
                                 reg, as.integer(maxIter), implicitPrefs, alpha, nonnegative,
                                 as.integer(numUserBlocks), as.integer(numItemBlocks),
                                 as.integer(checkpointInterval), as.integer(seed))
-            return(new("ALSModel", jobj = jobj))
+            new("ALSModel", jobj = jobj)
           })
 
 # Returns a summary of the ALS model produced by spark.als.
@@ -1163,17 +1144,17 @@ setMethod("spark.als", signature(data = "SparkDataFrame"),
 #' @export
 #' @note summary(ALSModel) since 2.1.0
 setMethod("summary", signature(object = "ALSModel"),
-function(object, ...) {
-    jobj <- object@jobj
-    user <- callJMethod(jobj, "userCol")
-    item <- callJMethod(jobj, "itemCol")
-    rating <- callJMethod(jobj, "ratingCol")
-    userFactors <- dataFrame(callJMethod(jobj, "userFactors"))
-    itemFactors <- dataFrame(callJMethod(jobj, "itemFactors"))
-    rank <- callJMethod(jobj, "rank")
-    return(list(user = user, item = item, rating = rating, userFactors = userFactors,
-                itemFactors = itemFactors, rank = rank))
-})
+          function(object) {
+            jobj <- object@jobj
+            user <- callJMethod(jobj, "userCol")
+            item <- callJMethod(jobj, "itemCol")
+            rating <- callJMethod(jobj, "ratingCol")
+            userFactors <- dataFrame(callJMethod(jobj, "userFactors"))
+            itemFactors <- dataFrame(callJMethod(jobj, "itemFactors"))
+            rank <- callJMethod(jobj, "rank")
+            list(user = user, item = item, rating = rating, userFactors = userFactors,
+                 itemFactors = itemFactors, rank = rank)
+          })
 
 
 # Makes predictions from an ALS model or a model produced by spark.als.
@@ -1185,9 +1166,9 @@ function(object, ...) {
 #' @export
 #' @note predict(ALSModel) since 2.1.0
 setMethod("predict", signature(object = "ALSModel"),
-function(object, newData) {
-    return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf)))
-})
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
 
 
 # Saves the ALS model to the input path.
@@ -1203,10 +1184,6 @@ function(object, newData) {
 #' @seealso \link{read.ml}
 #' @note write.ml(ALSModel, character) since 2.1.0
 setMethod("write.ml", signature(object = "ALSModel", path = "character"),
-function(object, path, overwrite = FALSE) {
-    writer <- callJMethod(object@jobj, "write")
-    if (overwrite) {
-        writer <- callJMethod(writer, "overwrite")
-    }
-    invisible(callJMethod(writer, "save", path))
-})
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R
index d15c2393b9..de9bd48662 100644
--- a/R/pkg/inst/tests/testthat/test_mllib.R
+++ b/R/pkg/inst/tests/testthat/test_mllib.R
@@ -95,6 +95,10 @@ test_that("spark.glm summary", {
   expect_equal(stats$df.residual, rStats$df.residual)
   expect_equal(stats$aic, rStats$aic)
 
+  out <- capture.output(print(stats))
+  expect_match(out[2], "Deviance Residuals:")
+  expect_true(any(grepl("AIC: 59.22", out)))
+
   # binomial family
   df <- suppressWarnings(createDataFrame(iris))
   training <- df[df$Species %in% c("versicolor", "virginica"), ]
@@ -409,7 +413,7 @@ test_that("spark.naiveBayes", {
 
   # Test e1071::naiveBayes
   if (requireNamespace("e1071", quietly = TRUE)) {
-    expect_that(m <- e1071::naiveBayes(Survived ~ ., data = t1), not(throws_error()))
+    expect_error(m <- e1071::naiveBayes(Survived ~ ., data = t1), NA)
     expect_equal(as.character(predict(m, t1[1, ])), "Yes")
   }
 })
@@ -487,7 +491,7 @@ test_that("spark.isotonicRegression", {
                         weightCol = "weight")
   # only allow one variable on the right hand side of the formula
   expect_error(model2 <- spark.isoreg(df, ~., isotonic = FALSE))
-  result <- summary(model, df)
+  result <- summary(model)
   expect_equal(result$predictions, list(7, 5, 4, 4, 1))
 
   # Test model prediction
@@ -503,7 +507,7 @@ test_that("spark.isotonicRegression", {
   expect_error(write.ml(model, modelPath))
   write.ml(model, modelPath, overwrite = TRUE)
   model2 <- read.ml(modelPath)
-  expect_equal(result, summary(model2, df))
+  expect_equal(result, summary(model2))
 
   unlink(modelPath)
 })