[SPARK-18862][SPARKR][ML] Split SparkR mllib.R into multiple files

## What changes were proposed in this pull request? SparkR ```mllib.R``` is getting bigger as we add more ML wrappers, I'd like to split it into multiple files to make us easy to maintain: * mllib_classification.R * mllib_clustering.R * mllib_recommendation.R * mllib_regression.R * mllib_stat.R * mllib_tree.R * mllib_utils.R Note: Only reorg, no actual code change. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #16312 from yanboliang/spark-18862.
author: Yanbo Liang <ybliang8@gmail.com> 2017-01-08 01:10:36 -0800
committer: Yanbo Liang <ybliang8@gmail.com> 2017-01-08 01:10:36 -0800
commit: 6b6b555a1e667a9f03dfe4a21e56c513a353a58d (patch)
tree: f58f251a2f0789f3207c75f4792cbd915df07558 /R
parent: 923e594844a7ad406195b91877f0fb374d5a454b (diff)
download: spark-6b6b555a1e667a9f03dfe4a21e56c513a353a58d.tar.gz
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16 files changed, 3535 insertions, 3285 deletions
diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION
index 6a36748597..cc471edc37 100644
--- a/R/pkg/DESCRIPTION
+++ b/R/pkg/DESCRIPTION
@@ -41,7 +41,13 @@ Collate:
     'functions.R'
     'install.R'
     'jvm.R'
-    'mllib.R'
+    'mllib_classification.R'
+    'mllib_clustering.R'
+    'mllib_recommendation.R'
+    'mllib_regression.R'
+    'mllib_stat.R'
+    'mllib_tree.R'
+    'mllib_utils.R'
     'serialize.R'
     'sparkR.R'
     'stats.R'
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
deleted file mode 100644
index d736bbb5e9..0000000000
--- a/R/pkg/R/mllib.R
+++ /dev/null
@@ -1,2114 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-# mllib.R: Provides methods for MLlib integration
-
-# Integration with R's standard functions.
-# Most of MLlib's argorithms are provided in two flavours:
-# - a specialization of the default R methods (glm). These methods try to respect
-#   the inputs and the outputs of R's method to the largest extent, but some small differences
-#   may exist.
-# - a set of methods that reflect the arguments of the other languages supported by Spark. These
-#   methods are prefixed with the `spark.` prefix: spark.glm, spark.kmeans, etc.
-
-#' S4 class that represents a generalized linear model
-#'
-#' @param jobj a Java object reference to the backing Scala GeneralizedLinearRegressionWrapper
-#' @export
-#' @note GeneralizedLinearRegressionModel since 2.0.0
-setClass("GeneralizedLinearRegressionModel", representation(jobj = "jobj"))
-
-#' S4 class that represents a NaiveBayesModel
-#'
-#' @param jobj a Java object reference to the backing Scala NaiveBayesWrapper
-#' @export
-#' @note NaiveBayesModel since 2.0.0
-setClass("NaiveBayesModel", representation(jobj = "jobj"))
-
-#' S4 class that represents an LDAModel
-#'
-#' @param jobj a Java object reference to the backing Scala LDAWrapper
-#' @export
-#' @note LDAModel since 2.1.0
-setClass("LDAModel", representation(jobj = "jobj"))
-
-#' S4 class that represents a AFTSurvivalRegressionModel
-#'
-#' @param jobj a Java object reference to the backing Scala AFTSurvivalRegressionWrapper
-#' @export
-#' @note AFTSurvivalRegressionModel since 2.0.0
-setClass("AFTSurvivalRegressionModel", representation(jobj = "jobj"))
-
-#' S4 class that represents a KMeansModel
-#'
-#' @param jobj a Java object reference to the backing Scala KMeansModel
-#' @export
-#' @note KMeansModel since 2.0.0
-setClass("KMeansModel", representation(jobj = "jobj"))
-
-#' S4 class that represents a MultilayerPerceptronClassificationModel
-#'
-#' @param jobj a Java object reference to the backing Scala MultilayerPerceptronClassifierWrapper
-#' @export
-#' @note MultilayerPerceptronClassificationModel since 2.1.0
-setClass("MultilayerPerceptronClassificationModel", representation(jobj = "jobj"))
-
-#' S4 class that represents an IsotonicRegressionModel
-#'
-#' @param jobj a Java object reference to the backing Scala IsotonicRegressionModel
-#' @export
-#' @note IsotonicRegressionModel since 2.1.0
-setClass("IsotonicRegressionModel", representation(jobj = "jobj"))
-
-#' S4 class that represents a GaussianMixtureModel
-#'
-#' @param jobj a Java object reference to the backing Scala GaussianMixtureModel
-#' @export
-#' @note GaussianMixtureModel since 2.1.0
-setClass("GaussianMixtureModel", representation(jobj = "jobj"))
-
-#' S4 class that represents an ALSModel
-#'
-#' @param jobj a Java object reference to the backing Scala ALSWrapper
-#' @export
-#' @note ALSModel since 2.1.0
-setClass("ALSModel", representation(jobj = "jobj"))
-
-#' S4 class that represents an KSTest
-#'
-#' @param jobj a Java object reference to the backing Scala KSTestWrapper
-#' @export
-#' @note KSTest since 2.1.0
-setClass("KSTest", representation(jobj = "jobj"))
-
-#' S4 class that represents an LogisticRegressionModel
-#'
-#' @param jobj a Java object reference to the backing Scala LogisticRegressionModel
-#' @export
-#' @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"))
-
-#' S4 class that represents a GBTRegressionModel
-#'
-#' @param jobj a Java object reference to the backing Scala GBTRegressionModel
-#' @export
-#' @note GBTRegressionModel since 2.1.0
-setClass("GBTRegressionModel", representation(jobj = "jobj"))
-
-#' S4 class that represents a GBTClassificationModel
-#'
-#' @param jobj a Java object reference to the backing Scala GBTClassificationModel
-#' @export
-#' @note GBTClassificationModel since 2.1.0
-setClass("GBTClassificationModel", 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
-#' MLlib model below.
-#' @rdname write.ml
-#' @name write.ml
-#' @export
-#' @seealso \link{spark.glm}, \link{glm},
-#' @seealso \link{spark.als}, \link{spark.gaussianMixture}, \link{spark.gbt}, \link{spark.isoreg},
-#' @seealso \link{spark.kmeans},
-#' @seealso \link{spark.lda}, \link{spark.logit}, \link{spark.mlp}, \link{spark.naiveBayes},
-#' @seealso \link{spark.randomForest}, \link{spark.survreg},
-#' @seealso \link{read.ml}
-NULL
-
-#' Makes predictions from a MLlib model
-#'
-#' Makes predictions from a MLlib model. For more information, see the specific
-#' MLlib model below.
-#' @rdname predict
-#' @name predict
-#' @export
-#' @seealso \link{spark.glm}, \link{glm},
-#' @seealso \link{spark.als}, \link{spark.gaussianMixture}, \link{spark.gbt}, \link{spark.isoreg},
-#' @seealso \link{spark.kmeans},
-#' @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) {
-  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
-#'
-#' Fits generalized linear model against a Spark DataFrame.
-#' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
-#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
-#'
-#' @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 family a description of the error distribution and link function to be used in the model.
-#'               This can be a character string naming a family function, a family function or
-#'               the result of a call to a family function. Refer R family at
-#'               \url{https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html}.
-#' @param tol positive convergence tolerance of iterations.
-#' @param maxIter integer giving the maximal number of IRLS iterations.
-#' @param weightCol the weight column name. If this is not set or \code{NULL}, we treat all instance
-#'                  weights as 1.0.
-#' @param regParam regularization parameter for L2 regularization.
-#' @param ... additional arguments passed to the method.
-#' @aliases spark.glm,SparkDataFrame,formula-method
-#' @return \code{spark.glm} returns a fitted generalized linear model.
-#' @rdname spark.glm
-#' @name spark.glm
-#' @export
-#' @examples
-#' \dontrun{
-#' sparkR.session()
-#' data(iris)
-#' df <- createDataFrame(iris)
-#' model <- spark.glm(df, Sepal_Length ~ Sepal_Width, family = "gaussian")
-#' summary(model)
-#'
-#' # fitted values on training data
-#' fitted <- predict(model, df)
-#' head(select(fitted, "Sepal_Length", "prediction"))
-#'
-#' # save fitted model to input path
-#' path <- "path/to/model"
-#' write.ml(model, path)
-#'
-#' # can also read back the saved model and print
-#' savedModel <- read.ml(path)
-#' summary(savedModel)
-#' }
-#' @note spark.glm since 2.0.0
-#' @seealso \link{glm}, \link{read.ml}
-setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"),
-          function(data, formula, family = gaussian, tol = 1e-6, maxIter = 25, weightCol = NULL,
-                   regParam = 0.0) {
-            if (is.character(family)) {
-              family <- get(family, mode = "function", envir = parent.frame())
-            }
-            if (is.function(family)) {
-              family <- family()
-            }
-            if (is.null(family$family)) {
-              print(family)
-              stop("'family' not recognized")
-            }
-
-            formula <- paste(deparse(formula), collapse = "")
-            if (is.null(weightCol)) {
-              weightCol <- ""
-            }
-
-            jobj <- callJStatic("org.apache.spark.ml.r.GeneralizedLinearRegressionWrapper",
-                                "fit", formula, data@sdf, family$family, family$link,
-                                tol, as.integer(maxIter), as.character(weightCol), regParam)
-            new("GeneralizedLinearRegressionModel", jobj = jobj)
-          })
-
-#' Generalized Linear Models (R-compliant)
-#'
-#' Fits a generalized linear model, similarly to R's glm().
-#' @param formula a symbolic description of the model to be fitted. Currently only a few formula
-#'                operators are supported, including '~', '.', ':', '+', and '-'.
-#' @param data a SparkDataFrame or R's glm data for training.
-#' @param family a description of the error distribution and link function to be used in the model.
-#'               This can be a character string naming a family function, a family function or
-#'               the result of a call to a family function. Refer R family at
-#'               \url{https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html}.
-#' @param weightCol the weight column name. If this is not set or \code{NULL}, we treat all instance
-#'                  weights as 1.0.
-#' @param epsilon positive convergence tolerance of iterations.
-#' @param maxit integer giving the maximal number of IRLS iterations.
-#' @return \code{glm} returns a fitted generalized linear model.
-#' @rdname glm
-#' @export
-#' @examples
-#' \dontrun{
-#' sparkR.session()
-#' data(iris)
-#' df <- createDataFrame(iris)
-#' model <- glm(Sepal_Length ~ Sepal_Width, df, family = "gaussian")
-#' summary(model)
-#' }
-#' @note glm since 1.5.0
-#' @seealso \link{spark.glm}
-setMethod("glm", signature(formula = "formula", family = "ANY", data = "SparkDataFrame"),
-          function(formula, family = gaussian, data, epsilon = 1e-6, maxit = 25, weightCol = NULL) {
-            spark.glm(data, formula, family, tol = epsilon, maxIter = maxit, weightCol = weightCol)
-          })
-
-#  Returns the summary of a model produced by glm() or spark.glm(), similarly to R's summary().
-
-#' @param object a fitted generalized linear model.
-#' @return \code{summary} returns summary information of the fitted model, which is a list.
-#'         The list of components includes at least the \code{coefficients} (coefficients matrix, which includes
-#'         coefficients, standard error of coefficients, t value and p value),
-#'         \code{null.deviance} (null/residual degrees of freedom), \code{aic} (AIC)
-#'         and \code{iter} (number of iterations IRLS takes). If there are collinear columns in the data,
-#'         the coefficients matrix only provides coefficients.
-#' @rdname spark.glm
-#' @export
-#' @note summary(GeneralizedLinearRegressionModel) since 2.0.0
-setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"),
-          function(object) {
-            jobj <- object@jobj
-            is.loaded <- callJMethod(jobj, "isLoaded")
-            features <- callJMethod(jobj, "rFeatures")
-            coefficients <- callJMethod(jobj, "rCoefficients")
-            dispersion <- callJMethod(jobj, "rDispersion")
-            null.deviance <- callJMethod(jobj, "rNullDeviance")
-            deviance <- callJMethod(jobj, "rDeviance")
-            df.null <- callJMethod(jobj, "rResidualDegreeOfFreedomNull")
-            df.residual <- callJMethod(jobj, "rResidualDegreeOfFreedom")
-            aic <- callJMethod(jobj, "rAic")
-            iter <- callJMethod(jobj, "rNumIterations")
-            family <- callJMethod(jobj, "rFamily")
-            deviance.resid <- if (is.loaded) {
-              NULL
-            } else {
-              dataFrame(callJMethod(jobj, "rDevianceResiduals"))
-            }
-            # If the underlying WeightedLeastSquares using "normal" solver, we can provide
-            # coefficients, standard error of coefficients, t value and p value. Otherwise,
-            # it will be fitted by local "l-bfgs", we can only provide coefficients.
-            if (length(features) == length(coefficients)) {
-              coefficients <- matrix(coefficients, ncol = 1)
-              colnames(coefficients) <- c("Estimate")
-              rownames(coefficients) <- unlist(features)
-            } else {
-              coefficients <- matrix(coefficients, ncol = 4)
-              colnames(coefficients) <- c("Estimate", "Std. Error", "t value", "Pr(>|t|)")
-              rownames(coefficients) <- unlist(features)
-            }
-            ans <- list(deviance.resid = deviance.resid, coefficients = coefficients,
-                        dispersion = dispersion, null.deviance = null.deviance,
-                        deviance = deviance, df.null = df.null, df.residual = df.residual,
-                        aic = aic, iter = iter, family = family, is.loaded = is.loaded)
-            class(ans) <- "summary.GeneralizedLinearRegressionModel"
-            ans
-          })
-
-#  Prints the summary of GeneralizedLinearRegressionModel
-
-#' @rdname spark.glm
-#' @param x summary object of fitted generalized linear model returned by \code{summary} function.
-#' @export
-#' @note print.summary.GeneralizedLinearRegressionModel since 2.0.0
-print.summary.GeneralizedLinearRegressionModel <- function(x, ...) {
-  if (x$is.loaded) {
-    cat("\nSaved-loaded model does not support output 'Deviance Residuals'.\n")
-  } else {
-    x$deviance.resid <- setNames(unlist(approxQuantile(x$deviance.resid, "devianceResiduals",
-    c(0.0, 0.25, 0.5, 0.75, 1.0), 0.01)), c("Min", "1Q", "Median", "3Q", "Max"))
-    x$deviance.resid <- zapsmall(x$deviance.resid, 5L)
-    cat("\nDeviance Residuals: \n")
-    cat("(Note: These are approximate quantiles with relative error <= 0.01)\n")
-    print.default(x$deviance.resid, digits = 5L, na.print = "", print.gap = 2L)
-  }
-
-  cat("\nCoefficients:\n")
-  print.default(x$coefficients, digits = 5L, na.print = "", print.gap = 2L)
-
-  cat("\n(Dispersion parameter for ", x$family, " family taken to be ", format(x$dispersion),
-    ")\n\n", apply(cbind(paste(format(c("Null", "Residual"), justify = "right"), "deviance:"),
-    format(unlist(x[c("null.deviance", "deviance")]), digits = 5L),
-    " 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\n", sep = "")
-  invisible(x)
-  }
-
-#  Makes predictions from a generalized linear model produced by glm() or spark.glm(),
-#  similarly to R's predict().
-
-#' @param newData a SparkDataFrame for testing.
-#' @return \code{predict} returns a SparkDataFrame containing predicted labels in a column named
-#'         "prediction".
-#' @rdname spark.glm
-#' @export
-#' @note predict(GeneralizedLinearRegressionModel) since 1.5.0
-setMethod("predict", signature(object = "GeneralizedLinearRegressionModel"),
-          function(object, newData) {
-            predict_internal(object, newData)
-          })
-
-# Makes predictions from a naive Bayes model or a model produced by spark.naiveBayes(),
-# similarly to R package e1071's predict.
-
-#' @param newData a SparkDataFrame for testing.
-#' @return \code{predict} returns a SparkDataFrame containing predicted labeled in a column named
-#' "prediction".
-#' @rdname spark.naiveBayes
-#' @export
-#' @note predict(NaiveBayesModel) since 2.0.0
-setMethod("predict", signature(object = "NaiveBayesModel"),
-          function(object, newData) {
-            predict_internal(object, newData)
-          })
-
-# Returns the summary of a naive Bayes model produced by \code{spark.naiveBayes}
-
-#' @param object a naive Bayes model fitted by \code{spark.naiveBayes}.
-#' @return \code{summary} returns summary information of the fitted model, which is a list.
-#'         The list includes \code{apriori} (the label distribution) and
-#'         \code{tables} (conditional probabilities given the target label).
-#' @rdname spark.naiveBayes
-#' @export
-#' @note summary(NaiveBayesModel) since 2.0.0
-setMethod("summary", signature(object = "NaiveBayesModel"),
-          function(object) {
-            jobj <- object@jobj
-            features <- callJMethod(jobj, "features")
-            labels <- callJMethod(jobj, "labels")
-            apriori <- callJMethod(jobj, "apriori")
-            apriori <- t(as.matrix(unlist(apriori)))
-            colnames(apriori) <- unlist(labels)
-            tables <- callJMethod(jobj, "tables")
-            tables <- matrix(tables, nrow = length(labels))
-            rownames(tables) <- unlist(labels)
-            colnames(tables) <- unlist(features)
-            list(apriori = apriori, tables = tables)
-          })
-
-# Returns posterior probabilities from a Latent Dirichlet Allocation model produced by spark.lda()
-
-#' @param newData A SparkDataFrame for testing.
-#' @return \code{spark.posterior} returns a SparkDataFrame containing posterior probabilities
-#'         vectors named "topicDistribution".
-#' @rdname spark.lda
-#' @aliases spark.posterior,LDAModel,SparkDataFrame-method
-#' @export
-#' @note spark.posterior(LDAModel) since 2.1.0
-setMethod("spark.posterior", signature(object = "LDAModel", newData = "SparkDataFrame"),
-          function(object, newData) {
-            predict_internal(object, newData)
-          })
-
-# Returns the summary of a Latent Dirichlet Allocation model produced by \code{spark.lda}
-
-#' @param object A Latent Dirichlet Allocation model fitted by \code{spark.lda}.
-#' @param maxTermsPerTopic Maximum number of terms to collect for each topic. Default value of 10.
-#' @return \code{summary} returns summary information of the fitted model, which is a list.
-#'         The list includes
-#'         \item{\code{docConcentration}}{concentration parameter commonly named \code{alpha} for
-#'               the prior placed on documents distributions over topics \code{theta}}
-#'         \item{\code{topicConcentration}}{concentration parameter commonly named \code{beta} or
-#'               \code{eta} for the prior placed on topic distributions over terms}
-#'         \item{\code{logLikelihood}}{log likelihood of the entire corpus}
-#'         \item{\code{logPerplexity}}{log perplexity}
-#'         \item{\code{isDistributed}}{TRUE for distributed model while FALSE for local model}
-#'         \item{\code{vocabSize}}{number of terms in the corpus}
-#'         \item{\code{topics}}{top 10 terms and their weights of all topics}
-#'         \item{\code{vocabulary}}{whole terms of the training corpus, NULL if libsvm format file
-#'               used as training set}
-#' @rdname spark.lda
-#' @aliases summary,LDAModel-method
-#' @export
-#' @note summary(LDAModel) since 2.1.0
-setMethod("summary", signature(object = "LDAModel"),
-          function(object, maxTermsPerTopic) {
-            maxTermsPerTopic <- as.integer(ifelse(missing(maxTermsPerTopic), 10, maxTermsPerTopic))
-            jobj <- object@jobj
-            docConcentration <- callJMethod(jobj, "docConcentration")
-            topicConcentration <- callJMethod(jobj, "topicConcentration")
-            logLikelihood <- callJMethod(jobj, "logLikelihood")
-            logPerplexity <- callJMethod(jobj, "logPerplexity")
-            isDistributed <- callJMethod(jobj, "isDistributed")
-            vocabSize <- callJMethod(jobj, "vocabSize")
-            topics <- dataFrame(callJMethod(jobj, "topics", maxTermsPerTopic))
-            vocabulary <- callJMethod(jobj, "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}
-
-#' @return \code{spark.perplexity} returns the log perplexity of given SparkDataFrame, or the log
-#'         perplexity of the training data if missing argument "data".
-#' @rdname spark.lda
-#' @aliases spark.perplexity,LDAModel-method
-#' @export
-#' @note spark.perplexity(LDAModel) since 2.1.0
-setMethod("spark.perplexity", signature(object = "LDAModel", data = "SparkDataFrame"),
-          function(object, data) {
-            ifelse(missing(data), callJMethod(object@jobj, "logPerplexity"),
-                   callJMethod(object@jobj, "computeLogPerplexity", data@sdf))
-         })
-
-# Saves the Latent Dirichlet Allocation model to the input path.
-
-#' @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 spark.lda
-#' @aliases write.ml,LDAModel,character-method
-#' @export
-#' @seealso \link{read.ml}
-#' @note write.ml(LDAModel, character) since 2.1.0
-setMethod("write.ml", signature(object = "LDAModel", path = "character"),
-          function(object, path, overwrite = FALSE) {
-            write_internal(object, path, overwrite)
-          })
-
-#' Isotonic Regression Model
-#'
-#' Fits an Isotonic Regression model against a Spark DataFrame, similarly to R's isoreg().
-#' Users can print, make predictions on the produced model and save the model to the input path.
-#'
-#' @param data 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 isotonic Whether the output sequence should be isotonic/increasing (TRUE) or
-#'                 antitonic/decreasing (FALSE).
-#' @param featureIndex The index of the feature if \code{featuresCol} is a vector column
-#'                     (default: 0), no effect otherwise.
-#' @param weightCol The weight column name.
-#' @param ... additional arguments passed to the method.
-#' @return \code{spark.isoreg} returns a fitted Isotonic Regression model.
-#' @rdname spark.isoreg
-#' @aliases spark.isoreg,SparkDataFrame,formula-method
-#' @name spark.isoreg
-#' @export
-#' @examples
-#' \dontrun{
-#' sparkR.session()
-#' data <- list(list(7.0, 0.0), list(5.0, 1.0), list(3.0, 2.0),
-#'         list(5.0, 3.0), list(1.0, 4.0))
-#' df <- createDataFrame(data, c("label", "feature"))
-#' model <- spark.isoreg(df, label ~ feature, isotonic = FALSE)
-#' # return model boundaries and prediction as lists
-#' result <- summary(model, df)
-#' # prediction based on fitted model
-#' predict_data <- list(list(-2.0), list(-1.0), list(0.5),
-#'                 list(0.75), list(1.0), list(2.0), list(9.0))
-#' predict_df <- createDataFrame(predict_data, c("feature"))
-#' # get prediction column
-#' predict_result <- collect(select(predict(model, predict_df), "prediction"))
-#'
-#' # save fitted model to input path
-#' path <- "path/to/model"
-#' write.ml(model, path)
-#'
-#' # can also read back the saved model and print
-#' savedModel <- read.ml(path)
-#' summary(savedModel)
-#' }
-#' @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 <- paste(deparse(formula), collapse = "")
-
-            if (is.null(weightCol)) {
-              weightCol <- ""
-            }
-
-            jobj <- callJStatic("org.apache.spark.ml.r.IsotonicRegressionWrapper", "fit",
-                                data@sdf, formula, as.logical(isotonic), as.integer(featureIndex),
-                                as.character(weightCol))
-            new("IsotonicRegressionModel", jobj = jobj)
-          })
-
-#  Predicted values based on an isotonicRegression model
-
-#' @param object a fitted IsotonicRegressionModel.
-#' @param newData SparkDataFrame for testing.
-#' @return \code{predict} returns a SparkDataFrame containing predicted values.
-#' @rdname spark.isoreg
-#' @aliases predict,IsotonicRegressionModel,SparkDataFrame-method
-#' @export
-#' @note predict(IsotonicRegressionModel) since 2.1.0
-setMethod("predict", signature(object = "IsotonicRegressionModel"),
-          function(object, newData) {
-            predict_internal(object, newData)
-          })
-
-#  Get the summary of an IsotonicRegressionModel model
-
-#' @return \code{summary} returns summary information of the fitted model, which is a list.
-#'         The list includes model's \code{boundaries} (boundaries in increasing order)
-#'         and \code{predictions} (predictions associated with the boundaries at the same index).
-#' @rdname spark.isoreg
-#' @aliases summary,IsotonicRegressionModel-method
-#' @export
-#' @note summary(IsotonicRegressionModel) since 2.1.0
-setMethod("summary", signature(object = "IsotonicRegressionModel"),
-          function(object) {
-            jobj <- object@jobj
-            boundaries <- callJMethod(jobj, "boundaries")
-            predictions <- callJMethod(jobj, "predictions")
-            list(boundaries = boundaries, predictions = predictions)
-          })
-
-#' K-Means Clustering Model
-#'
-#' Fits a k-means clustering model against a Spark DataFrame, similarly to R's kmeans().
-#' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
-#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
-#'
-#' @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 '-'.
-#'                Note that the response variable of formula is empty in spark.kmeans.
-#' @param k number of centers.
-#' @param maxIter maximum iteration number.
-#' @param initMode the initialization algorithm choosen to fit the model.
-#' @param ... additional argument(s) passed to the method.
-#' @return \code{spark.kmeans} returns a fitted k-means model.
-#' @rdname spark.kmeans
-#' @aliases spark.kmeans,SparkDataFrame,formula-method
-#' @name spark.kmeans
-#' @export
-#' @examples
-#' \dontrun{
-#' sparkR.session()
-#' data(iris)
-#' df <- createDataFrame(iris)
-#' model <- spark.kmeans(df, Sepal_Length ~ Sepal_Width, k = 4, initMode = "random")
-#' summary(model)
-#'
-#' # fitted values on training data
-#' fitted <- predict(model, df)
-#' head(select(fitted, "Sepal_Length", "prediction"))
-#'
-#' # save fitted model to input path
-#' path <- "path/to/model"
-#' write.ml(model, path)
-#'
-#' # can also read back the saved model and print
-#' savedModel <- read.ml(path)
-#' summary(savedModel)
-#' }
-#' @note spark.kmeans since 2.0.0
-#' @seealso \link{predict}, \link{read.ml}, \link{write.ml}
-setMethod("spark.kmeans", signature(data = "SparkDataFrame", formula = "formula"),
-          function(data, formula, k = 2, maxIter = 20, initMode = c("k-means||", "random")) {
-            formula <- paste(deparse(formula), collapse = "")
-            initMode <- match.arg(initMode)
-            jobj <- callJStatic("org.apache.spark.ml.r.KMeansWrapper", "fit", data@sdf, formula,
-                                as.integer(k), as.integer(maxIter), initMode)
-            new("KMeansModel", jobj = jobj)
-          })
-
-#' Get fitted result from a k-means model
-#'
-#' Get fitted result from a k-means model, similarly to R's fitted().
-#' Note: A saved-loaded model does not support this method.
-#'
-#' @param object a fitted k-means model.
-#' @param method type of fitted results, \code{"centers"} for cluster centers
-#'        or \code{"classes"} for assigned classes.
-#' @param ... additional argument(s) passed to the method.
-#' @return \code{fitted} returns a SparkDataFrame containing fitted values.
-#' @rdname fitted
-#' @export
-#' @examples
-#' \dontrun{
-#' model <- spark.kmeans(trainingData, ~ ., 2)
-#' fitted.model <- fitted(model)
-#' showDF(fitted.model)
-#'}
-#' @note fitted since 2.0.0
-setMethod("fitted", signature(object = "KMeansModel"),
-          function(object, method = c("centers", "classes")) {
-            method <- match.arg(method)
-            jobj <- object@jobj
-            is.loaded <- callJMethod(jobj, "isLoaded")
-            if (is.loaded) {
-              stop("Saved-loaded k-means model does not support 'fitted' method")
-            } else {
-              dataFrame(callJMethod(jobj, "fitted", method))
-            }
-          })
-
-#  Get the summary of a k-means model
-
-#' @param object a fitted k-means model.
-#' @return \code{summary} returns summary information of the fitted model, which is a list.
-#'         The list includes the model's \code{k} (number of cluster centers),
-#'         \code{coefficients} (model cluster centers),
-#'         \code{size} (number of data points in each cluster), and \code{cluster}
-#'         (cluster centers of the transformed data).
-#' @rdname spark.kmeans
-#' @export
-#' @note summary(KMeansModel) since 2.0.0
-setMethod("summary", signature(object = "KMeansModel"),
-          function(object) {
-            jobj <- object@jobj
-            is.loaded <- callJMethod(jobj, "isLoaded")
-            features <- callJMethod(jobj, "features")
-            coefficients <- callJMethod(jobj, "coefficients")
-            k <- callJMethod(jobj, "k")
-            size <- callJMethod(jobj, "size")
-            coefficients <- t(matrix(coefficients, ncol = k))
-            colnames(coefficients) <- unlist(features)
-            rownames(coefficients) <- 1:k
-            cluster <- if (is.loaded) {
-              NULL
-            } else {
-              dataFrame(callJMethod(jobj, "cluster"))
-            }
-            list(k = k, coefficients = coefficients, size = size,
-                 cluster = cluster, is.loaded = is.loaded)
-          })
-
-#  Predicted values based on a k-means model
-
-#' @param newData a SparkDataFrame for testing.
-#' @return \code{predict} returns the predicted values based on a k-means model.
-#' @rdname spark.kmeans
-#' @export
-#' @note predict(KMeansModel) since 2.0.0
-setMethod("predict", signature(object = "KMeansModel"),
-          function(object, newData) {
-            predict_internal(object, newData)
-          })
-
-#' Logistic Regression Model
-#'
-#' Fits an logistic regression model against a Spark DataFrame. It supports "binomial": Binary logistic regression
-#' with pivoting; "multinomial": Multinomial logistic (softmax) regression without pivoting, similar to glmnet.
-#' Users can print, make predictions on the produced model and save the model to the input path.
-#'
-#' @param data 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 regParam the regularization parameter.
-#' @param elasticNetParam the ElasticNet mixing parameter. For alpha = 0.0, the penalty is an L2 penalty.
-#'                        For alpha = 1.0, it is an L1 penalty. For 0.0 < alpha < 1.0, the penalty is a combination
-#'                        of L1 and L2. Default is 0.0 which is an L2 penalty.
-#' @param maxIter maximum iteration number.
-#' @param tol convergence tolerance of iterations.
-#' @param family the name of family which is a description of the label distribution to be used in the model.
-#'               Supported options:
-#'                 \itemize{
-#'                   \item{"auto": Automatically select the family based on the number of classes:
-#'                           If number of classes == 1 || number of classes == 2, set to "binomial".
-#'                           Else, set to "multinomial".}
-#'                   \item{"binomial": Binary logistic regression with pivoting.}
-#'                   \item{"multinomial": Multinomial logistic (softmax) regression without pivoting.}
-#'                 }
-#' @param standardization whether to standardize the training features before fitting the model. The coefficients
-#'                        of models will be always returned on the original scale, so it will be transparent for
-#'                        users. Note that with/without standardization, the models should be always converged
-#'                        to the same solution when no regularization is applied. Default is TRUE, same as glmnet.
-#' @param thresholds in binary classification, in range [0, 1]. If the estimated probability of class label 1
-#'                  is > threshold, then predict 1, else 0. A high threshold encourages the model to predict 0
-#'                  more often; a low threshold encourages the model to predict 1 more often. Note: Setting this with
-#'                  threshold p is equivalent to setting thresholds c(1-p, p). In multiclass (or binary) classification to adjust the probability of
-#'                  predicting each class. Array must have length equal to the number of classes, with values > 0,
-#'                  excepting that at most one value may be 0. The class with largest value p/t is predicted, where p
-#'                  is the original probability of that class and t is the class's threshold.
-#' @param weightCol The weight column name.
-#' @param ... additional arguments passed to the method.
-#' @return \code{spark.logit} returns a fitted logistic regression model.
-#' @rdname spark.logit
-#' @aliases spark.logit,SparkDataFrame,formula-method
-#' @name spark.logit
-#' @export
-#' @examples
-#' \dontrun{
-#' sparkR.session()
-#' # binary logistic regression
-#' df <- createDataFrame(iris)
-#' training <- df[df$Species %in% c("versicolor", "virginica"), ]
-#' model <- spark.logit(training, Species ~ ., regParam = 0.5)
-#' summary <- summary(model)
-#'
-#' # fitted values on training data
-#' fitted <- predict(model, training)
-#'
-#' # save fitted model to input path
-#' path <- "path/to/model"
-#' write.ml(model, path)
-#'
-#' # can also read back the saved model and predict
-#' # Note that summary deos not work on loaded model
-#' savedModel <- read.ml(path)
-#' summary(savedModel)
-#'
-#' # multinomial logistic regression
-#'
-#' df <- createDataFrame(iris)
-#' model <- spark.logit(df, Species ~ ., regParam = 0.5)
-#' summary <- summary(model)
-#'
-#' }
-#' @note spark.logit since 2.1.0
-setMethod("spark.logit", signature(data = "SparkDataFrame", formula = "formula"),
-          function(data, formula, regParam = 0.0, elasticNetParam = 0.0, maxIter = 100,
-                   tol = 1E-6, family = "auto", standardization = TRUE,
-                   thresholds = 0.5, weightCol = NULL) {
-            formula <- paste(deparse(formula), collapse = "")
-
-            if (is.null(weightCol)) {
-              weightCol <- ""
-            }
-
-            jobj <- callJStatic("org.apache.spark.ml.r.LogisticRegressionWrapper", "fit",
-                                data@sdf, formula, as.numeric(regParam),
-                                as.numeric(elasticNetParam), as.integer(maxIter),
-                                as.numeric(tol), as.character(family),
-                                as.logical(standardization), as.array(thresholds),
-                                as.character(weightCol))
-            new("LogisticRegressionModel", jobj = jobj)
-          })
-
-#  Predicted values based on an LogisticRegressionModel model
-
-#' @param newData a SparkDataFrame for testing.
-#' @return \code{predict} returns the predicted values based on an LogisticRegressionModel.
-#' @rdname spark.logit
-#' @aliases predict,LogisticRegressionModel,SparkDataFrame-method
-#' @export
-#' @note predict(LogisticRegressionModel) since 2.1.0
-setMethod("predict", signature(object = "LogisticRegressionModel"),
-          function(object, newData) {
-            predict_internal(object, newData)
-          })
-
-#  Get the summary of an LogisticRegressionModel
-
-#' @param object an LogisticRegressionModel fitted by \code{spark.logit}.
-#' @return \code{summary} returns summary information of the fitted model, which is a list.
-#'         The list includes \code{coefficients} (coefficients matrix of the fitted model).
-#' @rdname spark.logit
-#' @aliases summary,LogisticRegressionModel-method
-#' @export
-#' @note summary(LogisticRegressionModel) since 2.1.0
-setMethod("summary", signature(object = "LogisticRegressionModel"),
-          function(object) {
-            jobj <- object@jobj
-            features <- callJMethod(jobj, "rFeatures")
-            labels <- callJMethod(jobj, "labels")
-            coefficients <- callJMethod(jobj, "rCoefficients")
-            nCol <- length(coefficients) / length(features)
-            coefficients <- matrix(coefficients, ncol = nCol)
-            # If nCol == 1, means this is a binomial logistic regression model with pivoting.
-            # Otherwise, it's a multinomial logistic regression model without pivoting.
-            if (nCol == 1) {
-              colnames(coefficients) <- c("Estimate")
-            } else {
-              colnames(coefficients) <- unlist(labels)
-            }
-            rownames(coefficients) <- unlist(features)
-
-            list(coefficients = coefficients)
-          })
-
-#' Multilayer Perceptron Classification Model
-#'
-#' \code{spark.mlp} fits a multi-layer perceptron neural network model against a SparkDataFrame.
-#' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
-#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
-#' Only categorical data is supported.
-#' For more details, see
-#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html}{
-#'   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".
-#' @param maxIter maximum iteration number.
-#' @param tol convergence tolerance of iterations.
-#' @param stepSize stepSize parameter.
-#' @param seed seed parameter for weights initialization.
-#' @param initialWeights initialWeights parameter for weights initialization, it should be a
-#' numeric vector.
-#' @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,formula-method
-#' @name spark.mlp
-#' @seealso \link{read.ml}
-#' @export
-#' @examples
-#' \dontrun{
-#' df <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm")
-#'
-#' # fit a Multilayer Perceptron Classification Model
-#' 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))
-#'
-#' # 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)
-#' }
-#' @note spark.mlp since 2.1.0
-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.")
-            }
-            layers <- as.integer(na.omit(layers))
-            if (length(layers) <= 1) {
-              stop ("layers must be a integer vector with length > 1.")
-            }
-            if (!is.null(seed)) {
-              seed <- as.character(as.integer(seed))
-            }
-            if (!is.null(initialWeights)) {
-              initialWeights <- as.array(as.numeric(na.omit(initialWeights)))
-            }
-            jobj <- callJStatic("org.apache.spark.ml.r.MultilayerPerceptronClassifierWrapper",
-                                "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)
-          })
-
-# Makes predictions from a model produced by spark.mlp().
-
-#' @param newData a SparkDataFrame for testing.
-#' @return \code{predict} returns a SparkDataFrame containing predicted labeled in a column named
-#' "prediction".
-#' @rdname spark.mlp
-#' @aliases predict,MultilayerPerceptronClassificationModel-method
-#' @export
-#' @note predict(MultilayerPerceptronClassificationModel) since 2.1.0
-setMethod("predict", signature(object = "MultilayerPerceptronClassificationModel"),
-          function(object, newData) {
-            predict_internal(object, newData)
-          })
-
-# 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 summary information of the fitted model, which is a list.
-#'         The list includes \code{numOfInputs} (number of inputs), \code{numOfOutputs}
-#'         (number of outputs), \code{layers} (array of layer sizes including input
-#'         and output layers), and \code{weights} (the weights of layers).
-#'         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
-#' @note summary(MultilayerPerceptronClassificationModel) since 2.1.0
-setMethod("summary", signature(object = "MultilayerPerceptronClassificationModel"),
-          function(object) {
-            jobj <- object@jobj
-            layers <- unlist(callJMethod(jobj, "layers"))
-            numOfInputs <- head(layers, n = 1)
-            numOfOutputs <- tail(layers, n = 1)
-            weights <- callJMethod(jobj, "weights")
-            list(numOfInputs = numOfInputs, numOfOutputs = numOfOutputs,
-                 layers = layers, weights = weights)
-          })
-
-#' Naive Bayes Models
-#'
-#' \code{spark.naiveBayes} fits a Bernoulli naive Bayes model against a SparkDataFrame.
-#' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
-#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
-#' Only categorical data is supported.
-#'
-#' @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 smoothing smoothing parameter.
-#' @param ... additional argument(s) passed to the method. Currently only \code{smoothing}.
-#' @return \code{spark.naiveBayes} returns a fitted naive Bayes model.
-#' @rdname spark.naiveBayes
-#' @aliases spark.naiveBayes,SparkDataFrame,formula-method
-#' @name spark.naiveBayes
-#' @seealso e1071: \url{https://cran.r-project.org/package=e1071}
-#' @export
-#' @examples
-#' \dontrun{
-#' data <- as.data.frame(UCBAdmissions)
-#' df <- createDataFrame(data)
-#'
-#' # fit a Bernoulli naive Bayes model
-#' model <- spark.naiveBayes(df, Admit ~ Gender + Dept, smoothing = 0)
-#'
-#' # 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)
-#' }
-#' @note spark.naiveBayes since 2.0.0
-setMethod("spark.naiveBayes", signature(data = "SparkDataFrame", formula = "formula"),
-          function(data, formula, smoothing = 1.0) {
-            formula <- paste(deparse(formula), collapse = "")
-            jobj <- callJStatic("org.apache.spark.ml.r.NaiveBayesWrapper", "fit",
-            formula, data@sdf, smoothing)
-            new("NaiveBayesModel", jobj = jobj)
-          })
-
-# Saves the Bernoulli naive Bayes model to the input path.
-
-#' @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 spark.naiveBayes
-#' @export
-#' @seealso \link{write.ml}
-#' @note write.ml(NaiveBayesModel, character) since 2.0.0
-setMethod("write.ml", signature(object = "NaiveBayesModel", path = "character"),
-          function(object, path, overwrite = FALSE) {
-            write_internal(object, path, overwrite)
-          })
-
-# Saves the AFT survival regression model to the input path.
-
-#' @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 spark.survreg
-#' @export
-#' @note write.ml(AFTSurvivalRegressionModel, character) since 2.0.0
-#' @seealso \link{write.ml}
-setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "character"),
-          function(object, path, overwrite = FALSE) {
-            write_internal(object, path, overwrite)
-          })
-
-#  Saves the generalized linear model to the input path.
-
-#' @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 spark.glm
-#' @export
-#' @note write.ml(GeneralizedLinearRegressionModel, character) since 2.0.0
-setMethod("write.ml", signature(object = "GeneralizedLinearRegressionModel", path = "character"),
-          function(object, path, overwrite = FALSE) {
-            write_internal(object, path, overwrite)
-          })
-
-#  Save fitted MLlib model to the input path
-
-#' @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 spark.kmeans
-#' @export
-#' @note write.ml(KMeansModel, character) since 2.0.0
-setMethod("write.ml", signature(object = "KMeansModel", path = "character"),
-          function(object, path, overwrite = FALSE) {
-            write_internal(object, path, overwrite)
-          })
-
-# Saves the Multilayer Perceptron Classification Model to the input path.
-
-#' @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 spark.mlp
-#' @aliases write.ml,MultilayerPerceptronClassificationModel,character-method
-#' @export
-#' @seealso \link{write.ml}
-#' @note write.ml(MultilayerPerceptronClassificationModel, character) since 2.1.0
-setMethod("write.ml", signature(object = "MultilayerPerceptronClassificationModel",
-          path = "character"),
-          function(object, path, overwrite = FALSE) {
-            write_internal(object, path, overwrite)
-          })
-
-#  Save fitted IsotonicRegressionModel to the input path
-
-#' @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 spark.isoreg
-#' @aliases write.ml,IsotonicRegressionModel,character-method
-#' @export
-#' @note write.ml(IsotonicRegression, character) since 2.1.0
-setMethod("write.ml", signature(object = "IsotonicRegressionModel", path = "character"),
-          function(object, path, overwrite = FALSE) {
-            write_internal(object, path, overwrite)
-          })
-
-#  Save fitted LogisticRegressionModel to the input path
-
-#' @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 spark.logit
-#' @aliases write.ml,LogisticRegressionModel,character-method
-#' @export
-#' @note write.ml(LogisticRegression, character) since 2.1.0
-setMethod("write.ml", signature(object = "LogisticRegressionModel", path = "character"),
-          function(object, path, overwrite = FALSE) {
-            write_internal(object, path, overwrite)
-          })
-
-#  Save fitted MLlib model to the input path
-
-#' @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,GaussianMixtureModel,character-method
-#' @rdname spark.gaussianMixture
-#' @export
-#' @note write.ml(GaussianMixtureModel, character) since 2.1.0
-setMethod("write.ml", signature(object = "GaussianMixtureModel", path = "character"),
-          function(object, path, overwrite = FALSE) {
-            write_internal(object, path, overwrite)
-          })
-
-#' Load a fitted MLlib model from the input path.
-#'
-#' @param path path of the model to read.
-#' @return A fitted MLlib model.
-#' @rdname read.ml
-#' @name read.ml
-#' @export
-#' @seealso \link{write.ml}
-#' @examples
-#' \dontrun{
-#' path <- "path/to/model"
-#' model <- read.ml(path)
-#' }
-#' @note read.ml since 2.0.0
-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")) {
-    new("NaiveBayesModel", jobj = jobj)
-  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.AFTSurvivalRegressionWrapper")) {
-    new("AFTSurvivalRegressionModel", jobj = jobj)
-  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.GeneralizedLinearRegressionWrapper")) {
-    new("GeneralizedLinearRegressionModel", jobj = jobj)
-  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.KMeansWrapper")) {
-    new("KMeansModel", jobj = jobj)
-  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.LDAWrapper")) {
-    new("LDAModel", jobj = jobj)
-  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.MultilayerPerceptronClassifierWrapper")) {
-    new("MultilayerPerceptronClassificationModel", jobj = jobj)
-  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.IsotonicRegressionWrapper")) {
-    new("IsotonicRegressionModel", jobj = jobj)
-  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.GaussianMixtureWrapper")) {
-    new("GaussianMixtureModel", jobj = jobj)
-  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.ALSWrapper")) {
-    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 if (isInstanceOf(jobj, "org.apache.spark.ml.r.GBTRegressorWrapper")) {
-    new("GBTRegressionModel", jobj = jobj)
-  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.GBTClassifierWrapper")) {
-    new("GBTClassificationModel", jobj = jobj)
-  } else {
-    stop("Unsupported model: ", jobj)
-  }
-}
-
-#' Accelerated Failure Time (AFT) Survival Regression Model
-#'
-#' \code{spark.survreg} fits an accelerated failure time (AFT) survival regression model on
-#' a SparkDataFrame. Users can call \code{summary} to get a summary of the fitted AFT model,
-#' \code{predict} to make predictions on new data, and \code{write.ml}/\code{read.ml} to
-#' save/load fitted models.
-#'
-#' @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 '-'.
-#'                Note that operator '.' is not supported currently.
-#' @return \code{spark.survreg} returns a fitted AFT survival regression model.
-#' @rdname spark.survreg
-#' @seealso survival: \url{https://cran.r-project.org/package=survival}
-#' @export
-#' @examples
-#' \dontrun{
-#' df <- createDataFrame(ovarian)
-#' model <- spark.survreg(df, Surv(futime, fustat) ~ ecog_ps + rx)
-#'
-#' # get a summary of the model
-#' summary(model)
-#'
-#' # make predictions
-#' predicted <- predict(model, df)
-#' showDF(predicted)
-#'
-#' # save and load the model
-#' path <- "path/to/model"
-#' write.ml(model, path)
-#' savedModel <- read.ml(path)
-#' summary(savedModel)
-#' }
-#' @note spark.survreg since 2.0.0
-setMethod("spark.survreg", signature(data = "SparkDataFrame", formula = "formula"),
-          function(data, formula) {
-            formula <- paste(deparse(formula), collapse = "")
-            jobj <- callJStatic("org.apache.spark.ml.r.AFTSurvivalRegressionWrapper",
-                                "fit", formula, data@sdf)
-            new("AFTSurvivalRegressionModel", jobj = jobj)
-          })
-
-#' Latent Dirichlet Allocation
-#'
-#' \code{spark.lda} fits a Latent Dirichlet Allocation model on a SparkDataFrame. Users can call
-#' \code{summary} to get a summary of the fitted LDA model, \code{spark.posterior} to compute
-#' posterior probabilities on new data, \code{spark.perplexity} to compute log perplexity on new
-#' data and \code{write.ml}/\code{read.ml} to save/load fitted models.
-#'
-#' @param data A SparkDataFrame for training.
-#' @param features Features column name. Either libSVM-format column or character-format column is
-#'        valid.
-#' @param k Number of topics.
-#' @param maxIter Maximum iterations.
-#' @param optimizer Optimizer to train an LDA model, "online" or "em", default is "online".
-#' @param subsamplingRate (For online optimizer) Fraction of the corpus to be sampled and used in
-#'        each iteration of mini-batch gradient descent, in range (0, 1].
-#' @param topicConcentration concentration parameter (commonly named \code{beta} or \code{eta}) for
-#'        the prior placed on topic distributions over terms, default -1 to set automatically on the
-#'        Spark side. Use \code{summary} to retrieve the effective topicConcentration. Only 1-size
-#'        numeric is accepted.
-#' @param docConcentration concentration parameter (commonly named \code{alpha}) for the
-#'        prior placed on documents distributions over topics (\code{theta}), default -1 to set
-#'        automatically on the Spark side. Use \code{summary} to retrieve the effective
-#'        docConcentration. Only 1-size or \code{k}-size numeric is accepted.
-#' @param customizedStopWords stopwords that need to be removed from the given corpus. Ignore the
-#'        parameter if libSVM-format column is used as the features column.
-#' @param maxVocabSize maximum vocabulary size, default 1 << 18
-#' @param ... additional argument(s) passed to the method.
-#' @return \code{spark.lda} returns a fitted Latent Dirichlet Allocation model.
-#' @rdname spark.lda
-#' @aliases spark.lda,SparkDataFrame-method
-#' @seealso topicmodels: \url{https://cran.r-project.org/package=topicmodels}
-#' @export
-#' @examples
-#' \dontrun{
-#' # nolint start
-#' # An example "path/to/file" can be
-#' # paste0(Sys.getenv("SPARK_HOME"), "/data/mllib/sample_lda_libsvm_data.txt")
-#' # nolint end
-#' text <- read.df("path/to/file", source = "libsvm")
-#' model <- spark.lda(data = text, optimizer = "em")
-#'
-#' # get a summary of the model
-#' summary(model)
-#'
-#' # compute posterior probabilities
-#' posterior <- spark.posterior(model, text)
-#' showDF(posterior)
-#'
-#' # compute perplexity
-#' perplexity <- spark.perplexity(model, text)
-#'
-#' # save and load the model
-#' path <- "path/to/model"
-#' write.ml(model, path)
-#' savedModel <- read.ml(path)
-#' summary(savedModel)
-#' }
-#' @note spark.lda since 2.1.0
-setMethod("spark.lda", signature(data = "SparkDataFrame"),
-          function(data, features = "features", k = 10, maxIter = 20, optimizer = c("online", "em"),
-                   subsamplingRate = 0.05, topicConcentration = -1, docConcentration = -1,
-                   customizedStopWords = "", maxVocabSize = bitwShiftL(1, 18)) {
-            optimizer <- match.arg(optimizer)
-            jobj <- callJStatic("org.apache.spark.ml.r.LDAWrapper", "fit", data@sdf, features,
-                                as.integer(k), as.integer(maxIter), optimizer,
-                                as.numeric(subsamplingRate), topicConcentration,
-                                as.array(docConcentration), as.array(customizedStopWords),
-                                maxVocabSize)
-            new("LDAModel", jobj = jobj)
-          })
-
-# Returns a summary of the AFT survival regression model produced by spark.survreg,
-# similarly to R's summary().
-
-#' @param object a fitted AFT survival regression model.
-#' @return \code{summary} returns summary information of the fitted model, which is a list.
-#'         The list includes the model's \code{coefficients} (features, coefficients,
-#'         intercept and log(scale)).
-#' @rdname spark.survreg
-#' @export
-#' @note summary(AFTSurvivalRegressionModel) since 2.0.0
-setMethod("summary", signature(object = "AFTSurvivalRegressionModel"),
-          function(object) {
-            jobj <- object@jobj
-            features <- callJMethod(jobj, "rFeatures")
-            coefficients <- callJMethod(jobj, "rCoefficients")
-            coefficients <- as.matrix(unlist(coefficients))
-            colnames(coefficients) <- c("Value")
-            rownames(coefficients) <- unlist(features)
-            list(coefficients = coefficients)
-          })
-
-# Makes predictions from an AFT survival regression model or a model produced by
-# spark.survreg, similarly to R package survival's predict.
-
-#' @param newData a SparkDataFrame for testing.
-#' @return \code{predict} returns a SparkDataFrame containing predicted values
-#'         on the original scale of the data (mean predicted value at scale = 1.0).
-#' @rdname spark.survreg
-#' @export
-#' @note predict(AFTSurvivalRegressionModel) since 2.0.0
-setMethod("predict", signature(object = "AFTSurvivalRegressionModel"),
-          function(object, newData) {
-            predict_internal(object, newData)
-          })
-
-#' Multivariate Gaussian Mixture Model (GMM)
-#'
-#' Fits multivariate gaussian mixture model against a Spark DataFrame, similarly to R's
-#' mvnormalmixEM(). Users can call \code{summary} to print a summary of the fitted model,
-#' \code{predict} to make predictions on new data, and \code{write.ml}/\code{read.ml}
-#' to save/load fitted models.
-#'
-#' @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 '-'.
-#'                Note that the response variable of formula is empty in spark.gaussianMixture.
-#' @param k number of independent Gaussians in the mixture model.
-#' @param maxIter maximum iteration number.
-#' @param tol the convergence tolerance.
-#' @param ... additional arguments passed to the method.
-#' @aliases spark.gaussianMixture,SparkDataFrame,formula-method
-#' @return \code{spark.gaussianMixture} returns a fitted multivariate gaussian mixture model.
-#' @rdname spark.gaussianMixture
-#' @name spark.gaussianMixture
-#' @seealso mixtools: \url{https://cran.r-project.org/package=mixtools}
-#' @export
-#' @examples
-#' \dontrun{
-#' sparkR.session()
-#' library(mvtnorm)
-#' set.seed(100)
-#' a <- rmvnorm(4, c(0, 0))
-#' b <- rmvnorm(6, c(3, 4))
-#' data <- rbind(a, b)
-#' df <- createDataFrame(as.data.frame(data))
-#' model <- spark.gaussianMixture(df, ~ V1 + V2, k = 2)
-#' summary(model)
-#'
-#' # fitted values on training data
-#' fitted <- predict(model, df)
-#' head(select(fitted, "V1", "prediction"))
-#'
-#' # save fitted model to input path
-#' path <- "path/to/model"
-#' write.ml(model, path)
-#'
-#' # can also read back the saved model and print
-#' savedModel <- read.ml(path)
-#' summary(savedModel)
-#' }
-#' @note spark.gaussianMixture since 2.1.0
-#' @seealso \link{predict}, \link{read.ml}, \link{write.ml}
-setMethod("spark.gaussianMixture", signature(data = "SparkDataFrame", formula = "formula"),
-          function(data, formula, k = 2, maxIter = 100, tol = 0.01) {
-            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))
-            new("GaussianMixtureModel", jobj = jobj)
-          })
-
-#  Get the summary of a multivariate gaussian mixture model
-
-#' @param object a fitted gaussian mixture model.
-#' @return \code{summary} returns summary of the fitted model, which is a list.
-#'         The list includes the model's \code{lambda} (lambda), \code{mu} (mu),
-#'         \code{sigma} (sigma), and \code{posterior} (posterior).
-#' @aliases spark.gaussianMixture,SparkDataFrame,formula-method
-#' @rdname spark.gaussianMixture
-#' @export
-#' @note summary(GaussianMixtureModel) since 2.1.0
-setMethod("summary", signature(object = "GaussianMixtureModel"),
-          function(object) {
-            jobj <- object@jobj
-            is.loaded <- callJMethod(jobj, "isLoaded")
-            lambda <- unlist(callJMethod(jobj, "lambda"))
-            muList <- callJMethod(jobj, "mu")
-            sigmaList <- callJMethod(jobj, "sigma")
-            k <- callJMethod(jobj, "k")
-            dim <- callJMethod(jobj, "dim")
-            mu <- c()
-            for (i in 1 : k) {
-              start <- (i - 1) * dim + 1
-              end <- i * dim
-              mu[[i]] <- unlist(muList[start : end])
-            }
-            sigma <- c()
-            for (i in 1 : k) {
-              start <- (i - 1) * dim * dim + 1
-              end <- i * dim * dim
-              sigma[[i]] <- t(matrix(sigmaList[start : end], ncol = dim))
-            }
-            posterior <- if (is.loaded) {
-              NULL
-            } else {
-              dataFrame(callJMethod(jobj, "posterior"))
-            }
-            list(lambda = lambda, mu = mu, sigma = sigma,
-                 posterior = posterior, is.loaded = is.loaded)
-          })
-
-#  Predicted values based on a gaussian mixture model
-
-#' @param newData a SparkDataFrame for testing.
-#' @return \code{predict} returns a SparkDataFrame containing predicted labels in a column named
-#'         "prediction".
-#' @aliases predict,GaussianMixtureModel,SparkDataFrame-method
-#' @rdname spark.gaussianMixture
-#' @export
-#' @note predict(GaussianMixtureModel) since 2.1.0
-setMethod("predict", signature(object = "GaussianMixtureModel"),
-          function(object, newData) {
-            predict_internal(object, newData)
-          })
-
-#' Alternating Least Squares (ALS) for Collaborative Filtering
-#'
-#' \code{spark.als} learns latent factors in collaborative filtering via alternating least
-#' squares. Users can call \code{summary} to obtain fitted latent factors, \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-collaborative-filtering.html}{MLlib:
-#' Collaborative Filtering}.
-#'
-#' @param data a SparkDataFrame for training.
-#' @param ratingCol column name for ratings.
-#' @param userCol column name for user ids. Ids must be (or can be coerced into) integers.
-#' @param itemCol column name for item ids. Ids must be (or can be coerced into) integers.
-#' @param rank rank of the matrix factorization (> 0).
-#' @param regParam regularization parameter (>= 0).
-#' @param maxIter maximum number of iterations (>= 0).
-#' @param nonnegative logical value indicating whether to apply nonnegativity constraints.
-#' @param implicitPrefs logical value indicating whether to use implicit preference.
-#' @param alpha alpha parameter in the implicit preference formulation (>= 0).
-#' @param seed integer seed for random number generation.
-#' @param numUserBlocks number of user blocks used to parallelize computation (> 0).
-#' @param numItemBlocks number of item blocks used to parallelize computation (> 0).
-#' @param checkpointInterval number of checkpoint intervals (>= 1) or disable checkpoint (-1).
-#' @param ... additional argument(s) passed to the method.
-#' @return \code{spark.als} returns a fitted ALS model.
-#' @rdname spark.als
-#' @aliases spark.als,SparkDataFrame-method
-#' @name spark.als
-#' @export
-#' @examples
-#' \dontrun{
-#' ratings <- list(list(0, 0, 4.0), list(0, 1, 2.0), list(1, 1, 3.0), list(1, 2, 4.0),
-#'                 list(2, 1, 1.0), list(2, 2, 5.0))
-#' df <- createDataFrame(ratings, c("user", "item", "rating"))
-#' model <- spark.als(df, "rating", "user", "item")
-#'
-#' # extract latent factors
-#' stats <- summary(model)
-#' userFactors <- stats$userFactors
-#' itemFactors <- stats$itemFactors
-#'
-#' # make predictions
-#' predicted <- predict(model, df)
-#' showDF(predicted)
-#'
-#' # save and load the model
-#' path <- "path/to/model"
-#' write.ml(model, path)
-#' savedModel <- read.ml(path)
-#' summary(savedModel)
-#'
-#' # set other arguments
-#' modelS <- spark.als(df, "rating", "user", "item", rank = 20,
-#'                     regParam = 0.1, nonnegative = TRUE)
-#' statsS <- summary(modelS)
-#' }
-#' @note spark.als since 2.1.0
-setMethod("spark.als", signature(data = "SparkDataFrame"),
-          function(data, ratingCol = "rating", userCol = "user", itemCol = "item",
-                   rank = 10, regParam = 0.1, maxIter = 10, nonnegative = FALSE,
-                   implicitPrefs = FALSE, alpha = 1.0, numUserBlocks = 10, numItemBlocks = 10,
-                   checkpointInterval = 10, seed = 0) {
-
-            if (!is.numeric(rank) || rank <= 0) {
-              stop("rank should be a positive number.")
-            }
-            if (!is.numeric(regParam) || regParam < 0) {
-              stop("regParam should be a nonnegative number.")
-            }
-            if (!is.numeric(maxIter) || maxIter <= 0) {
-              stop("maxIter should be a positive number.")
-            }
-
-            jobj <- callJStatic("org.apache.spark.ml.r.ALSWrapper",
-                                "fit", data@sdf, ratingCol, userCol, itemCol, as.integer(rank),
-                                regParam, as.integer(maxIter), implicitPrefs, alpha, nonnegative,
-                                as.integer(numUserBlocks), as.integer(numItemBlocks),
-                                as.integer(checkpointInterval), as.integer(seed))
-            new("ALSModel", jobj = jobj)
-          })
-
-# Returns a summary of the ALS model produced by spark.als.
-
-#' @param object a fitted ALS model.
-#' @return \code{summary} returns summary information of the fitted model, which is a list.
-#'         The list includes \code{user} (the names of the user column),
-#'         \code{item} (the item column), \code{rating} (the rating column), \code{userFactors}
-#'         (the estimated user factors), \code{itemFactors} (the estimated item factors),
-#'         and \code{rank} (rank of the matrix factorization model).
-#' @rdname spark.als
-#' @aliases summary,ALSModel-method
-#' @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")
-            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.
-
-#' @param newData a SparkDataFrame for testing.
-#' @return \code{predict} returns a SparkDataFrame containing predicted values.
-#' @rdname spark.als
-#' @aliases predict,ALSModel-method
-#' @export
-#' @note predict(ALSModel) since 2.1.0
-setMethod("predict", signature(object = "ALSModel"),
-          function(object, newData) {
-            predict_internal(object, newData)
-          })
-
-
-# Saves the ALS model to the input path.
-
-#' @param path the directory where the model is saved.
-#' @param overwrite logical value indicating whether to overwrite if the output path
-#'                  already exists. Default is FALSE which means throw exception
-#'                  if the output path exists.
-#'
-#' @rdname spark.als
-#' @aliases write.ml,ALSModel,character-method
-#' @export
-#' @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) {
-            write_internal(object, path, overwrite)
-          })
-
-#' (One-Sample) Kolmogorov-Smirnov Test
-#'
-#' @description
-#' \code{spark.kstest} Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a
-#' continuous distribution.
-#'
-#' By comparing the largest difference between the empirical cumulative
-#' distribution of the sample data and the theoretical distribution we can provide a test for the
-#' the null hypothesis that the sample data comes from that theoretical distribution.
-#'
-#' Users can call \code{summary} to obtain a summary of the test, and \code{print.summary.KSTest}
-#' to print out a summary result.
-#'
-#' @param data a SparkDataFrame of user data.
-#' @param testCol column name where the test data is from. It should be a column of double type.
-#' @param nullHypothesis name of the theoretical distribution tested against. Currently only
-#'                       \code{"norm"} for normal distribution is supported.
-#' @param distParams parameters(s) of the distribution. For \code{nullHypothesis = "norm"},
-#'                   we can provide as a vector the mean and standard deviation of
-#'                   the distribution. If none is provided, then standard normal will be used.
-#'                   If only one is provided, then the standard deviation will be set to be one.
-#' @param ... additional argument(s) passed to the method.
-#' @return \code{spark.kstest} returns a test result object.
-#' @rdname spark.kstest
-#' @aliases spark.kstest,SparkDataFrame-method
-#' @name spark.kstest
-#' @seealso \href{http://spark.apache.org/docs/latest/mllib-statistics.html#hypothesis-testing}{
-#'          MLlib: Hypothesis Testing}
-#' @export
-#' @examples
-#' \dontrun{
-#' data <- data.frame(test = c(0.1, 0.15, 0.2, 0.3, 0.25))
-#' df <- createDataFrame(data)
-#' test <- spark.kstest(df, "test", "norm", c(0, 1))
-#'
-#' # get a summary of the test result
-#' testSummary <- summary(test)
-#' testSummary
-#'
-#' # print out the summary in an organized way
-#' print.summary.KSTest(testSummary)
-#' }
-#' @note spark.kstest since 2.1.0
-setMethod("spark.kstest", signature(data = "SparkDataFrame"),
-          function(data, testCol = "test", nullHypothesis = c("norm"), distParams = c(0, 1)) {
-            tryCatch(match.arg(nullHypothesis),
-                     error = function(e) {
-                       msg <- paste("Distribution", nullHypothesis, "is not supported.")
-                       stop(msg)
-                     })
-            if (nullHypothesis == "norm") {
-              distParams <- as.numeric(distParams)
-              mu <- ifelse(length(distParams) < 1, 0, distParams[1])
-              sigma <- ifelse(length(distParams) < 2, 1, distParams[2])
-              jobj <- callJStatic("org.apache.spark.ml.r.KSTestWrapper",
-                                  "test", data@sdf, testCol, nullHypothesis,
-                                  as.array(c(mu, sigma)))
-              new("KSTest", jobj = jobj)
-            }
-})
-
-#  Get the summary of Kolmogorov-Smirnov (KS) Test.
-#' @param object test result object of KSTest by \code{spark.kstest}.
-#' @return \code{summary} returns summary information of KSTest object, which is a list.
-#'         The list includes the \code{p.value} (p-value), \code{statistic} (test statistic
-#'         computed for the test), \code{nullHypothesis} (the null hypothesis with its
-#'         parameters tested against) and \code{degreesOfFreedom} (degrees of freedom of the test).
-#' @rdname spark.kstest
-#' @aliases summary,KSTest-method
-#' @export
-#' @note summary(KSTest) since 2.1.0
-setMethod("summary", signature(object = "KSTest"),
-          function(object) {
-            jobj <- object@jobj
-            pValue <- callJMethod(jobj, "pValue")
-            statistic <- callJMethod(jobj, "statistic")
-            nullHypothesis <- callJMethod(jobj, "nullHypothesis")
-            distName <- callJMethod(jobj, "distName")
-            distParams <- unlist(callJMethod(jobj, "distParams"))
-            degreesOfFreedom <- callJMethod(jobj, "degreesOfFreedom")
-
-            ans <- list(p.value = pValue, statistic = statistic, nullHypothesis = nullHypothesis,
-                        nullHypothesis.name = distName, nullHypothesis.parameters = distParams,
-                        degreesOfFreedom = degreesOfFreedom, jobj = jobj)
-            class(ans) <- "summary.KSTest"
-            ans
-          })
-
-#  Prints the summary of KSTest
-
-#' @rdname spark.kstest
-#' @param x summary object of KSTest returned by \code{summary}.
-#' @export
-#' @note print.summary.KSTest since 2.1.0
-print.summary.KSTest <- function(x, ...) {
-  jobj <- x$jobj
-  summaryStr <- callJMethod(jobj, "summary")
-  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-regression}{
-#' Random Forest Regression} and
-#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-classifier}{
-#' Random Forest Classification}
-#'
-#' @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).
-#' @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.
-#' @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 is "gini".
-#' @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].
-#' @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 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. If TRUE, the algorithm will cache node IDs for each instance. Caching
-#'                     can speed up training of deeper trees. Users can set how often should the
-#'                     cache be checkpointed or disable it by setting checkpointInterval.
-#' @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,
-                   featureSubsetStrategy = "auto", seed = NULL, subsamplingRate = 1.0,
-                   minInstancesPerNode = 1, minInfoGain = 0.0, checkpointInterval = 10,
-                   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.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)
-          })
-
-# Create the summary of a tree ensemble model (eg. Random Forest, GBT)
-summary.treeEnsemble <- 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)
-}
-
-#  Get the summary of a Random Forest Regression Model
-
-#' @return \code{summary} returns summary information of the fitted model, which is a list.
-#'         The list of components includes \code{formula} (formula),
-#'         \code{numFeatures} (number of features), \code{features} (list of features),
-#'         \code{featureImportances} (feature importances), \code{numTrees} (number of trees),
-#'         and \code{treeWeights} (tree weights).
-#' @rdname spark.randomForest
-#' @aliases summary,RandomForestRegressionModel-method
-#' @export
-#' @note summary(RandomForestRegressionModel) since 2.1.0
-setMethod("summary", signature(object = "RandomForestRegressionModel"),
-          function(object) {
-            ans <- summary.treeEnsemble(object)
-            class(ans) <- "summary.RandomForestRegressionModel"
-            ans
-          })
-
-#  Get the summary of a Random Forest Classification 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.treeEnsemble(object)
-            class(ans) <- "summary.RandomForestClassificationModel"
-            ans
-          })
-
-#  Prints the summary of tree ensemble models (eg. Random Forest, GBT)
-print.summary.treeEnsemble <- 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)
-}
-
-#  Prints the summary of Random Forest Regression Model
-
-#' @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.treeEnsemble(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.treeEnsemble(x)
-}
-
-#' Gradient Boosted Tree Model for Regression and Classification
-#'
-#' \code{spark.gbt} fits a Gradient Boosted Tree Regression model or Classification model on a
-#' SparkDataFrame. Users can call \code{summary} to get a summary of the fitted
-#' Gradient Boosted Tree 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#gradient-boosted-tree-regression}{
-#' GBT Regression} and
-#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-classifier}{
-#' GBT Classification}
-#'
-#' @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).
-#' @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.
-#' @param maxIter Param for maximum number of iterations (>= 0).
-#' @param stepSize Param for Step size to be used for each iteration of optimization.
-#' @param lossType Loss function which GBT tries to minimize.
-#'                 For classification, must be "logistic". For regression, must be one of
-#'                 "squared" (L2) and "absolute" (L1), default is "squared".
-#' @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].
-#' @param minInstancesPerNode Minimum number of instances each child must have after split. If a
-#'                            split causes the left or right child to have fewer than
-#'                            minInstancesPerNode, the split will be discarded as invalid. Should be
-#'                            >= 1.
-#' @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 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. If TRUE, the algorithm will cache node IDs for each instance. Caching
-#'                     can speed up training of deeper trees. Users can set how often should the
-#'                     cache be checkpointed or disable it by setting checkpointInterval.
-#' @param ... additional arguments passed to the method.
-#' @aliases spark.gbt,SparkDataFrame,formula-method
-#' @return \code{spark.gbt} returns a fitted Gradient Boosted Tree model.
-#' @rdname spark.gbt
-#' @name spark.gbt
-#' @export
-#' @examples
-#' \dontrun{
-#' # fit a Gradient Boosted Tree Regression Model
-#' df <- createDataFrame(longley)
-#' model <- spark.gbt(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 Gradient Boosted Tree Classification Model
-#' # label must be binary - Only binary classification is supported for GBT.
-#' df <- createDataFrame(iris[iris$Species != "virginica", ])
-#' model <- spark.gbt(df, Species ~ Petal_Length + Petal_Width, "classification")
-#'
-#' # numeric label is also supported
-#' iris2 <- iris[iris$Species != "virginica", ]
-#' iris2$NumericSpecies <- ifelse(iris2$Species == "setosa", 0, 1)
-#' df <- createDataFrame(iris2)
-#' model <- spark.gbt(df, NumericSpecies ~ ., type = "classification")
-#' }
-#' @note spark.gbt since 2.1.0
-setMethod("spark.gbt", signature(data = "SparkDataFrame", formula = "formula"),
-          function(data, formula, type = c("regression", "classification"),
-                   maxDepth = 5, maxBins = 32, maxIter = 20, stepSize = 0.1, lossType = NULL,
-                   seed = NULL, subsamplingRate = 1.0, minInstancesPerNode = 1, minInfoGain = 0.0,
-                   checkpointInterval = 10, 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(lossType)) lossType <- "squared"
-                     lossType <- match.arg(lossType, c("squared", "absolute"))
-                     jobj <- callJStatic("org.apache.spark.ml.r.GBTRegressorWrapper",
-                                         "fit", data@sdf, formula, as.integer(maxDepth),
-                                         as.integer(maxBins), as.integer(maxIter),
-                                         as.numeric(stepSize), as.integer(minInstancesPerNode),
-                                         as.numeric(minInfoGain), as.integer(checkpointInterval),
-                                         lossType, seed, as.numeric(subsamplingRate),
-                                         as.integer(maxMemoryInMB), as.logical(cacheNodeIds))
-                     new("GBTRegressionModel", jobj = jobj)
-                   },
-                   classification = {
-                     if (is.null(lossType)) lossType <- "logistic"
-                     lossType <- match.arg(lossType, "logistic")
-                     jobj <- callJStatic("org.apache.spark.ml.r.GBTClassifierWrapper",
-                                         "fit", data@sdf, formula, as.integer(maxDepth),
-                                         as.integer(maxBins), as.integer(maxIter),
-                                         as.numeric(stepSize), as.integer(minInstancesPerNode),
-                                         as.numeric(minInfoGain), as.integer(checkpointInterval),
-                                         lossType, seed, as.numeric(subsamplingRate),
-                                         as.integer(maxMemoryInMB), as.logical(cacheNodeIds))
-                     new("GBTClassificationModel", jobj = jobj)
-                   }
-            )
-          })
-
-# Makes predictions from a Gradient Boosted Tree 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.gbt
-#' @aliases predict,GBTRegressionModel-method
-#' @export
-#' @note predict(GBTRegressionModel) since 2.1.0
-setMethod("predict", signature(object = "GBTRegressionModel"),
-          function(object, newData) {
-            predict_internal(object, newData)
-          })
-
-#' @rdname spark.gbt
-#' @aliases predict,GBTClassificationModel-method
-#' @export
-#' @note predict(GBTClassificationModel) since 2.1.0
-setMethod("predict", signature(object = "GBTClassificationModel"),
-          function(object, newData) {
-            predict_internal(object, newData)
-          })
-
-# Save the Gradient Boosted Tree Regression or Classification model to the input path.
-
-#' @param object A fitted Gradient Boosted Tree 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,GBTRegressionModel,character-method
-#' @rdname spark.gbt
-#' @export
-#' @note write.ml(GBTRegressionModel, character) since 2.1.0
-setMethod("write.ml", signature(object = "GBTRegressionModel", path = "character"),
-          function(object, path, overwrite = FALSE) {
-            write_internal(object, path, overwrite)
-          })
-
-#' @aliases write.ml,GBTClassificationModel,character-method
-#' @rdname spark.gbt
-#' @export
-#' @note write.ml(GBTClassificationModel, character) since 2.1.0
-setMethod("write.ml", signature(object = "GBTClassificationModel", path = "character"),
-          function(object, path, overwrite = FALSE) {
-            write_internal(object, path, overwrite)
-          })
-
-#  Get the summary of a Gradient Boosted Tree Regression Model
-
-#' @return \code{summary} returns summary information of the fitted model, which is a list.
-#'         The list of components includes \code{formula} (formula),
-#'         \code{numFeatures} (number of features), \code{features} (list of features),
-#'         \code{featureImportances} (feature importances), \code{numTrees} (number of trees),
-#'         and \code{treeWeights} (tree weights).
-#' @rdname spark.gbt
-#' @aliases summary,GBTRegressionModel-method
-#' @export
-#' @note summary(GBTRegressionModel) since 2.1.0
-setMethod("summary", signature(object = "GBTRegressionModel"),
-          function(object) {
-            ans <- summary.treeEnsemble(object)
-            class(ans) <- "summary.GBTRegressionModel"
-            ans
-          })
-
-#  Get the summary of a Gradient Boosted Tree Classification Model
-
-#' @rdname spark.gbt
-#' @aliases summary,GBTClassificationModel-method
-#' @export
-#' @note summary(GBTClassificationModel) since 2.1.0
-setMethod("summary", signature(object = "GBTClassificationModel"),
-          function(object) {
-            ans <- summary.treeEnsemble(object)
-            class(ans) <- "summary.GBTClassificationModel"
-            ans
-          })
-
-#  Prints the summary of Gradient Boosted Tree Regression Model
-
-#' @param x summary object of Gradient Boosted Tree regression model or classification model
-#'          returned by \code{summary}.
-#' @rdname spark.gbt
-#' @export
-#' @note print.summary.GBTRegressionModel since 2.1.0
-print.summary.GBTRegressionModel <- function(x, ...) {
-  print.summary.treeEnsemble(x)
-}
-
-#  Prints the summary of Gradient Boosted Tree Classification Model
-
-#' @rdname spark.gbt
-#' @export
-#' @note print.summary.GBTClassificationModel since 2.1.0
-print.summary.GBTClassificationModel <- function(x, ...) {
-  print.summary.treeEnsemble(x)
-}
diff --git a/R/pkg/R/mllib_classification.R b/R/pkg/R/mllib_classification.R
new file mode 100644
index 0000000000..8da84499df
--- /dev/null
+++ b/R/pkg/R/mllib_classification.R
@@ -0,0 +1,417 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# mllib_regression.R: Provides methods for MLlib classification algorithms
+#                     (except for tree-based algorithms) integration
+
+#' S4 class that represents an LogisticRegressionModel
+#'
+#' @param jobj a Java object reference to the backing Scala LogisticRegressionModel
+#' @export
+#' @note LogisticRegressionModel since 2.1.0
+setClass("LogisticRegressionModel", representation(jobj = "jobj"))
+
+#' S4 class that represents a MultilayerPerceptronClassificationModel
+#'
+#' @param jobj a Java object reference to the backing Scala MultilayerPerceptronClassifierWrapper
+#' @export
+#' @note MultilayerPerceptronClassificationModel since 2.1.0
+setClass("MultilayerPerceptronClassificationModel", representation(jobj = "jobj"))
+
+#' S4 class that represents a NaiveBayesModel
+#'
+#' @param jobj a Java object reference to the backing Scala NaiveBayesWrapper
+#' @export
+#' @note NaiveBayesModel since 2.0.0
+setClass("NaiveBayesModel", representation(jobj = "jobj"))
+
+#' Logistic Regression Model
+#'
+#' Fits an logistic regression model against a Spark DataFrame. It supports "binomial": Binary logistic regression
+#' with pivoting; "multinomial": Multinomial logistic (softmax) regression without pivoting, similar to glmnet.
+#' Users can print, make predictions on the produced model and save the model to the input path.
+#'
+#' @param data 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 regParam the regularization parameter.
+#' @param elasticNetParam the ElasticNet mixing parameter. For alpha = 0.0, the penalty is an L2 penalty.
+#'                        For alpha = 1.0, it is an L1 penalty. For 0.0 < alpha < 1.0, the penalty is a combination
+#'                        of L1 and L2. Default is 0.0 which is an L2 penalty.
+#' @param maxIter maximum iteration number.
+#' @param tol convergence tolerance of iterations.
+#' @param family the name of family which is a description of the label distribution to be used in the model.
+#'               Supported options:
+#'                 \itemize{
+#'                   \item{"auto": Automatically select the family based on the number of classes:
+#'                           If number of classes == 1 || number of classes == 2, set to "binomial".
+#'                           Else, set to "multinomial".}
+#'                   \item{"binomial": Binary logistic regression with pivoting.}
+#'                   \item{"multinomial": Multinomial logistic (softmax) regression without pivoting.}
+#'                 }
+#' @param standardization whether to standardize the training features before fitting the model. The coefficients
+#'                        of models will be always returned on the original scale, so it will be transparent for
+#'                        users. Note that with/without standardization, the models should be always converged
+#'                        to the same solution when no regularization is applied. Default is TRUE, same as glmnet.
+#' @param thresholds in binary classification, in range [0, 1]. If the estimated probability of class label 1
+#'                  is > threshold, then predict 1, else 0. A high threshold encourages the model to predict 0
+#'                  more often; a low threshold encourages the model to predict 1 more often. Note: Setting this with
+#'                  threshold p is equivalent to setting thresholds c(1-p, p). In multiclass (or binary) classification to adjust the probability of
+#'                  predicting each class. Array must have length equal to the number of classes, with values > 0,
+#'                  excepting that at most one value may be 0. The class with largest value p/t is predicted, where p
+#'                  is the original probability of that class and t is the class's threshold.
+#' @param weightCol The weight column name.
+#' @param ... additional arguments passed to the method.
+#' @return \code{spark.logit} returns a fitted logistic regression model.
+#' @rdname spark.logit
+#' @aliases spark.logit,SparkDataFrame,formula-method
+#' @name spark.logit
+#' @export
+#' @examples
+#' \dontrun{
+#' sparkR.session()
+#' # binary logistic regression
+#' df <- createDataFrame(iris)
+#' training <- df[df$Species %in% c("versicolor", "virginica"), ]
+#' model <- spark.logit(training, Species ~ ., regParam = 0.5)
+#' summary <- summary(model)
+#'
+#' # fitted values on training data
+#' fitted <- predict(model, training)
+#'
+#' # save fitted model to input path
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#'
+#' # can also read back the saved model and predict
+#' # Note that summary deos not work on loaded model
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#'
+#' # multinomial logistic regression
+#'
+#' df <- createDataFrame(iris)
+#' model <- spark.logit(df, Species ~ ., regParam = 0.5)
+#' summary <- summary(model)
+#'
+#' }
+#' @note spark.logit since 2.1.0
+setMethod("spark.logit", signature(data = "SparkDataFrame", formula = "formula"),
+          function(data, formula, regParam = 0.0, elasticNetParam = 0.0, maxIter = 100,
+                   tol = 1E-6, family = "auto", standardization = TRUE,
+                   thresholds = 0.5, weightCol = NULL) {
+            formula <- paste(deparse(formula), collapse = "")
+
+            if (is.null(weightCol)) {
+              weightCol <- ""
+            }
+
+            jobj <- callJStatic("org.apache.spark.ml.r.LogisticRegressionWrapper", "fit",
+                                data@sdf, formula, as.numeric(regParam),
+                                as.numeric(elasticNetParam), as.integer(maxIter),
+                                as.numeric(tol), as.character(family),
+                                as.logical(standardization), as.array(thresholds),
+                                as.character(weightCol))
+            new("LogisticRegressionModel", jobj = jobj)
+          })
+
+#  Get the summary of an LogisticRegressionModel
+
+#' @param object an LogisticRegressionModel fitted by \code{spark.logit}.
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes \code{coefficients} (coefficients matrix of the fitted model).
+#' @rdname spark.logit
+#' @aliases summary,LogisticRegressionModel-method
+#' @export
+#' @note summary(LogisticRegressionModel) since 2.1.0
+setMethod("summary", signature(object = "LogisticRegressionModel"),
+          function(object) {
+            jobj <- object@jobj
+            features <- callJMethod(jobj, "rFeatures")
+            labels <- callJMethod(jobj, "labels")
+            coefficients <- callJMethod(jobj, "rCoefficients")
+            nCol <- length(coefficients) / length(features)
+            coefficients <- matrix(coefficients, ncol = nCol)
+            # If nCol == 1, means this is a binomial logistic regression model with pivoting.
+            # Otherwise, it's a multinomial logistic regression model without pivoting.
+            if (nCol == 1) {
+              colnames(coefficients) <- c("Estimate")
+            } else {
+              colnames(coefficients) <- unlist(labels)
+            }
+            rownames(coefficients) <- unlist(features)
+
+            list(coefficients = coefficients)
+          })
+
+#  Predicted values based on an LogisticRegressionModel model
+
+#' @param newData a SparkDataFrame for testing.
+#' @return \code{predict} returns the predicted values based on an LogisticRegressionModel.
+#' @rdname spark.logit
+#' @aliases predict,LogisticRegressionModel,SparkDataFrame-method
+#' @export
+#' @note predict(LogisticRegressionModel) since 2.1.0
+setMethod("predict", signature(object = "LogisticRegressionModel"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+#  Save fitted LogisticRegressionModel to the input path
+
+#' @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 spark.logit
+#' @aliases write.ml,LogisticRegressionModel,character-method
+#' @export
+#' @note write.ml(LogisticRegression, character) since 2.1.0
+setMethod("write.ml", signature(object = "LogisticRegressionModel", path = "character"),
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
+
+#' Multilayer Perceptron Classification Model
+#'
+#' \code{spark.mlp} fits a multi-layer perceptron neural network model against a SparkDataFrame.
+#' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
+#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
+#' Only categorical data is supported.
+#' For more details, see
+#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html}{
+#'   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".
+#' @param maxIter maximum iteration number.
+#' @param tol convergence tolerance of iterations.
+#' @param stepSize stepSize parameter.
+#' @param seed seed parameter for weights initialization.
+#' @param initialWeights initialWeights parameter for weights initialization, it should be a
+#' numeric vector.
+#' @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,formula-method
+#' @name spark.mlp
+#' @seealso \link{read.ml}
+#' @export
+#' @examples
+#' \dontrun{
+#' df <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm")
+#'
+#' # fit a Multilayer Perceptron Classification Model
+#' 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))
+#'
+#' # 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)
+#' }
+#' @note spark.mlp since 2.1.0
+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.")
+            }
+            layers <- as.integer(na.omit(layers))
+            if (length(layers) <= 1) {
+              stop ("layers must be a integer vector with length > 1.")
+            }
+            if (!is.null(seed)) {
+              seed <- as.character(as.integer(seed))
+            }
+            if (!is.null(initialWeights)) {
+              initialWeights <- as.array(as.numeric(na.omit(initialWeights)))
+            }
+            jobj <- callJStatic("org.apache.spark.ml.r.MultilayerPerceptronClassifierWrapper",
+                                "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)
+          })
+
+#  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 summary information of the fitted model, which is a list.
+#'         The list includes \code{numOfInputs} (number of inputs), \code{numOfOutputs}
+#'         (number of outputs), \code{layers} (array of layer sizes including input
+#'         and output layers), and \code{weights} (the weights of layers).
+#'         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
+#' @note summary(MultilayerPerceptronClassificationModel) since 2.1.0
+setMethod("summary", signature(object = "MultilayerPerceptronClassificationModel"),
+          function(object) {
+            jobj <- object@jobj
+            layers <- unlist(callJMethod(jobj, "layers"))
+            numOfInputs <- head(layers, n = 1)
+            numOfOutputs <- tail(layers, n = 1)
+            weights <- callJMethod(jobj, "weights")
+            list(numOfInputs = numOfInputs, numOfOutputs = numOfOutputs,
+                 layers = layers, weights = weights)
+          })
+
+#  Makes predictions from a model produced by spark.mlp().
+
+#' @param newData a SparkDataFrame for testing.
+#' @return \code{predict} returns a SparkDataFrame containing predicted labeled in a column named
+#' "prediction".
+#' @rdname spark.mlp
+#' @aliases predict,MultilayerPerceptronClassificationModel-method
+#' @export
+#' @note predict(MultilayerPerceptronClassificationModel) since 2.1.0
+setMethod("predict", signature(object = "MultilayerPerceptronClassificationModel"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+#  Saves the Multilayer Perceptron Classification Model to the input path.
+
+#' @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 spark.mlp
+#' @aliases write.ml,MultilayerPerceptronClassificationModel,character-method
+#' @export
+#' @seealso \link{write.ml}
+#' @note write.ml(MultilayerPerceptronClassificationModel, character) since 2.1.0
+setMethod("write.ml", signature(object = "MultilayerPerceptronClassificationModel",
+          path = "character"),
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
+
+#' Naive Bayes Models
+#'
+#' \code{spark.naiveBayes} fits a Bernoulli naive Bayes model against a SparkDataFrame.
+#' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
+#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
+#' Only categorical data is supported.
+#'
+#' @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 smoothing smoothing parameter.
+#' @param ... additional argument(s) passed to the method. Currently only \code{smoothing}.
+#' @return \code{spark.naiveBayes} returns a fitted naive Bayes model.
+#' @rdname spark.naiveBayes
+#' @aliases spark.naiveBayes,SparkDataFrame,formula-method
+#' @name spark.naiveBayes
+#' @seealso e1071: \url{https://cran.r-project.org/package=e1071}
+#' @export
+#' @examples
+#' \dontrun{
+#' data <- as.data.frame(UCBAdmissions)
+#' df <- createDataFrame(data)
+#'
+#' # fit a Bernoulli naive Bayes model
+#' model <- spark.naiveBayes(df, Admit ~ Gender + Dept, smoothing = 0)
+#'
+#' # 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)
+#' }
+#' @note spark.naiveBayes since 2.0.0
+setMethod("spark.naiveBayes", signature(data = "SparkDataFrame", formula = "formula"),
+          function(data, formula, smoothing = 1.0) {
+            formula <- paste(deparse(formula), collapse = "")
+            jobj <- callJStatic("org.apache.spark.ml.r.NaiveBayesWrapper", "fit",
+            formula, data@sdf, smoothing)
+            new("NaiveBayesModel", jobj = jobj)
+          })
+
+#  Returns the summary of a naive Bayes model produced by \code{spark.naiveBayes}
+
+#' @param object a naive Bayes model fitted by \code{spark.naiveBayes}.
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes \code{apriori} (the label distribution) and
+#'         \code{tables} (conditional probabilities given the target label).
+#' @rdname spark.naiveBayes
+#' @export
+#' @note summary(NaiveBayesModel) since 2.0.0
+setMethod("summary", signature(object = "NaiveBayesModel"),
+          function(object) {
+            jobj <- object@jobj
+            features <- callJMethod(jobj, "features")
+            labels <- callJMethod(jobj, "labels")
+            apriori <- callJMethod(jobj, "apriori")
+            apriori <- t(as.matrix(unlist(apriori)))
+            colnames(apriori) <- unlist(labels)
+            tables <- callJMethod(jobj, "tables")
+            tables <- matrix(tables, nrow = length(labels))
+            rownames(tables) <- unlist(labels)
+            colnames(tables) <- unlist(features)
+            list(apriori = apriori, tables = tables)
+          })
+
+#  Makes predictions from a naive Bayes model or a model produced by spark.naiveBayes(),
+#  similarly to R package e1071's predict.
+
+#' @param newData a SparkDataFrame for testing.
+#' @return \code{predict} returns a SparkDataFrame containing predicted labeled in a column named
+#' "prediction".
+#' @rdname spark.naiveBayes
+#' @export
+#' @note predict(NaiveBayesModel) since 2.0.0
+setMethod("predict", signature(object = "NaiveBayesModel"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+#  Saves the Bernoulli naive Bayes model to the input path.
+
+#' @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 spark.naiveBayes
+#' @export
+#' @seealso \link{write.ml}
+#' @note write.ml(NaiveBayesModel, character) since 2.0.0
+setMethod("write.ml", signature(object = "NaiveBayesModel", path = "character"),
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
diff --git a/R/pkg/R/mllib_clustering.R b/R/pkg/R/mllib_clustering.R
new file mode 100644
index 0000000000..c443588387
--- /dev/null
+++ b/R/pkg/R/mllib_clustering.R
@@ -0,0 +1,456 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# mllib_clustering.R: Provides methods for MLlib clustering algorithms integration
+
+#' S4 class that represents a GaussianMixtureModel
+#'
+#' @param jobj a Java object reference to the backing Scala GaussianMixtureModel
+#' @export
+#' @note GaussianMixtureModel since 2.1.0
+setClass("GaussianMixtureModel", representation(jobj = "jobj"))
+
+#' S4 class that represents a KMeansModel
+#'
+#' @param jobj a Java object reference to the backing Scala KMeansModel
+#' @export
+#' @note KMeansModel since 2.0.0
+setClass("KMeansModel", representation(jobj = "jobj"))
+
+#' S4 class that represents an LDAModel
+#'
+#' @param jobj a Java object reference to the backing Scala LDAWrapper
+#' @export
+#' @note LDAModel since 2.1.0
+setClass("LDAModel", representation(jobj = "jobj"))
+
+#' Multivariate Gaussian Mixture Model (GMM)
+#'
+#' Fits multivariate gaussian mixture model against a Spark DataFrame, similarly to R's
+#' mvnormalmixEM(). Users can call \code{summary} to print a summary of the fitted model,
+#' \code{predict} to make predictions on new data, and \code{write.ml}/\code{read.ml}
+#' to save/load fitted models.
+#'
+#' @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 '-'.
+#'                Note that the response variable of formula is empty in spark.gaussianMixture.
+#' @param k number of independent Gaussians in the mixture model.
+#' @param maxIter maximum iteration number.
+#' @param tol the convergence tolerance.
+#' @param ... additional arguments passed to the method.
+#' @aliases spark.gaussianMixture,SparkDataFrame,formula-method
+#' @return \code{spark.gaussianMixture} returns a fitted multivariate gaussian mixture model.
+#' @rdname spark.gaussianMixture
+#' @name spark.gaussianMixture
+#' @seealso mixtools: \url{https://cran.r-project.org/package=mixtools}
+#' @export
+#' @examples
+#' \dontrun{
+#' sparkR.session()
+#' library(mvtnorm)
+#' set.seed(100)
+#' a <- rmvnorm(4, c(0, 0))
+#' b <- rmvnorm(6, c(3, 4))
+#' data <- rbind(a, b)
+#' df <- createDataFrame(as.data.frame(data))
+#' model <- spark.gaussianMixture(df, ~ V1 + V2, k = 2)
+#' summary(model)
+#'
+#' # fitted values on training data
+#' fitted <- predict(model, df)
+#' head(select(fitted, "V1", "prediction"))
+#'
+#' # save fitted model to input path
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#'
+#' # can also read back the saved model and print
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#' }
+#' @note spark.gaussianMixture since 2.1.0
+#' @seealso \link{predict}, \link{read.ml}, \link{write.ml}
+setMethod("spark.gaussianMixture", signature(data = "SparkDataFrame", formula = "formula"),
+          function(data, formula, k = 2, maxIter = 100, tol = 0.01) {
+            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))
+            new("GaussianMixtureModel", jobj = jobj)
+          })
+
+#  Get the summary of a multivariate gaussian mixture model
+
+#' @param object a fitted gaussian mixture model.
+#' @return \code{summary} returns summary of the fitted model, which is a list.
+#'         The list includes the model's \code{lambda} (lambda), \code{mu} (mu),
+#'         \code{sigma} (sigma), and \code{posterior} (posterior).
+#' @aliases spark.gaussianMixture,SparkDataFrame,formula-method
+#' @rdname spark.gaussianMixture
+#' @export
+#' @note summary(GaussianMixtureModel) since 2.1.0
+setMethod("summary", signature(object = "GaussianMixtureModel"),
+          function(object) {
+            jobj <- object@jobj
+            is.loaded <- callJMethod(jobj, "isLoaded")
+            lambda <- unlist(callJMethod(jobj, "lambda"))
+            muList <- callJMethod(jobj, "mu")
+            sigmaList <- callJMethod(jobj, "sigma")
+            k <- callJMethod(jobj, "k")
+            dim <- callJMethod(jobj, "dim")
+            mu <- c()
+            for (i in 1 : k) {
+              start <- (i - 1) * dim + 1
+              end <- i * dim
+              mu[[i]] <- unlist(muList[start : end])
+            }
+            sigma <- c()
+            for (i in 1 : k) {
+              start <- (i - 1) * dim * dim + 1
+              end <- i * dim * dim
+              sigma[[i]] <- t(matrix(sigmaList[start : end], ncol = dim))
+            }
+            posterior <- if (is.loaded) {
+              NULL
+            } else {
+              dataFrame(callJMethod(jobj, "posterior"))
+            }
+            list(lambda = lambda, mu = mu, sigma = sigma,
+                 posterior = posterior, is.loaded = is.loaded)
+          })
+
+#  Predicted values based on a gaussian mixture model
+
+#' @param newData a SparkDataFrame for testing.
+#' @return \code{predict} returns a SparkDataFrame containing predicted labels in a column named
+#'         "prediction".
+#' @aliases predict,GaussianMixtureModel,SparkDataFrame-method
+#' @rdname spark.gaussianMixture
+#' @export
+#' @note predict(GaussianMixtureModel) since 2.1.0
+setMethod("predict", signature(object = "GaussianMixtureModel"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+#  Save fitted MLlib model to the input path
+
+#' @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,GaussianMixtureModel,character-method
+#' @rdname spark.gaussianMixture
+#' @export
+#' @note write.ml(GaussianMixtureModel, character) since 2.1.0
+setMethod("write.ml", signature(object = "GaussianMixtureModel", path = "character"),
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
+
+#' K-Means Clustering Model
+#'
+#' Fits a k-means clustering model against a Spark DataFrame, similarly to R's kmeans().
+#' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
+#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
+#'
+#' @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 '-'.
+#'                Note that the response variable of formula is empty in spark.kmeans.
+#' @param k number of centers.
+#' @param maxIter maximum iteration number.
+#' @param initMode the initialization algorithm choosen to fit the model.
+#' @param ... additional argument(s) passed to the method.
+#' @return \code{spark.kmeans} returns a fitted k-means model.
+#' @rdname spark.kmeans
+#' @aliases spark.kmeans,SparkDataFrame,formula-method
+#' @name spark.kmeans
+#' @export
+#' @examples
+#' \dontrun{
+#' sparkR.session()
+#' data(iris)
+#' df <- createDataFrame(iris)
+#' model <- spark.kmeans(df, Sepal_Length ~ Sepal_Width, k = 4, initMode = "random")
+#' summary(model)
+#'
+#' # fitted values on training data
+#' fitted <- predict(model, df)
+#' head(select(fitted, "Sepal_Length", "prediction"))
+#'
+#' # save fitted model to input path
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#'
+#' # can also read back the saved model and print
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#' }
+#' @note spark.kmeans since 2.0.0
+#' @seealso \link{predict}, \link{read.ml}, \link{write.ml}
+setMethod("spark.kmeans", signature(data = "SparkDataFrame", formula = "formula"),
+          function(data, formula, k = 2, maxIter = 20, initMode = c("k-means||", "random")) {
+            formula <- paste(deparse(formula), collapse = "")
+            initMode <- match.arg(initMode)
+            jobj <- callJStatic("org.apache.spark.ml.r.KMeansWrapper", "fit", data@sdf, formula,
+                                as.integer(k), as.integer(maxIter), initMode)
+            new("KMeansModel", jobj = jobj)
+          })
+
+#  Get the summary of a k-means model
+
+#' @param object a fitted k-means model.
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes the model's \code{k} (number of cluster centers),
+#'         \code{coefficients} (model cluster centers),
+#'         \code{size} (number of data points in each cluster), and \code{cluster}
+#'         (cluster centers of the transformed data).
+#' @rdname spark.kmeans
+#' @export
+#' @note summary(KMeansModel) since 2.0.0
+setMethod("summary", signature(object = "KMeansModel"),
+          function(object) {
+            jobj <- object@jobj
+            is.loaded <- callJMethod(jobj, "isLoaded")
+            features <- callJMethod(jobj, "features")
+            coefficients <- callJMethod(jobj, "coefficients")
+            k <- callJMethod(jobj, "k")
+            size <- callJMethod(jobj, "size")
+            coefficients <- t(matrix(coefficients, ncol = k))
+            colnames(coefficients) <- unlist(features)
+            rownames(coefficients) <- 1:k
+            cluster <- if (is.loaded) {
+              NULL
+            } else {
+              dataFrame(callJMethod(jobj, "cluster"))
+            }
+            list(k = k, coefficients = coefficients, size = size,
+                 cluster = cluster, is.loaded = is.loaded)
+          })
+
+#  Predicted values based on a k-means model
+
+#' @param newData a SparkDataFrame for testing.
+#' @return \code{predict} returns the predicted values based on a k-means model.
+#' @rdname spark.kmeans
+#' @export
+#' @note predict(KMeansModel) since 2.0.0
+setMethod("predict", signature(object = "KMeansModel"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+#' Get fitted result from a k-means model
+#'
+#' Get fitted result from a k-means model, similarly to R's fitted().
+#' Note: A saved-loaded model does not support this method.
+#'
+#' @param object a fitted k-means model.
+#' @param method type of fitted results, \code{"centers"} for cluster centers
+#'        or \code{"classes"} for assigned classes.
+#' @param ... additional argument(s) passed to the method.
+#' @return \code{fitted} returns a SparkDataFrame containing fitted values.
+#' @rdname fitted
+#' @export
+#' @examples
+#' \dontrun{
+#' model <- spark.kmeans(trainingData, ~ ., 2)
+#' fitted.model <- fitted(model)
+#' showDF(fitted.model)
+#'}
+#' @note fitted since 2.0.0
+setMethod("fitted", signature(object = "KMeansModel"),
+          function(object, method = c("centers", "classes")) {
+            method <- match.arg(method)
+            jobj <- object@jobj
+            is.loaded <- callJMethod(jobj, "isLoaded")
+            if (is.loaded) {
+              stop("Saved-loaded k-means model does not support 'fitted' method")
+            } else {
+              dataFrame(callJMethod(jobj, "fitted", method))
+            }
+          })
+
+#  Save fitted MLlib model to the input path
+
+#' @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 spark.kmeans
+#' @export
+#' @note write.ml(KMeansModel, character) since 2.0.0
+setMethod("write.ml", signature(object = "KMeansModel", path = "character"),
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
+
+#' Latent Dirichlet Allocation
+#'
+#' \code{spark.lda} fits a Latent Dirichlet Allocation model on a SparkDataFrame. Users can call
+#' \code{summary} to get a summary of the fitted LDA model, \code{spark.posterior} to compute
+#' posterior probabilities on new data, \code{spark.perplexity} to compute log perplexity on new
+#' data and \code{write.ml}/\code{read.ml} to save/load fitted models.
+#'
+#' @param data A SparkDataFrame for training.
+#' @param features Features column name. Either libSVM-format column or character-format column is
+#'        valid.
+#' @param k Number of topics.
+#' @param maxIter Maximum iterations.
+#' @param optimizer Optimizer to train an LDA model, "online" or "em", default is "online".
+#' @param subsamplingRate (For online optimizer) Fraction of the corpus to be sampled and used in
+#'        each iteration of mini-batch gradient descent, in range (0, 1].
+#' @param topicConcentration concentration parameter (commonly named \code{beta} or \code{eta}) for
+#'        the prior placed on topic distributions over terms, default -1 to set automatically on the
+#'        Spark side. Use \code{summary} to retrieve the effective topicConcentration. Only 1-size
+#'        numeric is accepted.
+#' @param docConcentration concentration parameter (commonly named \code{alpha}) for the
+#'        prior placed on documents distributions over topics (\code{theta}), default -1 to set
+#'        automatically on the Spark side. Use \code{summary} to retrieve the effective
+#'        docConcentration. Only 1-size or \code{k}-size numeric is accepted.
+#' @param customizedStopWords stopwords that need to be removed from the given corpus. Ignore the
+#'        parameter if libSVM-format column is used as the features column.
+#' @param maxVocabSize maximum vocabulary size, default 1 << 18
+#' @param ... additional argument(s) passed to the method.
+#' @return \code{spark.lda} returns a fitted Latent Dirichlet Allocation model.
+#' @rdname spark.lda
+#' @aliases spark.lda,SparkDataFrame-method
+#' @seealso topicmodels: \url{https://cran.r-project.org/package=topicmodels}
+#' @export
+#' @examples
+#' \dontrun{
+#' # nolint start
+#' # An example "path/to/file" can be
+#' # paste0(Sys.getenv("SPARK_HOME"), "/data/mllib/sample_lda_libsvm_data.txt")
+#' # nolint end
+#' text <- read.df("path/to/file", source = "libsvm")
+#' model <- spark.lda(data = text, optimizer = "em")
+#'
+#' # get a summary of the model
+#' summary(model)
+#'
+#' # compute posterior probabilities
+#' posterior <- spark.posterior(model, text)
+#' showDF(posterior)
+#'
+#' # compute perplexity
+#' perplexity <- spark.perplexity(model, text)
+#'
+#' # save and load the model
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#' }
+#' @note spark.lda since 2.1.0
+setMethod("spark.lda", signature(data = "SparkDataFrame"),
+          function(data, features = "features", k = 10, maxIter = 20, optimizer = c("online", "em"),
+                   subsamplingRate = 0.05, topicConcentration = -1, docConcentration = -1,
+                   customizedStopWords = "", maxVocabSize = bitwShiftL(1, 18)) {
+            optimizer <- match.arg(optimizer)
+            jobj <- callJStatic("org.apache.spark.ml.r.LDAWrapper", "fit", data@sdf, features,
+                                as.integer(k), as.integer(maxIter), optimizer,
+                                as.numeric(subsamplingRate), topicConcentration,
+                                as.array(docConcentration), as.array(customizedStopWords),
+                                maxVocabSize)
+            new("LDAModel", jobj = jobj)
+          })
+
+#  Returns the summary of a Latent Dirichlet Allocation model produced by \code{spark.lda}
+
+#' @param object A Latent Dirichlet Allocation model fitted by \code{spark.lda}.
+#' @param maxTermsPerTopic Maximum number of terms to collect for each topic. Default value of 10.
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes
+#'         \item{\code{docConcentration}}{concentration parameter commonly named \code{alpha} for
+#'               the prior placed on documents distributions over topics \code{theta}}
+#'         \item{\code{topicConcentration}}{concentration parameter commonly named \code{beta} or
+#'               \code{eta} for the prior placed on topic distributions over terms}
+#'         \item{\code{logLikelihood}}{log likelihood of the entire corpus}
+#'         \item{\code{logPerplexity}}{log perplexity}
+#'         \item{\code{isDistributed}}{TRUE for distributed model while FALSE for local model}
+#'         \item{\code{vocabSize}}{number of terms in the corpus}
+#'         \item{\code{topics}}{top 10 terms and their weights of all topics}
+#'         \item{\code{vocabulary}}{whole terms of the training corpus, NULL if libsvm format file
+#'               used as training set}
+#' @rdname spark.lda
+#' @aliases summary,LDAModel-method
+#' @export
+#' @note summary(LDAModel) since 2.1.0
+setMethod("summary", signature(object = "LDAModel"),
+          function(object, maxTermsPerTopic) {
+            maxTermsPerTopic <- as.integer(ifelse(missing(maxTermsPerTopic), 10, maxTermsPerTopic))
+            jobj <- object@jobj
+            docConcentration <- callJMethod(jobj, "docConcentration")
+            topicConcentration <- callJMethod(jobj, "topicConcentration")
+            logLikelihood <- callJMethod(jobj, "logLikelihood")
+            logPerplexity <- callJMethod(jobj, "logPerplexity")
+            isDistributed <- callJMethod(jobj, "isDistributed")
+            vocabSize <- callJMethod(jobj, "vocabSize")
+            topics <- dataFrame(callJMethod(jobj, "topics", maxTermsPerTopic))
+            vocabulary <- callJMethod(jobj, "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}
+
+#' @return \code{spark.perplexity} returns the log perplexity of given SparkDataFrame, or the log
+#'         perplexity of the training data if missing argument "data".
+#' @rdname spark.lda
+#' @aliases spark.perplexity,LDAModel-method
+#' @export
+#' @note spark.perplexity(LDAModel) since 2.1.0
+setMethod("spark.perplexity", signature(object = "LDAModel", data = "SparkDataFrame"),
+          function(object, data) {
+            ifelse(missing(data), callJMethod(object@jobj, "logPerplexity"),
+                   callJMethod(object@jobj, "computeLogPerplexity", data@sdf))
+         })
+
+#  Returns posterior probabilities from a Latent Dirichlet Allocation model produced by spark.lda()
+
+#' @param newData A SparkDataFrame for testing.
+#' @return \code{spark.posterior} returns a SparkDataFrame containing posterior probabilities
+#'         vectors named "topicDistribution".
+#' @rdname spark.lda
+#' @aliases spark.posterior,LDAModel,SparkDataFrame-method
+#' @export
+#' @note spark.posterior(LDAModel) since 2.1.0
+setMethod("spark.posterior", signature(object = "LDAModel", newData = "SparkDataFrame"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+#  Saves the Latent Dirichlet Allocation model to the input path.
+
+#' @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 spark.lda
+#' @aliases write.ml,LDAModel,character-method
+#' @export
+#' @seealso \link{read.ml}
+#' @note write.ml(LDAModel, character) since 2.1.0
+setMethod("write.ml", signature(object = "LDAModel", path = "character"),
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
diff --git a/R/pkg/R/mllib_recommendation.R b/R/pkg/R/mllib_recommendation.R
new file mode 100644
index 0000000000..fa79424908
--- /dev/null
+++ b/R/pkg/R/mllib_recommendation.R
@@ -0,0 +1,162 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# mllib_recommendation.R: Provides methods for MLlib recommendation algorithms integration
+
+#' S4 class that represents an ALSModel
+#'
+#' @param jobj a Java object reference to the backing Scala ALSWrapper
+#' @export
+#' @note ALSModel since 2.1.0
+setClass("ALSModel", representation(jobj = "jobj"))
+
+#' Alternating Least Squares (ALS) for Collaborative Filtering
+#'
+#' \code{spark.als} learns latent factors in collaborative filtering via alternating least
+#' squares. Users can call \code{summary} to obtain fitted latent factors, \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-collaborative-filtering.html}{MLlib:
+#' Collaborative Filtering}.
+#'
+#' @param data a SparkDataFrame for training.
+#' @param ratingCol column name for ratings.
+#' @param userCol column name for user ids. Ids must be (or can be coerced into) integers.
+#' @param itemCol column name for item ids. Ids must be (or can be coerced into) integers.
+#' @param rank rank of the matrix factorization (> 0).
+#' @param regParam regularization parameter (>= 0).
+#' @param maxIter maximum number of iterations (>= 0).
+#' @param nonnegative logical value indicating whether to apply nonnegativity constraints.
+#' @param implicitPrefs logical value indicating whether to use implicit preference.
+#' @param alpha alpha parameter in the implicit preference formulation (>= 0).
+#' @param seed integer seed for random number generation.
+#' @param numUserBlocks number of user blocks used to parallelize computation (> 0).
+#' @param numItemBlocks number of item blocks used to parallelize computation (> 0).
+#' @param checkpointInterval number of checkpoint intervals (>= 1) or disable checkpoint (-1).
+#' @param ... additional argument(s) passed to the method.
+#' @return \code{spark.als} returns a fitted ALS model.
+#' @rdname spark.als
+#' @aliases spark.als,SparkDataFrame-method
+#' @name spark.als
+#' @export
+#' @examples
+#' \dontrun{
+#' ratings <- list(list(0, 0, 4.0), list(0, 1, 2.0), list(1, 1, 3.0), list(1, 2, 4.0),
+#'                 list(2, 1, 1.0), list(2, 2, 5.0))
+#' df <- createDataFrame(ratings, c("user", "item", "rating"))
+#' model <- spark.als(df, "rating", "user", "item")
+#'
+#' # extract latent factors
+#' stats <- summary(model)
+#' userFactors <- stats$userFactors
+#' itemFactors <- stats$itemFactors
+#'
+#' # make predictions
+#' predicted <- predict(model, df)
+#' showDF(predicted)
+#'
+#' # save and load the model
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#'
+#' # set other arguments
+#' modelS <- spark.als(df, "rating", "user", "item", rank = 20,
+#'                     regParam = 0.1, nonnegative = TRUE)
+#' statsS <- summary(modelS)
+#' }
+#' @note spark.als since 2.1.0
+setMethod("spark.als", signature(data = "SparkDataFrame"),
+          function(data, ratingCol = "rating", userCol = "user", itemCol = "item",
+                   rank = 10, regParam = 0.1, maxIter = 10, nonnegative = FALSE,
+                   implicitPrefs = FALSE, alpha = 1.0, numUserBlocks = 10, numItemBlocks = 10,
+                   checkpointInterval = 10, seed = 0) {
+
+            if (!is.numeric(rank) || rank <= 0) {
+              stop("rank should be a positive number.")
+            }
+            if (!is.numeric(regParam) || regParam < 0) {
+              stop("regParam should be a nonnegative number.")
+            }
+            if (!is.numeric(maxIter) || maxIter <= 0) {
+              stop("maxIter should be a positive number.")
+            }
+
+            jobj <- callJStatic("org.apache.spark.ml.r.ALSWrapper",
+                                "fit", data@sdf, ratingCol, userCol, itemCol, as.integer(rank),
+                                regParam, as.integer(maxIter), implicitPrefs, alpha, nonnegative,
+                                as.integer(numUserBlocks), as.integer(numItemBlocks),
+                                as.integer(checkpointInterval), as.integer(seed))
+            new("ALSModel", jobj = jobj)
+          })
+
+#  Returns a summary of the ALS model produced by spark.als.
+
+#' @param object a fitted ALS model.
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes \code{user} (the names of the user column),
+#'         \code{item} (the item column), \code{rating} (the rating column), \code{userFactors}
+#'         (the estimated user factors), \code{itemFactors} (the estimated item factors),
+#'         and \code{rank} (rank of the matrix factorization model).
+#' @rdname spark.als
+#' @aliases summary,ALSModel-method
+#' @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")
+            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.
+
+#' @param newData a SparkDataFrame for testing.
+#' @return \code{predict} returns a SparkDataFrame containing predicted values.
+#' @rdname spark.als
+#' @aliases predict,ALSModel-method
+#' @export
+#' @note predict(ALSModel) since 2.1.0
+setMethod("predict", signature(object = "ALSModel"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+#  Saves the ALS model to the input path.
+
+#' @param path the directory where the model is saved.
+#' @param overwrite logical value indicating whether to overwrite if the output path
+#'                  already exists. Default is FALSE which means throw exception
+#'                  if the output path exists.
+#'
+#' @rdname spark.als
+#' @aliases write.ml,ALSModel,character-method
+#' @export
+#' @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) {
+            write_internal(object, path, overwrite)
+          })
diff --git a/R/pkg/R/mllib_regression.R b/R/pkg/R/mllib_regression.R
new file mode 100644
index 0000000000..a480168a29
--- /dev/null
+++ b/R/pkg/R/mllib_regression.R
@@ -0,0 +1,448 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# mllib_regression.R: Provides methods for MLlib regression algorithms
+#                     (except for tree-based algorithms) integration
+
+#' S4 class that represents a AFTSurvivalRegressionModel
+#'
+#' @param jobj a Java object reference to the backing Scala AFTSurvivalRegressionWrapper
+#' @export
+#' @note AFTSurvivalRegressionModel since 2.0.0
+setClass("AFTSurvivalRegressionModel", representation(jobj = "jobj"))
+
+#' S4 class that represents a generalized linear model
+#'
+#' @param jobj a Java object reference to the backing Scala GeneralizedLinearRegressionWrapper
+#' @export
+#' @note GeneralizedLinearRegressionModel since 2.0.0
+setClass("GeneralizedLinearRegressionModel", representation(jobj = "jobj"))
+
+#' S4 class that represents an IsotonicRegressionModel
+#'
+#' @param jobj a Java object reference to the backing Scala IsotonicRegressionModel
+#' @export
+#' @note IsotonicRegressionModel since 2.1.0
+setClass("IsotonicRegressionModel", representation(jobj = "jobj"))
+
+#' Generalized Linear Models
+#'
+#' Fits generalized linear model against a Spark DataFrame.
+#' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
+#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
+#'
+#' @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 family a description of the error distribution and link function to be used in the model.
+#'               This can be a character string naming a family function, a family function or
+#'               the result of a call to a family function. Refer R family at
+#'               \url{https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html}.
+#' @param tol positive convergence tolerance of iterations.
+#' @param maxIter integer giving the maximal number of IRLS iterations.
+#' @param weightCol the weight column name. If this is not set or \code{NULL}, we treat all instance
+#'                  weights as 1.0.
+#' @param regParam regularization parameter for L2 regularization.
+#' @param ... additional arguments passed to the method.
+#' @aliases spark.glm,SparkDataFrame,formula-method
+#' @return \code{spark.glm} returns a fitted generalized linear model.
+#' @rdname spark.glm
+#' @name spark.glm
+#' @export
+#' @examples
+#' \dontrun{
+#' sparkR.session()
+#' data(iris)
+#' df <- createDataFrame(iris)
+#' model <- spark.glm(df, Sepal_Length ~ Sepal_Width, family = "gaussian")
+#' summary(model)
+#'
+#' # fitted values on training data
+#' fitted <- predict(model, df)
+#' head(select(fitted, "Sepal_Length", "prediction"))
+#'
+#' # save fitted model to input path
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#'
+#' # can also read back the saved model and print
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#' }
+#' @note spark.glm since 2.0.0
+#' @seealso \link{glm}, \link{read.ml}
+setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"),
+          function(data, formula, family = gaussian, tol = 1e-6, maxIter = 25, weightCol = NULL,
+                   regParam = 0.0) {
+            if (is.character(family)) {
+              family <- get(family, mode = "function", envir = parent.frame())
+            }
+            if (is.function(family)) {
+              family <- family()
+            }
+            if (is.null(family$family)) {
+              print(family)
+              stop("'family' not recognized")
+            }
+
+            formula <- paste(deparse(formula), collapse = "")
+            if (is.null(weightCol)) {
+              weightCol <- ""
+            }
+
+            jobj <- callJStatic("org.apache.spark.ml.r.GeneralizedLinearRegressionWrapper",
+                                "fit", formula, data@sdf, family$family, family$link,
+                                tol, as.integer(maxIter), as.character(weightCol), regParam)
+            new("GeneralizedLinearRegressionModel", jobj = jobj)
+          })
+
+#' Generalized Linear Models (R-compliant)
+#'
+#' Fits a generalized linear model, similarly to R's glm().
+#' @param formula a symbolic description of the model to be fitted. Currently only a few formula
+#'                operators are supported, including '~', '.', ':', '+', and '-'.
+#' @param data a SparkDataFrame or R's glm data for training.
+#' @param family a description of the error distribution and link function to be used in the model.
+#'               This can be a character string naming a family function, a family function or
+#'               the result of a call to a family function. Refer R family at
+#'               \url{https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html}.
+#' @param weightCol the weight column name. If this is not set or \code{NULL}, we treat all instance
+#'                  weights as 1.0.
+#' @param epsilon positive convergence tolerance of iterations.
+#' @param maxit integer giving the maximal number of IRLS iterations.
+#' @return \code{glm} returns a fitted generalized linear model.
+#' @rdname glm
+#' @export
+#' @examples
+#' \dontrun{
+#' sparkR.session()
+#' data(iris)
+#' df <- createDataFrame(iris)
+#' model <- glm(Sepal_Length ~ Sepal_Width, df, family = "gaussian")
+#' summary(model)
+#' }
+#' @note glm since 1.5.0
+#' @seealso \link{spark.glm}
+setMethod("glm", signature(formula = "formula", family = "ANY", data = "SparkDataFrame"),
+          function(formula, family = gaussian, data, epsilon = 1e-6, maxit = 25, weightCol = NULL) {
+            spark.glm(data, formula, family, tol = epsilon, maxIter = maxit, weightCol = weightCol)
+          })
+
+#  Returns the summary of a model produced by glm() or spark.glm(), similarly to R's summary().
+
+#' @param object a fitted generalized linear model.
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list of components includes at least the \code{coefficients} (coefficients matrix, which includes
+#'         coefficients, standard error of coefficients, t value and p value),
+#'         \code{null.deviance} (null/residual degrees of freedom), \code{aic} (AIC)
+#'         and \code{iter} (number of iterations IRLS takes). If there are collinear columns in the data,
+#'         the coefficients matrix only provides coefficients.
+#' @rdname spark.glm
+#' @export
+#' @note summary(GeneralizedLinearRegressionModel) since 2.0.0
+setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"),
+          function(object) {
+            jobj <- object@jobj
+            is.loaded <- callJMethod(jobj, "isLoaded")
+            features <- callJMethod(jobj, "rFeatures")
+            coefficients <- callJMethod(jobj, "rCoefficients")
+            dispersion <- callJMethod(jobj, "rDispersion")
+            null.deviance <- callJMethod(jobj, "rNullDeviance")
+            deviance <- callJMethod(jobj, "rDeviance")
+            df.null <- callJMethod(jobj, "rResidualDegreeOfFreedomNull")
+            df.residual <- callJMethod(jobj, "rResidualDegreeOfFreedom")
+            aic <- callJMethod(jobj, "rAic")
+            iter <- callJMethod(jobj, "rNumIterations")
+            family <- callJMethod(jobj, "rFamily")
+            deviance.resid <- if (is.loaded) {
+              NULL
+            } else {
+              dataFrame(callJMethod(jobj, "rDevianceResiduals"))
+            }
+            # If the underlying WeightedLeastSquares using "normal" solver, we can provide
+            # coefficients, standard error of coefficients, t value and p value. Otherwise,
+            # it will be fitted by local "l-bfgs", we can only provide coefficients.
+            if (length(features) == length(coefficients)) {
+              coefficients <- matrix(coefficients, ncol = 1)
+              colnames(coefficients) <- c("Estimate")
+              rownames(coefficients) <- unlist(features)
+            } else {
+              coefficients <- matrix(coefficients, ncol = 4)
+              colnames(coefficients) <- c("Estimate", "Std. Error", "t value", "Pr(>|t|)")
+              rownames(coefficients) <- unlist(features)
+            }
+            ans <- list(deviance.resid = deviance.resid, coefficients = coefficients,
+                        dispersion = dispersion, null.deviance = null.deviance,
+                        deviance = deviance, df.null = df.null, df.residual = df.residual,
+                        aic = aic, iter = iter, family = family, is.loaded = is.loaded)
+            class(ans) <- "summary.GeneralizedLinearRegressionModel"
+            ans
+          })
+
+#  Prints the summary of GeneralizedLinearRegressionModel
+
+#' @rdname spark.glm
+#' @param x summary object of fitted generalized linear model returned by \code{summary} function.
+#' @export
+#' @note print.summary.GeneralizedLinearRegressionModel since 2.0.0
+print.summary.GeneralizedLinearRegressionModel <- function(x, ...) {
+  if (x$is.loaded) {
+    cat("\nSaved-loaded model does not support output 'Deviance Residuals'.\n")
+  } else {
+    x$deviance.resid <- setNames(unlist(approxQuantile(x$deviance.resid, "devianceResiduals",
+    c(0.0, 0.25, 0.5, 0.75, 1.0), 0.01)), c("Min", "1Q", "Median", "3Q", "Max"))
+    x$deviance.resid <- zapsmall(x$deviance.resid, 5L)
+    cat("\nDeviance Residuals: \n")
+    cat("(Note: These are approximate quantiles with relative error <= 0.01)\n")
+    print.default(x$deviance.resid, digits = 5L, na.print = "", print.gap = 2L)
+  }
+
+  cat("\nCoefficients:\n")
+  print.default(x$coefficients, digits = 5L, na.print = "", print.gap = 2L)
+
+  cat("\n(Dispersion parameter for ", x$family, " family taken to be ", format(x$dispersion),
+    ")\n\n", apply(cbind(paste(format(c("Null", "Residual"), justify = "right"), "deviance:"),
+    format(unlist(x[c("null.deviance", "deviance")]), digits = 5L),
+    " 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\n", sep = "")
+  invisible(x)
+  }
+
+#  Makes predictions from a generalized linear model produced by glm() or spark.glm(),
+#  similarly to R's predict().
+
+#' @param newData a SparkDataFrame for testing.
+#' @return \code{predict} returns a SparkDataFrame containing predicted labels in a column named
+#'         "prediction".
+#' @rdname spark.glm
+#' @export
+#' @note predict(GeneralizedLinearRegressionModel) since 1.5.0
+setMethod("predict", signature(object = "GeneralizedLinearRegressionModel"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+#  Saves the generalized linear model to the input path.
+
+#' @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 spark.glm
+#' @export
+#' @note write.ml(GeneralizedLinearRegressionModel, character) since 2.0.0
+setMethod("write.ml", signature(object = "GeneralizedLinearRegressionModel", path = "character"),
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
+
+#' Isotonic Regression Model
+#'
+#' Fits an Isotonic Regression model against a Spark DataFrame, similarly to R's isoreg().
+#' Users can print, make predictions on the produced model and save the model to the input path.
+#'
+#' @param data 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 isotonic Whether the output sequence should be isotonic/increasing (TRUE) or
+#'                 antitonic/decreasing (FALSE).
+#' @param featureIndex The index of the feature if \code{featuresCol} is a vector column
+#'                     (default: 0), no effect otherwise.
+#' @param weightCol The weight column name.
+#' @param ... additional arguments passed to the method.
+#' @return \code{spark.isoreg} returns a fitted Isotonic Regression model.
+#' @rdname spark.isoreg
+#' @aliases spark.isoreg,SparkDataFrame,formula-method
+#' @name spark.isoreg
+#' @export
+#' @examples
+#' \dontrun{
+#' sparkR.session()
+#' data <- list(list(7.0, 0.0), list(5.0, 1.0), list(3.0, 2.0),
+#'         list(5.0, 3.0), list(1.0, 4.0))
+#' df <- createDataFrame(data, c("label", "feature"))
+#' model <- spark.isoreg(df, label ~ feature, isotonic = FALSE)
+#' # return model boundaries and prediction as lists
+#' result <- summary(model, df)
+#' # prediction based on fitted model
+#' predict_data <- list(list(-2.0), list(-1.0), list(0.5),
+#'                 list(0.75), list(1.0), list(2.0), list(9.0))
+#' predict_df <- createDataFrame(predict_data, c("feature"))
+#' # get prediction column
+#' predict_result <- collect(select(predict(model, predict_df), "prediction"))
+#'
+#' # save fitted model to input path
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#'
+#' # can also read back the saved model and print
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#' }
+#' @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 <- paste(deparse(formula), collapse = "")
+
+            if (is.null(weightCol)) {
+              weightCol <- ""
+            }
+
+            jobj <- callJStatic("org.apache.spark.ml.r.IsotonicRegressionWrapper", "fit",
+                                data@sdf, formula, as.logical(isotonic), as.integer(featureIndex),
+                                as.character(weightCol))
+            new("IsotonicRegressionModel", jobj = jobj)
+          })
+
+#  Get the summary of an IsotonicRegressionModel model
+
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes model's \code{boundaries} (boundaries in increasing order)
+#'         and \code{predictions} (predictions associated with the boundaries at the same index).
+#' @rdname spark.isoreg
+#' @aliases summary,IsotonicRegressionModel-method
+#' @export
+#' @note summary(IsotonicRegressionModel) since 2.1.0
+setMethod("summary", signature(object = "IsotonicRegressionModel"),
+          function(object) {
+            jobj <- object@jobj
+            boundaries <- callJMethod(jobj, "boundaries")
+            predictions <- callJMethod(jobj, "predictions")
+            list(boundaries = boundaries, predictions = predictions)
+          })
+
+#  Predicted values based on an isotonicRegression model
+
+#' @param object a fitted IsotonicRegressionModel.
+#' @param newData SparkDataFrame for testing.
+#' @return \code{predict} returns a SparkDataFrame containing predicted values.
+#' @rdname spark.isoreg
+#' @aliases predict,IsotonicRegressionModel,SparkDataFrame-method
+#' @export
+#' @note predict(IsotonicRegressionModel) since 2.1.0
+setMethod("predict", signature(object = "IsotonicRegressionModel"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+#  Save fitted IsotonicRegressionModel to the input path
+
+#' @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 spark.isoreg
+#' @aliases write.ml,IsotonicRegressionModel,character-method
+#' @export
+#' @note write.ml(IsotonicRegression, character) since 2.1.0
+setMethod("write.ml", signature(object = "IsotonicRegressionModel", path = "character"),
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
+
+#' Accelerated Failure Time (AFT) Survival Regression Model
+#'
+#' \code{spark.survreg} fits an accelerated failure time (AFT) survival regression model on
+#' a SparkDataFrame. Users can call \code{summary} to get a summary of the fitted AFT model,
+#' \code{predict} to make predictions on new data, and \code{write.ml}/\code{read.ml} to
+#' save/load fitted models.
+#'
+#' @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 '-'.
+#'                Note that operator '.' is not supported currently.
+#' @return \code{spark.survreg} returns a fitted AFT survival regression model.
+#' @rdname spark.survreg
+#' @seealso survival: \url{https://cran.r-project.org/package=survival}
+#' @export
+#' @examples
+#' \dontrun{
+#' df <- createDataFrame(ovarian)
+#' model <- spark.survreg(df, Surv(futime, fustat) ~ ecog_ps + rx)
+#'
+#' # get a summary of the model
+#' summary(model)
+#'
+#' # make predictions
+#' predicted <- predict(model, df)
+#' showDF(predicted)
+#'
+#' # save and load the model
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#' }
+#' @note spark.survreg since 2.0.0
+setMethod("spark.survreg", signature(data = "SparkDataFrame", formula = "formula"),
+          function(data, formula) {
+            formula <- paste(deparse(formula), collapse = "")
+            jobj <- callJStatic("org.apache.spark.ml.r.AFTSurvivalRegressionWrapper",
+                                "fit", formula, data@sdf)
+            new("AFTSurvivalRegressionModel", jobj = jobj)
+          })
+
+#  Returns a summary of the AFT survival regression model produced by spark.survreg,
+#  similarly to R's summary().
+
+#' @param object a fitted AFT survival regression model.
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list includes the model's \code{coefficients} (features, coefficients,
+#'         intercept and log(scale)).
+#' @rdname spark.survreg
+#' @export
+#' @note summary(AFTSurvivalRegressionModel) since 2.0.0
+setMethod("summary", signature(object = "AFTSurvivalRegressionModel"),
+          function(object) {
+            jobj <- object@jobj
+            features <- callJMethod(jobj, "rFeatures")
+            coefficients <- callJMethod(jobj, "rCoefficients")
+            coefficients <- as.matrix(unlist(coefficients))
+            colnames(coefficients) <- c("Value")
+            rownames(coefficients) <- unlist(features)
+            list(coefficients = coefficients)
+          })
+
+#  Makes predictions from an AFT survival regression model or a model produced by
+#  spark.survreg, similarly to R package survival's predict.
+
+#' @param newData a SparkDataFrame for testing.
+#' @return \code{predict} returns a SparkDataFrame containing predicted values
+#'         on the original scale of the data (mean predicted value at scale = 1.0).
+#' @rdname spark.survreg
+#' @export
+#' @note predict(AFTSurvivalRegressionModel) since 2.0.0
+setMethod("predict", signature(object = "AFTSurvivalRegressionModel"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+#  Saves the AFT survival regression model to the input path.
+
+#' @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 spark.survreg
+#' @export
+#' @note write.ml(AFTSurvivalRegressionModel, character) since 2.0.0
+#' @seealso \link{write.ml}
+setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "character"),
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
diff --git a/R/pkg/R/mllib_stat.R b/R/pkg/R/mllib_stat.R
new file mode 100644
index 0000000000..3e013f1d45
--- /dev/null
+++ b/R/pkg/R/mllib_stat.R
@@ -0,0 +1,127 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# mllib_stat.R: Provides methods for MLlib statistics algorithms integration
+
+#' S4 class that represents an KSTest
+#'
+#' @param jobj a Java object reference to the backing Scala KSTestWrapper
+#' @export
+#' @note KSTest since 2.1.0
+setClass("KSTest", representation(jobj = "jobj"))
+
+#' (One-Sample) Kolmogorov-Smirnov Test
+#'
+#' @description
+#' \code{spark.kstest} Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a
+#' continuous distribution.
+#'
+#' By comparing the largest difference between the empirical cumulative
+#' distribution of the sample data and the theoretical distribution we can provide a test for the
+#' the null hypothesis that the sample data comes from that theoretical distribution.
+#'
+#' Users can call \code{summary} to obtain a summary of the test, and \code{print.summary.KSTest}
+#' to print out a summary result.
+#'
+#' @param data a SparkDataFrame of user data.
+#' @param testCol column name where the test data is from. It should be a column of double type.
+#' @param nullHypothesis name of the theoretical distribution tested against. Currently only
+#'                       \code{"norm"} for normal distribution is supported.
+#' @param distParams parameters(s) of the distribution. For \code{nullHypothesis = "norm"},
+#'                   we can provide as a vector the mean and standard deviation of
+#'                   the distribution. If none is provided, then standard normal will be used.
+#'                   If only one is provided, then the standard deviation will be set to be one.
+#' @param ... additional argument(s) passed to the method.
+#' @return \code{spark.kstest} returns a test result object.
+#' @rdname spark.kstest
+#' @aliases spark.kstest,SparkDataFrame-method
+#' @name spark.kstest
+#' @seealso \href{http://spark.apache.org/docs/latest/mllib-statistics.html#hypothesis-testing}{
+#'          MLlib: Hypothesis Testing}
+#' @export
+#' @examples
+#' \dontrun{
+#' data <- data.frame(test = c(0.1, 0.15, 0.2, 0.3, 0.25))
+#' df <- createDataFrame(data)
+#' test <- spark.kstest(df, "test", "norm", c(0, 1))
+#'
+#' # get a summary of the test result
+#' testSummary <- summary(test)
+#' testSummary
+#'
+#' # print out the summary in an organized way
+#' print.summary.KSTest(testSummary)
+#' }
+#' @note spark.kstest since 2.1.0
+setMethod("spark.kstest", signature(data = "SparkDataFrame"),
+          function(data, testCol = "test", nullHypothesis = c("norm"), distParams = c(0, 1)) {
+            tryCatch(match.arg(nullHypothesis),
+                     error = function(e) {
+                       msg <- paste("Distribution", nullHypothesis, "is not supported.")
+                       stop(msg)
+                     })
+            if (nullHypothesis == "norm") {
+              distParams <- as.numeric(distParams)
+              mu <- ifelse(length(distParams) < 1, 0, distParams[1])
+              sigma <- ifelse(length(distParams) < 2, 1, distParams[2])
+              jobj <- callJStatic("org.apache.spark.ml.r.KSTestWrapper",
+                                  "test", data@sdf, testCol, nullHypothesis,
+                                  as.array(c(mu, sigma)))
+              new("KSTest", jobj = jobj)
+            }
+})
+
+#  Get the summary of Kolmogorov-Smirnov (KS) Test.
+
+#' @param object test result object of KSTest by \code{spark.kstest}.
+#' @return \code{summary} returns summary information of KSTest object, which is a list.
+#'         The list includes the \code{p.value} (p-value), \code{statistic} (test statistic
+#'         computed for the test), \code{nullHypothesis} (the null hypothesis with its
+#'         parameters tested against) and \code{degreesOfFreedom} (degrees of freedom of the test).
+#' @rdname spark.kstest
+#' @aliases summary,KSTest-method
+#' @export
+#' @note summary(KSTest) since 2.1.0
+setMethod("summary", signature(object = "KSTest"),
+          function(object) {
+            jobj <- object@jobj
+            pValue <- callJMethod(jobj, "pValue")
+            statistic <- callJMethod(jobj, "statistic")
+            nullHypothesis <- callJMethod(jobj, "nullHypothesis")
+            distName <- callJMethod(jobj, "distName")
+            distParams <- unlist(callJMethod(jobj, "distParams"))
+            degreesOfFreedom <- callJMethod(jobj, "degreesOfFreedom")
+
+            ans <- list(p.value = pValue, statistic = statistic, nullHypothesis = nullHypothesis,
+                        nullHypothesis.name = distName, nullHypothesis.parameters = distParams,
+                        degreesOfFreedom = degreesOfFreedom, jobj = jobj)
+            class(ans) <- "summary.KSTest"
+            ans
+          })
+
+#  Prints the summary of KSTest
+
+#' @rdname spark.kstest
+#' @param x summary object of KSTest returned by \code{summary}.
+#' @export
+#' @note print.summary.KSTest since 2.1.0
+print.summary.KSTest <- function(x, ...) {
+  jobj <- x$jobj
+  summaryStr <- callJMethod(jobj, "summary")
+  cat(summaryStr, "\n")
+  invisible(x)
+}
diff --git a/R/pkg/R/mllib_tree.R b/R/pkg/R/mllib_tree.R
new file mode 100644
index 0000000000..0d53fad061
--- /dev/null
+++ b/R/pkg/R/mllib_tree.R
@@ -0,0 +1,496 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# mllib_tree.R: Provides methods for MLlib tree-based algorithms integration
+
+#' S4 class that represents a GBTRegressionModel
+#'
+#' @param jobj a Java object reference to the backing Scala GBTRegressionModel
+#' @export
+#' @note GBTRegressionModel since 2.1.0
+setClass("GBTRegressionModel", representation(jobj = "jobj"))
+
+#' S4 class that represents a GBTClassificationModel
+#'
+#' @param jobj a Java object reference to the backing Scala GBTClassificationModel
+#' @export
+#' @note GBTClassificationModel since 2.1.0
+setClass("GBTClassificationModel", 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"))
+
+# Create the summary of a tree ensemble model (eg. Random Forest, GBT)
+summary.treeEnsemble <- 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)
+}
+
+# Prints the summary of tree ensemble models (eg. Random Forest, GBT)
+print.summary.treeEnsemble <- 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)
+}
+
+#' Gradient Boosted Tree Model for Regression and Classification
+#'
+#' \code{spark.gbt} fits a Gradient Boosted Tree Regression model or Classification model on a
+#' SparkDataFrame. Users can call \code{summary} to get a summary of the fitted
+#' Gradient Boosted Tree 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#gradient-boosted-tree-regression}{
+#' GBT Regression} and
+#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-classifier}{
+#' GBT Classification}
+#'
+#' @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).
+#' @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.
+#' @param maxIter Param for maximum number of iterations (>= 0).
+#' @param stepSize Param for Step size to be used for each iteration of optimization.
+#' @param lossType Loss function which GBT tries to minimize.
+#'                 For classification, must be "logistic". For regression, must be one of
+#'                 "squared" (L2) and "absolute" (L1), default is "squared".
+#' @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].
+#' @param minInstancesPerNode Minimum number of instances each child must have after split. If a
+#'                            split causes the left or right child to have fewer than
+#'                            minInstancesPerNode, the split will be discarded as invalid. Should be
+#'                            >= 1.
+#' @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 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. If TRUE, the algorithm will cache node IDs for each instance. Caching
+#'                     can speed up training of deeper trees. Users can set how often should the
+#'                     cache be checkpointed or disable it by setting checkpointInterval.
+#' @param ... additional arguments passed to the method.
+#' @aliases spark.gbt,SparkDataFrame,formula-method
+#' @return \code{spark.gbt} returns a fitted Gradient Boosted Tree model.
+#' @rdname spark.gbt
+#' @name spark.gbt
+#' @export
+#' @examples
+#' \dontrun{
+#' # fit a Gradient Boosted Tree Regression Model
+#' df <- createDataFrame(longley)
+#' model <- spark.gbt(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 Gradient Boosted Tree Classification Model
+#' # label must be binary - Only binary classification is supported for GBT.
+#' df <- createDataFrame(iris[iris$Species != "virginica", ])
+#' model <- spark.gbt(df, Species ~ Petal_Length + Petal_Width, "classification")
+#'
+#' # numeric label is also supported
+#' iris2 <- iris[iris$Species != "virginica", ]
+#' iris2$NumericSpecies <- ifelse(iris2$Species == "setosa", 0, 1)
+#' df <- createDataFrame(iris2)
+#' model <- spark.gbt(df, NumericSpecies ~ ., type = "classification")
+#' }
+#' @note spark.gbt since 2.1.0
+setMethod("spark.gbt", signature(data = "SparkDataFrame", formula = "formula"),
+          function(data, formula, type = c("regression", "classification"),
+                   maxDepth = 5, maxBins = 32, maxIter = 20, stepSize = 0.1, lossType = NULL,
+                   seed = NULL, subsamplingRate = 1.0, minInstancesPerNode = 1, minInfoGain = 0.0,
+                   checkpointInterval = 10, 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(lossType)) lossType <- "squared"
+                     lossType <- match.arg(lossType, c("squared", "absolute"))
+                     jobj <- callJStatic("org.apache.spark.ml.r.GBTRegressorWrapper",
+                                         "fit", data@sdf, formula, as.integer(maxDepth),
+                                         as.integer(maxBins), as.integer(maxIter),
+                                         as.numeric(stepSize), as.integer(minInstancesPerNode),
+                                         as.numeric(minInfoGain), as.integer(checkpointInterval),
+                                         lossType, seed, as.numeric(subsamplingRate),
+                                         as.integer(maxMemoryInMB), as.logical(cacheNodeIds))
+                     new("GBTRegressionModel", jobj = jobj)
+                   },
+                   classification = {
+                     if (is.null(lossType)) lossType <- "logistic"
+                     lossType <- match.arg(lossType, "logistic")
+                     jobj <- callJStatic("org.apache.spark.ml.r.GBTClassifierWrapper",
+                                         "fit", data@sdf, formula, as.integer(maxDepth),
+                                         as.integer(maxBins), as.integer(maxIter),
+                                         as.numeric(stepSize), as.integer(minInstancesPerNode),
+                                         as.numeric(minInfoGain), as.integer(checkpointInterval),
+                                         lossType, seed, as.numeric(subsamplingRate),
+                                         as.integer(maxMemoryInMB), as.logical(cacheNodeIds))
+                     new("GBTClassificationModel", jobj = jobj)
+                   }
+            )
+          })
+
+#  Get the summary of a Gradient Boosted Tree Regression Model
+
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list of components includes \code{formula} (formula),
+#'         \code{numFeatures} (number of features), \code{features} (list of features),
+#'         \code{featureImportances} (feature importances), \code{numTrees} (number of trees),
+#'         and \code{treeWeights} (tree weights).
+#' @rdname spark.gbt
+#' @aliases summary,GBTRegressionModel-method
+#' @export
+#' @note summary(GBTRegressionModel) since 2.1.0
+setMethod("summary", signature(object = "GBTRegressionModel"),
+          function(object) {
+            ans <- summary.treeEnsemble(object)
+            class(ans) <- "summary.GBTRegressionModel"
+            ans
+          })
+
+#  Prints the summary of Gradient Boosted Tree Regression Model
+
+#' @param x summary object of Gradient Boosted Tree regression model or classification model
+#'          returned by \code{summary}.
+#' @rdname spark.gbt
+#' @export
+#' @note print.summary.GBTRegressionModel since 2.1.0
+print.summary.GBTRegressionModel <- function(x, ...) {
+  print.summary.treeEnsemble(x)
+}
+
+#  Get the summary of a Gradient Boosted Tree Classification Model
+
+#' @rdname spark.gbt
+#' @aliases summary,GBTClassificationModel-method
+#' @export
+#' @note summary(GBTClassificationModel) since 2.1.0
+setMethod("summary", signature(object = "GBTClassificationModel"),
+          function(object) {
+            ans <- summary.treeEnsemble(object)
+            class(ans) <- "summary.GBTClassificationModel"
+            ans
+          })
+
+#  Prints the summary of Gradient Boosted Tree Classification Model
+
+#' @rdname spark.gbt
+#' @export
+#' @note print.summary.GBTClassificationModel since 2.1.0
+print.summary.GBTClassificationModel <- function(x, ...) {
+  print.summary.treeEnsemble(x)
+}
+
+#  Makes predictions from a Gradient Boosted Tree 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.gbt
+#' @aliases predict,GBTRegressionModel-method
+#' @export
+#' @note predict(GBTRegressionModel) since 2.1.0
+setMethod("predict", signature(object = "GBTRegressionModel"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+#' @rdname spark.gbt
+#' @aliases predict,GBTClassificationModel-method
+#' @export
+#' @note predict(GBTClassificationModel) since 2.1.0
+setMethod("predict", signature(object = "GBTClassificationModel"),
+          function(object, newData) {
+            predict_internal(object, newData)
+          })
+
+#  Save the Gradient Boosted Tree Regression or Classification model to the input path.
+
+#' @param object A fitted Gradient Boosted Tree 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,GBTRegressionModel,character-method
+#' @rdname spark.gbt
+#' @export
+#' @note write.ml(GBTRegressionModel, character) since 2.1.0
+setMethod("write.ml", signature(object = "GBTRegressionModel", path = "character"),
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
+
+#' @aliases write.ml,GBTClassificationModel,character-method
+#' @rdname spark.gbt
+#' @export
+#' @note write.ml(GBTClassificationModel, character) since 2.1.0
+setMethod("write.ml", signature(object = "GBTClassificationModel", path = "character"),
+          function(object, path, overwrite = FALSE) {
+            write_internal(object, path, overwrite)
+          })
+
+#' 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-regression}{
+#' Random Forest Regression} and
+#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-classifier}{
+#' Random Forest Classification}
+#'
+#' @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).
+#' @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.
+#' @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 is "gini".
+#' @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].
+#' @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 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. If TRUE, the algorithm will cache node IDs for each instance. Caching
+#'                     can speed up training of deeper trees. Users can set how often should the
+#'                     cache be checkpointed or disable it by setting checkpointInterval.
+#' @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,
+                   featureSubsetStrategy = "auto", seed = NULL, subsamplingRate = 1.0,
+                   minInstancesPerNode = 1, minInfoGain = 0.0, checkpointInterval = 10,
+                   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.integer(maxMemoryInMB), as.logical(cacheNodeIds))
+                     new("RandomForestClassificationModel", jobj = jobj)
+                   }
+            )
+          })
+
+#  Get the summary of a Random Forest Regression Model
+
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'         The list of components includes \code{formula} (formula),
+#'         \code{numFeatures} (number of features), \code{features} (list of features),
+#'         \code{featureImportances} (feature importances), \code{numTrees} (number of trees),
+#'         and \code{treeWeights} (tree weights).
+#' @rdname spark.randomForest
+#' @aliases summary,RandomForestRegressionModel-method
+#' @export
+#' @note summary(RandomForestRegressionModel) since 2.1.0
+setMethod("summary", signature(object = "RandomForestRegressionModel"),
+          function(object) {
+            ans <- summary.treeEnsemble(object)
+            class(ans) <- "summary.RandomForestRegressionModel"
+            ans
+          })
+
+#  Prints the summary of Random Forest Regression Model
+
+#' @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.treeEnsemble(x)
+}
+
+#  Get the summary of a Random Forest Classification 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.treeEnsemble(object)
+            class(ans) <- "summary.RandomForestClassificationModel"
+            ans
+          })
+
+#  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.treeEnsemble(x)
+}
+
+#  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)
+          })
diff --git a/R/pkg/R/mllib_utils.R b/R/pkg/R/mllib_utils.R
new file mode 100644
index 0000000000..720ee41c58
--- /dev/null
+++ b/R/pkg/R/mllib_utils.R
@@ -0,0 +1,119 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# mllib_utils.R: Utilities for MLlib integration
+
+# Integration with R's standard functions.
+# Most of MLlib's argorithms are provided in two flavours:
+# - a specialization of the default R methods (glm). These methods try to respect
+#   the inputs and the outputs of R's method to the largest extent, but some small differences
+#   may exist.
+# - a set of methods that reflect the arguments of the other languages supported by Spark. These
+#   methods are prefixed with the `spark.` prefix: spark.glm, spark.kmeans, etc.
+
+#' Saves the MLlib model to the input path
+#'
+#' Saves the MLlib model to the input path. For more information, see the specific
+#' MLlib model below.
+#' @rdname write.ml
+#' @name write.ml
+#' @export
+#' @seealso \link{spark.glm}, \link{glm},
+#' @seealso \link{spark.als}, \link{spark.gaussianMixture}, \link{spark.gbt}, \link{spark.isoreg},
+#' @seealso \link{spark.kmeans},
+#' @seealso \link{spark.lda}, \link{spark.logit}, \link{spark.mlp}, \link{spark.naiveBayes},
+#' @seealso \link{spark.randomForest}, \link{spark.survreg},
+#' @seealso \link{read.ml}
+NULL
+
+#' Makes predictions from a MLlib model
+#'
+#' Makes predictions from a MLlib model. For more information, see the specific
+#' MLlib model below.
+#' @rdname predict
+#' @name predict
+#' @export
+#' @seealso \link{spark.glm}, \link{glm},
+#' @seealso \link{spark.als}, \link{spark.gaussianMixture}, \link{spark.gbt}, \link{spark.isoreg},
+#' @seealso \link{spark.kmeans},
+#' @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) {
+  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))
+}
+
+#' Load a fitted MLlib model from the input path.
+#'
+#' @param path path of the model to read.
+#' @return A fitted MLlib model.
+#' @rdname read.ml
+#' @name read.ml
+#' @export
+#' @seealso \link{write.ml}
+#' @examples
+#' \dontrun{
+#' path <- "path/to/model"
+#' model <- read.ml(path)
+#' }
+#' @note read.ml since 2.0.0
+read.ml <- function(path) {
+  path <- suppressWarnings(normalizePath(path))
+  sparkSession <- getSparkSession()
+  callJStatic("org.apache.spark.ml.r.RWrappers", "session", sparkSession)
+  jobj <- callJStatic("org.apache.spark.ml.r.RWrappers", "load", path)
+  if (isInstanceOf(jobj, "org.apache.spark.ml.r.NaiveBayesWrapper")) {
+    new("NaiveBayesModel", jobj = jobj)
+  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.AFTSurvivalRegressionWrapper")) {
+    new("AFTSurvivalRegressionModel", jobj = jobj)
+  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.GeneralizedLinearRegressionWrapper")) {
+    new("GeneralizedLinearRegressionModel", jobj = jobj)
+  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.KMeansWrapper")) {
+    new("KMeansModel", jobj = jobj)
+  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.LDAWrapper")) {
+    new("LDAModel", jobj = jobj)
+  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.MultilayerPerceptronClassifierWrapper")) {
+    new("MultilayerPerceptronClassificationModel", jobj = jobj)
+  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.IsotonicRegressionWrapper")) {
+    new("IsotonicRegressionModel", jobj = jobj)
+  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.GaussianMixtureWrapper")) {
+    new("GaussianMixtureModel", jobj = jobj)
+  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.ALSWrapper")) {
+    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 if (isInstanceOf(jobj, "org.apache.spark.ml.r.GBTRegressorWrapper")) {
+    new("GBTRegressionModel", jobj = jobj)
+  } else if (isInstanceOf(jobj, "org.apache.spark.ml.r.GBTClassifierWrapper")) {
+    new("GBTClassificationModel", jobj = jobj)
+  } else {
+    stop("Unsupported model: ", jobj)
+  }
+}
diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R
deleted file mode 100644
index 0f0d831c6f..0000000000
--- a/R/pkg/inst/tests/testthat/test_mllib.R
+++ /dev/null
@@ -1,1170 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-library(testthat)
-
-context("MLlib functions")
-
-# Tests for MLlib functions in SparkR
-sparkSession <- sparkR.session(enableHiveSupport = FALSE)
-
-absoluteSparkPath <- function(x) {
-  sparkHome <- sparkR.conf("spark.home")
-  file.path(sparkHome, x)
-}
-
-test_that("formula of spark.glm", {
-  training <- suppressWarnings(createDataFrame(iris))
-  # directly calling the spark API
-  # dot minus and intercept vs native glm
-  model <- spark.glm(training, Sepal_Width ~ . - Species + 0)
-  vals <- collect(select(predict(model, training), "prediction"))
-  rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-
-  # feature interaction vs native glm
-  model <- spark.glm(training, Sepal_Width ~ Species:Sepal_Length)
-  vals <- collect(select(predict(model, training), "prediction"))
-  rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris)
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-
-  # glm should work with long formula
-  training <- suppressWarnings(createDataFrame(iris))
-  training$LongLongLongLongLongName <- training$Sepal_Width
-  training$VeryLongLongLongLonLongName <- training$Sepal_Length
-  training$AnotherLongLongLongLongName <- training$Species
-  model <- spark.glm(training, LongLongLongLongLongName ~ VeryLongLongLongLonLongName +
-    AnotherLongLongLongLongName)
-  vals <- collect(select(predict(model, training), "prediction"))
-  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-})
-
-test_that("spark.glm and predict", {
-  training <- suppressWarnings(createDataFrame(iris))
-  # gaussian family
-  model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species)
-  prediction <- predict(model, training)
-  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
-  vals <- collect(select(prediction, "prediction"))
-  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-
-  # poisson family
-  model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species,
-  family = poisson(link = identity))
-  prediction <- predict(model, training)
-  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
-  vals <- collect(select(prediction, "prediction"))
-  rVals <- suppressWarnings(predict(glm(Sepal.Width ~ Sepal.Length + Species,
-  data = iris, family = poisson(link = identity)), iris))
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-
-  # Test stats::predict is working
-  x <- rnorm(15)
-  y <- x + rnorm(15)
-  expect_equal(length(predict(lm(y ~ x))), 15)
-})
-
-test_that("spark.glm summary", {
-  # gaussian family
-  training <- suppressWarnings(createDataFrame(iris))
-  stats <- summary(spark.glm(training, Sepal_Width ~ Sepal_Length + Species))
-
-  rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris))
-
-  coefs <- unlist(stats$coefficients)
-  rCoefs <- unlist(rStats$coefficients)
-  expect_true(all(abs(rCoefs - coefs) < 1e-4))
-  expect_true(all(
-    rownames(stats$coefficients) ==
-    c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
-  expect_equal(stats$dispersion, rStats$dispersion)
-  expect_equal(stats$null.deviance, rStats$null.deviance)
-  expect_equal(stats$deviance, rStats$deviance)
-  expect_equal(stats$df.null, rStats$df.null)
-  expect_equal(stats$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"), ]
-  stats <- summary(spark.glm(training, Species ~ Sepal_Length + Sepal_Width,
-    family = binomial(link = "logit")))
-
-  rTraining <- iris[iris$Species %in% c("versicolor", "virginica"), ]
-  rStats <- summary(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
-  family = binomial(link = "logit")))
-
-  coefs <- unlist(stats$coefficients)
-  rCoefs <- unlist(rStats$coefficients)
-  expect_true(all(abs(rCoefs - coefs) < 1e-4))
-  expect_true(all(
-    rownames(stats$coefficients) ==
-    c("(Intercept)", "Sepal_Length", "Sepal_Width")))
-  expect_equal(stats$dispersion, rStats$dispersion)
-  expect_equal(stats$null.deviance, rStats$null.deviance)
-  expect_equal(stats$deviance, rStats$deviance)
-  expect_equal(stats$df.null, rStats$df.null)
-  expect_equal(stats$df.residual, rStats$df.residual)
-  expect_equal(stats$aic, rStats$aic)
-
-  # Test spark.glm works with weighted dataset
-  a1 <- c(0, 1, 2, 3)
-  a2 <- c(5, 2, 1, 3)
-  w <- c(1, 2, 3, 4)
-  b <- c(1, 0, 1, 0)
-  data <- as.data.frame(cbind(a1, a2, w, b))
-  df <- createDataFrame(data)
-
-  stats <- summary(spark.glm(df, b ~ a1 + a2, family = "binomial", weightCol = "w"))
-  rStats <- summary(glm(b ~ a1 + a2, family = "binomial", data = data, weights = w))
-
-  coefs <- unlist(stats$coefficients)
-  rCoefs <- unlist(rStats$coefficients)
-  expect_true(all(abs(rCoefs - coefs) < 1e-3))
-  expect_true(all(rownames(stats$coefficients) == c("(Intercept)", "a1", "a2")))
-  expect_equal(stats$dispersion, rStats$dispersion)
-  expect_equal(stats$null.deviance, rStats$null.deviance)
-  expect_equal(stats$deviance, rStats$deviance)
-  expect_equal(stats$df.null, rStats$df.null)
-  expect_equal(stats$df.residual, rStats$df.residual)
-  expect_equal(stats$aic, rStats$aic)
-
-  # Test summary works on base GLM models
-  baseModel <- stats::glm(Sepal.Width ~ Sepal.Length + Species, data = iris)
-  baseSummary <- summary(baseModel)
-  expect_true(abs(baseSummary$deviance - 12.19313) < 1e-4)
-
-  # Test spark.glm works with regularization parameter
-  data <- as.data.frame(cbind(a1, a2, b))
-  df <- suppressWarnings(createDataFrame(data))
-  regStats <- summary(spark.glm(df, b ~ a1 + a2, regParam = 1.0))
-  expect_equal(regStats$aic, 13.32836, tolerance = 1e-4) # 13.32836 is from summary() result
-
-  # Test spark.glm works on collinear data
-  A <- matrix(c(1, 2, 3, 4, 2, 4, 6, 8), 4, 2)
-  b <- c(1, 2, 3, 4)
-  data <- as.data.frame(cbind(A, b))
-  df <- createDataFrame(data)
-  stats <- summary(spark.glm(df, b ~ . - 1))
-  coefs <- unlist(stats$coefficients)
-  expect_true(all(abs(c(0.5, 0.25) - coefs) < 1e-4))
-})
-
-test_that("spark.glm save/load", {
-  training <- suppressWarnings(createDataFrame(iris))
-  m <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species)
-  s <- summary(m)
-
-  modelPath <- tempfile(pattern = "spark-glm", fileext = ".tmp")
-  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)
-  expect_equal(rownames(s$coefficients), rownames(s2$coefficients))
-  expect_equal(s$dispersion, s2$dispersion)
-  expect_equal(s$null.deviance, s2$null.deviance)
-  expect_equal(s$deviance, s2$deviance)
-  expect_equal(s$df.null, s2$df.null)
-  expect_equal(s$df.residual, s2$df.residual)
-  expect_equal(s$aic, s2$aic)
-  expect_equal(s$iter, s2$iter)
-  expect_true(!s$is.loaded)
-  expect_true(s2$is.loaded)
-
-  unlink(modelPath)
-})
-
-
-
-test_that("formula of glm", {
-  training <- suppressWarnings(createDataFrame(iris))
-  # dot minus and intercept vs native glm
-  model <- glm(Sepal_Width ~ . - Species + 0, data = training)
-  vals <- collect(select(predict(model, training), "prediction"))
-  rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-
-  # feature interaction vs native glm
-  model <- glm(Sepal_Width ~ Species:Sepal_Length, data = training)
-  vals <- collect(select(predict(model, training), "prediction"))
-  rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris)
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-
-  # glm should work with long formula
-  training <- suppressWarnings(createDataFrame(iris))
-  training$LongLongLongLongLongName <- training$Sepal_Width
-  training$VeryLongLongLongLonLongName <- training$Sepal_Length
-  training$AnotherLongLongLongLongName <- training$Species
-  model <- glm(LongLongLongLongLongName ~ VeryLongLongLongLonLongName + AnotherLongLongLongLongName,
-               data = training)
-  vals <- collect(select(predict(model, training), "prediction"))
-  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-})
-
-test_that("glm and predict", {
-  training <- suppressWarnings(createDataFrame(iris))
-  # gaussian family
-  model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
-  prediction <- predict(model, training)
-  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
-  vals <- collect(select(prediction, "prediction"))
-  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-
-  # poisson family
-  model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training,
-               family = poisson(link = identity))
-  prediction <- predict(model, training)
-  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
-  vals <- collect(select(prediction, "prediction"))
-  rVals <- suppressWarnings(predict(glm(Sepal.Width ~ Sepal.Length + Species,
-           data = iris, family = poisson(link = identity)), iris))
-  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
-
-  # Test stats::predict is working
-  x <- rnorm(15)
-  y <- x + rnorm(15)
-  expect_equal(length(predict(lm(y ~ x))), 15)
-})
-
-test_that("glm summary", {
-  # gaussian family
-  training <- suppressWarnings(createDataFrame(iris))
-  stats <- summary(glm(Sepal_Width ~ Sepal_Length + Species, data = training))
-
-  rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris))
-
-  coefs <- unlist(stats$coefficients)
-  rCoefs <- unlist(rStats$coefficients)
-  expect_true(all(abs(rCoefs - coefs) < 1e-4))
-  expect_true(all(
-    rownames(stats$coefficients) ==
-    c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
-  expect_equal(stats$dispersion, rStats$dispersion)
-  expect_equal(stats$null.deviance, rStats$null.deviance)
-  expect_equal(stats$deviance, rStats$deviance)
-  expect_equal(stats$df.null, rStats$df.null)
-  expect_equal(stats$df.residual, rStats$df.residual)
-  expect_equal(stats$aic, rStats$aic)
-
-  # binomial family
-  df <- suppressWarnings(createDataFrame(iris))
-  training <- df[df$Species %in% c("versicolor", "virginica"), ]
-  stats <- summary(glm(Species ~ Sepal_Length + Sepal_Width, data = training,
-    family = binomial(link = "logit")))
-
-  rTraining <- iris[iris$Species %in% c("versicolor", "virginica"), ]
-  rStats <- summary(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
-    family = binomial(link = "logit")))
-
-  coefs <- unlist(stats$coefficients)
-  rCoefs <- unlist(rStats$coefficients)
-  expect_true(all(abs(rCoefs - coefs) < 1e-4))
-  expect_true(all(
-    rownames(stats$coefficients) ==
-    c("(Intercept)", "Sepal_Length", "Sepal_Width")))
-  expect_equal(stats$dispersion, rStats$dispersion)
-  expect_equal(stats$null.deviance, rStats$null.deviance)
-  expect_equal(stats$deviance, rStats$deviance)
-  expect_equal(stats$df.null, rStats$df.null)
-  expect_equal(stats$df.residual, rStats$df.residual)
-  expect_equal(stats$aic, rStats$aic)
-
-  # Test summary works on base GLM models
-  baseModel <- stats::glm(Sepal.Width ~ Sepal.Length + Species, data = iris)
-  baseSummary <- summary(baseModel)
-  expect_true(abs(baseSummary$deviance - 12.19313) < 1e-4)
-})
-
-test_that("glm save/load", {
-  training <- suppressWarnings(createDataFrame(iris))
-  m <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
-  s <- summary(m)
-
-  modelPath <- tempfile(pattern = "glm", fileext = ".tmp")
-  write.ml(m, modelPath)
-  expect_error(write.ml(m, modelPath))
-  write.ml(m, modelPath, overwrite = TRUE)
-  m2 <- read.ml(modelPath)
-  s2 <- summary(m2)
-
-  expect_equal(s$coefficients, s2$coefficients)
-  expect_equal(rownames(s$coefficients), rownames(s2$coefficients))
-  expect_equal(s$dispersion, s2$dispersion)
-  expect_equal(s$null.deviance, s2$null.deviance)
-  expect_equal(s$deviance, s2$deviance)
-  expect_equal(s$df.null, s2$df.null)
-  expect_equal(s$df.residual, s2$df.residual)
-  expect_equal(s$aic, s2$aic)
-  expect_equal(s$iter, s2$iter)
-  expect_true(!s$is.loaded)
-  expect_true(s2$is.loaded)
-
-  unlink(modelPath)
-})
-
-test_that("spark.kmeans", {
-  newIris <- iris
-  newIris$Species <- NULL
-  training <- suppressWarnings(createDataFrame(newIris))
-
-  take(training, 1)
-
-  model <- spark.kmeans(data = training, ~ ., k = 2, maxIter = 10, initMode = "random")
-  sample <- take(select(predict(model, training), "prediction"), 1)
-  expect_equal(typeof(sample$prediction), "integer")
-  expect_equal(sample$prediction, 1)
-
-  # Test stats::kmeans is working
-  statsModel <- kmeans(x = newIris, centers = 2)
-  expect_equal(sort(unique(statsModel$cluster)), c(1, 2))
-
-  # Test fitted works on KMeans
-  fitted.model <- fitted(model)
-  expect_equal(sort(collect(distinct(select(fitted.model, "prediction")))$prediction), c(0, 1))
-
-  # Test summary works on KMeans
-  summary.model <- summary(model)
-  cluster <- summary.model$cluster
-  k <- summary.model$k
-  expect_equal(k, 2)
-  expect_equal(sort(collect(distinct(select(cluster, "prediction")))$prediction), c(0, 1))
-
-  # Test model save/load
-  modelPath <- tempfile(pattern = "spark-kmeans", fileext = ".tmp")
-  write.ml(model, modelPath)
-  expect_error(write.ml(model, modelPath))
-  write.ml(model, modelPath, overwrite = TRUE)
-  model2 <- read.ml(modelPath)
-  summary2 <- summary(model2)
-  expect_equal(sort(unlist(summary.model$size)), sort(unlist(summary2$size)))
-  expect_equal(summary.model$coefficients, summary2$coefficients)
-  expect_true(!summary.model$is.loaded)
-  expect_true(summary2$is.loaded)
-
-  unlink(modelPath)
-})
-
-test_that("spark.mlp", {
-  df <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
-                source = "libsvm")
-  model <- spark.mlp(df, label ~ features, blockSize = 128, layers = c(4, 5, 4, 3),
-                     solver = "l-bfgs", maxIter = 100, tol = 0.5, stepSize = 1, seed = 1)
-
-  # Test summary method
-  summary <- summary(model)
-  expect_equal(summary$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),
-               tolerance = 1e-6)
-
-  # Test predict method
-  mlpTestDF <- df
-  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
-  expect_equal(head(mlpPredictions$prediction, 6), c("1.0", "0.0", "0.0", "0.0", "0.0", "0.0"))
-
-  # Test model save/load
-  modelPath <- tempfile(pattern = "spark-mlp", fileext = ".tmp")
-  write.ml(model, modelPath)
-  expect_error(write.ml(model, modelPath))
-  write.ml(model, modelPath, overwrite = TRUE)
-  model2 <- read.ml(modelPath)
-  summary2 <- summary(model2)
-
-  expect_equal(summary2$numOfInputs, 4)
-  expect_equal(summary2$numOfOutputs, 3)
-  expect_equal(summary2$layers, c(4, 5, 4, 3))
-  expect_equal(length(summary2$weights), 64)
-
-  unlink(modelPath)
-
-  # Test default parameter
-  model <- spark.mlp(df, label ~ features, layers = c(4, 5, 4, 3))
-  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
-  expect_equal(head(mlpPredictions$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, 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, label ~ features, layers = c(4, 5, 4, 3), maxIter = 10)
-  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
-  expect_equal(head(mlpPredictions$prediction, 10),
-               c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
-  # seed equals 10
-  model <- spark.mlp(df, label ~ features, layers = c(4, 5, 4, 3), maxIter = 10, seed = 10)
-  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
-  expect_equal(head(mlpPredictions$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, 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$prediction, 10),
-               c("1.0", "1.0", "1.0", "1.0", "2.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
-
-  model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2, initialWeights =
-    c(0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 5.0, 5.0, 5.0, 5.0, 9.0, 9.0, 9.0, 9.0, 9.0))
-  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
-  expect_equal(head(mlpPredictions$prediction, 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, label ~ features, layers = c(4, 3), maxIter = 2)
-  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
-  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", {
-  # R code to reproduce the result.
-  # We do not support instance weights yet. So we ignore the frequencies.
-  #
-  #' library(e1071)
-  #' t <- as.data.frame(Titanic)
-  #' t1 <- t[t$Freq > 0, -5]
-  #' m <- naiveBayes(Survived ~ ., data = t1)
-  #' m
-  #' predict(m, t1)
-  #
-  # -- output of 'm'
-  #
-  # A-priori probabilities:
-  # Y
-  #        No       Yes
-  # 0.4166667 0.5833333
-  #
-  # Conditional probabilities:
-  #      Class
-  # Y           1st       2nd       3rd      Crew
-  #   No  0.2000000 0.2000000 0.4000000 0.2000000
-  #   Yes 0.2857143 0.2857143 0.2857143 0.1428571
-  #
-  #      Sex
-  # Y     Male Female
-  #   No   0.5    0.5
-  #   Yes  0.5    0.5
-  #
-  #      Age
-  # Y         Child     Adult
-  #   No  0.2000000 0.8000000
-  #   Yes 0.4285714 0.5714286
-  #
-  # -- output of 'predict(m, t1)'
-  #
-  # Yes Yes Yes Yes No  No  Yes Yes No  No  Yes Yes Yes Yes Yes Yes Yes Yes No  No  Yes Yes No  No
-  #
-
-  t <- as.data.frame(Titanic)
-  t1 <- t[t$Freq > 0, -5]
-  df <- suppressWarnings(createDataFrame(t1))
-  m <- spark.naiveBayes(df, Survived ~ ., smoothing = 0.0)
-  s <- summary(m)
-  expect_equal(as.double(s$apriori[1, "Yes"]), 0.5833333, tolerance = 1e-6)
-  expect_equal(sum(s$apriori), 1)
-  expect_equal(as.double(s$tables["Yes", "Age_Adult"]), 0.5714286, tolerance = 1e-6)
-  p <- collect(select(predict(m, df), "prediction"))
-  expect_equal(p$prediction, c("Yes", "Yes", "Yes", "Yes", "No", "No", "Yes", "Yes", "No", "No",
-                               "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "No",
-                               "Yes", "Yes", "No", "No"))
-
-  # Test model save/load
-  modelPath <- tempfile(pattern = "spark-naiveBayes", fileext = ".tmp")
-  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)
-
-  unlink(modelPath)
-
-  # Test e1071::naiveBayes
-  if (requireNamespace("e1071", quietly = TRUE)) {
-    expect_error(m <- e1071::naiveBayes(Survived ~ ., data = t1), NA)
-    expect_equal(as.character(predict(m, t1[1, ])), "Yes")
-  }
-
-  # Test numeric response variable
-  t1$NumericSurvived <- ifelse(t1$Survived == "No", 0, 1)
-  t2 <- t1[-4]
-  df <- suppressWarnings(createDataFrame(t2))
-  m <- spark.naiveBayes(df, NumericSurvived ~ ., smoothing = 0.0)
-  s <- summary(m)
-  expect_equal(as.double(s$apriori[1, 1]), 0.5833333, tolerance = 1e-6)
-  expect_equal(sum(s$apriori), 1)
-  expect_equal(as.double(s$tables[1, "Age_Adult"]), 0.5714286, tolerance = 1e-6)
-})
-
-test_that("spark.survreg", {
-  # R code to reproduce the result.
-  #
-  #' rData <- list(time = c(4, 3, 1, 1, 2, 2, 3), status = c(1, 1, 1, 0, 1, 1, 0),
-  #'               x = c(0, 2, 1, 1, 1, 0, 0), sex = c(0, 0, 0, 0, 1, 1, 1))
-  #' library(survival)
-  #' model <- survreg(Surv(time, status) ~ x + sex, rData)
-  #' summary(model)
-  #' predict(model, data)
-  #
-  # -- output of 'summary(model)'
-  #
-  #              Value Std. Error     z        p
-  # (Intercept)  1.315      0.270  4.88 1.07e-06
-  # x           -0.190      0.173 -1.10 2.72e-01
-  # sex         -0.253      0.329 -0.77 4.42e-01
-  # Log(scale)  -1.160      0.396 -2.93 3.41e-03
-  #
-  # -- output of 'predict(model, data)'
-  #
-  #        1        2        3        4        5        6        7
-  # 3.724591 2.545368 3.079035 3.079035 2.390146 2.891269 2.891269
-  #
-  data <- list(list(4, 1, 0, 0), list(3, 1, 2, 0), list(1, 1, 1, 0),
-          list(1, 0, 1, 0), list(2, 1, 1, 1), list(2, 1, 0, 1), list(3, 0, 0, 1))
-  df <- createDataFrame(data, c("time", "status", "x", "sex"))
-  model <- spark.survreg(df, Surv(time, status) ~ x + sex)
-  stats <- summary(model)
-  coefs <- as.vector(stats$coefficients[, 1])
-  rCoefs <- c(1.3149571, -0.1903409, -0.2532618, -1.1599800)
-  expect_equal(coefs, rCoefs, tolerance = 1e-4)
-  expect_true(all(
-    rownames(stats$coefficients) ==
-    c("(Intercept)", "x", "sex", "Log(scale)")))
-  p <- collect(select(predict(model, df), "prediction"))
-  expect_equal(p$prediction, c(3.724591, 2.545368, 3.079035, 3.079035,
-               2.390146, 2.891269, 2.891269), tolerance = 1e-4)
-
-  # Test model save/load
-  modelPath <- tempfile(pattern = "spark-survreg", 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)
-  coefs2 <- as.vector(stats2$coefficients[, 1])
-  expect_equal(coefs, coefs2)
-  expect_equal(rownames(stats$coefficients), rownames(stats2$coefficients))
-
-  unlink(modelPath)
-
-  # Test survival::survreg
-  if (requireNamespace("survival", quietly = TRUE)) {
-    rData <- list(time = c(4, 3, 1, 1, 2, 2, 3), status = c(1, 1, 1, 0, 1, 1, 0),
-                 x = c(0, 2, 1, 1, 1, 0, 0), sex = c(0, 0, 0, 0, 1, 1, 1))
-    expect_error(
-      model <- survival::survreg(formula = survival::Surv(time, status) ~ x + sex, data = rData),
-      NA)
-    expect_equal(predict(model, rData)[[1]], 3.724591, tolerance = 1e-4)
-  }
-})
-
-test_that("spark.isotonicRegression", {
-  label <- c(7.0, 5.0, 3.0, 5.0, 1.0)
-  feature <- c(0.0, 1.0, 2.0, 3.0, 4.0)
-  weight <- c(1.0, 1.0, 1.0, 1.0, 1.0)
-  data <- as.data.frame(cbind(label, feature, weight))
-  df <- createDataFrame(data)
-
-  model <- spark.isoreg(df, label ~ feature, isotonic = FALSE,
-                        weightCol = "weight")
-  # only allow one variable on the right hand side of the formula
-  expect_error(model2 <- spark.isoreg(df, ~., isotonic = FALSE))
-  result <- summary(model)
-  expect_equal(result$predictions, list(7, 5, 4, 4, 1))
-
-  # Test model prediction
-  predict_data <- list(list(-2.0), list(-1.0), list(0.5),
-                       list(0.75), list(1.0), list(2.0), list(9.0))
-  predict_df <- createDataFrame(predict_data, c("feature"))
-  predict_result <- collect(select(predict(model, predict_df), "prediction"))
-  expect_equal(predict_result$prediction, c(7.0, 7.0, 6.0, 5.5, 5.0, 4.0, 1.0))
-
-  # Test model save/load
-  modelPath <- tempfile(pattern = "spark-isotonicRegression", fileext = ".tmp")
-  write.ml(model, modelPath)
-  expect_error(write.ml(model, modelPath))
-  write.ml(model, modelPath, overwrite = TRUE)
-  model2 <- read.ml(modelPath)
-  expect_equal(result, summary(model2))
-
-  unlink(modelPath)
-})
-
-test_that("spark.logit", {
-  # R code to reproduce the result.
-  # nolint start
-  #' library(glmnet)
-  #' iris.x = as.matrix(iris[, 1:4])
-  #' iris.y = as.factor(as.character(iris[, 5]))
-  #' logit = glmnet(iris.x, iris.y, family="multinomial", alpha=0, lambda=0.5)
-  #' coef(logit)
-  #
-  # $setosa
-  # 5 x 1 sparse Matrix of class "dgCMatrix"
-  # s0
-  #               1.0981324
-  # Sepal.Length -0.2909860
-  # Sepal.Width   0.5510907
-  # Petal.Length -0.1915217
-  # Petal.Width  -0.4211946
-  #
-  # $versicolor
-  # 5 x 1 sparse Matrix of class "dgCMatrix"
-  # s0
-  #               1.520061e+00
-  # Sepal.Length  2.524501e-02
-  # Sepal.Width  -5.310313e-01
-  # Petal.Length  3.656543e-02
-  # Petal.Width  -3.144464e-05
-  #
-  # $virginica
-  # 5 x 1 sparse Matrix of class "dgCMatrix"
-  # s0
-  #              -2.61819385
-  # Sepal.Length  0.26574097
-  # Sepal.Width  -0.02005932
-  # Petal.Length  0.15495629
-  # Petal.Width   0.42122607
-  # nolint end
-
-  # Test multinomial logistic regression againt three classes
-  df <- suppressWarnings(createDataFrame(iris))
-  model <- spark.logit(df, Species ~ ., regParam = 0.5)
-  summary <- summary(model)
-  versicolorCoefsR <- c(1.52, 0.03, -0.53, 0.04, 0.00)
-  virginicaCoefsR <- c(-2.62, 0.27, -0.02, 0.16, 0.42)
-  setosaCoefsR <- c(1.10, -0.29, 0.55, -0.19, -0.42)
-  versicolorCoefs <- unlist(summary$coefficients[, "versicolor"])
-  virginicaCoefs <- unlist(summary$coefficients[, "virginica"])
-  setosaCoefs <- unlist(summary$coefficients[, "setosa"])
-  expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1))
-  expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1))
-  expect_true(all(abs(setosaCoefs - setosaCoefs) < 0.1))
-
-  # Test model save and load
-  modelPath <- tempfile(pattern = "spark-logit", fileext = ".tmp")
-  write.ml(model, modelPath)
-  expect_error(write.ml(model, modelPath))
-  write.ml(model, modelPath, overwrite = TRUE)
-  model2 <- read.ml(modelPath)
-  coefs <- summary(model)$coefficients
-  coefs2 <- summary(model2)$coefficients
-  expect_equal(coefs, coefs2)
-  unlink(modelPath)
-
-  # R code to reproduce the result.
-  # nolint start
-  #' library(glmnet)
-  #' iris2 <- iris[iris$Species %in% c("versicolor", "virginica"), ]
-  #' iris.x = as.matrix(iris2[, 1:4])
-  #' iris.y = as.factor(as.character(iris2[, 5]))
-  #' logit = glmnet(iris.x, iris.y, family="multinomial", alpha=0, lambda=0.5)
-  #' coef(logit)
-  #
-  # $versicolor
-  # 5 x 1 sparse Matrix of class "dgCMatrix"
-  # s0
-  #               3.93844796
-  # Sepal.Length -0.13538675
-  # Sepal.Width  -0.02386443
-  # Petal.Length -0.35076451
-  # Petal.Width  -0.77971954
-  #
-  # $virginica
-  # 5 x 1 sparse Matrix of class "dgCMatrix"
-  # s0
-  #              -3.93844796
-  # Sepal.Length  0.13538675
-  # Sepal.Width   0.02386443
-  # Petal.Length  0.35076451
-  # Petal.Width   0.77971954
-  #
-  #' logit = glmnet(iris.x, iris.y, family="binomial", alpha=0, lambda=0.5)
-  #' coef(logit)
-  #
-  # 5 x 1 sparse Matrix of class "dgCMatrix"
-  # s0
-  # (Intercept)  -6.0824412
-  # Sepal.Length  0.2458260
-  # Sepal.Width   0.1642093
-  # Petal.Length  0.4759487
-  # Petal.Width   1.0383948
-  #
-  # nolint end
-
-  # Test multinomial logistic regression againt two classes
-  df <- suppressWarnings(createDataFrame(iris))
-  training <- df[df$Species %in% c("versicolor", "virginica"), ]
-  model <- spark.logit(training, Species ~ ., regParam = 0.5, family = "multinomial")
-  summary <- summary(model)
-  versicolorCoefsR <- c(3.94, -0.16, -0.02, -0.35, -0.78)
-  virginicaCoefsR <- c(-3.94, 0.16, -0.02, 0.35, 0.78)
-  versicolorCoefs <- unlist(summary$coefficients[, "versicolor"])
-  virginicaCoefs <- unlist(summary$coefficients[, "virginica"])
-  expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1))
-  expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1))
-
-  # Test binomial logistic regression againt two classes
-  model <- spark.logit(training, Species ~ ., regParam = 0.5)
-  summary <- summary(model)
-  coefsR <- c(-6.08, 0.25, 0.16, 0.48, 1.04)
-  coefs <- unlist(summary$coefficients[, "Estimate"])
-  expect_true(all(abs(coefsR - coefs) < 0.1))
-
-  # Test prediction with string label
-  prediction <- predict(model, training)
-  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "character")
-  expected <- c("versicolor", "versicolor", "virginica", "versicolor", "versicolor",
-                "versicolor", "versicolor", "versicolor", "versicolor", "versicolor")
-  expect_equal(as.list(take(select(prediction, "prediction"), 10))[[1]], expected)
-
-  # Test prediction with numeric label
-  label <- c(0.0, 0.0, 0.0, 1.0, 1.0)
-  feature <- c(1.1419053, 0.9194079, -0.9498666, -1.1069903, 0.2809776)
-  data <- as.data.frame(cbind(label, feature))
-  df <- createDataFrame(data)
-  model <- spark.logit(df, label ~ feature)
-  prediction <- collect(select(predict(model, df), "prediction"))
-  expect_equal(prediction$prediction, c("0.0", "0.0", "1.0", "1.0", "0.0"))
-})
-
-test_that("spark.gaussianMixture", {
-  # R code to reproduce the result.
-  # nolint start
-  #' library(mvtnorm)
-  #' set.seed(1)
-  #' a <- rmvnorm(7, c(0, 0))
-  #' b <- rmvnorm(8, c(10, 10))
-  #' data <- rbind(a, b)
-  #' model <- mvnormalmixEM(data, k = 2)
-  #' model$lambda
-  #
-  #  [1] 0.4666667 0.5333333
-  #
-  #' model$mu
-  #
-  #  [1] 0.11731091 -0.06192351
-  #  [1] 10.363673  9.897081
-  #
-  #' model$sigma
-  #
-  #  [[1]]
-  #             [,1]       [,2]
-  #  [1,] 0.62049934 0.06880802
-  #  [2,] 0.06880802 1.27431874
-  #
-  #  [[2]]
-  #            [,1]     [,2]
-  #  [1,] 0.2961543 0.160783
-  #  [2,] 0.1607830 1.008878
-  # nolint end
-  data <- list(list(-0.6264538, 0.1836433), list(-0.8356286, 1.5952808),
-               list(0.3295078, -0.8204684), list(0.4874291, 0.7383247),
-               list(0.5757814, -0.3053884), list(1.5117812, 0.3898432),
-               list(-0.6212406, -2.2146999), list(11.1249309, 9.9550664),
-               list(9.9838097, 10.9438362), list(10.8212212, 10.5939013),
-               list(10.9189774, 10.7821363), list(10.0745650, 8.0106483),
-               list(10.6198257, 9.9438713), list(9.8442045, 8.5292476),
-               list(9.5218499, 10.4179416))
-  df <- createDataFrame(data, c("x1", "x2"))
-  model <- spark.gaussianMixture(df, ~ x1 + x2, k = 2)
-  stats <- summary(model)
-  rLambda <- c(0.4666667, 0.5333333)
-  rMu <- c(0.11731091, -0.06192351, 10.363673, 9.897081)
-  rSigma <- c(0.62049934, 0.06880802, 0.06880802, 1.27431874,
-              0.2961543, 0.160783, 0.1607830, 1.008878)
-  expect_equal(stats$lambda, rLambda, tolerance = 1e-3)
-  expect_equal(unlist(stats$mu), rMu, tolerance = 1e-3)
-  expect_equal(unlist(stats$sigma), rSigma, tolerance = 1e-3)
-  p <- collect(select(predict(model, df), "prediction"))
-  expect_equal(p$prediction, c(0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1))
-
-  # Test model save/load
-  modelPath <- tempfile(pattern = "spark-gaussianMixture", 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$lambda, stats2$lambda)
-  expect_equal(unlist(stats$mu), unlist(stats2$mu))
-  expect_equal(unlist(stats$sigma), unlist(stats2$sigma))
-
-  unlink(modelPath)
-})
-
-test_that("spark.lda with libsvm", {
-  text <- read.df(absoluteSparkPath("data/mllib/sample_lda_libsvm_data.txt"), source = "libsvm")
-  model <- spark.lda(text, optimizer = "em")
-
-  stats <- summary(model, 10)
-  isDistributed <- stats$isDistributed
-  logLikelihood <- stats$logLikelihood
-  logPerplexity <- stats$logPerplexity
-  vocabSize <- stats$vocabSize
-  topics <- stats$topicTopTerms
-  weights <- stats$topicTopTermsWeights
-  vocabulary <- stats$vocabulary
-
-  expect_false(isDistributed)
-  expect_true(logLikelihood <= 0 & is.finite(logLikelihood))
-  expect_true(logPerplexity >= 0 & is.finite(logPerplexity))
-  expect_equal(vocabSize, 11)
-  expect_true(is.null(vocabulary))
-
-  # Test model save/load
-  modelPath <- tempfile(pattern = "spark-lda", fileext = ".tmp")
-  write.ml(model, modelPath)
-  expect_error(write.ml(model, modelPath))
-  write.ml(model, modelPath, overwrite = TRUE)
-  model2 <- read.ml(modelPath)
-  stats2 <- summary(model2)
-
-  expect_false(stats2$isDistributed)
-  expect_equal(logLikelihood, stats2$logLikelihood)
-  expect_equal(logPerplexity, stats2$logPerplexity)
-  expect_equal(vocabSize, stats2$vocabSize)
-  expect_equal(vocabulary, stats2$vocabulary)
-
-  unlink(modelPath)
-})
-
-test_that("spark.lda with text input", {
-  text <- read.text(absoluteSparkPath("data/mllib/sample_lda_data.txt"))
-  model <- spark.lda(text, optimizer = "online", features = "value")
-
-  stats <- summary(model)
-  isDistributed <- stats$isDistributed
-  logLikelihood <- stats$logLikelihood
-  logPerplexity <- stats$logPerplexity
-  vocabSize <- stats$vocabSize
-  topics <- stats$topicTopTerms
-  weights <- stats$topicTopTermsWeights
-  vocabulary <- stats$vocabulary
-
-  expect_false(isDistributed)
-  expect_true(logLikelihood <= 0 & is.finite(logLikelihood))
-  expect_true(logPerplexity >= 0 & is.finite(logPerplexity))
-  expect_equal(vocabSize, 10)
-  expect_true(setequal(stats$vocabulary, c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9")))
-
-  # Test model save/load
-  modelPath <- tempfile(pattern = "spark-lda-text", 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_false(stats2$isDistributed)
-  expect_equal(logLikelihood, stats2$logLikelihood)
-  expect_equal(logPerplexity, stats2$logPerplexity)
-  expect_equal(vocabSize, stats2$vocabSize)
-  expect_true(all.equal(vocabulary, stats2$vocabulary))
-
-  unlink(modelPath)
-})
-
-test_that("spark.posterior and spark.perplexity", {
-  text <- read.text(absoluteSparkPath("data/mllib/sample_lda_data.txt"))
-  model <- spark.lda(text, features = "value", k = 3)
-
-  # Assert perplexities are equal
-  stats <- summary(model)
-  logPerplexity <- spark.perplexity(model, text)
-  expect_equal(logPerplexity, stats$logPerplexity)
-
-  # Assert the sum of every topic distribution is equal to 1
-  posterior <- spark.posterior(model, text)
-  local.posterior <- collect(posterior)$topicDistribution
-  expect_equal(length(local.posterior), sum(unlist(local.posterior)))
-})
-
-test_that("spark.als", {
-  data <- list(list(0, 0, 4.0), list(0, 1, 2.0), list(1, 1, 3.0), list(1, 2, 4.0),
-               list(2, 1, 1.0), list(2, 2, 5.0))
-  df <- createDataFrame(data, c("user", "item", "score"))
-  model <- spark.als(df, ratingCol = "score", userCol = "user", itemCol = "item",
-                     rank = 10, maxIter = 5, seed = 0, regParam = 0.1)
-  stats <- summary(model)
-  expect_equal(stats$rank, 10)
-  test <- createDataFrame(list(list(0, 2), list(1, 0), list(2, 0)), c("user", "item"))
-  predictions <- collect(predict(model, test))
-
-  expect_equal(predictions$prediction, c(-0.1380762, 2.6258414, -1.5018409),
-  tolerance = 1e-4)
-
-  # Test model save/load
-  modelPath <- tempfile(pattern = "spark-als", fileext = ".tmp")
-  write.ml(model, modelPath)
-  expect_error(write.ml(model, modelPath))
-  write.ml(model, modelPath, overwrite = TRUE)
-  model2 <- read.ml(modelPath)
-  stats2 <- summary(model2)
-  expect_equal(stats2$rating, "score")
-  userFactors <- collect(stats$userFactors)
-  itemFactors <- collect(stats$itemFactors)
-  userFactors2 <- collect(stats2$userFactors)
-  itemFactors2 <- collect(stats2$itemFactors)
-
-  orderUser <- order(userFactors$id)
-  orderUser2 <- order(userFactors2$id)
-  expect_equal(userFactors$id[orderUser], userFactors2$id[orderUser2])
-  expect_equal(userFactors$features[orderUser], userFactors2$features[orderUser2])
-
-  orderItem <- order(itemFactors$id)
-  orderItem2 <- order(itemFactors2$id)
-  expect_equal(itemFactors$id[orderItem], itemFactors2$id[orderItem2])
-  expect_equal(itemFactors$features[orderItem], itemFactors2$features[orderItem2])
-
-  unlink(modelPath)
-})
-
-test_that("spark.kstest", {
-  data <- data.frame(test = c(0.1, 0.15, 0.2, 0.3, 0.25, -1, -0.5))
-  df <- createDataFrame(data)
-  testResult <- spark.kstest(df, "test", "norm")
-  stats <- summary(testResult)
-
-  rStats <- ks.test(data$test, "pnorm", alternative = "two.sided")
-
-  expect_equal(stats$p.value, rStats$p.value, tolerance = 1e-4)
-  expect_equal(stats$statistic, unname(rStats$statistic), tolerance = 1e-4)
-  expect_match(capture.output(stats)[1], "Kolmogorov-Smirnov test summary:")
-
-  testResult <- spark.kstest(df, "test", "norm", -0.5)
-  stats <- summary(testResult)
-
-  rStats <- ks.test(data$test, "pnorm", -0.5, 1, alternative = "two.sided")
-
-  expect_equal(stats$p.value, rStats$p.value, tolerance = 1e-4)
-  expect_equal(stats$statistic, unname(rStats$statistic), tolerance = 1e-4)
-  expect_match(capture.output(stats)[1], "Kolmogorov-Smirnov test summary:")
-
-  # Test print.summary.KSTest
-  printStats <- capture.output(print.summary.KSTest(stats))
-  expect_match(printStats[1], "Kolmogorov-Smirnov test summary:")
-  expect_match(printStats[5],
-               "Low presumption against null hypothesis: Sample follows theoretical distribution. ")
-})
-
-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$prediction, c(60.32820, 61.22315, 60.69025, 62.11070,
-                                         63.53160, 64.05470, 65.12710, 64.30450,
-                                         66.70910, 67.86125, 68.08700, 67.21865,
-                                         68.89275, 69.53180, 69.39640, 69.68250),
-
-               tolerance = 1e-4)
-  stats <- summary(model)
-  expect_equal(stats$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)
-
-  # 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)
-  # Test string prediction values
-  predictions <- collect(predict(model, data))$prediction
-  expect_equal(length(grep("setosa", predictions)), 50)
-  expect_equal(length(grep("versicolor", predictions)), 50)
-
-  modelPath <- tempfile(pattern = "spark-randomForestClassification", fileext = ".tmp")
-  write.ml(model, modelPath)
-  expect_error(write.ml(model, modelPath))
-  write.ml(model, modelPath, overwrite = TRUE)
-  model2 <- read.ml(modelPath)
-  stats2 <- summary(model2)
-  expect_equal(stats$depth, stats2$depth)
-  expect_equal(stats$numNodes, stats2$numNodes)
-  expect_equal(stats$numClasses, stats2$numClasses)
-
-  unlink(modelPath)
-
-  # Test numeric response variable
-  labelToIndex <- function(species) {
-    switch(as.character(species),
-      setosa = 0.0,
-      versicolor = 1.0,
-      virginica = 2.0
-    )
-  }
-  iris$NumericSpecies <- lapply(iris$Species, labelToIndex)
-  data <- suppressWarnings(createDataFrame(iris[-5]))
-  model <- spark.randomForest(data, NumericSpecies ~ Petal_Length + Petal_Width, "classification",
-                              maxDepth = 5, maxBins = 16)
-  stats <- summary(model)
-  expect_equal(stats$numFeatures, 2)
-  expect_equal(stats$numTrees, 20)
-  # Test numeric prediction values
-  predictions <- collect(predict(model, data))$prediction
-  expect_equal(length(grep("1.0", predictions)), 50)
-  expect_equal(length(grep("2.0", predictions)), 50)
-
-  # spark.randomForest classification can work on libsvm data
-  data <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
-                source = "libsvm")
-  model <- spark.randomForest(data, label ~ features, "classification")
-  expect_equal(summary(model)$numFeatures, 4)
-})
-
-test_that("spark.gbt", {
-  # regression
-  data <- suppressWarnings(createDataFrame(longley))
-  model <- spark.gbt(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16, seed = 123)
-  predictions <- collect(predict(model, data))
-  expect_equal(predictions$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$numTrees, 20)
-  expect_equal(stats$formula, "Employed ~ .")
-  expect_equal(stats$numFeatures, 6)
-  expect_equal(length(stats$treeWeights), 20)
-
-  modelPath <- tempfile(pattern = "spark-gbtRegression", 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)
-
-  # classification
-  # label must be binary - GBTClassifier currently only supports binary classification.
-  iris2 <- iris[iris$Species != "virginica", ]
-  data <- suppressWarnings(createDataFrame(iris2))
-  model <- spark.gbt(data, Species ~ Petal_Length + Petal_Width, "classification")
-  stats <- summary(model)
-  expect_equal(stats$numFeatures, 2)
-  expect_equal(stats$numTrees, 20)
-  expect_error(capture.output(stats), NA)
-  expect_true(length(capture.output(stats)) > 6)
-  predictions <- collect(predict(model, data))$prediction
-  # test string prediction values
-  expect_equal(length(grep("setosa", predictions)), 50)
-  expect_equal(length(grep("versicolor", predictions)), 50)
-
-  modelPath <- tempfile(pattern = "spark-gbtClassification", 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)
-
-  iris2$NumericSpecies <- ifelse(iris2$Species == "setosa", 0, 1)
-  df <- suppressWarnings(createDataFrame(iris2))
-  m <- spark.gbt(df, NumericSpecies ~ ., type = "classification")
-  s <- summary(m)
-  # test numeric prediction values
-  expect_equal(iris2$NumericSpecies, as.double(collect(predict(m, df))$prediction))
-  expect_equal(s$numFeatures, 5)
-  expect_equal(s$numTrees, 20)
-
-  # spark.gbt classification can work on libsvm data
-  data <- read.df(absoluteSparkPath("data/mllib/sample_binary_classification_data.txt"),
-                source = "libsvm")
-  model <- spark.gbt(data, label ~ features, "classification")
-  expect_equal(summary(model)$numFeatures, 692)
-})
-
-sparkR.session.stop()
diff --git a/R/pkg/inst/tests/testthat/test_mllib_classification.R b/R/pkg/inst/tests/testthat/test_mllib_classification.R
new file mode 100644
index 0000000000..2e0dea321e
--- /dev/null
+++ b/R/pkg/inst/tests/testthat/test_mllib_classification.R
@@ -0,0 +1,341 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+library(testthat)
+
+context("MLlib classification algorithms, except for tree-based algorithms")
+
+# Tests for MLlib classification algorithms in SparkR
+sparkSession <- sparkR.session(enableHiveSupport = FALSE)
+
+absoluteSparkPath <- function(x) {
+  sparkHome <- sparkR.conf("spark.home")
+  file.path(sparkHome, x)
+}
+
+test_that("spark.logit", {
+  # R code to reproduce the result.
+  # nolint start
+  #' library(glmnet)
+  #' iris.x = as.matrix(iris[, 1:4])
+  #' iris.y = as.factor(as.character(iris[, 5]))
+  #' logit = glmnet(iris.x, iris.y, family="multinomial", alpha=0, lambda=0.5)
+  #' coef(logit)
+  #
+  # $setosa
+  # 5 x 1 sparse Matrix of class "dgCMatrix"
+  # s0
+  #               1.0981324
+  # Sepal.Length -0.2909860
+  # Sepal.Width   0.5510907
+  # Petal.Length -0.1915217
+  # Petal.Width  -0.4211946
+  #
+  # $versicolor
+  # 5 x 1 sparse Matrix of class "dgCMatrix"
+  # s0
+  #               1.520061e+00
+  # Sepal.Length  2.524501e-02
+  # Sepal.Width  -5.310313e-01
+  # Petal.Length  3.656543e-02
+  # Petal.Width  -3.144464e-05
+  #
+  # $virginica
+  # 5 x 1 sparse Matrix of class "dgCMatrix"
+  # s0
+  #              -2.61819385
+  # Sepal.Length  0.26574097
+  # Sepal.Width  -0.02005932
+  # Petal.Length  0.15495629
+  # Petal.Width   0.42122607
+  # nolint end
+
+  # Test multinomial logistic regression againt three classes
+  df <- suppressWarnings(createDataFrame(iris))
+  model <- spark.logit(df, Species ~ ., regParam = 0.5)
+  summary <- summary(model)
+  versicolorCoefsR <- c(1.52, 0.03, -0.53, 0.04, 0.00)
+  virginicaCoefsR <- c(-2.62, 0.27, -0.02, 0.16, 0.42)
+  setosaCoefsR <- c(1.10, -0.29, 0.55, -0.19, -0.42)
+  versicolorCoefs <- unlist(summary$coefficients[, "versicolor"])
+  virginicaCoefs <- unlist(summary$coefficients[, "virginica"])
+  setosaCoefs <- unlist(summary$coefficients[, "setosa"])
+  expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1))
+  expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1))
+  expect_true(all(abs(setosaCoefs - setosaCoefs) < 0.1))
+
+  # Test model save and load
+  modelPath <- tempfile(pattern = "spark-logit", fileext = ".tmp")
+  write.ml(model, modelPath)
+  expect_error(write.ml(model, modelPath))
+  write.ml(model, modelPath, overwrite = TRUE)
+  model2 <- read.ml(modelPath)
+  coefs <- summary(model)$coefficients
+  coefs2 <- summary(model2)$coefficients
+  expect_equal(coefs, coefs2)
+  unlink(modelPath)
+
+  # R code to reproduce the result.
+  # nolint start
+  #' library(glmnet)
+  #' iris2 <- iris[iris$Species %in% c("versicolor", "virginica"), ]
+  #' iris.x = as.matrix(iris2[, 1:4])
+  #' iris.y = as.factor(as.character(iris2[, 5]))
+  #' logit = glmnet(iris.x, iris.y, family="multinomial", alpha=0, lambda=0.5)
+  #' coef(logit)
+  #
+  # $versicolor
+  # 5 x 1 sparse Matrix of class "dgCMatrix"
+  # s0
+  #               3.93844796
+  # Sepal.Length -0.13538675
+  # Sepal.Width  -0.02386443
+  # Petal.Length -0.35076451
+  # Petal.Width  -0.77971954
+  #
+  # $virginica
+  # 5 x 1 sparse Matrix of class "dgCMatrix"
+  # s0
+  #              -3.93844796
+  # Sepal.Length  0.13538675
+  # Sepal.Width   0.02386443
+  # Petal.Length  0.35076451
+  # Petal.Width   0.77971954
+  #
+  #' logit = glmnet(iris.x, iris.y, family="binomial", alpha=0, lambda=0.5)
+  #' coef(logit)
+  #
+  # 5 x 1 sparse Matrix of class "dgCMatrix"
+  # s0
+  # (Intercept)  -6.0824412
+  # Sepal.Length  0.2458260
+  # Sepal.Width   0.1642093
+  # Petal.Length  0.4759487
+  # Petal.Width   1.0383948
+  #
+  # nolint end
+
+  # Test multinomial logistic regression againt two classes
+  df <- suppressWarnings(createDataFrame(iris))
+  training <- df[df$Species %in% c("versicolor", "virginica"), ]
+  model <- spark.logit(training, Species ~ ., regParam = 0.5, family = "multinomial")
+  summary <- summary(model)
+  versicolorCoefsR <- c(3.94, -0.16, -0.02, -0.35, -0.78)
+  virginicaCoefsR <- c(-3.94, 0.16, -0.02, 0.35, 0.78)
+  versicolorCoefs <- unlist(summary$coefficients[, "versicolor"])
+  virginicaCoefs <- unlist(summary$coefficients[, "virginica"])
+  expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1))
+  expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1))
+
+  # Test binomial logistic regression againt two classes
+  model <- spark.logit(training, Species ~ ., regParam = 0.5)
+  summary <- summary(model)
+  coefsR <- c(-6.08, 0.25, 0.16, 0.48, 1.04)
+  coefs <- unlist(summary$coefficients[, "Estimate"])
+  expect_true(all(abs(coefsR - coefs) < 0.1))
+
+  # Test prediction with string label
+  prediction <- predict(model, training)
+  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "character")
+  expected <- c("versicolor", "versicolor", "virginica", "versicolor", "versicolor",
+                "versicolor", "versicolor", "versicolor", "versicolor", "versicolor")
+  expect_equal(as.list(take(select(prediction, "prediction"), 10))[[1]], expected)
+
+  # Test prediction with numeric label
+  label <- c(0.0, 0.0, 0.0, 1.0, 1.0)
+  feature <- c(1.1419053, 0.9194079, -0.9498666, -1.1069903, 0.2809776)
+  data <- as.data.frame(cbind(label, feature))
+  df <- createDataFrame(data)
+  model <- spark.logit(df, label ~ feature)
+  prediction <- collect(select(predict(model, df), "prediction"))
+  expect_equal(prediction$prediction, c("0.0", "0.0", "1.0", "1.0", "0.0"))
+})
+
+test_that("spark.mlp", {
+  df <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
+                source = "libsvm")
+  model <- spark.mlp(df, label ~ features, blockSize = 128, layers = c(4, 5, 4, 3),
+                     solver = "l-bfgs", maxIter = 100, tol = 0.5, stepSize = 1, seed = 1)
+
+  # Test summary method
+  summary <- summary(model)
+  expect_equal(summary$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),
+               tolerance = 1e-6)
+
+  # Test predict method
+  mlpTestDF <- df
+  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
+  expect_equal(head(mlpPredictions$prediction, 6), c("1.0", "0.0", "0.0", "0.0", "0.0", "0.0"))
+
+  # Test model save/load
+  modelPath <- tempfile(pattern = "spark-mlp", fileext = ".tmp")
+  write.ml(model, modelPath)
+  expect_error(write.ml(model, modelPath))
+  write.ml(model, modelPath, overwrite = TRUE)
+  model2 <- read.ml(modelPath)
+  summary2 <- summary(model2)
+
+  expect_equal(summary2$numOfInputs, 4)
+  expect_equal(summary2$numOfOutputs, 3)
+  expect_equal(summary2$layers, c(4, 5, 4, 3))
+  expect_equal(length(summary2$weights), 64)
+
+  unlink(modelPath)
+
+  # Test default parameter
+  model <- spark.mlp(df, label ~ features, layers = c(4, 5, 4, 3))
+  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
+  expect_equal(head(mlpPredictions$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, 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, label ~ features, layers = c(4, 5, 4, 3), maxIter = 10)
+  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
+  expect_equal(head(mlpPredictions$prediction, 10),
+               c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
+  # seed equals 10
+  model <- spark.mlp(df, label ~ features, layers = c(4, 5, 4, 3), maxIter = 10, seed = 10)
+  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
+  expect_equal(head(mlpPredictions$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, 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$prediction, 10),
+               c("1.0", "1.0", "1.0", "1.0", "2.0", "1.0", "2.0", "2.0", "1.0", "0.0"))
+
+  model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2, initialWeights =
+    c(0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 5.0, 5.0, 5.0, 5.0, 9.0, 9.0, 9.0, 9.0, 9.0))
+  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
+  expect_equal(head(mlpPredictions$prediction, 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, label ~ features, layers = c(4, 3), maxIter = 2)
+  mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction"))
+  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", {
+  # R code to reproduce the result.
+  # We do not support instance weights yet. So we ignore the frequencies.
+  #
+  #' library(e1071)
+  #' t <- as.data.frame(Titanic)
+  #' t1 <- t[t$Freq > 0, -5]
+  #' m <- naiveBayes(Survived ~ ., data = t1)
+  #' m
+  #' predict(m, t1)
+  #
+  # -- output of 'm'
+  #
+  # A-priori probabilities:
+  # Y
+  #        No       Yes
+  # 0.4166667 0.5833333
+  #
+  # Conditional probabilities:
+  #      Class
+  # Y           1st       2nd       3rd      Crew
+  #   No  0.2000000 0.2000000 0.4000000 0.2000000
+  #   Yes 0.2857143 0.2857143 0.2857143 0.1428571
+  #
+  #      Sex
+  # Y     Male Female
+  #   No   0.5    0.5
+  #   Yes  0.5    0.5
+  #
+  #      Age
+  # Y         Child     Adult
+  #   No  0.2000000 0.8000000
+  #   Yes 0.4285714 0.5714286
+  #
+  # -- output of 'predict(m, t1)'
+  #
+  # Yes Yes Yes Yes No  No  Yes Yes No  No  Yes Yes Yes Yes Yes Yes Yes Yes No  No  Yes Yes No  No
+  #
+
+  t <- as.data.frame(Titanic)
+  t1 <- t[t$Freq > 0, -5]
+  df <- suppressWarnings(createDataFrame(t1))
+  m <- spark.naiveBayes(df, Survived ~ ., smoothing = 0.0)
+  s <- summary(m)
+  expect_equal(as.double(s$apriori[1, "Yes"]), 0.5833333, tolerance = 1e-6)
+  expect_equal(sum(s$apriori), 1)
+  expect_equal(as.double(s$tables["Yes", "Age_Adult"]), 0.5714286, tolerance = 1e-6)
+  p <- collect(select(predict(m, df), "prediction"))
+  expect_equal(p$prediction, c("Yes", "Yes", "Yes", "Yes", "No", "No", "Yes", "Yes", "No", "No",
+                               "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", "No",
+                               "Yes", "Yes", "No", "No"))
+
+  # Test model save/load
+  modelPath <- tempfile(pattern = "spark-naiveBayes", fileext = ".tmp")
+  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)
+
+  unlink(modelPath)
+
+  # Test e1071::naiveBayes
+  if (requireNamespace("e1071", quietly = TRUE)) {
+    expect_error(m <- e1071::naiveBayes(Survived ~ ., data = t1), NA)
+    expect_equal(as.character(predict(m, t1[1, ])), "Yes")
+  }
+
+  # Test numeric response variable
+  t1$NumericSurvived <- ifelse(t1$Survived == "No", 0, 1)
+  t2 <- t1[-4]
+  df <- suppressWarnings(createDataFrame(t2))
+  m <- spark.naiveBayes(df, NumericSurvived ~ ., smoothing = 0.0)
+  s <- summary(m)
+  expect_equal(as.double(s$apriori[1, 1]), 0.5833333, tolerance = 1e-6)
+  expect_equal(sum(s$apriori), 1)
+  expect_equal(as.double(s$tables[1, "Age_Adult"]), 0.5714286, tolerance = 1e-6)
+})
+
+sparkR.session.stop()
diff --git a/R/pkg/inst/tests/testthat/test_mllib_clustering.R b/R/pkg/inst/tests/testthat/test_mllib_clustering.R
new file mode 100644
index 0000000000..1980fffd80
--- /dev/null
+++ b/R/pkg/inst/tests/testthat/test_mllib_clustering.R
@@ -0,0 +1,224 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+library(testthat)
+
+context("MLlib clustering algorithms")
+
+# Tests for MLlib clustering algorithms in SparkR
+sparkSession <- sparkR.session(enableHiveSupport = FALSE)
+
+absoluteSparkPath <- function(x) {
+  sparkHome <- sparkR.conf("spark.home")
+  file.path(sparkHome, x)
+}
+
+test_that("spark.gaussianMixture", {
+  # R code to reproduce the result.
+  # nolint start
+  #' library(mvtnorm)
+  #' set.seed(1)
+  #' a <- rmvnorm(7, c(0, 0))
+  #' b <- rmvnorm(8, c(10, 10))
+  #' data <- rbind(a, b)
+  #' model <- mvnormalmixEM(data, k = 2)
+  #' model$lambda
+  #
+  #  [1] 0.4666667 0.5333333
+  #
+  #' model$mu
+  #
+  #  [1] 0.11731091 -0.06192351
+  #  [1] 10.363673  9.897081
+  #
+  #' model$sigma
+  #
+  #  [[1]]
+  #             [,1]       [,2]
+  #  [1,] 0.62049934 0.06880802
+  #  [2,] 0.06880802 1.27431874
+  #
+  #  [[2]]
+  #            [,1]     [,2]
+  #  [1,] 0.2961543 0.160783
+  #  [2,] 0.1607830 1.008878
+  # nolint end
+  data <- list(list(-0.6264538, 0.1836433), list(-0.8356286, 1.5952808),
+               list(0.3295078, -0.8204684), list(0.4874291, 0.7383247),
+               list(0.5757814, -0.3053884), list(1.5117812, 0.3898432),
+               list(-0.6212406, -2.2146999), list(11.1249309, 9.9550664),
+               list(9.9838097, 10.9438362), list(10.8212212, 10.5939013),
+               list(10.9189774, 10.7821363), list(10.0745650, 8.0106483),
+               list(10.6198257, 9.9438713), list(9.8442045, 8.5292476),
+               list(9.5218499, 10.4179416))
+  df <- createDataFrame(data, c("x1", "x2"))
+  model <- spark.gaussianMixture(df, ~ x1 + x2, k = 2)
+  stats <- summary(model)
+  rLambda <- c(0.4666667, 0.5333333)
+  rMu <- c(0.11731091, -0.06192351, 10.363673, 9.897081)
+  rSigma <- c(0.62049934, 0.06880802, 0.06880802, 1.27431874,
+              0.2961543, 0.160783, 0.1607830, 1.008878)
+  expect_equal(stats$lambda, rLambda, tolerance = 1e-3)
+  expect_equal(unlist(stats$mu), rMu, tolerance = 1e-3)
+  expect_equal(unlist(stats$sigma), rSigma, tolerance = 1e-3)
+  p <- collect(select(predict(model, df), "prediction"))
+  expect_equal(p$prediction, c(0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1))
+
+  # Test model save/load
+  modelPath <- tempfile(pattern = "spark-gaussianMixture", 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$lambda, stats2$lambda)
+  expect_equal(unlist(stats$mu), unlist(stats2$mu))
+  expect_equal(unlist(stats$sigma), unlist(stats2$sigma))
+
+  unlink(modelPath)
+})
+
+test_that("spark.kmeans", {
+  newIris <- iris
+  newIris$Species <- NULL
+  training <- suppressWarnings(createDataFrame(newIris))
+
+  take(training, 1)
+
+  model <- spark.kmeans(data = training, ~ ., k = 2, maxIter = 10, initMode = "random")
+  sample <- take(select(predict(model, training), "prediction"), 1)
+  expect_equal(typeof(sample$prediction), "integer")
+  expect_equal(sample$prediction, 1)
+
+  # Test stats::kmeans is working
+  statsModel <- kmeans(x = newIris, centers = 2)
+  expect_equal(sort(unique(statsModel$cluster)), c(1, 2))
+
+  # Test fitted works on KMeans
+  fitted.model <- fitted(model)
+  expect_equal(sort(collect(distinct(select(fitted.model, "prediction")))$prediction), c(0, 1))
+
+  # Test summary works on KMeans
+  summary.model <- summary(model)
+  cluster <- summary.model$cluster
+  k <- summary.model$k
+  expect_equal(k, 2)
+  expect_equal(sort(collect(distinct(select(cluster, "prediction")))$prediction), c(0, 1))
+
+  # Test model save/load
+  modelPath <- tempfile(pattern = "spark-kmeans", fileext = ".tmp")
+  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)
+  expect_true(!summary.model$is.loaded)
+  expect_true(summary2$is.loaded)
+
+  unlink(modelPath)
+})
+
+test_that("spark.lda with libsvm", {
+  text <- read.df(absoluteSparkPath("data/mllib/sample_lda_libsvm_data.txt"), source = "libsvm")
+  model <- spark.lda(text, optimizer = "em")
+
+  stats <- summary(model, 10)
+  isDistributed <- stats$isDistributed
+  logLikelihood <- stats$logLikelihood
+  logPerplexity <- stats$logPerplexity
+  vocabSize <- stats$vocabSize
+  topics <- stats$topicTopTerms
+  weights <- stats$topicTopTermsWeights
+  vocabulary <- stats$vocabulary
+
+  expect_false(isDistributed)
+  expect_true(logLikelihood <= 0 & is.finite(logLikelihood))
+  expect_true(logPerplexity >= 0 & is.finite(logPerplexity))
+  expect_equal(vocabSize, 11)
+  expect_true(is.null(vocabulary))
+
+  # Test model save/load
+  modelPath <- tempfile(pattern = "spark-lda", fileext = ".tmp")
+  write.ml(model, modelPath)
+  expect_error(write.ml(model, modelPath))
+  write.ml(model, modelPath, overwrite = TRUE)
+  model2 <- read.ml(modelPath)
+  stats2 <- summary(model2)
+
+  expect_false(stats2$isDistributed)
+  expect_equal(logLikelihood, stats2$logLikelihood)
+  expect_equal(logPerplexity, stats2$logPerplexity)
+  expect_equal(vocabSize, stats2$vocabSize)
+  expect_equal(vocabulary, stats2$vocabulary)
+
+  unlink(modelPath)
+})
+
+test_that("spark.lda with text input", {
+  text <- read.text(absoluteSparkPath("data/mllib/sample_lda_data.txt"))
+  model <- spark.lda(text, optimizer = "online", features = "value")
+
+  stats <- summary(model)
+  isDistributed <- stats$isDistributed
+  logLikelihood <- stats$logLikelihood
+  logPerplexity <- stats$logPerplexity
+  vocabSize <- stats$vocabSize
+  topics <- stats$topicTopTerms
+  weights <- stats$topicTopTermsWeights
+  vocabulary <- stats$vocabulary
+
+  expect_false(isDistributed)
+  expect_true(logLikelihood <= 0 & is.finite(logLikelihood))
+  expect_true(logPerplexity >= 0 & is.finite(logPerplexity))
+  expect_equal(vocabSize, 10)
+  expect_true(setequal(stats$vocabulary, c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9")))
+
+  # Test model save/load
+  modelPath <- tempfile(pattern = "spark-lda-text", 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_false(stats2$isDistributed)
+  expect_equal(logLikelihood, stats2$logLikelihood)
+  expect_equal(logPerplexity, stats2$logPerplexity)
+  expect_equal(vocabSize, stats2$vocabSize)
+  expect_true(all.equal(vocabulary, stats2$vocabulary))
+
+  unlink(modelPath)
+})
+
+test_that("spark.posterior and spark.perplexity", {
+  text <- read.text(absoluteSparkPath("data/mllib/sample_lda_data.txt"))
+  model <- spark.lda(text, features = "value", k = 3)
+
+  # Assert perplexities are equal
+  stats <- summary(model)
+  logPerplexity <- spark.perplexity(model, text)
+  expect_equal(logPerplexity, stats$logPerplexity)
+
+  # Assert the sum of every topic distribution is equal to 1
+  posterior <- spark.posterior(model, text)
+  local.posterior <- collect(posterior)$topicDistribution
+  expect_equal(length(local.posterior), sum(unlist(local.posterior)))
+})
+
+sparkR.session.stop()
diff --git a/R/pkg/inst/tests/testthat/test_mllib_recommendation.R b/R/pkg/inst/tests/testthat/test_mllib_recommendation.R
new file mode 100644
index 0000000000..6b1040db93
--- /dev/null
+++ b/R/pkg/inst/tests/testthat/test_mllib_recommendation.R
@@ -0,0 +1,65 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+library(testthat)
+
+context("MLlib recommendation algorithms")
+
+# Tests for MLlib recommendation algorithms in SparkR
+sparkSession <- sparkR.session(enableHiveSupport = FALSE)
+
+test_that("spark.als", {
+  data <- list(list(0, 0, 4.0), list(0, 1, 2.0), list(1, 1, 3.0), list(1, 2, 4.0),
+               list(2, 1, 1.0), list(2, 2, 5.0))
+  df <- createDataFrame(data, c("user", "item", "score"))
+  model <- spark.als(df, ratingCol = "score", userCol = "user", itemCol = "item",
+                     rank = 10, maxIter = 5, seed = 0, regParam = 0.1)
+  stats <- summary(model)
+  expect_equal(stats$rank, 10)
+  test <- createDataFrame(list(list(0, 2), list(1, 0), list(2, 0)), c("user", "item"))
+  predictions <- collect(predict(model, test))
+
+  expect_equal(predictions$prediction, c(-0.1380762, 2.6258414, -1.5018409),
+  tolerance = 1e-4)
+
+  # Test model save/load
+  modelPath <- tempfile(pattern = "spark-als", fileext = ".tmp")
+  write.ml(model, modelPath)
+  expect_error(write.ml(model, modelPath))
+  write.ml(model, modelPath, overwrite = TRUE)
+  model2 <- read.ml(modelPath)
+  stats2 <- summary(model2)
+  expect_equal(stats2$rating, "score")
+  userFactors <- collect(stats$userFactors)
+  itemFactors <- collect(stats$itemFactors)
+  userFactors2 <- collect(stats2$userFactors)
+  itemFactors2 <- collect(stats2$itemFactors)
+
+  orderUser <- order(userFactors$id)
+  orderUser2 <- order(userFactors2$id)
+  expect_equal(userFactors$id[orderUser], userFactors2$id[orderUser2])
+  expect_equal(userFactors$features[orderUser], userFactors2$features[orderUser2])
+
+  orderItem <- order(itemFactors$id)
+  orderItem2 <- order(itemFactors2$id)
+  expect_equal(itemFactors$id[orderItem], itemFactors2$id[orderItem2])
+  expect_equal(itemFactors$features[orderItem], itemFactors2$features[orderItem2])
+
+  unlink(modelPath)
+})
+
+sparkR.session.stop()
diff --git a/R/pkg/inst/tests/testthat/test_mllib_regression.R b/R/pkg/inst/tests/testthat/test_mllib_regression.R
new file mode 100644
index 0000000000..e20dafa414
--- /dev/null
+++ b/R/pkg/inst/tests/testthat/test_mllib_regression.R
@@ -0,0 +1,417 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+library(testthat)
+
+context("MLlib regression algorithms, except for tree-based algorithms")
+
+# Tests for MLlib regression algorithms in SparkR
+sparkSession <- sparkR.session(enableHiveSupport = FALSE)
+
+test_that("formula of spark.glm", {
+  training <- suppressWarnings(createDataFrame(iris))
+  # directly calling the spark API
+  # dot minus and intercept vs native glm
+  model <- spark.glm(training, Sepal_Width ~ . - Species + 0)
+  vals <- collect(select(predict(model, training), "prediction"))
+  rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
+  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
+
+  # feature interaction vs native glm
+  model <- spark.glm(training, Sepal_Width ~ Species:Sepal_Length)
+  vals <- collect(select(predict(model, training), "prediction"))
+  rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris)
+  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
+
+  # glm should work with long formula
+  training <- suppressWarnings(createDataFrame(iris))
+  training$LongLongLongLongLongName <- training$Sepal_Width
+  training$VeryLongLongLongLonLongName <- training$Sepal_Length
+  training$AnotherLongLongLongLongName <- training$Species
+  model <- spark.glm(training, LongLongLongLongLongName ~ VeryLongLongLongLonLongName +
+    AnotherLongLongLongLongName)
+  vals <- collect(select(predict(model, training), "prediction"))
+  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
+  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
+})
+
+test_that("spark.glm and predict", {
+  training <- suppressWarnings(createDataFrame(iris))
+  # gaussian family
+  model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species)
+  prediction <- predict(model, training)
+  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
+  vals <- collect(select(prediction, "prediction"))
+  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
+  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
+
+  # poisson family
+  model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species,
+  family = poisson(link = identity))
+  prediction <- predict(model, training)
+  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
+  vals <- collect(select(prediction, "prediction"))
+  rVals <- suppressWarnings(predict(glm(Sepal.Width ~ Sepal.Length + Species,
+  data = iris, family = poisson(link = identity)), iris))
+  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
+
+  # Test stats::predict is working
+  x <- rnorm(15)
+  y <- x + rnorm(15)
+  expect_equal(length(predict(lm(y ~ x))), 15)
+})
+
+test_that("spark.glm summary", {
+  # gaussian family
+  training <- suppressWarnings(createDataFrame(iris))
+  stats <- summary(spark.glm(training, Sepal_Width ~ Sepal_Length + Species))
+
+  rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris))
+
+  coefs <- unlist(stats$coefficients)
+  rCoefs <- unlist(rStats$coefficients)
+  expect_true(all(abs(rCoefs - coefs) < 1e-4))
+  expect_true(all(
+    rownames(stats$coefficients) ==
+    c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
+  expect_equal(stats$dispersion, rStats$dispersion)
+  expect_equal(stats$null.deviance, rStats$null.deviance)
+  expect_equal(stats$deviance, rStats$deviance)
+  expect_equal(stats$df.null, rStats$df.null)
+  expect_equal(stats$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"), ]
+  stats <- summary(spark.glm(training, Species ~ Sepal_Length + Sepal_Width,
+    family = binomial(link = "logit")))
+
+  rTraining <- iris[iris$Species %in% c("versicolor", "virginica"), ]
+  rStats <- summary(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
+  family = binomial(link = "logit")))
+
+  coefs <- unlist(stats$coefficients)
+  rCoefs <- unlist(rStats$coefficients)
+  expect_true(all(abs(rCoefs - coefs) < 1e-4))
+  expect_true(all(
+    rownames(stats$coefficients) ==
+    c("(Intercept)", "Sepal_Length", "Sepal_Width")))
+  expect_equal(stats$dispersion, rStats$dispersion)
+  expect_equal(stats$null.deviance, rStats$null.deviance)
+  expect_equal(stats$deviance, rStats$deviance)
+  expect_equal(stats$df.null, rStats$df.null)
+  expect_equal(stats$df.residual, rStats$df.residual)
+  expect_equal(stats$aic, rStats$aic)
+
+  # Test spark.glm works with weighted dataset
+  a1 <- c(0, 1, 2, 3)
+  a2 <- c(5, 2, 1, 3)
+  w <- c(1, 2, 3, 4)
+  b <- c(1, 0, 1, 0)
+  data <- as.data.frame(cbind(a1, a2, w, b))
+  df <- createDataFrame(data)
+
+  stats <- summary(spark.glm(df, b ~ a1 + a2, family = "binomial", weightCol = "w"))
+  rStats <- summary(glm(b ~ a1 + a2, family = "binomial", data = data, weights = w))
+
+  coefs <- unlist(stats$coefficients)
+  rCoefs <- unlist(rStats$coefficients)
+  expect_true(all(abs(rCoefs - coefs) < 1e-3))
+  expect_true(all(rownames(stats$coefficients) == c("(Intercept)", "a1", "a2")))
+  expect_equal(stats$dispersion, rStats$dispersion)
+  expect_equal(stats$null.deviance, rStats$null.deviance)
+  expect_equal(stats$deviance, rStats$deviance)
+  expect_equal(stats$df.null, rStats$df.null)
+  expect_equal(stats$df.residual, rStats$df.residual)
+  expect_equal(stats$aic, rStats$aic)
+
+  # Test summary works on base GLM models
+  baseModel <- stats::glm(Sepal.Width ~ Sepal.Length + Species, data = iris)
+  baseSummary <- summary(baseModel)
+  expect_true(abs(baseSummary$deviance - 12.19313) < 1e-4)
+
+  # Test spark.glm works with regularization parameter
+  data <- as.data.frame(cbind(a1, a2, b))
+  df <- suppressWarnings(createDataFrame(data))
+  regStats <- summary(spark.glm(df, b ~ a1 + a2, regParam = 1.0))
+  expect_equal(regStats$aic, 13.32836, tolerance = 1e-4) # 13.32836 is from summary() result
+
+  # Test spark.glm works on collinear data
+  A <- matrix(c(1, 2, 3, 4, 2, 4, 6, 8), 4, 2)
+  b <- c(1, 2, 3, 4)
+  data <- as.data.frame(cbind(A, b))
+  df <- createDataFrame(data)
+  stats <- summary(spark.glm(df, b ~ . - 1))
+  coefs <- unlist(stats$coefficients)
+  expect_true(all(abs(c(0.5, 0.25) - coefs) < 1e-4))
+})
+
+test_that("spark.glm save/load", {
+  training <- suppressWarnings(createDataFrame(iris))
+  m <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species)
+  s <- summary(m)
+
+  modelPath <- tempfile(pattern = "spark-glm", fileext = ".tmp")
+  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)
+  expect_equal(rownames(s$coefficients), rownames(s2$coefficients))
+  expect_equal(s$dispersion, s2$dispersion)
+  expect_equal(s$null.deviance, s2$null.deviance)
+  expect_equal(s$deviance, s2$deviance)
+  expect_equal(s$df.null, s2$df.null)
+  expect_equal(s$df.residual, s2$df.residual)
+  expect_equal(s$aic, s2$aic)
+  expect_equal(s$iter, s2$iter)
+  expect_true(!s$is.loaded)
+  expect_true(s2$is.loaded)
+
+  unlink(modelPath)
+})
+
+test_that("formula of glm", {
+  training <- suppressWarnings(createDataFrame(iris))
+  # dot minus and intercept vs native glm
+  model <- glm(Sepal_Width ~ . - Species + 0, data = training)
+  vals <- collect(select(predict(model, training), "prediction"))
+  rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
+  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
+
+  # feature interaction vs native glm
+  model <- glm(Sepal_Width ~ Species:Sepal_Length, data = training)
+  vals <- collect(select(predict(model, training), "prediction"))
+  rVals <- predict(glm(Sepal.Width ~ Species:Sepal.Length, data = iris), iris)
+  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
+
+  # glm should work with long formula
+  training <- suppressWarnings(createDataFrame(iris))
+  training$LongLongLongLongLongName <- training$Sepal_Width
+  training$VeryLongLongLongLonLongName <- training$Sepal_Length
+  training$AnotherLongLongLongLongName <- training$Species
+  model <- glm(LongLongLongLongLongName ~ VeryLongLongLongLonLongName + AnotherLongLongLongLongName,
+               data = training)
+  vals <- collect(select(predict(model, training), "prediction"))
+  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
+  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
+})
+
+test_that("glm and predict", {
+  training <- suppressWarnings(createDataFrame(iris))
+  # gaussian family
+  model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
+  prediction <- predict(model, training)
+  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
+  vals <- collect(select(prediction, "prediction"))
+  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
+  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
+
+  # poisson family
+  model <- glm(Sepal_Width ~ Sepal_Length + Species, data = training,
+               family = poisson(link = identity))
+  prediction <- predict(model, training)
+  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
+  vals <- collect(select(prediction, "prediction"))
+  rVals <- suppressWarnings(predict(glm(Sepal.Width ~ Sepal.Length + Species,
+           data = iris, family = poisson(link = identity)), iris))
+  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
+
+  # Test stats::predict is working
+  x <- rnorm(15)
+  y <- x + rnorm(15)
+  expect_equal(length(predict(lm(y ~ x))), 15)
+})
+
+test_that("glm summary", {
+  # gaussian family
+  training <- suppressWarnings(createDataFrame(iris))
+  stats <- summary(glm(Sepal_Width ~ Sepal_Length + Species, data = training))
+
+  rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris))
+
+  coefs <- unlist(stats$coefficients)
+  rCoefs <- unlist(rStats$coefficients)
+  expect_true(all(abs(rCoefs - coefs) < 1e-4))
+  expect_true(all(
+    rownames(stats$coefficients) ==
+    c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
+  expect_equal(stats$dispersion, rStats$dispersion)
+  expect_equal(stats$null.deviance, rStats$null.deviance)
+  expect_equal(stats$deviance, rStats$deviance)
+  expect_equal(stats$df.null, rStats$df.null)
+  expect_equal(stats$df.residual, rStats$df.residual)
+  expect_equal(stats$aic, rStats$aic)
+
+  # binomial family
+  df <- suppressWarnings(createDataFrame(iris))
+  training <- df[df$Species %in% c("versicolor", "virginica"), ]
+  stats <- summary(glm(Species ~ Sepal_Length + Sepal_Width, data = training,
+    family = binomial(link = "logit")))
+
+  rTraining <- iris[iris$Species %in% c("versicolor", "virginica"), ]
+  rStats <- summary(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
+    family = binomial(link = "logit")))
+
+  coefs <- unlist(stats$coefficients)
+  rCoefs <- unlist(rStats$coefficients)
+  expect_true(all(abs(rCoefs - coefs) < 1e-4))
+  expect_true(all(
+    rownames(stats$coefficients) ==
+    c("(Intercept)", "Sepal_Length", "Sepal_Width")))
+  expect_equal(stats$dispersion, rStats$dispersion)
+  expect_equal(stats$null.deviance, rStats$null.deviance)
+  expect_equal(stats$deviance, rStats$deviance)
+  expect_equal(stats$df.null, rStats$df.null)
+  expect_equal(stats$df.residual, rStats$df.residual)
+  expect_equal(stats$aic, rStats$aic)
+
+  # Test summary works on base GLM models
+  baseModel <- stats::glm(Sepal.Width ~ Sepal.Length + Species, data = iris)
+  baseSummary <- summary(baseModel)
+  expect_true(abs(baseSummary$deviance - 12.19313) < 1e-4)
+})
+
+test_that("glm save/load", {
+  training <- suppressWarnings(createDataFrame(iris))
+  m <- glm(Sepal_Width ~ Sepal_Length + Species, data = training)
+  s <- summary(m)
+
+  modelPath <- tempfile(pattern = "glm", fileext = ".tmp")
+  write.ml(m, modelPath)
+  expect_error(write.ml(m, modelPath))
+  write.ml(m, modelPath, overwrite = TRUE)
+  m2 <- read.ml(modelPath)
+  s2 <- summary(m2)
+
+  expect_equal(s$coefficients, s2$coefficients)
+  expect_equal(rownames(s$coefficients), rownames(s2$coefficients))
+  expect_equal(s$dispersion, s2$dispersion)
+  expect_equal(s$null.deviance, s2$null.deviance)
+  expect_equal(s$deviance, s2$deviance)
+  expect_equal(s$df.null, s2$df.null)
+  expect_equal(s$df.residual, s2$df.residual)
+  expect_equal(s$aic, s2$aic)
+  expect_equal(s$iter, s2$iter)
+  expect_true(!s$is.loaded)
+  expect_true(s2$is.loaded)
+
+  unlink(modelPath)
+})
+
+test_that("spark.isoreg", {
+  label <- c(7.0, 5.0, 3.0, 5.0, 1.0)
+  feature <- c(0.0, 1.0, 2.0, 3.0, 4.0)
+  weight <- c(1.0, 1.0, 1.0, 1.0, 1.0)
+  data <- as.data.frame(cbind(label, feature, weight))
+  df <- createDataFrame(data)
+
+  model <- spark.isoreg(df, label ~ feature, isotonic = FALSE,
+                        weightCol = "weight")
+  # only allow one variable on the right hand side of the formula
+  expect_error(model2 <- spark.isoreg(df, ~., isotonic = FALSE))
+  result <- summary(model)
+  expect_equal(result$predictions, list(7, 5, 4, 4, 1))
+
+  # Test model prediction
+  predict_data <- list(list(-2.0), list(-1.0), list(0.5),
+                       list(0.75), list(1.0), list(2.0), list(9.0))
+  predict_df <- createDataFrame(predict_data, c("feature"))
+  predict_result <- collect(select(predict(model, predict_df), "prediction"))
+  expect_equal(predict_result$prediction, c(7.0, 7.0, 6.0, 5.5, 5.0, 4.0, 1.0))
+
+  # Test model save/load
+  modelPath <- tempfile(pattern = "spark-isoreg", fileext = ".tmp")
+  write.ml(model, modelPath)
+  expect_error(write.ml(model, modelPath))
+  write.ml(model, modelPath, overwrite = TRUE)
+  model2 <- read.ml(modelPath)
+  expect_equal(result, summary(model2))
+
+  unlink(modelPath)
+})
+
+test_that("spark.survreg", {
+  # R code to reproduce the result.
+  #
+  #' rData <- list(time = c(4, 3, 1, 1, 2, 2, 3), status = c(1, 1, 1, 0, 1, 1, 0),
+  #'               x = c(0, 2, 1, 1, 1, 0, 0), sex = c(0, 0, 0, 0, 1, 1, 1))
+  #' library(survival)
+  #' model <- survreg(Surv(time, status) ~ x + sex, rData)
+  #' summary(model)
+  #' predict(model, data)
+  #
+  # -- output of 'summary(model)'
+  #
+  #              Value Std. Error     z        p
+  # (Intercept)  1.315      0.270  4.88 1.07e-06
+  # x           -0.190      0.173 -1.10 2.72e-01
+  # sex         -0.253      0.329 -0.77 4.42e-01
+  # Log(scale)  -1.160      0.396 -2.93 3.41e-03
+  #
+  # -- output of 'predict(model, data)'
+  #
+  #        1        2        3        4        5        6        7
+  # 3.724591 2.545368 3.079035 3.079035 2.390146 2.891269 2.891269
+  #
+  data <- list(list(4, 1, 0, 0), list(3, 1, 2, 0), list(1, 1, 1, 0),
+          list(1, 0, 1, 0), list(2, 1, 1, 1), list(2, 1, 0, 1), list(3, 0, 0, 1))
+  df <- createDataFrame(data, c("time", "status", "x", "sex"))
+  model <- spark.survreg(df, Surv(time, status) ~ x + sex)
+  stats <- summary(model)
+  coefs <- as.vector(stats$coefficients[, 1])
+  rCoefs <- c(1.3149571, -0.1903409, -0.2532618, -1.1599800)
+  expect_equal(coefs, rCoefs, tolerance = 1e-4)
+  expect_true(all(
+    rownames(stats$coefficients) ==
+    c("(Intercept)", "x", "sex", "Log(scale)")))
+  p <- collect(select(predict(model, df), "prediction"))
+  expect_equal(p$prediction, c(3.724591, 2.545368, 3.079035, 3.079035,
+               2.390146, 2.891269, 2.891269), tolerance = 1e-4)
+
+  # Test model save/load
+  modelPath <- tempfile(pattern = "spark-survreg", 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)
+  coefs2 <- as.vector(stats2$coefficients[, 1])
+  expect_equal(coefs, coefs2)
+  expect_equal(rownames(stats$coefficients), rownames(stats2$coefficients))
+
+  unlink(modelPath)
+
+  # Test survival::survreg
+  if (requireNamespace("survival", quietly = TRUE)) {
+    rData <- list(time = c(4, 3, 1, 1, 2, 2, 3), status = c(1, 1, 1, 0, 1, 1, 0),
+                 x = c(0, 2, 1, 1, 1, 0, 0), sex = c(0, 0, 0, 0, 1, 1, 1))
+    expect_error(
+      model <- survival::survreg(formula = survival::Surv(time, status) ~ x + sex, data = rData),
+      NA)
+    expect_equal(predict(model, rData)[[1]], 3.724591, tolerance = 1e-4)
+  }
+})
+
+sparkR.session.stop()
diff --git a/R/pkg/inst/tests/testthat/test_mllib_stat.R b/R/pkg/inst/tests/testthat/test_mllib_stat.R
new file mode 100644
index 0000000000..beb148e770
--- /dev/null
+++ b/R/pkg/inst/tests/testthat/test_mllib_stat.R
@@ -0,0 +1,53 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+library(testthat)
+
+context("MLlib statistics algorithms")
+
+# Tests for MLlib statistics algorithms in SparkR
+sparkSession <- sparkR.session(enableHiveSupport = FALSE)
+
+test_that("spark.kstest", {
+  data <- data.frame(test = c(0.1, 0.15, 0.2, 0.3, 0.25, -1, -0.5))
+  df <- createDataFrame(data)
+  testResult <- spark.kstest(df, "test", "norm")
+  stats <- summary(testResult)
+
+  rStats <- ks.test(data$test, "pnorm", alternative = "two.sided")
+
+  expect_equal(stats$p.value, rStats$p.value, tolerance = 1e-4)
+  expect_equal(stats$statistic, unname(rStats$statistic), tolerance = 1e-4)
+  expect_match(capture.output(stats)[1], "Kolmogorov-Smirnov test summary:")
+
+  testResult <- spark.kstest(df, "test", "norm", -0.5)
+  stats <- summary(testResult)
+
+  rStats <- ks.test(data$test, "pnorm", -0.5, 1, alternative = "two.sided")
+
+  expect_equal(stats$p.value, rStats$p.value, tolerance = 1e-4)
+  expect_equal(stats$statistic, unname(rStats$statistic), tolerance = 1e-4)
+  expect_match(capture.output(stats)[1], "Kolmogorov-Smirnov test summary:")
+
+  # Test print.summary.KSTest
+  printStats <- capture.output(print.summary.KSTest(stats))
+  expect_match(printStats[1], "Kolmogorov-Smirnov test summary:")
+  expect_match(printStats[5],
+               "Low presumption against null hypothesis: Sample follows theoretical distribution. ")
+})
+
+sparkR.session.stop()
diff --git a/R/pkg/inst/tests/testthat/test_mllib_tree.R b/R/pkg/inst/tests/testthat/test_mllib_tree.R
new file mode 100644
index 0000000000..5d13539be8
--- /dev/null
+++ b/R/pkg/inst/tests/testthat/test_mllib_tree.R
@@ -0,0 +1,203 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+library(testthat)
+
+context("MLlib tree-based algorithms")
+
+# Tests for MLlib tree-based algorithms in SparkR
+sparkSession <- sparkR.session(enableHiveSupport = FALSE)
+
+absoluteSparkPath <- function(x) {
+  sparkHome <- sparkR.conf("spark.home")
+  file.path(sparkHome, x)
+}
+
+test_that("spark.gbt", {
+  # regression
+  data <- suppressWarnings(createDataFrame(longley))
+  model <- spark.gbt(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16, seed = 123)
+  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$numTrees, 20)
+  expect_equal(stats$formula, "Employed ~ .")
+  expect_equal(stats$numFeatures, 6)
+  expect_equal(length(stats$treeWeights), 20)
+
+  modelPath <- tempfile(pattern = "spark-gbtRegression", 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)
+
+  # classification
+  # label must be binary - GBTClassifier currently only supports binary classification.
+  iris2 <- iris[iris$Species != "virginica", ]
+  data <- suppressWarnings(createDataFrame(iris2))
+  model <- spark.gbt(data, Species ~ Petal_Length + Petal_Width, "classification")
+  stats <- summary(model)
+  expect_equal(stats$numFeatures, 2)
+  expect_equal(stats$numTrees, 20)
+  expect_error(capture.output(stats), NA)
+  expect_true(length(capture.output(stats)) > 6)
+  predictions <- collect(predict(model, data))$prediction
+  # test string prediction values
+  expect_equal(length(grep("setosa", predictions)), 50)
+  expect_equal(length(grep("versicolor", predictions)), 50)
+
+  modelPath <- tempfile(pattern = "spark-gbtClassification", 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)
+
+  iris2$NumericSpecies <- ifelse(iris2$Species == "setosa", 0, 1)
+  df <- suppressWarnings(createDataFrame(iris2))
+  m <- spark.gbt(df, NumericSpecies ~ ., type = "classification")
+  s <- summary(m)
+  # test numeric prediction values
+  expect_equal(iris2$NumericSpecies, as.double(collect(predict(m, df))$prediction))
+  expect_equal(s$numFeatures, 5)
+  expect_equal(s$numTrees, 20)
+
+  # spark.gbt classification can work on libsvm data
+  data <- read.df(absoluteSparkPath("data/mllib/sample_binary_classification_data.txt"),
+                source = "libsvm")
+  model <- spark.gbt(data, label ~ features, "classification")
+  expect_equal(summary(model)$numFeatures, 692)
+})
+
+test_that("spark.randomForest", {
+  # regression
+  data <- suppressWarnings(createDataFrame(longley))
+  model <- spark.randomForest(data, Employed ~ ., "regression", maxDepth = 5, maxBins = 16,
+                              numTrees = 1)
+
+  predictions <- collect(predict(model, data))
+  expect_equal(predictions$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(60.32820, 61.22315, 60.69025, 62.11070,
+                                         63.53160, 64.05470, 65.12710, 64.30450,
+                                         66.70910, 67.86125, 68.08700, 67.21865,
+                                         68.89275, 69.53180, 69.39640, 69.68250),
+
+               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$treeWeights)
+
+  unlink(modelPath)
+
+  # classification
+  data <- suppressWarnings(createDataFrame(iris))
+  model <- spark.randomForest(data, Species ~ Petal_Length + Petal_Width, "classification",
+                              maxDepth = 5, maxBins = 16)
+
+  stats <- summary(model)
+  expect_equal(stats$numFeatures, 2)
+  expect_equal(stats$numTrees, 20)
+  expect_error(capture.output(stats), NA)
+  expect_true(length(capture.output(stats)) > 6)
+  # Test string prediction values
+  predictions <- collect(predict(model, data))$prediction
+  expect_equal(length(grep("setosa", predictions)), 50)
+  expect_equal(length(grep("versicolor", predictions)), 50)
+
+  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)
+
+  # Test numeric response variable
+  labelToIndex <- function(species) {
+    switch(as.character(species),
+      setosa = 0.0,
+      versicolor = 1.0,
+      virginica = 2.0
+    )
+  }
+  iris$NumericSpecies <- lapply(iris$Species, labelToIndex)
+  data <- suppressWarnings(createDataFrame(iris[-5]))
+  model <- spark.randomForest(data, NumericSpecies ~ Petal_Length + Petal_Width, "classification",
+                              maxDepth = 5, maxBins = 16)
+  stats <- summary(model)
+  expect_equal(stats$numFeatures, 2)
+  expect_equal(stats$numTrees, 20)
+  # Test numeric prediction values
+  predictions <- collect(predict(model, data))$prediction
+  expect_equal(length(grep("1.0", predictions)), 50)
+  expect_equal(length(grep("2.0", predictions)), 50)
+
+  # spark.randomForest classification can work on libsvm data
+  data <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
+                source = "libsvm")
+  model <- spark.randomForest(data, label ~ features, "classification")
+  expect_equal(summary(model)$numFeatures, 4)
+})
+
+sparkR.session.stop()
author	Yanbo Liang <ybliang8@gmail.com>	2017-01-08 01:10:36 -0800
committer	Yanbo Liang <ybliang8@gmail.com>	2017-01-08 01:10:36 -0800
commit	6b6b555a1e667a9f03dfe4a21e56c513a353a58d (patch)
tree	f58f251a2f0789f3207c75f4792cbd915df07558 /R
parent	923e594844a7ad406195b91877f0fb374d5a454b (diff)
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