[GitHub] [spark] huaxingao commented on a change in pull request #27593: [SPARK-30818][SPARKR][ML] Add SparkR LinearRegression wrapper

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huaxingao commented on a change in pull request #27593: [SPARK-30818][SPARKR][ML] Add SparkR LinearRegression wrapper
URL: https://github.com/apache/spark/pull/27593#discussion_r380403796
 
 

 ##########
 File path: R/pkg/R/mllib_regression.R
 ##########
 @@ -540,3 +546,147 @@ setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "c
           function(object, path, overwrite = FALSE) {
             write_internal(object, path, overwrite)
           })
+
+#' Linear Regression Model
+#'
+#' \code{spark.lm} fits a linear regression model against a SparkDataFrame.
+#' Users can call \code{predict} to make
+#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
+#'
+#' @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 maxIter maximum iteration number.
+#' @param regParam the regularization parameter.
+#' @param elasticNetParam the ElasticNet mixing parameter, in range [0, 1].
+#'        For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
+#' @param tol convergence tolerance of iterations.
+#' @param standardization whether to standardize the training features before fitting the model.
+#' @param weightCol weight column name.
+#' @param aggregationDepth suggested depth for treeAggregate (>= 2).
+#' @param loss the loss function to be optimized. Supported options: "squaredError" and "huber"
+#' @param epsilon the shape parameter to control the amount of robustnes
+#' @param solver The solver algorithm for optimization.
+#'        Supported options: "l-bfgs", "normal" and "auto".
+#' @param stringIndexerOrderType how to order categories of a string feature column. This is used to
+#'                               decide the base level of a string feature as the last category
+#'                               after ordering is dropped when encoding strings. Supported options
+#'                               are "frequencyDesc", "frequencyAsc", "alphabetDesc", and
+#'                               "alphabetAsc". The default value is "frequencyDesc". When the
+#'                               ordering is set to "alphabetDesc", this drops the same category
+#'                               as R when encoding strings.
+#' @param ... additional arguments passed to the method.
+#' @return \code{spark.lm} returns a fitted Linear Regression Model.
+#'
+#' @rdname spark.lm
+#' @aliases spark.lm,SparkDataFrame,formula-method
+#' @name spark.lm
+#' @seealso \link{read.ml}
+#' @examples
+#' \dontrun{
+#' df <- read.df("data/mllib/sample_linear_regression_data.txt", source = "libsvm")
+#'
+#' # fit Linear Regression Model
+#' model <- spark.lm(
+#'            df, label ~ features,
+#'            regParam = 0.01, maxIter = 10, fitLinear = TRUE
+#'          )
+#'
+#' # 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.lm since 3.1.0
+setMethod("spark.lm", signature(data = "SparkDataFrame", formula = "formula"),
+          function(data, formula,
+                   maxIter = 100L, regParam = 0.0, elasticNetParam = 0.0,
+                   tol = 1e-6, standardization = TRUE,
+                   solver = c("auto", "l-bfgs", "normal"),
+                   weightCol = NULL, aggregationDepth = 2L,
+                   loss = c("squaredError", "huber"), epsilon = 1.35,
+                   stringIndexerOrderType = c("frequencyDesc", "frequencyAsc",
+                                              "alphabetDesc", "alphabetAsc")) {
+
+
 
 Review comment:
   nit: delete extra blank line? There are several other places that have two blank lines as well. 

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