huaxingao commented on a change in pull request #27571: 
[SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r379882358
 
 

 ##########
 File path: R/pkg/R/mllib_regression.R
 ##########
 @@ -540,3 +546,150 @@ setMethod("write.ml", signature(object = 
"AFTSurvivalRegressionModel", path = "c
           function(object, path, overwrite = FALSE) {
             write_internal(object, path, overwrite)
           })
+
+
+#' Factorization Machines Regression Model Model
+#'
+#' \code{spark.fmRegressor} fits a factorization 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 factorSize dimensionality of the factors.
+#' @param fitLinear whether to fit linear term.  # TODO Can we express this 
with formula?
+#' @param regParam the regularization parameter.
+#' @param miniBatchFraction the mini-batch fraction parameter.
+#' @param initStd the standard deviation of initial coefficients.
+#' @param maxIter maximum iteration number.
+#' @param stepSize stepSize parameter.
+#' @param tol convergence tolerance of iterations.
+#' @param solver solver parameter, supported options: "gd" (minibatch gradient 
descent) or "adamW".
+#' @param seed seed parameter for weights initialization.
+#' @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.fmRegressor} returns a fitted Factorization Machines 
Regression Model.
+#'
+#' @rdname spark.fmRegressor
+#' @aliases spark.fmRegressor,SparkDataFrame,formula-method
+#' @name spark.fmRegressor
+#' @seealso \link{read.ml}
+#' @examples
+#' \dontrun{
+#' df <- read.df("data/mllib/sample_linear_regression_data.txt", source = 
"libsvm")
+#'
+#' # fit Factorization Machines Regression Model
+#' model <- spark.fmRegressor(
+#'            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.fmRegressor since 3.1.0
+setMethod("spark.fmRegressor", signature(data = "SparkDataFrame", formula = 
"formula"),
+          function(data, formula, factorSize = 8, fitLinear = TRUE, regParam = 
0.0,
+                   miniBatchFraction = 1.0, initStd = 0.01, maxIter = 100, 
stepSize=1.0,
+                   tol = 1e-6, solver = c("adamW", "gd"), seed = NULL,
+                   stringIndexerOrderType = c("frequencyDesc", "frequencyAsc",
+                                              "alphabetDesc", "alphabetAsc")) {
+
+            formula <- paste(deparse(formula), collapse = "")
+
+            if (!is.null(seed)) {
+              seed <- as.character(as.integer(seed))
+            }
+
+            solver <- match.arg(solver)
+            stringIndexerOrderType <- match.arg(stringIndexerOrderType)
+
+            jobj <- callJStatic("org.apache.spark.ml.r.FMRegressorWrapper",
+                                "fit",
+                                data@sdf,
+                                formula,
+                                as.integer(factorSize),
+                                as.logical(fitLinear),
+                                as.numeric(regParam),
+                                as.numeric(miniBatchFraction),
+                                as.numeric(initStd),
+                                as.integer(maxIter),
+                                as.numeric(stepSize),
+                                as.numeric(tol),
+                                solver,
+                                seed,
+                                stringIndexerOrderType)
+            new("FMRegressionModel", jobj = jobj)
+          })
+
+
+#  Returns the summary of a FM Regression model produced by 
\code{spark.fmRegressor}
+
+#' @param object a FM Regression Model model fitted by 
\code{spark.fmRegressor}.
+#' @return \code{summary} returns summary information of the fitted model, 
which is a list.
+#'
+#' @rdname spark.fmRegressor
+#' @note summary(FMRegressionModel) since 3.1.0
+setMethod("summary", signature(object = "FMRegressionModel"),
+          function(object) {
+            jobj <- object@jobj
+            features <- callJMethod(jobj, "rFeatures")
+            coefficients <- callJMethod(jobj, "rCoefficients")
+            coefficients <- as.matrix(unlist(coefficients))
+            colnames(coefficients) <- c("Estimate")
+            rownames(coefficients) <- unlist(features)
+            numFeatures <- callJMethod(jobj, "numFeatures")
+            raw_factors <- unlist(callJMethod(jobj, "rFactors"))
+            factor_size <- callJMethod(jobj, "factorSize")
+
+            list(
+              coefficients = coefficients,
+              factors = matrix(raw_factors, ncol = factor_size),
+              numFeatures = numFeatures,
+              factorSize = factor_size
+            )
+          })
+
+
 
 Review comment:
   diddo

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