Github user junyangq commented on a diff in the pull request:
https://github.com/apache/spark/pull/14384#discussion_r74116056
--- Diff: R/pkg/R/mllib.R ---
@@ -632,3 +642,147 @@ setMethod("predict", signature(object =
"AFTSurvivalRegressionModel"),
function(object, newData) {
return(dataFrame(callJMethod(object@jobj, "transform",
newData@sdf)))
})
+
+
+#' 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}.
+#' Additional arguments can be passed to the methods.
+#' \describe{
+#' \item{nonnegative}{logical value indicating whether to apply
nonnegativity constraints.
+#' Default: FALSE}
+#' \item{implicitPrefs}{logical value indicating whether to use
implicit preference.
+#' Default: FALSE}
+#' \item{alpha}{alpha parameter in the implicit preference formulation
(>= 0). Default: 1.0}
+#' \item{seed}{integer seed for random number generation. Default: 0}
+#' \item{numUserBlocks}{number of user blocks used to parallelize
computation (> 0).
+#' Default: 10}
+#' \item{numItemBlocks}{number of item blocks used to parallelize
computation (> 0).
+#' Default: 10}
+#' \item{checkpointInterval}{number of checkpoint intervals (>= 1) or
disable checkpoint (-1).
+#' Default: 10}
+#' }
+#'
+#' @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 reg regularization parameter (>= 0)
+#' @param maxIter maximum number of iterations (>= 0)
+
+#' @return \code{spark.als} returns a fitted ALS model
+#' @rdname spark.als
+#' @aliases spark.als,SparkDataFrame
+#' @name spark.als
+#' @export
+#' @examples
+#' \dontrun{
+#' df <- createDataFrame(ratings)
+#' 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,
+#' reg = 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, reg = 1.0, maxIter = 10, ...) {
+
+ `%||%` <- function(a, b) if (!is.null(a)) a else b
+
+ args <- list(...)
+ numUserBlocks <- args$numUserBlocks %||% 10
+ numItemBlocks <- args$numItemBlocks %||% 10
+ implicitPrefs <- args$implicitPrefs %||% FALSE
+ alpha <- args$alpha %||% 1.0
+ nonnegative <- args$nonnegative %||% FALSE
+ checkpointInterval <- args$checkpointInterval %||% 10
+ seed <- args$seed %||% 0
+
+ features <- array(c(ratingCol, userCol, itemCol))
+ distParams <- array(as.integer(c(numUserBlocks, numItemBlocks,
+ checkpointInterval, seed)))
+
+ jobj <- callJStatic("org.apache.spark.ml.r.ALSWrapper",
+ "fit", data@sdf, features,
as.integer(rank),
+ reg, as.integer(maxIter), implicitPrefs,
alpha, nonnegative,
--- End diff --
Done. Thanks!
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