huaxingao commented on a change in pull request #27570:
[SPARK-30820][SPARKR][ML] Add FMClassifier to SparkR
URL: https://github.com/apache/spark/pull/27570#discussion_r379880740
##########
File path: R/pkg/R/mllib_classification.R
##########
@@ -649,3 +655,155 @@ setMethod("write.ml", signature(object =
"NaiveBayesModel", path = "character"),
function(object, path, overwrite = FALSE) {
write_internal(object, path, overwrite)
})
+
+
+#' Factorization Machines Classification Model
+#'
+#' \code{spark.fmClassifier} fits a factorization classification 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 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 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).
+#' @param seed seed parameter for weights initialization.
+#' @param handleInvalid How to handle invalid data (unseen labels or NULL
values) in features and
+#' label column of string type.
+#' Supported options: "skip" (filter out rows with
invalid data),
+#' "error" (throw an error), "keep"
(put invalid data in
+#' a special additional bucket, at
index numLabels). Default
+#' is "error".
+#' @param ... additional arguments passed to the method.
+#' @return \code{spark.fmClassifier} returns a fitted Factorization Machines
Classification Model.
+#' @rdname spark.fmClassifier
+#' @aliases spark.fmClassifier,SparkDataFrame,formula-method
+#' @name spark.fmClassifier
+#' @seealso \link{read.ml}
+#' @examples
+#' \dontrun{
+#' df <- read.df("data/mllib/sample_binary_classification_data.txt", source =
"libsvm")
+#'
+#' # fit Factorization Machines Classification Model
+#' model <- spark.fmClassifier(
+#' 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.fmClassifier since 3.0.0
Review comment:
3.1.0?
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