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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