diff options
Diffstat (limited to 'R')
| -rw-r--r-- | R/pkg/R/mllib.R | 10 | ||||
| -rw-r--r-- | R/pkg/inst/tests/testthat/test_mllib.R | 6 |
2 files changed, 12 insertions, 4 deletions
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R index 64d19fab7e..9a53f757b4 100644 --- a/R/pkg/R/mllib.R +++ b/R/pkg/R/mllib.R @@ -138,10 +138,11 @@ predict_internal <- function(object, newData) { #' This can be a character string naming a family function, a family function or #' the result of a call to a family function. Refer R family at #' \url{https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html}. -#' @param weightCol the weight column name. If this is not set or \code{NULL}, we treat all instance -#' weights as 1.0. #' @param tol positive convergence tolerance of iterations. #' @param maxIter integer giving the maximal number of IRLS iterations. +#' @param weightCol the weight column name. If this is not set or \code{NULL}, we treat all instance +#' weights as 1.0. +#' @param regParam regularization parameter for L2 regularization. #' @param ... additional arguments passed to the method. #' @aliases spark.glm,SparkDataFrame,formula-method #' @return \code{spark.glm} returns a fitted generalized linear model @@ -171,7 +172,8 @@ predict_internal <- function(object, newData) { #' @note spark.glm since 2.0.0 #' @seealso \link{glm}, \link{read.ml} setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"), - function(data, formula, family = gaussian, tol = 1e-6, maxIter = 25, weightCol = NULL) { + function(data, formula, family = gaussian, tol = 1e-6, maxIter = 25, weightCol = NULL, + regParam = 0.0) { if (is.character(family)) { family <- get(family, mode = "function", envir = parent.frame()) } @@ -190,7 +192,7 @@ setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"), jobj <- callJStatic("org.apache.spark.ml.r.GeneralizedLinearRegressionWrapper", "fit", formula, data@sdf, family$family, family$link, - tol, as.integer(maxIter), as.character(weightCol)) + tol, as.integer(maxIter), as.character(weightCol), regParam) new("GeneralizedLinearRegressionModel", jobj = jobj) }) diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R index 1e6da650d1..825a24073b 100644 --- a/R/pkg/inst/tests/testthat/test_mllib.R +++ b/R/pkg/inst/tests/testthat/test_mllib.R @@ -148,6 +148,12 @@ test_that("spark.glm summary", { baseModel <- stats::glm(Sepal.Width ~ Sepal.Length + Species, data = iris) baseSummary <- summary(baseModel) expect_true(abs(baseSummary$deviance - 12.19313) < 1e-4) + + # Test spark.glm works with regularization parameter + data <- as.data.frame(cbind(a1, a2, b)) + df <- suppressWarnings(createDataFrame(data)) + regStats <- summary(spark.glm(df, b ~ a1 + a2, regParam = 1.0)) + expect_equal(regStats$aic, 13.32836, tolerance = 1e-4) # 13.32836 is from summary() result }) test_that("spark.glm save/load", { |