diff options
| -rw-r--r-- | R/pkg/R/mllib.R | 80 |
1 files changed, 36 insertions, 44 deletions
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R index b83b3b3d3f..dbff1b900d 100644 --- a/R/pkg/R/mllib.R +++ b/R/pkg/R/mllib.R @@ -53,9 +53,10 @@ setClass("AFTSurvivalRegressionModel", representation(jobj = "jobj")) #' @note KMeansModel since 2.0.0 setClass("KMeansModel", representation(jobj = "jobj")) -#' Fits a generalized linear model +#' Generalized Linear Models #' -#' Fits a generalized linear model against a Spark DataFrame. +#' Fits generalized linear model against a Spark DataFrame. Users can print, make predictions on the +#' produced model and save the model to the input path. #' #' @param data SparkDataFrame for training. #' @param formula A symbolic description of the model to be fitted. Currently only a few formula @@ -66,8 +67,9 @@ setClass("KMeansModel", representation(jobj = "jobj")) #' \url{https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html}. #' @param tol Positive convergence tolerance of iterations. #' @param maxIter Integer giving the maximal number of IRLS iterations. -#' @return a fitted generalized linear model +#' @return \code{spark.glm} returns a fitted generalized linear model #' @rdname spark.glm +#' @name spark.glm #' @export #' @examples #' \dontrun{ @@ -76,8 +78,21 @@ setClass("KMeansModel", representation(jobj = "jobj")) #' df <- createDataFrame(iris) #' model <- spark.glm(df, Sepal_Length ~ Sepal_Width, family = "gaussian") #' summary(model) +#' +#' # fitted values on training data +#' fitted <- predict(model, df) +#' head(select(fitted, "Sepal_Length", "prediction")) +#' +#' # save fitted model to input path +#' path <- "path/to/model" +#' write.ml(model, path) +#' +#' # can also read back the saved model and print +#' savedModel <- read.ml(path) +#' summary(savedModel) #' } #' @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) { if (is.character(family)) { @@ -99,10 +114,9 @@ setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"), return(new("GeneralizedLinearRegressionModel", jobj = jobj)) }) -#' Fits a generalized linear model (R-compliant). +#' Generalized Linear Models (R-compliant) #' #' Fits a generalized linear model, similarly to R's glm(). -#' #' @param formula A symbolic description of the model to be fitted. Currently only a few formula #' operators are supported, including '~', '.', ':', '+', and '-'. #' @param data SparkDataFrame for training. @@ -112,7 +126,7 @@ setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"), #' \url{https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html}. #' @param epsilon Positive convergence tolerance of iterations. #' @param maxit Integer giving the maximal number of IRLS iterations. -#' @return a fitted generalized linear model +#' @return \code{glm} returns a fitted generalized linear model. #' @rdname glm #' @export #' @examples @@ -124,24 +138,21 @@ setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"), #' summary(model) #' } #' @note glm since 1.5.0 +#' @seealso \link{spark.glm} setMethod("glm", signature(formula = "formula", family = "ANY", data = "SparkDataFrame"), function(formula, family = gaussian, data, epsilon = 1e-6, maxit = 25) { spark.glm(data, formula, family, tol = epsilon, maxIter = maxit) }) -#' Get the summary of a generalized linear model -#' -#' Returns the summary of a model produced by glm() or spark.glm(), similarly to R's summary(). +# Returns the summary of a model produced by glm() or spark.glm(), similarly to R's summary(). #' #' @param object A fitted generalized linear model -#' @return coefficients the model's coefficients, intercept -#' @rdname summary +#' @return \code{summary} returns a summary object of the fitted model, a list of components +#' including at least the coefficients, null/residual deviance, null/residual degrees +#' of freedom, AIC and number of iterations IRLS takes. +#' +#' @rdname spark.glm #' @export -#' @examples -#' \dontrun{ -#' model <- glm(y ~ x, trainingData) -#' summary(model) -#' } #' @note summary(GeneralizedLinearRegressionModel) since 2.0.0 setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"), function(object, ...) { @@ -173,10 +184,10 @@ setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"), return(ans) }) -#' Print the summary of GeneralizedLinearRegressionModel +# Prints the summary of GeneralizedLinearRegressionModel #' -#' @rdname print -#' @name print.summary.GeneralizedLinearRegressionModel +#' @rdname spark.glm +#' @param x Summary object of fitted generalized linear model returned by \code{summary} function #' @export #' @note print.summary.GeneralizedLinearRegressionModel since 2.0.0 print.summary.GeneralizedLinearRegressionModel <- function(x, ...) { @@ -205,22 +216,13 @@ print.summary.GeneralizedLinearRegressionModel <- function(x, ...) { invisible(x) } -#' Predicted values based on model +# Makes predictions from a generalized linear model produced by glm() or spark.glm(), +# similarly to R's predict(). #' -#' Makes predictions from a generalized linear model produced by glm() or spark.glm(), -#' similarly to R's predict(). -#' -#' @param object A fitted generalized linear model #' @param newData SparkDataFrame for testing -#' @return SparkDataFrame containing predicted labels in a column named "prediction" -#' @rdname predict +#' @return \code{predict} returns a SparkDataFrame containing predicted labels in a column named "prediction" +#' @rdname spark.glm #' @export -#' @examples -#' \dontrun{ -#' model <- glm(y ~ x, trainingData) -#' predicted <- predict(model, testData) -#' showDF(predicted) -#' } #' @note predict(GeneralizedLinearRegressionModel) since 1.5.0 setMethod("predict", signature(object = "GeneralizedLinearRegressionModel"), function(object, newData) { @@ -471,24 +473,14 @@ setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "c invisible(callJMethod(writer, "save", path)) }) -#' Save fitted MLlib model to the input path -#' -#' Save the generalized linear model to the input path. +# Saves the generalized linear model to the input path. #' -#' @param object A fitted generalized linear model #' @param path The directory where the model is saved #' @param overwrite Overwrites or not if the output path already exists. Default is FALSE #' which means throw exception if the output path exists. #' -#' @rdname write.ml -#' @name write.ml +#' @rdname spark.glm #' @export -#' @examples -#' \dontrun{ -#' model <- glm(y ~ x, trainingData) -#' path <- "path/to/model" -#' write.ml(model, path) -#' } #' @note write.ml(GeneralizedLinearRegressionModel, character) since 2.0.0 setMethod("write.ml", signature(object = "GeneralizedLinearRegressionModel", path = "character"), function(object, path, overwrite = FALSE) { |