[SPARK-16107][R] group glm methods in documentation
## What changes were proposed in this pull request? This groups GLM methods (spark.glm, summary, print, predict and write.ml) in the documentation. The example code was updated. ## How was this patch tested? N/A (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) ![screen shot 2016-06-21 at 2 31 37 pm](https://cloud.githubusercontent.com/assets/15318264/16247077/f6eafc04-37bc-11e6-89a8-7898ff3e4078.png) ![screen shot 2016-06-21 at 2 31 45 pm](https://cloud.githubusercontent.com/assets/15318264/16247078/f6eb1c16-37bc-11e6-940a-2b595b10617c.png) Author: Junyang Qian <junyangq@databricks.com> Author: Junyang Qian <junyangq@Junyangs-MacBook-Pro.local> Closes #13820 from junyangq/SPARK-16107.
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@ -53,9 +53,10 @@ setClass("AFTSurvivalRegressionModel", representation(jobj = "jobj"))
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#' @note KMeansModel since 2.0.0
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setClass("KMeansModel", representation(jobj = "jobj"))
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#' Fits a generalized linear model
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#' Generalized Linear Models
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#'
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#' Fits a generalized linear model against a Spark DataFrame.
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#' Fits generalized linear model against a Spark DataFrame. Users can print, make predictions on the
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#' produced model and save the model to the input path.
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#'
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#' @param data SparkDataFrame for training.
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#' @param formula A symbolic description of the model to be fitted. Currently only a few formula
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@ -66,8 +67,9 @@ setClass("KMeansModel", representation(jobj = "jobj"))
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#' \url{https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html}.
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#' @param tol Positive convergence tolerance of iterations.
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#' @param maxIter Integer giving the maximal number of IRLS iterations.
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#' @return a fitted generalized linear model
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#' @return \code{spark.glm} returns a fitted generalized linear model
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#' @rdname spark.glm
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#' @name spark.glm
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#' @export
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#' @examples
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#' \dontrun{
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@ -76,8 +78,21 @@ setClass("KMeansModel", representation(jobj = "jobj"))
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#' df <- createDataFrame(iris)
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#' model <- spark.glm(df, Sepal_Length ~ Sepal_Width, family = "gaussian")
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#' summary(model)
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#'
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#' # fitted values on training data
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#' fitted <- predict(model, df)
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#' head(select(fitted, "Sepal_Length", "prediction"))
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#'
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#' # save fitted model to input path
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#' path <- "path/to/model"
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#' write.ml(model, path)
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#'
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#' # can also read back the saved model and print
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#' savedModel <- read.ml(path)
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#' summary(savedModel)
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#' }
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#' @note spark.glm since 2.0.0
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#' @seealso \link{glm}, \link{read.ml}
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setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"),
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function(data, formula, family = gaussian, tol = 1e-6, maxIter = 25) {
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if (is.character(family)) {
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@ -99,10 +114,9 @@ setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"),
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return(new("GeneralizedLinearRegressionModel", jobj = jobj))
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})
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#' Fits a generalized linear model (R-compliant).
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#' Generalized Linear Models (R-compliant)
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#'
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#' Fits a generalized linear model, similarly to R's glm().
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#'
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#' @param formula A symbolic description of the model to be fitted. Currently only a few formula
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#' operators are supported, including '~', '.', ':', '+', and '-'.
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#' @param data SparkDataFrame for training.
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@ -112,7 +126,7 @@ setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"),
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#' \url{https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html}.
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#' @param epsilon Positive convergence tolerance of iterations.
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#' @param maxit Integer giving the maximal number of IRLS iterations.
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#' @return a fitted generalized linear model
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#' @return \code{glm} returns a fitted generalized linear model.
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#' @rdname glm
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#' @export
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#' @examples
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@ -124,24 +138,21 @@ setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"),
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#' summary(model)
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#' }
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#' @note glm since 1.5.0
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#' @seealso \link{spark.glm}
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setMethod("glm", signature(formula = "formula", family = "ANY", data = "SparkDataFrame"),
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function(formula, family = gaussian, data, epsilon = 1e-6, maxit = 25) {
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spark.glm(data, formula, family, tol = epsilon, maxIter = maxit)
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})
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#' Get the summary of a generalized linear model
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#'
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#' Returns the summary of a model produced by glm() or spark.glm(), similarly to R's summary().
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# Returns the summary of a model produced by glm() or spark.glm(), similarly to R's summary().
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#'
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#' @param object A fitted generalized linear model
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#' @return coefficients the model's coefficients, intercept
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#' @rdname summary
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#' @return \code{summary} returns a summary object of the fitted model, a list of components
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#' including at least the coefficients, null/residual deviance, null/residual degrees
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#' of freedom, AIC and number of iterations IRLS takes.
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#'
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#' @rdname spark.glm
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#' @export
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#' @examples
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#' \dontrun{
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#' model <- glm(y ~ x, trainingData)
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#' summary(model)
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#' }
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#' @note summary(GeneralizedLinearRegressionModel) since 2.0.0
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setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"),
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function(object, ...) {
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@ -173,10 +184,10 @@ setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"),
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return(ans)
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})
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#' Print the summary of GeneralizedLinearRegressionModel
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# Prints the summary of GeneralizedLinearRegressionModel
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#'
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#' @rdname print
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#' @name print.summary.GeneralizedLinearRegressionModel
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#' @rdname spark.glm
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#' @param x Summary object of fitted generalized linear model returned by \code{summary} function
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#' @export
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#' @note print.summary.GeneralizedLinearRegressionModel since 2.0.0
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print.summary.GeneralizedLinearRegressionModel <- function(x, ...) {
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@ -205,22 +216,13 @@ print.summary.GeneralizedLinearRegressionModel <- function(x, ...) {
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invisible(x)
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}
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#' Predicted values based on model
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# Makes predictions from a generalized linear model produced by glm() or spark.glm(),
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# similarly to R's predict().
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#'
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#' Makes predictions from a generalized linear model produced by glm() or spark.glm(),
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#' similarly to R's predict().
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#'
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#' @param object A fitted generalized linear model
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#' @param newData SparkDataFrame for testing
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#' @return SparkDataFrame containing predicted labels in a column named "prediction"
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#' @rdname predict
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#' @return \code{predict} returns a SparkDataFrame containing predicted labels in a column named "prediction"
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#' @rdname spark.glm
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#' @export
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#' @examples
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#' \dontrun{
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#' model <- glm(y ~ x, trainingData)
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#' predicted <- predict(model, testData)
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#' showDF(predicted)
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#' }
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#' @note predict(GeneralizedLinearRegressionModel) since 1.5.0
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setMethod("predict", signature(object = "GeneralizedLinearRegressionModel"),
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function(object, newData) {
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@ -471,24 +473,14 @@ setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "c
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invisible(callJMethod(writer, "save", path))
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})
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#' Save fitted MLlib model to the input path
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# Saves the generalized linear model to the input path.
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#'
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#' Save the generalized linear model to the input path.
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#'
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#' @param object A fitted generalized linear model
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#' @param path The directory where the model is saved
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#' @param overwrite Overwrites or not if the output path already exists. Default is FALSE
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#' which means throw exception if the output path exists.
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#'
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#' @rdname write.ml
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#' @name write.ml
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#' @rdname spark.glm
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#' @export
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#' @examples
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#' \dontrun{
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#' model <- glm(y ~ x, trainingData)
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#' path <- "path/to/model"
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#' write.ml(model, path)
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#' }
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#' @note write.ml(GeneralizedLinearRegressionModel, character) since 2.0.0
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setMethod("write.ml", signature(object = "GeneralizedLinearRegressionModel", path = "character"),
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function(object, path, overwrite = FALSE) {
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