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[SPARK-16144][SPARKR] update R API doc for mllib #13993

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[SPARK-16144][SPARKR] update R API doc for mllib #13993

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afc8ebe
update doc for mlliv
felixcheung Jun 30, 2016
1f9552e
update
felixcheung Jun 30, 2016
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2 changes: 0 additions & 2 deletions R/pkg/R/generics.R
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Original file line number Diff line number Diff line change
Expand Up @@ -1247,7 +1247,6 @@ setGeneric("spark.glm", function(data, formula, ...) { standardGeneric("spark.gl
#' @export
setGeneric("glm")

#' predict
#' @rdname predict
#' @export
setGeneric("predict", function(object, ...) { standardGeneric("predict") })
Expand All @@ -1272,7 +1271,6 @@ setGeneric("spark.naiveBayes", function(data, formula, ...) { standardGeneric("s
#' @export
setGeneric("spark.survreg", function(data, formula, ...) { standardGeneric("spark.survreg") })

#' write.ml
#' @rdname write.ml
#' @export
setGeneric("write.ml", function(object, path, ...) { standardGeneric("write.ml") })
34 changes: 28 additions & 6 deletions R/pkg/R/mllib.R
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Expand Up @@ -53,6 +53,28 @@ setClass("AFTSurvivalRegressionModel", representation(jobj = "jobj"))
#' @note KMeansModel since 2.0.0
setClass("KMeansModel", representation(jobj = "jobj"))

#' Saves the machine learning model to the input path
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@yanboliang yanboliang Jun 30, 2016

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I vote to use MLlib model rather than machine learning model in the whole context.

#'
#' Saves the machine learning model to the input path. For more information, see the specific
#' machine learning model below.
#' @rdname write.ml
#' @name write.ml
#' @export
#' @seealso \link{spark.glm}, \link{spark.kmeans}, \link{spark.naiveBayes}, \link{spark.survreg}
#' @seealso \link{read.ml}
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@yanboliang yanboliang Jun 30, 2016

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It's better to add @seealso \link{write.ml} in the docs of read.ml.

NULL

#' Predicted values based on a machine learning model
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@yanboliang yanboliang Jun 30, 2016

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Makes predictions from a MLlib model will be better?

#'
#' Predicted values based on a machine learning model. For more information, see the specific
#' machine learning model below.
#' @rdname predict
#' @name predict
#' @export
#' @seealso \link{spark.glm}, \link{spark.kmeans}, \link{spark.naiveBayes}, \link{spark.survreg}
#' @seealso \link{read.ml}
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It's not necessary to link read.ml to predict, I think here is typo.

NULL

#' Generalized Linear Models
#'
#' Fits generalized linear model against a Spark DataFrame. Users can print, make predictions on the
Expand Down Expand Up @@ -145,7 +167,7 @@ setMethod("glm", signature(formula = "formula", family = "ANY", data = "SparkDat
})

# 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 \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
Expand Down Expand Up @@ -185,7 +207,7 @@ setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"),
})

# Prints the summary of GeneralizedLinearRegressionModel
#'

#' @rdname spark.glm
#' @param x Summary object of fitted generalized linear model returned by \code{summary} function
#' @export
Expand Down Expand Up @@ -343,7 +365,7 @@ setMethod("fitted", signature(object = "KMeansModel"),
})

# Get the summary of a k-means model
#'

#' @param object A fitted k-means model
#' @return \code{summary} returns the model's coefficients, size and cluster
#' @rdname spark.kmeans
Expand All @@ -370,7 +392,7 @@ setMethod("summary", signature(object = "KMeansModel"),
})

# Predicted values based on a k-means model
#'

#' @return \code{predict} returns the predicted values based on a k-means model
#' @rdname spark.kmeans
#' @export
Expand Down Expand Up @@ -463,7 +485,7 @@ setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "c
})

# Saves the generalized linear model to the input path.
#'

#' @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.
Expand All @@ -481,7 +503,7 @@ setMethod("write.ml", signature(object = "GeneralizedLinearRegressionModel", pat
})

# Save fitted MLlib model to the input path
#'

#' @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.
Expand Down