diff options
| -rw-r--r-- | R/pkg/R/generics.R | 4 | ||||
| -rw-r--r-- | R/pkg/R/mllib.R | 8 | ||||
| -rw-r--r-- | docs/sparkr.md | 37 |
3 files changed, 42 insertions, 7 deletions
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R index c43b947129..379a78b1d8 100644 --- a/R/pkg/R/generics.R +++ b/R/pkg/R/generics.R @@ -535,8 +535,8 @@ setGeneric("showDF", function(x,...) { standardGeneric("showDF") }) #' @export setGeneric("summarize", function(x,...) { standardGeneric("summarize") }) -##' rdname summary -##' @export +#' @rdname summary +#' @export setGeneric("summary", function(x, ...) { standardGeneric("summary") }) # @rdname tojson diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R index b524d1fd87..cea3d760d0 100644 --- a/R/pkg/R/mllib.R +++ b/R/pkg/R/mllib.R @@ -56,10 +56,10 @@ setMethod("glm", signature(formula = "formula", family = "ANY", data = "DataFram #' #' Makes predictions from a model produced by glm(), similarly to R's predict(). #' -#' @param model A fitted MLlib model +#' @param object A fitted MLlib model #' @param newData DataFrame for testing #' @return DataFrame containing predicted values -#' @rdname glm +#' @rdname predict #' @export #' @examples #'\dontrun{ @@ -76,10 +76,10 @@ setMethod("predict", signature(object = "PipelineModel"), #' #' Returns the summary of a model produced by glm(), similarly to R's summary(). #' -#' @param model A fitted MLlib model +#' @param x A fitted MLlib model #' @return a list with a 'coefficient' component, which is the matrix of coefficients. See #' summary.glm for more information. -#' @rdname glm +#' @rdname summary #' @export #' @examples #'\dontrun{ diff --git a/docs/sparkr.md b/docs/sparkr.md index 4385a4eeac..7139d16b4a 100644 --- a/docs/sparkr.md +++ b/docs/sparkr.md @@ -11,7 +11,8 @@ title: SparkR (R on Spark) SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. In Spark {{site.SPARK_VERSION}}, SparkR provides a distributed data frame implementation that supports operations like selection, filtering, aggregation etc. (similar to R data frames, -[dplyr](https://github.com/hadley/dplyr)) but on large datasets. +[dplyr](https://github.com/hadley/dplyr)) but on large datasets. SparkR also supports distributed +machine learning using MLlib. # SparkR DataFrames @@ -230,3 +231,37 @@ head(teenagers) {% endhighlight %} </div> + +# Machine Learning + +SparkR allows the fitting of generalized linear models over DataFrames using the [glm()](api/R/glm.html) function. Under the hood, SparkR uses MLlib to train a model of the specified family. Currently the gaussian and binomial families are supported. We support a subset of the available R formula operators for model fitting, including '~', '.', '+', and '-'. The example below shows the use of building a gaussian GLM model using SparkR. + +<div data-lang="r" markdown="1"> +{% highlight r %} +# Create the DataFrame +df <- createDataFrame(sqlContext, iris) + +# Fit a linear model over the dataset. +model <- glm(Sepal_Length ~ Sepal_Width + Species, data = df, family = "gaussian") + +# Model coefficients are returned in a similar format to R's native glm(). +summary(model) +##$coefficients +## Estimate +##(Intercept) 2.2513930 +##Sepal_Width 0.8035609 +##Species_versicolor 1.4587432 +##Species_virginica 1.9468169 + +# Make predictions based on the model. +predictions <- predict(model, newData = df) +head(select(predictions, "Sepal_Length", "prediction")) +## Sepal_Length prediction +##1 5.1 5.063856 +##2 4.9 4.662076 +##3 4.7 4.822788 +##4 4.6 4.742432 +##5 5.0 5.144212 +##6 5.4 5.385281 +{% endhighlight %} +</div> |