Repository: spark Updated Branches: refs/heads/master 10b671447 -> af2a4b082
[SPARK-15129][R][DOC] R API changes in ML ## What changes were proposed in this pull request? Make user guide changes to SparkR documentation for all changes that happened in 2.0 to Machine Learning APIs Author: GayathriMurali <gayathr...@intel.com> Closes #13285 from GayathriMurali/SPARK-15129. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/af2a4b08 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/af2a4b08 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/af2a4b08 Branch: refs/heads/master Commit: af2a4b0826b2358c0fe75c3e4d7fd8f7bccdd8e5 Parents: 10b6714 Author: GayathriMurali <gayathr...@intel.com> Authored: Fri Jun 17 21:10:29 2016 -0700 Committer: Xiangrui Meng <m...@databricks.com> Committed: Fri Jun 17 21:10:29 2016 -0700 ---------------------------------------------------------------------- docs/sparkr.md | 77 +++++++++++-------------------------------- examples/src/main/r/ml.R | 4 +-- 2 files changed, 21 insertions(+), 60 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/af2a4b08/docs/sparkr.md ---------------------------------------------------------------------- diff --git a/docs/sparkr.md b/docs/sparkr.md index 59e486d..961bd32 100644 --- a/docs/sparkr.md +++ b/docs/sparkr.md @@ -285,71 +285,32 @@ head(teenagers) # 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 '-'. +SparkR supports the following Machine Learning algorithms. -The [summary()](api/R/summary.html) function gives the summary of a model produced by [glm()](api/R/glm.html). +* Generalized Linear Regression Model [spark.glm()](api/R/spark.glm.html) +* Naive Bayes [spark.naiveBayes()](api/R/spark.naiveBayes.html) +* KMeans [spark.kmeans()](api/R/spark.kmeans.html) +* AFT Survival Regression [spark.survreg()](api/R/spark.survreg.html) -* For gaussian GLM model, it returns a list with 'devianceResiduals' and 'coefficients' components. The 'devianceResiduals' gives the min/max deviance residuals of the estimation; the 'coefficients' gives the estimated coefficients and their estimated standard errors, t values and p-values. (It only available when model fitted by normal solver.) -* For binomial GLM model, it returns a list with 'coefficients' component which gives the estimated coefficients. +[Generalized Linear Regression](api/R/spark.glm.html) can be used to train a model from a specified family. Currently the Gaussian, Binomial, Poisson and Gamma families are supported. We support a subset of the available R formula operators for model fitting, including '~', '.', ':', '+', and '-'. -The examples below show the use of building gaussian GLM model and binomial GLM model using SparkR. +The [summary()](api/R/summary.html) function gives the summary of a model produced by different algorithms listed above. +It produces the similar result compared with R summary function. -## Gaussian GLM model +## Model persistence -<div data-lang="r" markdown="1"> -{% highlight r %} -# Create the DataFrame -df <- createDataFrame(sqlContext, iris) - -# Fit a gaussian GLM model over the dataset. -model <- glm(Sepal_Length ~ Sepal_Width + Species, data = df, family = "gaussian") - -# Model summary are returned in a similar format to R's native glm(). -summary(model) -##$devianceResiduals -## Min Max -## -1.307112 1.412532 -## -##$coefficients -## Estimate Std. Error t value Pr(>|t|) -##(Intercept) 2.251393 0.3697543 6.08889 9.568102e-09 -##Sepal_Width 0.8035609 0.106339 7.556598 4.187317e-12 -##Species_versicolor 1.458743 0.1121079 13.01195 0 -##Species_virginica 1.946817 0.100015 19.46525 0 - -# 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> +* [write.ml](api/R/write.ml.html) allows users to save a fitted model in a given input path +* [read.ml](api/R/read.ml.html) allows users to read/load the model which was saved using write.ml in a given path -## Binomial GLM model +Model persistence is supported for all Machine Learning algorithms for all families. -<div data-lang="r" markdown="1"> -{% highlight r %} -# Create the DataFrame -df <- createDataFrame(sqlContext, iris) -training <- filter(df, df$Species != "setosa") - -# Fit a binomial GLM model over the dataset. -model <- glm(Species ~ Sepal_Length + Sepal_Width, data = training, family = "binomial") - -# Model coefficients are returned in a similar format to R's native glm(). -summary(model) -##$coefficients -## Estimate -##(Intercept) -13.046005 -##Sepal_Length 1.902373 -##Sepal_Width 0.404655 -{% endhighlight %} -</div> +The examples below show how to build several models: +* GLM using the Gaussian and Binomial model families +* AFT survival regression model +* Naive Bayes model +* K-Means model + +{% include_example r/ml.R %} # R Function Name Conflicts http://git-wip-us.apache.org/repos/asf/spark/blob/af2a4b08/examples/src/main/r/ml.R ---------------------------------------------------------------------- diff --git a/examples/src/main/r/ml.R b/examples/src/main/r/ml.R index fd35936..495f392 100644 --- a/examples/src/main/r/ml.R +++ b/examples/src/main/r/ml.R @@ -25,6 +25,7 @@ library(SparkR) sc <- sparkR.init(appName="SparkR-ML-example") sqlContext <- sparkRSQL.init(sc) +# $example on$ ############################ spark.glm and glm ############################################## irisDF <- suppressWarnings(createDataFrame(sqlContext, iris)) @@ -57,7 +58,6 @@ binomialPredictions <- predict(binomialGLM, binomialTestDF) showDF(binomialPredictions) ############################ spark.survreg ############################################## - # Use the ovarian dataset available in R survival package library(survival) @@ -121,7 +121,7 @@ gaussianGLM <- spark.glm(gaussianDF, Sepal_Length ~ Sepal_Width + Species, famil modelPath <- tempfile(pattern = "ml", fileext = ".tmp") write.ml(gaussianGLM, modelPath) gaussianGLM2 <- read.ml(modelPath) - +# $example off$ # Check model summary summary(gaussianGLM2) --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
