From af2a4b0826b2358c0fe75c3e4d7fd8f7bccdd8e5 Mon Sep 17 00:00:00 2001
From: GayathriMurali <gayathri.m@intel.com>
Date: Fri, 17 Jun 2016 21:10:29 -0700
Subject: [PATCH] [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 <gayathri.m@intel.com>
Closes #13285 from GayathriMurali/SPARK-15129.
---
docs/sparkr.md | 77 ++++++++++------------------------------
examples/src/main/r/ml.R | 4 +--
2 files changed, 21 insertions(+), 60 deletions(-)
diff --git a/docs/sparkr.md b/docs/sparkr.md
index 59e486d192..961bd323fa 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
diff --git a/examples/src/main/r/ml.R b/examples/src/main/r/ml.R
index fd35936635..495f392c26 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)
--
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