diff options
Diffstat (limited to 'examples/src/main/r/ml.R')
| -rw-r--r-- | examples/src/main/r/ml.R | 21 |
1 files changed, 10 insertions, 11 deletions
diff --git a/examples/src/main/r/ml.R b/examples/src/main/r/ml.R index 495f392c26..940c98dcb9 100644 --- a/examples/src/main/r/ml.R +++ b/examples/src/main/r/ml.R @@ -21,14 +21,13 @@ # Load SparkR library into your R session library(SparkR) -# Initialize SparkContext and SQLContext -sc <- sparkR.init(appName="SparkR-ML-example") -sqlContext <- sparkRSQL.init(sc) +# Initialize SparkSession +sparkR.session(appName="SparkR-ML-example") # $example on$ ############################ spark.glm and glm ############################################## -irisDF <- suppressWarnings(createDataFrame(sqlContext, iris)) +irisDF <- suppressWarnings(createDataFrame(iris)) # Fit a generalized linear model of family "gaussian" with spark.glm gaussianDF <- irisDF gaussianTestDF <- irisDF @@ -62,7 +61,7 @@ showDF(binomialPredictions) library(survival) # Fit an accelerated failure time (AFT) survival regression model with spark.survreg -ovarianDF <- suppressWarnings(createDataFrame(sqlContext, ovarian)) +ovarianDF <- suppressWarnings(createDataFrame(ovarian)) aftDF <- ovarianDF aftTestDF <- ovarianDF aftModel <- spark.survreg(aftDF, Surv(futime, fustat) ~ ecog_ps + rx) @@ -78,7 +77,7 @@ showDF(aftPredictions) # Fit a Bernoulli naive Bayes model with spark.naiveBayes titanic <- as.data.frame(Titanic) -titanicDF <- suppressWarnings(createDataFrame(sqlContext, titanic[titanic$Freq > 0, -5])) +titanicDF <- createDataFrame(titanic[titanic$Freq > 0, -5]) nbDF <- titanicDF nbTestDF <- titanicDF nbModel <- spark.naiveBayes(nbDF, Survived ~ Class + Sex + Age) @@ -93,7 +92,7 @@ showDF(nbPredictions) ############################ spark.kmeans ############################################## # Fit a k-means model with spark.kmeans -irisDF <- suppressWarnings(createDataFrame(sqlContext, iris)) +irisDF <- suppressWarnings(createDataFrame(iris)) kmeansDF <- irisDF kmeansTestDF <- irisDF kmeansModel <- spark.kmeans(kmeansDF, ~ Sepal_Length + Sepal_Width + Petal_Length + Petal_Width, @@ -111,7 +110,7 @@ showDF(kmeansPredictions) ############################ model read/write ############################################## -irisDF <- suppressWarnings(createDataFrame(sqlContext, iris)) +irisDF <- suppressWarnings(createDataFrame(iris)) # Fit a generalized linear model of family "gaussian" with spark.glm gaussianDF <- irisDF gaussianTestDF <- irisDF @@ -139,11 +138,11 @@ train <- function(family) { model <- glm(Sepal.Length ~ Sepal.Width + Species, iris, family = family) summary(model) } -model.summaries <- spark.lapply(sc, families, train) +model.summaries <- spark.lapply(families, train) # Print the summary of each model print(model.summaries) -# Stop the SparkContext now -sparkR.stop() +# Stop the SparkSession now +sparkR.session.stop() |