examples/src/main/r/ml/ml.R

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# To run this example use
# ./bin/spark-submit examples/src/main/r/ml/ml.R

# Load SparkR library into your R session
library(SparkR)

# Initialize SparkSession
sparkR.session(appName = "SparkR-ML-example")

############################ model read/write ##############################################
# $example on:read_write$
training <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm")
# Fit a generalized linear model of family "gaussian" with spark.glm
df_list <- randomSplit(training, c(7,3), 2)
gaussianDF <- df_list[[1]]
gaussianTestDF <- df_list[[2]]
gaussianGLM <- spark.glm(gaussianDF, label ~ features, family = "gaussian")

# Save and then load a fitted MLlib model
modelPath <- tempfile(pattern = "ml", fileext = ".tmp")
write.ml(gaussianGLM, modelPath)
gaussianGLM2 <- read.ml(modelPath)

# Check model summary
summary(gaussianGLM2)

# Check model prediction
gaussianPredictions <- predict(gaussianGLM2, gaussianTestDF)
head(gaussianPredictions)

unlink(modelPath)
# $example off:read_write$

############################ fit models with spark.lapply #####################################
# Perform distributed training of multiple models with spark.lapply
algorithms <- c("Hartigan-Wong", "Lloyd", "MacQueen")
train <- function(algorithm) {
  model <- kmeans(x = iris[1:4], centers = 3, algorithm = algorithm)
  model$withinss
}

model.withinss <- spark.lapply(algorithms, train)

# Print the within-cluster sum of squares for each model
print(model.withinss)

# Stop the SparkSession now
sparkR.session.stop()