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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# 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()
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