ML with R
Questions
Is R suitable for Machine Learning (ML)?
How to run R ML jobs on a HPC system (UPPMAX, HPC2N, LUNARC, …)
Objectives
Short introduction to ML with R
Workflow
Show the structure of a suitable batch script
Examples to try
R provides many packages that are specifically designed for machine learning. R is also known for its statistical capabilities for analysis and interpretation of data.
This all makes it easier to develop and deploy models, also without having to write a lot of code yourself.
The R community has contributed many powerful packages, both for machine learning and data science. Some of the popular packages are:
Dplyr
Tidyr
Caret
MLR
ggplot2
randomForest
mlbench
tidyverse
and many many more.
Running your code
Workflow
Determine if you need any R libraries that are not already installed (load R module and R_packages/R-bundle-Bioconductor and check)
Determine if you want to run on CPUs or GPUs - some of the R version modules are not CUDA-aware
Install any missing R libraries in an isolated environment
Possibly download any datasets
Write a batch script
Submit the batch script
Example
Type-Along
We will run a simple example taken from https://machinelearningmastery.com/machine-learning-in-r-step-by-step/
If you cannot access remote data-sets, change the R code as mentioned inside to use a local data-set, which has already been downloaded
NOTE: normally we would not run this on the command line, but through a batch script, but since these are short examples we will run it on the command line.
$ module load R/4.1.1 R_packages/4.1.1
$ Rscript iris_ml.R
$ module load GCC/11.2.0 OpenMPI/4.1.1 R/4.1.2 R-bundle-Bioconductor/3.14-R-4.1.2
$ Rscript iris_ml.R
$ module load GCC/11.3.0 OpenMPI/4.1.4 R/4.2.1 R-bundle-Bioconductor/3.15-R-4.2.1
$ Rscript iris_ml.R
# Simple example taken from https://machinelearningmastery.com/machine-learning-in-r-step-by-step/
library(caret)
# COMMENT OUT THIS SECTION IF YOU CANNOT ACCESS REMOTE DATA-SETS
# --------------------------------------------------------------
# attach the iris dataset to the environment
data(iris)
# rename the dataset
dataset <- iris
# ---------------------------------------------------------------
# REMOVE THE COMMENTS ON THIS SECTION (except comments...) TO USE LOCAL DATA-SETS
# -------------------------------------------------------------------------------
# define the filename
#filename <- "iris.csv"
# load the CSV file from the local directory
#dataset <- read.csv(filename, header=FALSE)
# -------------------------------------------------------------------------------
# set the column names in the dataset
colnames(dataset) <- c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species")
# create a list of 80% of the rows in the original dataset we can use for training
validation_index <- createDataPartition(dataset$Species, p=0.80, list=FALSE)
# select 20% of the data for validation
validation <- dataset[-validation_index,]
# use the remaining 80% of data to training and testing the models
dataset <- dataset[validation_index,]
# Run algorithms using 10-fold cross validation
control <- trainControl(method="cv", number=10)
metric <- "Accuracy"
# a) linear algorithms
set.seed(7)
fit.lda <- train(Species~., data=dataset, method="lda", metric=metric, trControl=control)
# b) nonlinear algorithms
# CART
set.seed(7)
fit.cart <- train(Species~., data=dataset, method="rpart", metric=metric, trControl=control)
# kNN
set.seed(7)
fit.knn <- train(Species~., data=dataset, method="knn", metric=metric, trControl=control)
# c) advanced algorithms
# SVM
set.seed(7)
fit.svm <- train(Species~., data=dataset, method="svmRadial", metric=metric, trControl=control)
# Random Forest
set.seed(7)
fit.rf <- train(Species~., data=dataset, method="rf", metric=metric, trControl=control)
# summarize accuracy of models
results <- resamples(list(lda=fit.lda, cart=fit.cart, knn=fit.knn, svm=fit.svm, rf=fit.rf))
summary(results)
# summarize Best Model
print(fit.lda)
# estimate skill of LDA on the validation dataset
predictions <- predict(fit.lda, validation)
confusionMatrix(predictions, validation$Species)
R batch scripts for ML
Since most R codes for Machine Learning would run for a fairly long time, you would usually have to run them in a batch script.
ML on CPUs
Type-Along
Short serial batch example for running the R code above, iris_ml.R
Short serial example script for Rackham. Loading R/4.1.1 and R_packages/4.1.1
#!/bin/bash
#SBATCH -A naiss2024-22-1202 # Course project id. Change to your own project ID after the course
#SBATCH --time=00:10:00 # Asking for 10 minutes
#SBATCH -n 1 # Asking for 1 core
# Load any modules you need, here R/4.1.1 and R_packages/4.1.1
module load R/4.1.1 R_packages/4.1.1
# Run your R script (here 'iris_ml.R')
R --no-save --quiet < iris_ml.R
Short serial example for running on Kebnekaise. Loading R/4.2.1 and prerequisites, also R-bundle-Bioconductor/3.15-R-4.2.1
#!/bin/bash
#SBATCH -A hpc2n2024-114 # Change to your own project ID
#SBATCH --time=00:10:00 # Asking for 10 minutes
#SBATCH -n 1 # Asking for 1 core
# Load any modules you need, here R/4.2.1 and prerequisites + R-bundle-Bioconductor/3.15-R-4.2.1
module load GCC/11.3.0 OpenMPI/4.1.4 R/4.2.1 R-bundle-Bioconductor/3.15-R-4.2.1
# Run your R script (here 'iris_ml.R')
R --no-save --quiet < iris_ml.R
Short serial example for running on Cosmos. Loading R/4.2.1 and prerequisites, alsoa suitable R-bundle-Bioconductor
#!/bin/bash
#SBATCH -A lu2024-7-80 # Change to your own project ID
#SBATCH --time=00:10:00 # Asking for 10 minutes
#SBATCH -n 1 # Asking for 1 core
# Load any modules you need, here R/4.2.1 and prerequisites + R-bundle-Bioconductor
module load GCC/11.3.0 OpenMPI/4.1.4 R/4.2.1 R-bundle-Bioconductor/3.15-R-4.2.1
# Run your R script (here 'iris_ml.R')
R --no-save --quiet < iris_ml.R
Send the script to the batch:
$ sbatch <batch script>
ML on GPUs
Type-Along
Short ML example for running on Snowy.
#!/bin/bash
#SBATCH -A naiss2024-22-1202
#Asking for 10 min.
#SBATCH -t 00:10:00
#SBATCH --exclusive
#SBATCH -p node
#SBATCH -n 1
#SBATCH -M snowy
#SBATCH --gres=gpu:1
#Writing output and error files
#SBATCH --output=output%J.out
#SBATCH --error=error%J.error
ml purge > /dev/null 2>&1
ml R_packages/4.1.1
R --no-save --no-restore -f Rscript.R
Short ML example for running on Kebnekaise.
#!/bin/bash
#SBATCH -A hpc2n2024-114 # Change to your own project ID
#Asking for 10 min.
#SBATCH -t 00:10:00
#SBATCH -n 1
#SBATCH --gpus:1
#SBATCH -C l40s
#Writing output and error files
#SBATCH --output=output%J.out
#SBATCH --error=error%J.error
ml purge > /dev/null 2>&1
module load GCC/11.3.0 OpenMPI/4.1.4 R/4.2.1 CUDA/12.1.1
R --no-save --no-restore -f Rscript.R
Short ML example for running on Cosmos.
#!/bin/bash
#SBATCH -A lu2024-7-80 # Change to your own project ID
#Asking for 10 min.
#SBATCH -t 00:10:00
#SBATCH -n 1
#SBATCH --gres=gpus:1
#SBATCH -p gpua100
#Writing output and error files
#SBATCH --output=output%J.out
#SBATCH --error=error%J.error
ml purge > /dev/null 2>&1
module load GCC/11.3.0 OpenMPI/4.1.4 R/4.2.1 CUDA/12.1.1
R --no-save --no-restore -f Rscript.R
Short ML example.
#Example taken from https://github.com/lgreski/datasciencectacontent/blob/master/markdown/pml-randomForestPerformance.md
library(mlbench)
data(Sonar)
library(caret)
set.seed(95014)
# create training & testing data sets
inTraining <- createDataPartition(Sonar$Class, p = .75, list=FALSE)
training <- Sonar[inTraining,]
testing <- Sonar[-inTraining,]
# set up training run for x / y syntax because model format performs poorly
x <- training[,-61]
y <- training[,61]
#Serial mode
fitControl <- trainControl(method = "cv",
number = 25,
allowParallel = FALSE)
stime <- system.time(fit <- train(x,y, method="rf",data=Sonar,trControl = fitControl))
#Parallel mode
library(parallel)
library(doParallel)
cluster <- makeCluster(1)
registerDoParallel(cluster)
fitControl <- trainControl(method = "cv",
number = 25,
allowParallel = TRUE)
ptime <- system.time(fit <- train(x,y, method="rf",data=Sonar,trControl = fitControl))
stopCluster(cluster)
registerDoSEQ()
fit
fit$resample
confusionMatrix.train(fit)
#Timings
timing <- rbind(sequential = stime, parallel = ptime)
timing
$ sbatch <batch script>
Exercises
Run validation.R with Rscript
This example is taken from https://www.geeksforgeeks.org/cross-validation-in-r-programming/
validation.R
# R program to implement # validation set approach # Taken from https://www.geeksforgeeks.org/cross-validation-in-r-programming/ library(tidyverse) library(caret) library(datarium) # setting seed to generate a # reproducible random sampling set.seed(123) # creating training data as 80% of the dataset random_sample <- createDataPartition(marketing $ sales, p = 0.8, list = FALSE) # generating training dataset # from the random_sample training_dataset <- marketing[random_sample, ] # generating testing dataset # from rows which are not # included in random_sample testing_dataset <- marketing[-random_sample, ] # Building the model # training the model by assigning sales column # as target variable and rest other columns # as independent variables model <- lm(sales ~., data = training_dataset) # predicting the target variable predictions <- predict(model, testing_dataset) # computing model performance metrics data.frame( R2 = R2(predictions, testing_dataset $ sales), RMSE = RMSE(predictions, testing_dataset $ sales), MAE = MAE(predictions, testing_dataset $ sales))
Solution
$ Rscript validation.R
Create a batch script to run validation.R
You can find example batch scripts in the exercises/r directory.