In thiloshon/rautoalgo: Automated Machine Learning System in R

R Code of the Session, independent of automlr package

cat('## Loading Packages')
cat('\n')
cat('\n')

cat('library(mlr)')
cat('\n')
cat('\n')


cat("# Please pass your tabular data here")
cat('\n')
cat('data_original <- input_data_goes_here')
cat('\n')
cat(paste('target <- "', dataStore$mlPlan$target, '"'))
cat('\n')
cat('\n')
cat('\n')


for (i in 1: length(dataStore$mlPlan$ml.pipelines)) {
    cat(paste('################# Model', i, '################# '))
    cat('\n')
    cat('\n')


    cat("data <- subset(data_original, subset = !is.na(data_original[target]))")
    cat('\n')
    cat('\n')

    cat('resample <- mlr::makeResampleDesc("Holdout", split = 0.6)')
    cat('\n')
    cat('\n')
    cat('\n')





    cat('# Task')
    cat('\n')

    if(dataStore$learning.type == 'classification'){
            cat(paste('learning.task <- mlr::makeClassifTask(id =', dataStore$mlPlan$ml.pipelines[[i]]$id, 'data = data, target = target)'))
    } else {
        cat(paste('learning.task <- mlr::makeRegrTask(id =', dataStore$mlPlan$ml.pipelines[[i]]$id, 'data = data, target = target)'))
    }
    cat('\n')
    cat('\n')
    cat('\n')




    cat('# Learner')
    cat('\n')

    if(dataStore$learning.type == 'classification'){
            cat(paste('learner <- mlr::makeLearner(', dataStore$mlPlan$ml.pipelines[[i]]$learner, 'predict.type = "response", fix.factors.prediction = TRUE)'))
    } else {
        cat(paste('learner <- mlr::makeLearner(', dataStore$mlPlan$ml.pipelines[[i]]$learner, ')'))
    }
    cat('\n')
    cat('\n')
    cat('\n')





    cat('# Training and Testing')
    cat('\n')

    if(dataStore$learning.type == 'classification'){
            cat('mod = mlr::resample(learner, learning.task, resample, measures = list(mmce, acc, timetrain))')
    } else {
        cat('mod = mlr::resample(learner, learning.task, resample)')
    }
    cat('\n')
    cat('\n')
    cat('\n')

}