required_packages <- c("mlr3verse", "data.table", "xts", "openxlsx", "glmnet", "readxl", "PerformanceAnalytics", "outliers", "ggplot2") # Function to check and install the necessary packages install_and_load <- function(packages) { for (package in packages) { # Check if the package is installed if (!require(package, character.only = TRUE)) { # Install the package if it is not installed install.packages(package, dependencies = TRUE) # Load the package after installing library(package, character.only = TRUE) } } } install_and_load(required_packages)
setwd('..') source(file.path('sandbox', 'sampledata.R'))
setwd('..') source(file.path('sandbox', 'TSML.R')) source(file.path('sandbox', 'MLutils.R'))
Preprocessing
setwd('..') # Outlier detection and removal source(file.path('sandbox', 'outliers.R')) # Feature Selection source(file.path('sandbox', 'featureselection.R')) # Rescaling source(file.path('sandbox', 'rescale.R'))
Evaluation
setwd('..') # Constructing benchmarks source(file.path('sandbox', 'benchmark.R')) # Evaluation scores source(file.path('sandbox', 'evaluation.R'))
testtask <- TSML$new(data = regr_data, task = "regression", ts_var = "DATE", y = "WI.RET") testtask$train_test_split(cutoff = 0.8)
testtask$train_predict("regr.rpart", method = "default")
testtask$prevailing_means() testtask$mse() testtask$mae() testtask$rsq(benchmark = "zero") testtask$evals
setwd('..') source(file.path('sandbox', 'cvglmnet.R')) source(file.path('sandbox', 'rpart.R')) source(file.path('sandbox', 'ranger.R')) source(file.path('sandbox', 'svm.R')) source(file.path('sandbox', 'lda.R')) source(file.path('sandbox', 'qda.R')) source(file.path('sandbox', 'naiveBayes.R')) source(file.path('sandbox', 'KNN.R')) source(file.path('sandbox', 'nnet.R'))
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