├── analysis/               # Scripts used to create figures and tables in the paper
    ├── data/                   # Location where all experimental data is stored
    │   ├── raw/                # Raw datasets for the DNN surrogate benchmark
    │   ├── runs/               # Individual runs 
    ├── benchmarks/             # Code for experimental analysis (section 6)
    │   ├── synthetic           # Synthetic benchmark (section 6.1)
    │   ├── mlp                 # DNN surrogate benchmark (section 6.2)
    ├── renv/                   # renv configuration files to enable a reproducible setup 
    ├── R/                      # Implementation of methods 
    ├── LICENSE
    └── README.md               

library("renv")
renv::restore()

# Loading all scripts we need
source("R/tree_splitting.R")
source("R/helper.R")
source("R/marginal_effect.R")
source("R/plot_functions.R")

library(mlr)
library(mlrMBO)
library(e1071)
library(BBmisc)
library(data.table)

par.set = makeParamSet(
  makeNumericParam("cost", -10, 4, trafo = function(x) 2^x),
  makeNumericParam("gamma", -10, 4, trafo = function(x) 2^x)
)

ctrl = makeMBOControl()
ctrl = setMBOControlInfill(ctrl, crit = makeMBOInfillCritCB(cb.lambda = 1))
ctrl = setMBOControlTermination(ctrl, iters = 5)
tune.ctrl = makeTuneControlMBO(mbo.control = ctrl)
res = tuneParams(makeLearner("classif.svm"), iris.task, cv3, par.set = par.set, control = tune.ctrl,
  show.info = FALSE)
  
surrogate =  res$mbo.result$models[[1]]

print(surrogate)

FALSE Model for learner.id=regr.km; learner.class=regr.km
FALSE Trained on: task.id = data; obs = 13; features = 2
FALSE Hyperparameters: jitter=TRUE,covtype=matern3_2,optim.method=gen,nugget.estim=TRUE

##        cost      mean         sd
## 1 -9.998017 0.8085137 0.12850346
## 2 -9.261563 0.8223581 0.11260680
## 3 -8.525109 0.8271599 0.09651956
## 4 -7.788655 0.8161618 0.07913981
## 5 -7.052201 0.7814865 0.06697429
## 6 -6.315747 0.7200586 0.06511970

library(ggplot2)

p = plot_pdp_with_uncertainty_1D(me)
print(p)

predictor = Predictor$new(model = surrogate, data = data)
effects = FeatureEffect$new(predictor = predictor, feature = "cost", method = "pdp")

tree = compute_tree(effects, data, "SS_L2", 2)

plot_pdp_for_node(node = tree[[2]][[2]], testdata = data, model = surrogate, pdp.feature = "cost", grid.size = 20)