hyper-tuning.R
In SDMtune: Species Distribution Model Selection

## ----knitr-options, include=FALSE---------------------------------------------
knitr::opts_chunk$set(comment = "#>",
                      collapse = TRUE,
                      eval = FALSE,
                      fig.align = "center")

## ----load-data----------------------------------------------------------------
# library(SDMtune)
# library(zeallot)
# 
# # Prepare data
# files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
#                     pattern = "grd",
#                     full.names = TRUE)
# 
# predictors <- terra::rast(files)
# data <- prepareSWD(species = "Virtual species",
#                    p = virtualSp$presence,
#                    a = virtualSp$background,
#                    env = predictors,
#                    categorical = "biome")
# 
# # Split data in training, validation and testing datasets
# c(train, val, test) %<-% trainValTest(data,
#                                       val = 0.2,
#                                       test = 0.2,
#                                       only_presence = TRUE,
#                                       seed = 61516)
# 
# cat("# Training  : ", nrow(train@data))
# cat("\n# Validation: ", nrow(val@data))
# cat("\n# Testing   : ", nrow(test@data))
# 
# # Train Maxnet model with default settings
# model <- train("Maxnet",
#                data = train)

## ----tune-reg-----------------------------------------------------------------
# # Define the values for the regularization multiplier
# h <- list(reg = seq(0.2, 1, 0.1))
# 
# # Call the gridSearch function
# exp_1 <- gridSearch(model,
#                     hypers = h,
#                     metric = "auc",
#                     test = val)

## ----print-exp-1--------------------------------------------------------------
# exp_1

## ----plot-exp-1---------------------------------------------------------------
# plot(exp_1,
#      title = "Experiment 1")

## ----plot-exp-1-iter----------------------------------------------------------
# plot(exp_1,
#      title = "Experiment 1",
#      interactive = TRUE)

## ----slot-results-------------------------------------------------------------
# exp_1@results

## ----order-results------------------------------------------------------------
# exp_1@results[order(-exp_1@results$test_AUC), ]

## ----exercise-1, eval=FALSE, class.source='exercise'--------------------------
# # Define the values for reg
# h <- list(reg = 1:4)
# 
# # Call the gridSearch function
# exp_2 <- gridSearch(model,
#                     hypers = h,
#                     metric = "tss",
#                     test = val)

## ----exercise-2, eval=FALSE, class.source='exercise'--------------------------
# # Define the values for fc
# h <- list(fc = c("l", "lq", "lh", "lqp", "lqph", "lqpht"))
# 
# # Call the gridSearch function
# exp_3 <- gridSearch(model,
#                     hypers = h,
#                     metric = "auc",
#                     test = val)

## ----exercise-3, eval=FALSE, class.source='exercise'--------------------------
# maxent_model <- train("Maxent",
#                       data = data)
# 
# # Define the values for fc
# h <- list("iter" = seq(300, 1100, 200))
# 
# # Call the gridSearch function
# exp_4 <- gridSearch(maxent_model,
#                     hypers = h,
#                     metric = "auc",
#                     test = val)

## ----get-hypers---------------------------------------------------------------
# getTunableArgs(model)

## ----exp-5, eval=FALSE--------------------------------------------------------
# h <- list(reg = seq(0.2, 2, 0.2),
#           fc = c("l", "lq", "lh", "lqp", "lqph", "lqpht"))
# 
# exp_5 <- gridSearch(model,
#                     hypers = h,
#                     metric = "auc",
#                     test = val)

## ----random-search------------------------------------------------------------
# h <- list(reg = seq(0.2, 5, 0.2),
#           fc = c("l", "lq", "lh", "lp", "lqp", "lqph"))
# 
# exp_6 <- randomSearch(model,
#                       hypers = h,
#                       metric = "auc",
#                       test = val,
#                       pop = 10,
#                       seed = 65466)

## ----sdmtune-results----------------------------------------------------------
# exp_6@results

## ----optimize-model, eval=FALSE-----------------------------------------------
# exp_7 <- optimizeModel(model,
#                        hypers = h,
#                        metric = "auc",
#                        test = val,
#                        pop = 15,
#                        gen = 2,
#                        seed = 798)

## ----merge--------------------------------------------------------------------
# # Index of the best model in the experiment
# index <- which.max(exp_6@results$test_AUC)
# 
# # New train dataset containing only the selected variables
# new_train <- exp_6@models[[index]]@data
# 
# # Merge only presence data
# merged_data <- mergeSWD(new_train,
#                         val,
#                         only_presence = TRUE)

## ----final-model--------------------------------------------------------------
# final_model <- train("Maxnet",
#                      data = merged_data,
#                      fc = exp_6@results[index, 1],
#                      reg = exp_6@results[index, 2])

## ----final-evaluation---------------------------------------------------------
# auc(final_model, test = test)

## ----cross-validation---------------------------------------------------------
# # Create the folds from the training dataset
# folds <- randomFolds(train,
#                      k = 4,
#                      only_presence = TRUE,
#                      seed = 25)
# 
# # Train the model
# cv_model <- train("Maxent",
#                   data = train,
#                   folds = folds)

## ----randomSearch-cv----------------------------------------------------------
# h <- list(reg = seq(0.2, 5, 0.2),
#           fc = c("l", "lq", "lh", "lp", "lqp", "lqph"))
# 
# exp_8 <- randomSearch(cv_model,
#                       hypers = h,
#                       metric = "auc",
#                       pop = 10,
#                       seed = 65466)

## ----final-evaluation-cv------------------------------------------------------
# final_model <- combineCV(exp_8@models[[1]])
# 
# auc(final_model, test = test)