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)