Optimize Model

The function uses a Genetic Algorithm implementation to optimize the model hyperparameter configuration according to the chosen metric.

Usage

optimizeModel(
  model,
  hypers,
  metric,
  test = NULL,
  pop = 20,
  gen = 5,
  env = NULL,
  keep_best = 0.4,
  keep_random = 0.2,
  mutation_chance = 0.4,
  interactive = TRUE,
  progress = TRUE,
  seed = NULL
)

Arguments

model: SDMmodel or SDMmodelCV object.
hypers: named list containing the values of the hyperparameters that should be tuned, see details.
metric: character. The metric used to evaluate the models, possible values are: "auc", "tss" and "aicc".
test: SWD object. Testing dataset used to evaluate the model, not used with aicc and SDMmodelCV objects.
pop: numeric. Size of the population.
gen: numeric. Number of generations.
env: rast containing the environmental variables, used only with "aicc".
keep_best: numeric. Percentage of the best models in the population to be retained during each iteration, expressed as decimal number.
keep_random: numeric. Probability of retaining the excluded models during each iteration, expressed as decimal number.
mutation_chance: numeric. Probability of mutation of the child models, expressed as decimal number.
interactive: logical. If FALSE the interactive chart is not created.
progress: logical. If TRUE shows a progress bar.
seed: numeric. The value used to set the seed to have consistent results.

Value

SDMtune object.

Details

To know which hyperparameters can be tuned you can use the output of the function getTunableArgs. Hyperparameters not included in the hypers argument take the value that they have in the passed model.

An interactive chart showing in real-time the steps performed by the algorithm is displayed in the Viewer pane.

Part of the code is inspired by this post.

Author

Sergio Vignali

Examples

# \donttest{
# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")
#> ℹ Extracting predictor information for presence locations
#> ✔ Extracting predictor information for presence locations [27ms]
#> 
#> ℹ Extracting predictor information for absence/background locations
#> ✔ Extracting predictor information for absence/background locations [51ms]
#> 

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         val = 0.2,
                         test = 0.2,
                         only_presence = TRUE,
                         seed = 61516)
train <- datasets[[1]]
val <- datasets[[2]]

# Train a model
model <- train("Maxnet",
               data = train)

# Define the hyperparameters to test
h <- list(reg = seq(0.2, 5, 0.2),
          fc = c("l", "lq", "lh", "lp", "lqp", "lqph"))

# Run the function using as metric the AUC
if (FALSE) {
output <- optimizeModel(model,
                        hypers = h,
                        metric = "auc",
                        test = val,
                        pop = 15,
                        gen = 2,
                        seed = 798)
output@results
output@models
output@models[[1]]  # Best model
}
# }