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

optimizeModel(
  model,
  hypers,
  metric,
  test = NULL,
  pop = 20,
  gen = 5,
  env = NULL,
  keep_best = 0.4,
  keep_random = 0.2,
  mutation_chance = 0.4,
  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, default is NULL.

pop

numeric. Size of the population, default is 5.

gen

numeric. Number of generations, default is 20.

env

stack containing the environmental variables, used only with "aicc", default is NULL.

keep_best

numeric. Percentage of the best models in the population to be retained during each iteration, expressed as decimal number. Default is 0.4.

keep_random

numeric. Probability of retaining the excluded models during each iteration, expressed as decimal number. Default is 0.2.

mutation_chance

numeric. Probability of mutation of the child models, expressed as decimal number. Default is 0.4.

seed

numeric. The value used to set the seed to have consistent results, default is NULL.

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.

See also

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 <- raster::stack(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")
#> Extracting predictor information for presence locations...
#> Extracting predictor information for absence/background locations...
# 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 } # }