Given a set of possible hyperparameter values, the function trains models with all the possible combinations of hyperparameters.

gridSearch(model, hypers, metric, test = NULL, env = NULL, save_models = TRUE)

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.

env

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

save_models

logical, if FALSE the models are not saved and the output contains only a data frame with the metric values for each hyperparameter combination. Default is TRUE, set it to FALSE when there are many combinations to avoid R crashing for memory overload.

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, test = 0.2, only_presence = TRUE) train <- datasets[[1]] test <- datasets[[2]] # Train a model model <- train(method = "Maxnet", data = train, fc = "l") # Define the hyperparameters to test h <- list(reg = 1:2, fc = c("lqp", "lqph")) # Run the function using the AUC as metric output <- gridSearch(model, hypers = h, metric = "auc", test = test) output@results
#> fc reg train_AUC test_AUC diff_AUC #> 1 lqp 1 0.8620394 0.8550612 0.006978125 #> 2 lqph 1 0.8720638 0.8540037 0.018060000 #> 3 lqp 2 0.8588537 0.8488062 0.010047500 #> 4 lqph 2 0.8654625 0.8518813 0.013581250
output@models
#> [[1]] #> Object of class SDMmodel #> Method: Maxnet #> #> Species: Virtual species #> Presence locations: 320 #> Absence locations: 5000 #> #> Model configurations: #> -------------------- #> fc: lqp #> reg: 1 #> #> Variables: #> --------- #> Continuous: bio1 bio12 bio16 bio17 bio5 bio6 bio7 bio8 #> Categorical: biome #> [[2]] #> Object of class SDMmodel #> Method: Maxnet #> #> Species: Virtual species #> Presence locations: 320 #> Absence locations: 5000 #> #> Model configurations: #> -------------------- #> fc: lqph #> reg: 1 #> #> Variables: #> --------- #> Continuous: bio1 bio12 bio16 bio17 bio5 bio6 bio7 bio8 #> Categorical: biome #> [[3]] #> Object of class SDMmodel #> Method: Maxnet #> #> Species: Virtual species #> Presence locations: 320 #> Absence locations: 5000 #> #> Model configurations: #> -------------------- #> fc: lqp #> reg: 2 #> #> Variables: #> --------- #> Continuous: bio1 bio12 bio16 bio17 bio5 bio6 bio7 bio8 #> Categorical: biome #> [[4]] #> Object of class SDMmodel #> Method: Maxnet #> #> Species: Virtual species #> Presence locations: 320 #> Absence locations: 5000 #> #> Model configurations: #> -------------------- #> fc: lqph #> reg: 2 #> #> Variables: #> --------- #> Continuous: bio1 bio12 bio16 bio17 bio5 bio6 bio7 bio8 #> Categorical: biome
# Order rusults by highest test AUC head(output@results[order(-output@results$test_AUC), ])
#> fc reg train_AUC test_AUC diff_AUC #> 1 lqp 1 0.8620394 0.8550612 0.006978125 #> 2 lqph 1 0.8720638 0.8540037 0.018060000 #> 4 lqph 2 0.8654625 0.8518813 0.013581250 #> 3 lqp 2 0.8588537 0.8488062 0.010047500
# Run the function using the AICc as metric and without saving the trained # models, helpful when numerous hyperparameters are tested to avoid memory # problems output <- gridSearch(model, hypers = h, metric = "aicc", env = predictors, save_models = FALSE) output@results
#> fc reg AICc delta_AICc #> 1 lqp 1 5278.672 14.20438 #> 2 lqph 1 5283.754 19.28583 #> 3 lqp 2 5273.436 8.96854 #> 4 lqph 2 5264.468 0.00000
# }