Grid Search
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)
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 |
env |
stack containing the environmental variables, used
only with "aicc", default is |
save_models |
logical, if |
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.
SDMtune object.
Sergio Vignali
# 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")
# 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
output@models
# Order rusults by highest test AUC
head(output@results[order(-output@results$test_AUC), ])
# 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@resultsPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.