gridSearch {SDMtune}

Grid Search

Description

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

Usage

gridSearch(
  model,
  hypers,
  metric,
  test = NULL,
  env = NULL,
  save_models = TRUE,
  interactive = TRUE,
  progress = 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.

env

rast containing the environmental variables, used only with "aicc".

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. Set it to FALSE when there are many combinations to avoid R crashing for memory overload.

interactive

logical. If FALSE the interactive chart is not created.

progress

logical. If TRUE shows a progress bar.

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.

Value

SDMtune object.

Author(s)

Sergio Vignali

See Also

randomSearch and optimizeModel.

Examples

# 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")

# 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 results by highest test AUC
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@results