Fit and validate Deep Neural Network model

Usage

fit_abund_dnn(
  data,
  response,
  predictors,
  predictors_f = NULL,
  partition,
  predict_part = FALSE,
  learning_rate = 0.01,
  n_epochs = 10,
  batch_size = 32,
  validation_patience = 2,
  fitting_patience = 5,
  custom_architecture = NULL,
  verbose = TRUE
)

Arguments

data: tibble or data.frame. Database with response, predictors, and partition values
response: character. Column name with species abundance.
predictors: character. Vector with the column names of quantitative predictor variables (i.e. continuous variables). Usage predictors = c("temp", "precipt", "sand")
predictors_f: character. Vector with the column names of qualitative predictor variables (i.e. ordinal or nominal variables type). Usage predictors_f = c("landform")
partition: character. Column name with training and validation partition groups.
predict_part: logical. Save predicted abundance for testing data. Default = FALSE
learning_rate: numeric. The size of the step taken during the optimization process. Default = 0.01
n_epochs: numeric. Max number of times the learning algorithm will work through the training set. Default = 10
batch_size: numeric. A batch is a subset of the training set used in a single iteration of the training process. The size of each batch is referred to as the batch size. Default = 32
validation_patience: numerical. An integer indicating the number of epochs without loss improvement tolerated by the algorithm in the validation process. If the patience limit is exceeded, the training ends. Default 2
fitting_patience: numerical. The same as validation_patience, but in the final model fitting process. Default 5
custom_architecture: a Torch nn_module_generator object or a generate_dnn_architecture output. A neural network architecture to be used instead of the internal default one. Default NULL
verbose: logical. If FALSE, disables all console messages. Default TRUE

Value

A list object with:

model: A "luz_module_fitted" object from luz (torch framework). This object can be used to predicting.
predictors: A tibble with quantitative (c column names) and qualitative (f column names) variables use for modeling.
performance: Averaged performance metrics (see adm_eval).
performance_part: Performance metrics for each replica and partition.
predicted_part: Observed and predicted abundance for each test partition.

Examples

if (FALSE) {
require(dplyr)

# Database with species abundance and x and y coordinates
data("sppabund")

# Extract data for a single species
some_sp <- sppabund %>%
  dplyr::filter(species == "Species one") %>%
  dplyr::select(-.part2, -.part3)

# Explore reponse variables
some_sp$ind_ha %>% range()
some_sp$ind_ha %>% hist()

# Here we balance number of absences
some_sp <-
  balance_dataset(some_sp, response = "ind_ha", absence_ratio = 0.2)

# Generate a architecture
dnn_arch <- generate_dnn_architecture(
  number_of_features = 3,
  number_of_outputs = 1,
  number_of_hidden_layers = 3,
  hidden_layers_size = c(8, 16, 8),
  batch_norm = TRUE
)

# Fit a NET model
mdnn <- fit_abund_dnn(
  data = some_sp,
  response = "ind_ha",
  predictors = c("bio12", "elevation", "sand"),
  predictors_f = NULL,
  partition = ".part",
  learning_rate = 0.01,
  n_epochs = 10,
  batch_size = 32,
  validation_patience = 2,
  fitting_patience = 5,
  custom_architecture = dnn_arch,
  verbose = TRUE,
  predict_part = TRUE
)

mdnn
}