vaeac_get_save_file_names: Function that creates the save file names for the `vaeac` model

Description

Function that creates the save file names for the vaeac model

Usage

vaeac_get_save_file_names(
  model_description,
  n_features,
  n_train,
  depth,
  width,
  latent_dim,
  lr,
  epochs,
  save_every_nth_epoch,
  folder_to_save_model = NULL
)

Value

Array of string containing the save files to use when training the vaeac model. The first three names corresponds to the best, best_running, and last epochs, in that order.

Arguments

model_description: String (default is make.names(Sys.time())). String containing, e.g., the name of the data distribution or additional parameter information. Used in the save name of the fitted model. If not provided, then a name will be generated based on base::Sys.time() to ensure a unique name. We use base::make.names() to ensure a valid file name for all operating systems.
depth: Positive integer (default is 3). The number of hidden layers in the neural networks of the masked encoder, full encoder, and decoder.
width: Positive integer (default is 32). The number of neurons in each hidden layer in the neural networks of the masked encoder, full encoder, and decoder.
latent_dim: Positive integer (default is 8). The number of dimensions in the latent space.
lr: Positive numeric (default is 0.001). The learning rate used in the torch::optim_adam() optimizer.
epochs: Positive integer (default is 100). The number of epochs to train the final vaeac model. This includes epochs_initiation_phase, where the default is 2.
save_every_nth_epoch: Positive integer (default is NULL). If provided, then the vaeac model after every save_every_nth_epochth epoch will be saved.
folder_to_save_model: String (default is base::tempdir()). String specifying a path to a folder where the function is to save the fitted vaeac model. Note that the path will be removed from the returned explain() object if vaeac.save_model = FALSE.

Author

Lars Henry Berge Olsen