vaeac_train_model_auxiliary: Function used to train a 'vaeac' model
In shapr: Prediction Explanation with Dependence-Aware Shapley Values

vaeac_train_model_auxiliary

R Documentation

Function used to train a `vaeac` model

Description

This function can be applied both in the initialization phase when, we train several initiated vaeac models, and to keep training the best performing vaeac model for the remaining number of epochs. We are in the former setting when initialization_idx is provided and the latter when it is NULL. When it is NULL, we save the vaeac models with lowest VLB, IWAE, running IWAE, and the epochs according to save_every_nth_epoch to disk.

Usage

vaeac_train_model_auxiliary(
  vaeac_model,
  optimizer,
  train_dataloader,
  val_dataloader,
  val_iwae_n_samples,
  running_avg_n_values,
  verbose,
  cuda,
  epochs,
  save_every_nth_epoch,
  epochs_early_stopping,
  epochs_start = 1,
  progressr_bar = NULL,
  vaeac_save_file_names = NULL,
  state_list = NULL,
  initialization_idx = NULL,
  n_vaeacs_initialize = NULL,
  train_vlb = NULL,
  val_iwae = NULL,
  val_iwae_running = NULL
)

Arguments

`vaeac_model`	A `vaeac()` object. The `vaeac` model this function is to train.
`optimizer`	A `torch::optimizer()` object. See `vaeac_get_optimizer()`.
`train_dataloader`	A `torch::dataloader()` containing the training data for the `vaeac` model.
`val_dataloader`	A `torch::dataloader()` containing the validation data for the `vaeac` model.
`val_iwae_n_samples`	Positive integer (default is `25`). The number of generated samples used to compute the IWAE criterion when validating the vaeac model on the validation data.
`running_avg_n_values`	running_avg_n_values Positive integer (default is `5`). The number of previous IWAE values to include when we compute the running means of the IWAE criterion.
`verbose`	String vector or NULL. Specifies the verbosity (printout detail level) through one or more of strings `"basic"`, `"progress"`, `"convergence"`, `"shapley"` and `"vS_details"`. `"basic"` (default) displays basic information about the computation which is being performed. `⁠"progress⁠` displays information about where in the calculation process the function currently is. #' `"convergence"` displays information on how close to convergence the Shapley value estimates are (only when `iterative = TRUE`) . `"shapley"` displays intermediate Shapley value estimates and standard deviations (only when `iterative = TRUE`) the final estimates. `"vS_details"` displays information about the v_S estimates. This is most relevant for `⁠approach %in% c("regression_separate", "regression_surrogate", "vaeac"⁠`). `NULL` means no printout. Note that any combination of four strings can be used. E.g. `verbose = c("basic", "vS_details")` will display basic information + details about the v(S)-estimation process.
`cuda`	Logical (default is `FALSE`). If `TRUE`, then the `vaeac` model will be trained using cuda/GPU. If `torch::cuda_is_available()` is `FALSE`, the we fall back to use CPU. If `FALSE`, we use the CPU. Using a GPU for smaller tabular dataset often do not improve the efficiency. See `vignette("installation", package = "torch")` fo help to enable running on the GPU (only Linux and Windows).
`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_epoch`th epoch will be saved.
`epochs_early_stopping`	Positive integer (default is `NULL`). The training stops if there has been no improvement in the validation IWAE for `epochs_early_stopping` epochs. If the user wants the training process to be solely based on this training criterion, then `epochs` in `explain()` should be set to a large number. If `NULL`, then `shapr` will internally set `epochs_early_stopping = vaeac.epochs` such that early stopping does not occur.
`epochs_start`	Positive integer (default is `1`). At which epoch the training is starting at.
`progressr_bar`	A `progressr::progressor()` object (default is `NULL`) to keep track of progress.
`vaeac_save_file_names`	Array of strings containing the save file names for the `vaeac` model.
`state_list`	Named list containing the objects returned from `vaeac_get_full_state_list()`.
`initialization_idx`	Positive integer (default is `NULL`). The index of the current `vaeac` model in the initialization phase.
`n_vaeacs_initialize`	Positive integer (default is `4`). The number of different vaeac models to initiate in the start. Pick the best performing one after `epochs_initiation_phase` epochs (default is `2`) and continue training that one.
`train_vlb`	A `torch::torch_tensor()` (default is `NULL`) of one dimension containing previous values for the training VLB.
`val_iwae`	A `torch::torch_tensor()` (default is `NULL`) of one dimension containing previous values for the validation IWAE.
`val_iwae_running`	A `torch::torch_tensor()` (default is `NULL`) of one dimension containing previous values for the running validation IWAE.

Value

Depending on if we are in the initialization phase or not. Then either the trained vaeac model, or a list of where the vaeac models are stored on disk and the parameters of the model.