vaeac_get_data_objects function

Function to set up data loaders and save file names

vaeac_get_data_objects(
  x_train,
  log_exp_cont_feat,
  val_ratio,
  batch_size,
  paired_sampling,
  model_description,
  depth,
  width,
  latent_dim,
  lr,
  epochs,
  save_every_nth_epoch,
  folder_to_save_model,
  train_indices = NULL,
  val_indices = NULL
)

Arguments

• x_train: A data.table containing the training data. Categorical data must have class names $1,2,\dots,K$.
• log_exp_cont_feat: Logical (default is FALSE). If we are to $\log$ transform all continuous features before sending the data to vaeac(). The vaeac model creates unbounded Monte Carlo sample values. Thus, if the continuous features are strictly positive (as for, e.g., the Burr distribution and Abalone data set), it can be advantageous to $\log$ transform the data to unbounded form before using vaeac. If TRUE, then vaeac_postprocess_data() will take the $\exp$ of the results to get back to strictly positive values when using the vaeac model to impute missing values/generate the Monte Carlo samples.
• val_ratio: Numeric (default is 0.25). Scalar between 0 and 1 indicating the ratio of instances from the input data which will be used as validation data. That is, val_ratio = 0.25 means that 75% of the provided data is used as training data, while the remaining 25% is used as validation data.
• batch_size: Positive integer (default is 64). The number of samples to include in each batch during the training of the vaeac model. Used in torch::dataloader().
• paired_sampling: Logical (default is TRUE). If TRUE, we apply paired sampling to the training batches. That is, the training observations in each batch will be duplicated, where the first instance will be masked by $S$ while the second instance will be masked by $\bar{S}$. This ensures that the training of the vaeac model becomes more stable as the model has access to the full version of each training observation. However, this will increase the training time due to more complex implementation and doubling the size of each batch. See paired_sampler() for more information.
• 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.
• train_indices: Numeric array (optional) containing the indices of the training observations. There are conducted no checks to validate the indices.
• val_indices: Numeric array (optional) containing the indices of the validation observations. #' There are conducted no checks to validate the indices.

Returns

List of objects needed to train the vaeac model