vaeac_check_parameters function

Function that calls all vaeac parameters check functions

vaeac_check_parameters(
  x_train,
  model_description,
  folder_to_save_model,
  cuda,
  n_vaeacs_initialize,
  epochs_initiation_phase,
  epochs,
  epochs_early_stopping,
  save_every_nth_epoch,
  val_ratio,
  val_iwae_n_samples,
  depth,
  width,
  latent_dim,
  lr,
  batch_size,
  running_avg_n_values,
  activation_function,
  skip_conn_layer,
  skip_conn_masked_enc_dec,
  batch_normalization,
  paired_sampling,
  masking_ratio,
  mask_gen_coalitions,
  mask_gen_coalitions_prob,
  sigma_mu,
  sigma_sigma,
  save_data,
  log_exp_cont_feat,
  which_vaeac_model,
  verbose,
  seed,
  ...
)

Arguments

• x_train: A data.table containing the training data. Categorical data must have class names $1,2,\dots,K$.

• 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.

• 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.

• 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).

• 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.

• epochs_initiation_phase: Positive integer (default is 2). The number of epochs to run each of the n_vaeacs_initialize vaeac models before continuing to train only the best performing model.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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().

• 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.

• activation_function: An torch::nn_module() representing an activation function such as, e.g., torch::nn_relu() (default), torch::nn_leaky_relu(), torch::nn_selu(), or torch::nn_sigmoid().

• skip_conn_layer: Logical (default is TRUE). If TRUE, we apply identity skip connections in each layer, see skip_connection(). That is, we add the input $X$ to the outcome of each hidden layer, so the output becomes $X + activation(WX + b)$.

• skip_conn_masked_enc_dec: Logical (default is TRUE). If TRUE, we apply concatenate skip connections between the layers in the masked encoder and decoder. The first layer of the masked encoder will be linked to the last layer of the decoder. The second layer of the masked encoder will be linked to the second to last layer of the decoder, and so on.

• batch_normalization: Logical (default is FALSE). If TRUE, we apply batch normalization after the activation function. Note that if skip_conn_layer = TRUE, then the normalization is applied after the inclusion of the skip connection. That is, we batch normalize the whole quantity $X + activation(WX + b)$.

• 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.

• masking_ratio: Numeric (default is 0.5). Probability of masking a feature in the mcar_mask_generator() (MCAR = Missing Completely At Random). The MCAR masking scheme ensures that vaeac

  model can do arbitrary conditioning as all coalitions will be trained. masking_ratio will be overruled if mask_gen_coalitions is specified.

• mask_gen_coalitions: Matrix (default is NULL). Matrix containing the coalitions that the vaeac model will be trained on, see specified_masks_mask_generator(). This parameter is used internally in shapr when we only consider a subset of coalitions, i.e., when n_coalitions $< 2^{n_{\text{features}}}$, and for group Shapley, i.e., when group is specified in explain().

• mask_gen_coalitions_prob: Numeric array (default is NULL). Array of length equal to the height of mask_gen_coalitions containing the probabilities of sampling the corresponding coalitions in mask_gen_coalitions.

• sigma_mu: Numeric (default is 1e4). One of two hyperparameter values in the normal-gamma prior used in the masked encoder, see Section 3.3.1 in Olsen et al. (2022).

• sigma_sigma: Numeric (default is 1e-4). One of two hyperparameter values in the normal-gamma prior used in the masked encoder, see Section 3.3.1 in Olsen et al. (2022).

• save_data: Logical (default is FALSE). If TRUE, then the data is stored together with the model. Useful if one are to continue to train the model later using vaeac_train_model_continue().

• 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.

• which_vaeac_model: String (default is best). The name of the vaeac model (snapshots from different epochs) to use when generating the Monte Carlo samples. The standard choices are: "best" (epoch with lowest IWAE), "best_running" (epoch with lowest running IWAE, see vaeac.running_avg_n_values), and last (the last epoch). Note that additional choices are available if vaeac.save_every_nth_epoch is provided. For example, if vaeac.save_every_nth_epoch = 5, then vaeac.which_vaeac_model can also take the values "epoch_5", "epoch_10", "epoch_15", and so on.

• 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.

• seed: Positive integer (default is 1). Seed for reproducibility. Specifies the seed before any randomness based code is being run.

• ...: List of extra parameters, currently not used.

Returns

The function does not return anything.

Author(s)

Lars Henry Berge Olsen