Function to specify the extra parameters in the vaeac model — vaeac_get_extra_para

In this function, we specify the default values for the extra parameters used in explain() for approach = "vaeac".

Usage

vaeac_get_extra_para_default(
  vaeac.model_description = make.names(Sys.time()),
  vaeac.folder_to_save_model = tempdir(),
  vaeac.pretrained_vaeac_model = NULL,
  vaeac.cuda = FALSE,
  vaeac.epochs_initiation_phase = 2,
  vaeac.epochs_early_stopping = NULL,
  vaeac.save_every_nth_epoch = NULL,
  vaeac.val_ratio = 0.25,
  vaeac.val_iwae_n_samples = 25,
  vaeac.batch_size = 64,
  vaeac.batch_size_sampling = NULL,
  vaeac.running_avg_n_values = 5,
  vaeac.skip_conn_layer = TRUE,
  vaeac.skip_conn_masked_enc_dec = TRUE,
  vaeac.batch_normalization = FALSE,
  vaeac.paired_sampling = TRUE,
  vaeac.masking_ratio = 0.5,
  vaeac.mask_gen_coalitions = NULL,
  vaeac.mask_gen_coalitions_prob = NULL,
  vaeac.sigma_mu = 10000,
  vaeac.sigma_sigma = 1e-04,
  vaeac.sample_random = TRUE,
  vaeac.save_data = FALSE,
  vaeac.log_exp_cont_feat = FALSE,
  vaeac.which_vaeac_model = "best",
  vaeac.save_model = TRUE
)

Arguments

vaeac.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.
vaeac.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. Furthermore, the model cannot be moved from its original folder if we are to use the vaeac_train_model_continue() function to continue training the model.
vaeac.pretrained_vaeac_model: List or String (default is NULL). 1) Either a list of class vaeac, i.e., the list stored in explanation$internal$parameters$vaeac where explanation is the returned list from an earlier call to the explain() function. 2) A string containing the path to where the vaeac model is stored on disk, for example, explanation$internal$parameters$vaeac$models$best.
vaeac.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).
vaeac.epochs_initiation_phase: Positive integer (default is 2). The number of epochs to run each of the vaeac.n_vaeacs_initialize vaeac models before continuing to train only the best performing model.
vaeac.epochs_early_stopping: Positive integer (default is NULL). The training stops if there has been no improvement in the validation IWAE for vaeac.epochs_early_stopping epochs. If the user wants the training process to be solely based on this training criterion, then vaeac.epochs in explain() should be set to a large number. If NULL, then shapr will internally set vaeac.epochs_early_stopping = vaeac.epochs such that early stopping does not occur.
vaeac.save_every_nth_epoch: Positive integer (default is NULL). If provided, then the vaeac model after every vaeac.save_every_nth_epochth epoch will be saved.
vaeac.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, vaeac.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.
vaeac.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.
vaeac.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().
vaeac.batch_size_sampling: Positive integer (default is NULL) The number of samples to include in each batch when generating the Monte Carlo samples. If NULL, then the function generates the Monte Carlo samples for the provided coalitions and all explicands sent to explain() at the time. The number of coalitions are determined by the n_batches used by explain(). We recommend to tweak extra_computation_args$max_batch_size and extra_computation_args$min_n_batches rather than vaeac.batch_size_sampling. Larger batch sizes are often much faster provided sufficient memory.
vaeac.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.
vaeac.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 $X + activation(WX + b)$.
vaeac.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.
vaeac.batch_normalization: Logical (default is FALSE). If TRUE, we apply batch normalization after the activation function. Note that if vaeac.skip_conn_layer = TRUE, then the normalization is applied after the inclusion of the skip connection. That is, we batch normalize the whole quantity $X + activation(WX + b)$.
vaeac.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.
vaeac.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. vaeac.masking_ratio will be overruled if vaeac.mask_gen_coalitions is specified.
vaeac.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().
vaeac.mask_gen_coalitions_prob: Numeric array (default is NULL). Array of length equal to the height of vaeac.mask_gen_coalitions containing the probabilities of sampling the corresponding coalitions in vaeac.mask_gen_coalitions.
vaeac.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).
vaeac.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).
vaeac.sample_random: Logical (default is TRUE). If TRUE, the function generates random Monte Carlo samples from the inferred generative distributions. If FALSE, the function use the most likely values, i.e., the mean and class with highest probability for continuous and categorical, respectively.
vaeac.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().
vaeac.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.
vaeac.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.
vaeac.save_model: Boolean. If TRUE (default), the vaeac model will be saved either in a base::tempdir() folder or in a user specified location in vaeac.folder_to_save_model. If FALSE, then the paths to model and the model will will be deleted from the returned object from explain().

Value

Named list of the default values vaeac extra parameter arguments specified in this function call. Note that both vaeac.model_description and vaeac.folder_to_save_model will change with time and R session.

Details

The vaeac model consists of three neural network (a full encoder, a masked encoder, and a decoder) based on the provided vaeac.depth and vaeac.width. The encoders map the full and masked input representations to latent representations, respectively, where the dimension is given by vaeac.latent_dim. The latent representations are sent to the decoder to go back to the real feature space and provide a samplable probabilistic representation, from which the Monte Carlo samples are generated. We use the vaeac method at the epoch with the lowest validation error (IWAE) by default, but other possibilities are available but setting the vaeac.which_vaeac_model parameter. See Olsen et al. (2022) for more details.

References

Olsen, L. H., Glad, I. K., Jullum, M., & Aas, K. (2022). Using Shapley values and variational autoencoders to explain predictive models with dependent mixed features. Journal of machine learning research, 23(213), 1-51

Author

Lars Henry Berge Olsen