vaeac_train_model: Train the Vaeac Model
In shapr: Prediction Explanation with Dependence-Aware Shapley Values

vaeac_train_model

R Documentation

Train the Vaeac Model

Description

Function that fits a vaeac model to the given dataset based on the provided parameters, as described in Olsen et al. (2022). Note that all default parameters specified below origin from setup_approach.vaeac() and vaeac_get_extra_para_default().

Usage

vaeac_train_model(
  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_epoch`th 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.

Details

The vaeac model consists of three neural networks, i.e., a masked encoder, a full encoder, and a decoder. The networks have shared depth, width, and activation_function. The encoders maps the x_train to a latent representation of dimension latent_dim, while the decoder maps the latent representations back to the feature space. See Olsen et al. (2022) for more details. The function first initiates n_vaeacs_initialize vaeac models with different randomly initiated network parameter values to remedy poorly initiated values. After epochs_initiation_phase epochs, the n_vaeacs_initialize vaeac models are compared and the function continues to only train the best performing one for a total of epochs epochs. The networks are trained using the ADAM optimizer with the learning rate is lr.