Continue to Train the vaeac Model

Function that loads a previously trained vaeac model and continue the training, either on new data or on the same dataset as it was trained on before. If we are given a new dataset, then we assume that new dataset has the same distribution and one_hot_max_sizes as the original dataset.

vaeac_train_model_continue(
  explanation,
  epochs_new,
  lr_new = NULL,
  x_train = NULL,
  save_data = FALSE,
  verbose = NULL,
  seed = 1
)

Arguments

explanation

A explain() object and vaeac must be the used approach.

epochs_new

Positive integer. The number of extra epochs to conduct.

lr_new

Positive numeric. If we are to overwrite the old learning rate in the adam optimizer.

x_train

A data.table containing the training data. Categorical data must have class names \(1,2,\dots,K\).

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().

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.

Value

A list containing the training/validation errors and paths to where the vaeac models are saved on the disk.

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