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| #' Adversarial Debiasing | |
| #' @description Adversarial debiasing is an in-processing technique that learns a classifier to maximize prediction accuracy | |
| #' and simultaneously reduce an adversary's ability to determine the protected attribute from the predictions | |
| #' @param unprivileged_groups A list with two values: the column of the protected class and the value indicating representation for unprivileged group. | |
| #' @param privileged_groups A list with two values: the column of the protected class and the value indicating representation for privileged group. | |
| #' @param scope_name Scope name for the tensorflow variables. | |
| #' @param sess tensorflow session | |
| #' @param seed Seed to make \code{predict} repeatable. If not, \code{NULL}, must be an integer. | |
| #' @param adversary_loss_weight Hyperparameter that chooses the strength of the adversarial loss. | |
| #' @param num_epochs Number of training epochs. Must be an integer. | |
| #' @param batch_size Batch size. Must be an integer. | |
| #' @param classifier_num_hidden_units Number of hidden units in the classifier model. Must be an integer. | |
| #' @param debias Learn a classifier with or without debiasing. | |
| #' @examples | |
| #' \dontrun{ | |
| #' load_aif360_lib() | |
| #' ad <- adult_dataset() | |
| #' p <- list("race", 1) | |
| #' u <- list("race", 0) | |
| #' | |
| #' sess <- tf$compat$v1$Session() | |
| #' | |
| #' plain_model <- adversarial_debiasing(privileged_groups = p, | |
| #' unprivileged_groups = u, | |
| #' scope_name = "debiased_classifier", | |
| #' debias = TRUE, | |
| #' sess = sess) | |
| #' | |
| #' plain_model$fit(ad) | |
| #' ad_nodebiasing <- plain_model$predict(ad) | |
| #' } | |
| #' @export | |
| #' | |
| adversarial_debiasing <- function(unprivileged_groups, | |
| privileged_groups, | |
| scope_name = "current", | |
| sess = tf$compat$v1$Session(), | |
| seed = NULL, | |
| adversary_loss_weight = 0.1, | |
| num_epochs = 50L, | |
| batch_size = 128L, | |
| classifier_num_hidden_units = 200L, | |
| debias = TRUE) { | |
| unprivileged_dict <- dict_fn(unprivileged_groups) | |
| privileged_dict <- dict_fn(privileged_groups) | |
| # run check for variables that must be integers | |
| int_vars <- list(num_epochs = num_epochs, batch_size = batch_size, classifier_num_hidden_units = classifier_num_hidden_units) | |
| if (!is.null(seed)) int_vars <- append(int_vars, c(seed = seed)) | |
| is_int <- sapply(int_vars, is.integer) | |
| int_varnames <- names(int_vars) | |
| if (any(!is_int)) stop(paste(int_varnames[!is_int], collapse = ", "), " must be integer(s)") | |
| ad <- in_algo$AdversarialDebiasing(unprivileged_dict, | |
| privileged_dict, | |
| scope_name = scope_name, | |
| sess = sess, | |
| seed = seed, | |
| adversary_loss_weight = adversary_loss_weight, | |
| num_epochs = num_epochs, | |
| batch_size = batch_size, | |
| classifier_num_hidden_units = classifier_num_hidden_units, | |
| debias = debias) | |
| return(ad) | |
| } | |