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| #' Reject option classification | |
| #' | |
| #' @description Reject option classification is a postprocessing technique that gives | |
| #' favorable outcomes to unpriviliged groups and unfavorable outcomes to | |
| #' priviliged groups in a confidence band around the decision boundary with | |
| #' the highest uncertainty. | |
| #' @param unprivileged_groups A list epresentation for unprivileged group. | |
| #' @param privileged_groups A list representation for privileged group. | |
| #' @param low_class_thresh Smallest classification threshold to use in the optimization. Should be between 0. and 1. | |
| #' @param high_class_thresh Highest classification threshold to use in the optimization. Should be between 0. and 1. | |
| #' @param num_class_thresh Number of classification thresholds between low_class_thresh and high_class_thresh for the optimization search. Should be > 0. | |
| #' @param num_ROC_margin Number of relevant ROC margins to be used in the optimization search. Should be > 0. | |
| #' @param metric_name Name of the metric to use for the optimization. Allowed options are "Statistical parity difference", "Average odds difference", "Equal opportunity difference". | |
| #' @param metric_ub Upper bound of constraint on the metric value | |
| #' @param metric_lb Lower bound of constraint on the metric value | |
| #' @examples | |
| #' \dontrun{ | |
| #' # Example with Adult Dataset | |
| #' load_aif360_lib() | |
| #' ad <- adult_dataset() | |
| #' p <- list("race",1) | |
| #' u <- list("race", 0) | |
| #' | |
| #' col_names <- c(ad$feature_names, "label") | |
| #' ad_df <- data.frame(ad$features, ad$labels) | |
| #' colnames(ad_df) <- col_names | |
| #' | |
| #' lr <- glm(label ~ ., data=ad_df, family=binomial) | |
| #' | |
| #' ad_prob <- predict(lr, ad_df) | |
| #' ad_pred <- factor(ifelse(ad_prob> 0.5,1,0)) | |
| #' | |
| #' ad_df_pred <- data.frame(ad_df) | |
| #' ad_df_pred$label <- as.character(ad_pred) | |
| #' colnames(ad_df_pred) <- c(ad$feature_names, 'label') | |
| #' | |
| #' ad_ds <- binary_label_dataset(ad_df, target_column='label', favor_label = 1, | |
| #' unfavor_label = 0, unprivileged_protected_attribute = 0, | |
| #' privileged_protected_attribute = 1, protected_attribute='race') | |
| #' | |
| #' ad_ds_pred <- binary_label_dataset(ad_df_pred, target_column='label', favor_label = 1, | |
| #' unfavor_label = 0, unprivileged_protected_attribute = 0, | |
| #' privileged_protected_attribute = 1, protected_attribute='race') | |
| #' | |
| #' roc <- reject_option_classification(unprivileged_groups = u, | |
| #' privileged_groups = p, | |
| #' low_class_thresh = 0.01, | |
| #' high_class_thresh = 0.99, | |
| #' num_class_thresh = as.integer(100), | |
| #' num_ROC_margin = as.integer(50), | |
| #' metric_name = "Statistical parity difference", | |
| #' metric_ub = 0.05, | |
| #' metric_lb = -0.05) | |
| #' | |
| #' roc <- roc$fit(ad_ds, ad_ds_pred) | |
| #' | |
| #' ds_transformed_pred <- roc$predict(ad_ds_pred) | |
| #' } | |
| #' @export | |
| #' | |
| reject_option_classification <- function(unprivileged_groups, | |
| privileged_groups, | |
| low_class_thresh=0.01, | |
| high_class_thresh=0.99, | |
| num_class_thresh=as.integer(100), | |
| num_ROC_margin=as.integer(50), | |
| metric_name='Statistical parity difference', | |
| metric_ub=0.05, | |
| metric_lb=-0.05){ | |
| u_dict <- dict_fn(unprivileged_groups) | |
| p_dict <- dict_fn(privileged_groups) | |
| return(post_algo$RejectOptionClassification(u_dict, | |
| p_dict, | |
| low_class_thresh, | |
| high_class_thresh, | |
| num_class_thresh, | |
| num_ROC_margin, | |
| metric_name, | |
| metric_ub, | |
| metric_lb)) | |
| } | |