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| % Generated by roxygen2: do not edit by hand | |
| % Please edit documentation in R/postprocessing_reject_option_classification.R | |
| \name{reject_option_classification} | |
| \alias{reject_option_classification} | |
| \title{Reject option classification} | |
| \usage{ | |
| reject_option_classification( | |
| 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 | |
| ) | |
| } | |
| \arguments{ | |
| \item{unprivileged_groups}{A list epresentation for unprivileged group.} | |
| \item{privileged_groups}{A list representation for privileged group.} | |
| \item{low_class_thresh}{Smallest classification threshold to use in the optimization. Should be between 0. and 1.} | |
| \item{high_class_thresh}{Highest classification threshold to use in the optimization. Should be between 0. and 1.} | |
| \item{num_class_thresh}{Number of classification thresholds between low_class_thresh and high_class_thresh for the optimization search. Should be > 0.} | |
| \item{num_ROC_margin}{Number of relevant ROC margins to be used in the optimization search. Should be > 0.} | |
| \item{metric_name}{Name of the metric to use for the optimization. Allowed options are "Statistical parity difference", "Average odds difference", "Equal opportunity difference".} | |
| \item{metric_ub}{Upper bound of constraint on the metric value} | |
| \item{metric_lb}{Lower bound of constraint on the metric value} | |
| } | |
| \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. | |
| } | |
| \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) | |
| } | |
| } | |