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change examples for CRAN
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‎DESCRIPTION

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Package: fairmodels
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Type: Package
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Title: Flexible Tool for Bias Detection, Visualization, and Mitigation
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Version: 1.0.0
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Version: 1.0.1
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Authors@R:
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c(person("Jakub", "Wiśniewski", role = c("aut", "cre"),
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email = "[email protected]"),

‎NEWS.md

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# fairmodels 1.0.1
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* Changed examples - added parameter `num.threads = 1` to `ranger` and added *donttest{}* to examples with long computation time.
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# fairmodels 1.0.0
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* Added citation information
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* Added additional reference in `fairness_check()` documentation.

‎R/all_cutoffs.R

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#' data = german,
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#' family=binomial(link="logit"))
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm,
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#' protected = german$Sex,
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#' privileged = "male")
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#'
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#' ac <- all_cutoffs(fobject)
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#' plot(ac)
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#'
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#' \donttest{
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#' rf_model <- ranger::ranger(Risk ~.,
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#' data = german,
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#' probability = TRUE,
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#' num.trees = 200)
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#' num.trees = 100,
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#' seed = 1)
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#' explainer_rf <- DALEX::explain(rf_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm, explainer_rf,
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#' protected = german$Sex,
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#' privileged = "male")
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#' explainer_rf <- DALEX::explain(rf_model,
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#' data = german[,-1],
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#' y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_rf, fobject)
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#'
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#' ac <- all_cutoffs(fobject)
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#' plot(ac)
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#'
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#' }
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all_cutoffs <- function(x,
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grid_points = 101,

‎R/ceteris_paribus_cutoff.R

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#' y_numeric <- as.numeric(two_yr_recidivism) -1
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#' compas$Two_yr_Recidivism <- two_yr_recidivism
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#'
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#'
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#' lm_model <- glm(Two_yr_Recidivism~.,
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#' data=compas,
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#' family=binomial(link="logit"))
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = compas[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm,
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#' protected = compas$Ethnicity,
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#' privileged = "Caucasian")
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#'
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#' cpc <- ceteris_paribus_cutoff(fobject, "African_American")
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#' plot(cpc)
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#'
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#' \donttest{
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#' rf_model <- ranger::ranger(Two_yr_Recidivism ~.,
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#' data = compas,
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#' probability = TRUE,
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#' num.trees = 200)
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = compas[,-1], y = y_numeric)
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#' explainer_rf <- DALEX::explain(rf_model, data = compas[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm, explainer_rf,
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#' protected = compas$Ethnicity,
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#' privileged = "Caucasian")
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#'
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#' cpc <- ceteris_paribus_cutoff(fobject, "African_American")
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#'
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#' plot(cpc)
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#'
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#' }
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ceteris_paribus_cutoff <- function(x,
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subgroup,

‎R/choose_metric.R

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#' data = german,
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#' family=binomial(link="logit"))
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm,
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#' protected = german$Sex,
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#' privileged = "male")
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#'
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#'
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#' cm <- choose_metric(fobject, "TPR")
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#' plot(cm)
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#'
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#' \donttest{
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#' rf_model <- ranger::ranger(Risk ~.,
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#' data = german,
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#' probability = TRUE,
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#' num.trees = 200)
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#'
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#' explainer_rf <- DALEX::explain(rf_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm, explainer_rf,
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#' protected = german$Sex,
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#' privileged = "male")
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#' fobject <- fairness_check(explainer_rf, fobject)
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#'
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#' cm <- choose_metric(fobject, "TPR")
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#' plot(cm)
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#'
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#' }
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#'
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choose_metric <- function(x, fairness_metric = "FPR"){

‎R/expand_fairness_object.R

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#' data = german,
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#' family=binomial(link="logit"))
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm,
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#' protected = german$Sex,
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#' privileged = "male")
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#' expand_fairness_object(fobject, drop_metrics_with_na = TRUE)
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#'
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#' \donttest{
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#' rf_model <- ranger::ranger(Risk ~.,
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#' data = german,
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#' probability = TRUE,
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#' num.trees = 200)
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#'
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#' explainer_rf <- DALEX::explain(rf_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm, explainer_rf,
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#' protected = german$Sex,
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#' privileged = "male")
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#' fobject <- fairness_check(explainer_rf, fobject)
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#'
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#' expand_fairness_object(fobject, drop_metrics_with_na = TRUE)
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#'
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#' }
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expand_fairness_object <- function(x, scale = FALSE, drop_metrics_with_na = FALSE, fairness_metrics = NULL){
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‎R/fairness_check.R

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#' data = german,
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#' family=binomial(link="logit"))
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm,
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#' protected = german$Sex,
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#' privileged = "male")
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#' plot(fobject)
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#'
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#' \donttest{
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#' rf_model <- ranger::ranger(Risk ~.,
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#' data = german,
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#' probability = TRUE,
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#' max.depth = 3,
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#' num.trees = 100,
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#' seed = 1)
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#'
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#' explainer_rf <- DALEX::explain(rf_model,
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#' data = german[,-1],
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#' y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm, explainer_rf,
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#' protected = german$Sex,
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#' privileged = "male")
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#' fobject <- fairness_check(explainer_rf, fobject)
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#'
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#' plot(fobject)
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#'
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#'}
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fairness_check <- function(x,

‎R/fairness_heatmap.R

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#' rf_model <- ranger::ranger(Risk ~.,
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#' data = german,
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#' probability = TRUE,
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#' num.trees = 200)
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#' num.trees = 200,
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#' num.threads = 1)
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#' explainer_rf <- DALEX::explain(rf_model, data = german[,-1], y = y_numeric)

‎R/fairness_pca.R

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#' rf_model <- ranger::ranger(Risk ~.,
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#' data = german,
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#' probability = TRUE,
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#' num.trees = 200)
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#' num.trees = 200,
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#' num.threads = 1)
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#' explainer_rf <- DALEX::explain(rf_model, data = german[,-1], y = y_numeric)

‎R/fairness_radar.R

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#' data = german,
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#' family=binomial(link="logit"))
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm,
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#' protected = german$Sex,
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#' privileged = "male")
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#'
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#' fradar <- fairness_radar(fobject, fairness_metrics = c("ACC", "STP", "TNR",
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#' "TPR", "PPV"))
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#'
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#' plot(fradar)
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#'
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#' \donttest{
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#'
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#' rf_model <- ranger::ranger(Risk ~.,
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#' data = german,
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#' probability = TRUE,
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#' num.trees = 200)
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#' num.trees = 200,
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#' num.threads = 1)
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#'
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#' explainer_rf <- DALEX::explain(rf_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm, explainer_rf,
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#' protected = german$Sex,
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#' privileged = "male")
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#' fobject <- fairness_check(explainer_rf, fobject)
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#'
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#'
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#' fradar <- fairness_radar(fobject, fairness_metrics = c("ACC",
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#' "PPV"))
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#'
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#' plot(fradar)
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#' }
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fairness_radar <- function(x, fairness_metrics = c('ACC', 'TPR', 'PPV', 'FPR', 'STP')){

‎R/group_metric.R

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#' data = german,
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#' family=binomial(link="logit"))
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#'
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm,
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#' protected = german$Sex,
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#' privileged = "male")
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#'
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#' gm <- group_metric(fobject, "TPR", "f1", parity_loss = TRUE)
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#' plot(gm)
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#'
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#' \donttest{
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#'
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#' rf_model <- ranger::ranger(Risk ~.,
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#' data = german,
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#' probability = TRUE,
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#' num.trees = 200)
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#' explainer_rf <- DALEX::explain(rf_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm, explainer_rf,
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#' protected = german$Sex,
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#' privileged = "male")
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#' fobject <- fairness_check(explainer_rf, fobject)
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#'
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#' gm <- group_metric(fobject, "TPR", "f1", parity_loss = TRUE)
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#'
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#' plot(gm)
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#'
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#'}
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#'
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#' @return \code{group_metric} object.
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#' It is a list with following items:
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#' \itemize{

‎R/metric_scores.R

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#' data = german,
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#' family=binomial(link="logit"))
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#'
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm,
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#' protected = german$Sex,
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#' privileged = "male")
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#'
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#' ms <- metric_scores(fobject, fairness_metrics = c('ACC', 'TPR', 'PPV', 'FPR', 'STP'))
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#' plot(ms)
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#'
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#' \donttest{
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#'
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#' rf_model <- ranger::ranger(Risk ~.,
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#' data = german,
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#' probability = TRUE,
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#' num.trees = 200)
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#' explainer_rf <- DALEX::explain(rf_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm, explainer_rf,
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#' protected = german$Sex,
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#' privileged = "male")
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#' fobject <- fairness_check(explainer_rf, fobject)
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#'
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#' ms <- metric_scores(fobject, fairness_metrics = c('ACC', 'TPR', 'PPV', 'FPR', 'STP'))
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#' plot(ms)
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#'
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#' }
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#'
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‎R/performance_and_fairness.R

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#' data = german,
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#' family=binomial(link="logit"))
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#'
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm,
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#' protected = german$Sex,
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#' privileged = "male")
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#'
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#' paf <- performance_and_fairness(fobject)
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#' plot(paf)
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#'
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#' \donttest{
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#'
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#' rf_model <- ranger::ranger(Risk ~.,
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#' data = german,
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#' probability = TRUE,
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#' num.trees = 200)
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#'
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#' explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)
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#' explainer_rf <- DALEX::explain(rf_model, data = german[,-1], y = y_numeric)
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#'
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#' fobject <- fairness_check(explainer_lm, explainer_rf,
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#' protected = german$Sex,
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#' privileged = "male")
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#' fobject <- fairness_check(explainer_rf, fobject)
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#'
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#' # same explainers with different cutoffs for female
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#' fobject <- fairness_check(explainer_lm, explainer_rf, fobject,
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#'
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#' plot(paf)
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#'
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#'}
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