CRAN Package Check Results for Package fairmodels

Last updated on 2025-10-09 13:49:41 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.2.1 9.13 202.04 211.17 ERROR
r-devel-linux-x86_64-debian-gcc 1.2.1 6.57 141.76 148.33 ERROR
r-devel-linux-x86_64-fedora-clang 1.2.1 331.75 ERROR
r-devel-linux-x86_64-fedora-gcc 1.2.1 316.83 ERROR
r-devel-windows-x86_64 1.2.1 11.00 188.00 199.00 ERROR
r-patched-linux-x86_64 1.2.1 9.44 194.85 204.29 ERROR
r-release-linux-x86_64 1.2.1 8.10 193.55 201.65 ERROR
r-release-macos-arm64 1.2.1 111.00 NOTE
r-release-macos-x86_64 1.2.1 213.00 NOTE
r-release-windows-x86_64 1.2.1 11.00 190.00 201.00 ERROR
r-oldrel-macos-arm64 1.2.1 99.00 NOTE
r-oldrel-macos-x86_64 1.2.1 192.00 NOTE
r-oldrel-windows-x86_64 1.2.1 16.00 243.00 259.00 ERROR

Check Details

Version: 1.2.1
Check: CRAN incoming feasibility
Result: NOTE Maintainer: ‘Jakub Wiśniewski <jakwisn@gmail.com>’ The Description field contains <arXiv:2104.00507>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>. Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc

Version: 1.2.1
Check: Rd files
Result: NOTE checkRd: (-1) choose_metric.Rd:35: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) choose_metric.Rd:36: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) choose_metric.Rd:37: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) confusion_matrx.Rd:20: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) confusion_matrx.Rd:21: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) confusion_matrx.Rd:22: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) confusion_matrx.Rd:23: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) disparate_impact_remover.Rd:28: Lost braces 28 | pigeonholing. The number of pigeonholes is fixed and equal to min{101, unique(a)}, where a is vector with values for subgroup. So if some subgroup is not numerous and | ^ checkRd: (-1) fairness_check.Rd:47: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:48: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:49: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:50: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:51: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:52: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:53: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:54: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:55: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:56: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:57: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:58: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:61: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:62: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:63: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:64: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:65: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_check.Rd:66: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_heatmap.Rd:12: Lost braces 12 | \item{scale}{logical, if code{TRUE} metrics will be scaled to mean 0 and sd 1. Default \code{FALSE}} | ^ checkRd: (-1) fairness_heatmap.Rd:19: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_heatmap.Rd:20: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_heatmap.Rd:21: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_heatmap.Rd:22: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_pca.Rd:18: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_pca.Rd:19: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_pca.Rd:20: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_pca.Rd:21: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_pca.Rd:22: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_radar.Rd:18: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) fairness_radar.Rd:19: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_matrices.Rd:25: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_matrices.Rd:26: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_matrices.Rd:27: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_matrices.Rd:28: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_metric.Rd:30: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_metric.Rd:31: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_metric.Rd:32: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) group_metric.Rd:33: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) metric_scores.Rd:18: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) metric_scores.Rd:19: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) performance_and_fairness.Rd:20: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) performance_and_fairness.Rd:21: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) performance_and_fairness.Rd:22: Lost braces in \itemize; \value handles \item{}{} directly checkRd: (-1) performance_and_fairness.Rd:23: Lost braces in \itemize; \value handles \item{}{} directly Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64

Version: 1.2.1
Check: examples
Result: ERROR Running examples in ‘fairmodels-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: fairness_heatmap > ### Title: Fairness heatmap > ### Aliases: fairness_heatmap > > ### ** Examples > > > data("german") > > y_numeric <- as.numeric(german$Risk) - 1 > > lm_model <- glm(Risk ~ ., + data = german, + family = binomial(link = "logit") + ) > > rf_model <- ranger::ranger(Risk ~ ., + data = german, + probability = TRUE, + num.trees = 200, + num.threads = 1 + ) > > explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1369187 , mean = 0.7 , max = 0.9832426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9572803 , mean = 4.352454e-17 , max = 0.8283475 <1b>[32m A new explainer has been created! <1b>[39m > explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.07287302 , mean = 0.6989152 , max = 0.9974848 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.7219256 , mean = 0.001084826 , max = 0.6142332 <1b>[32m A new explainer has been created! <1b>[39m > > fobject <- fairness_check(explainer_lm, explainer_rf, + protected = german$Sex, + privileged = "male" + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > # same explainers with different cutoffs for female > fobject <- fairness_check(explainer_lm, explainer_rf, fobject, + protected = german$Sex, + privileged = "male", + cutoff = list(female = 0.4), + label = c("lm_2", "rf_2") + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : female: 0.4, male: 0.5 -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > > fh <- fairness_heatmap(fobject) > > plot(fh) Error in rep(yes, length.out = len) : attempt to replicate an object of type 'object' Calls: plot -> plot.fairness_heatmap -> ifelse Execution halted Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc

Version: 1.2.1
Check: tests
Result: ERROR Running ‘testthat.R’ [36s/40s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(fairmodels) > > > test_check("fairmodels") Welcome to DALEX (version: 2.5.2). Find examples and detailed introduction at: http://ema.drwhy.ai/ Additional features will be available after installation of: ggpubr. Use 'install_dependencies()' to get all suggested dependencies Loaded gbm 2.2.2 This version of gbm is no longer under development. Consider transitioning to gbm3, https://github.com/gbm-developers/gbm3 Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1575654 , mean = 0.5448133 , max = 0.8645892 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8479717 , mean = 6.679325e-05 , max = 0.7820503 <1b>[32m A new explainer has been created! <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.004522979 , mean = 0.5448801 , max = 0.8855426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8822826 , mean = -5.053611e-13 , max = 0.9767658 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 3 in total ( <1b>[31m model type not supported <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -174.6472 , mean = 742.9023 , max = 1690.151 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -307.5534 , mean = -6.003371e-13 , max = 302.2034 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models Fairness regression object created succesfully Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[31m model type not supported <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -174.6472 , mean = 742.9023 , max = 1690.151 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -307.5534 , mean = -6.003371e-13 , max = 302.2034 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[31m y not equal <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Fairness Metric not given, setting deafult ( TPR ) Performace metric not given, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Creating object with: Fairness metric: FPR Performance metric: f1 Fairness data top rows for FPR group score model 1 African_American 0.35204756 lm 2 Asian 0.04347826 lm 3 Caucasian 0.16393443 lm 4 Hispanic 0.11562500 lm 5 Native_American 0.16666667 lm 6 Other 0.07762557 lm Performance data for f1 : 1 lm 0.6039853 2 ranger 0.6343983 Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: non_existing Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: non_existing Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: auc Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: precision Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: recall Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1144574 , mean = 0.4551199 , max = 0.995477 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9767658 , mean = 5.053909e-13 , max = 0.8822826 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -119.546 , mean = 756.4906 , max = 1594.562 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -302.6659 , mean = 3.478115e-13 , max = 332.7938 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 361.6527 , mean = 756.1869 , max = 1136.792 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -669.0748 , mean = 0.3037205 , max = 630.6428 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m changing protected to factor Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 15 rows 2 cols -> target variable : 15 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 7.884924e-12 , mean = 0.4666667 , max = 1 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -7.884924e-12 , mean = -5.256659e-13 , max = 7.884915e-12 <1b>[32m A new explainer has been created! <1b>[39m [ FAIL 3 | WARN 1 | SKIP 0 | PASS 299 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_heatmap.R:2:3'): Test heatmap ────────────────────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::plot(fairness_heatmap(fobject)) at test_heatmap.R:2:3 2. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(fobject)) 3. └─base::ifelse(...) ── Failure ('test_plot_density.R:14:3'): Test plot_density ───────────────────── plt$labels$x not equal to "probability". target is NULL, current is character ── Error ('test_plot_fairmodels.R:8:3'): Test plot_fairmodels ────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::suppressWarnings(...) at test_plot_fairmodels.R:8:3 2. │ └─base::withCallingHandlers(...) 3. ├─fairmodels:::expect_s3_class(...) 4. │ ├─testthat::expect(...) at tests/testthat/helper_objects.R:70:20 5. │ └─base::class(object) %in% class 6. ├─fairmodels::plot_fairmodels(fc, type = "fairness_heatmap") 7. └─fairmodels:::plot_fairmodels.fairness_object(fc, type = "fairness_heatmap") 8. └─fairmodels:::plot_fairmodels.default(x, type, ...) 9. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(x, ...)) 10. └─base::ifelse(...) [ FAIL 3 | WARN 1 | SKIP 0 | PASS 299 ] Error: Test failures Execution halted Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.2.1
Check: re-building of vignette outputs
Result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘Advanced_tutorial.Rmd’ using rmarkdown --- finished re-building ‘Advanced_tutorial.Rmd’ --- re-building ‘Basic_tutorial.Rmd’ using rmarkdown Quitting from Basic_tutorial.Rmd:254-257 [unnamed-chunk-19] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <error/rlang_error> Error in `rep()`: ! attempt to replicate an object of type 'object' --- Backtrace: ▆ 1. ├─base::plot(fheatmap, text_size = 3) 2. └─fairmodels:::plot.fairness_heatmap(fheatmap, text_size = 3) 3. └─base::ifelse(...) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'Basic_tutorial.Rmd' failed with diagnostics: attempt to replicate an object of type 'object' --- failed re-building ‘Basic_tutorial.Rmd’ SUMMARY: processing the following file failed: ‘Basic_tutorial.Rmd’ Error: Vignette re-building failed. Execution halted Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.2.1
Check: tests
Result: ERROR Running ‘testthat.R’ [22s/25s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(fairmodels) > > > test_check("fairmodels") Welcome to DALEX (version: 2.5.2). Find examples and detailed introduction at: http://ema.drwhy.ai/ Additional features will be available after installation of: ggpubr. Use 'install_dependencies()' to get all suggested dependencies Loaded gbm 2.2.2 This version of gbm is no longer under development. Consider transitioning to gbm3, https://github.com/gbm-developers/gbm3 Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1661123 , mean = 0.5447374 , max = 0.8744903 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.852705 , mean = 0.0001427116 , max = 0.7740508 <1b>[32m A new explainer has been created! <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.004522979 , mean = 0.5448801 , max = 0.8855426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8822826 , mean = -5.053611e-13 , max = 0.9767658 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 3 in total ( <1b>[31m model type not supported <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -95.73285 , mean = 750.892 , max = 1547.596 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -314.4113 , mean = -9.066226e-14 , max = 269.3932 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models Fairness regression object created succesfully Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[31m model type not supported <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -95.73285 , mean = 750.892 , max = 1547.596 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -314.4113 , mean = -9.066226e-14 , max = 269.3932 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[31m y not equal <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 6/13 metrics calculated for all models ( <1b>[33m7 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Fairness Metric not given, setting deafult ( TPR ) Performace metric not given, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Creating object with: Fairness metric: FPR Performance metric: f1 Fairness data top rows for FPR group score model 1 African_American 0.35204756 lm 2 Asian 0.04347826 lm 3 Caucasian 0.16393443 lm 4 Hispanic 0.11562500 lm 5 Native_American 0.16666667 lm 6 Other 0.07762557 lm Performance data for f1 : 1 lm 0.6039853 2 ranger 0.6335761 Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: non_existing Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: non_existing Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: auc Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: precision Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: recall Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1144574 , mean = 0.4551199 , max = 0.995477 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9767658 , mean = 5.053909e-13 , max = 0.8822826 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -119.546 , mean = 756.4906 , max = 1594.562 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -302.6659 , mean = 3.478115e-13 , max = 332.7938 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 361.6527 , mean = 756.1869 , max = 1136.792 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -669.0748 , mean = 0.3037205 , max = 630.6428 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m changing protected to factor Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 15 rows 2 cols -> target variable : 15 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 7.884924e-12 , mean = 0.4666667 , max = 1 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -7.884924e-12 , mean = -5.256659e-13 , max = 7.884915e-12 <1b>[32m A new explainer has been created! <1b>[39m [ FAIL 3 | WARN 2 | SKIP 0 | PASS 299 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_heatmap.R:2:3'): Test heatmap ────────────────────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::plot(fairness_heatmap(fobject)) at test_heatmap.R:2:3 2. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(fobject)) 3. └─base::ifelse(...) ── Failure ('test_plot_density.R:14:3'): Test plot_density ───────────────────── plt$labels$x not equal to "probability". target is NULL, current is character ── Error ('test_plot_fairmodels.R:8:3'): Test plot_fairmodels ────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::suppressWarnings(...) at test_plot_fairmodels.R:8:3 2. │ └─base::withCallingHandlers(...) 3. ├─fairmodels:::expect_s3_class(...) 4. │ ├─testthat::expect(...) at tests/testthat/helper_objects.R:70:20 5. │ └─base::class(object) %in% class 6. ├─fairmodels::plot_fairmodels(fc, type = "fairness_heatmap") 7. └─fairmodels:::plot_fairmodels.fairness_object(fc, type = "fairness_heatmap") 8. └─fairmodels:::plot_fairmodels.default(x, type, ...) 9. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(x, ...)) 10. └─base::ifelse(...) [ FAIL 3 | WARN 2 | SKIP 0 | PASS 299 ] Error: Test failures Execution halted Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.2.1
Check: examples
Result: ERROR Running examples in ‘fairmodels-Ex.R’ failed The error most likely occurred in: > ### Name: fairness_heatmap > ### Title: Fairness heatmap > ### Aliases: fairness_heatmap > > ### ** Examples > > > data("german") > > y_numeric <- as.numeric(german$Risk) - 1 > > lm_model <- glm(Risk ~ ., + data = german, + family = binomial(link = "logit") + ) > > rf_model <- ranger::ranger(Risk ~ ., + data = german, + probability = TRUE, + num.trees = 200, + num.threads = 1 + ) > > explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1369187 , mean = 0.7 , max = 0.9832426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9572803 , mean = 1.280546e-17 , max = 0.8283475 <1b>[32m A new explainer has been created! <1b>[39m > explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.06718651 , mean = 0.6975836 , max = 0.9967857 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.7225752 , mean = 0.002416363 , max = 0.634748 <1b>[32m A new explainer has been created! <1b>[39m > > fobject <- fairness_check(explainer_lm, explainer_rf, + protected = german$Sex, + privileged = "male" + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > # same explainers with different cutoffs for female > fobject <- fairness_check(explainer_lm, explainer_rf, fobject, + protected = german$Sex, + privileged = "male", + cutoff = list(female = 0.4), + label = c("lm_2", "rf_2") + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : female: 0.4, male: 0.5 -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > > fh <- fairness_heatmap(fobject) > > plot(fh) Error in rep(yes, length.out = len) : attempt to replicate an object of type 'object' Calls: plot -> plot.fairness_heatmap -> ifelse Execution halted Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.2.1
Check: tests
Result: ERROR Running ‘testthat.R’ [61s/91s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(fairmodels) > > > test_check("fairmodels") Welcome to DALEX (version: 2.5.2). Find examples and detailed introduction at: http://ema.drwhy.ai/ Additional features will be available after installation of: ggpubr. Use 'install_dependencies()' to get all suggested dependencies Loaded gbm 2.2.2 This version of gbm is no longer under development. Consider transitioning to gbm3, https://github.com/gbm-developers/gbm3 Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1642965 , mean = 0.5447758 , max = 0.8658543 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8494785 , mean = 0.0001043454 , max = 0.7798716 <1b>[32m A new explainer has been created! <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.004522979 , mean = 0.5448801 , max = 0.8855426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8822826 , mean = -5.053611e-13 , max = 0.9767658 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 3 in total ( <1b>[31m model type not supported <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -165.8516 , mean = 756.1278 , max = 1708.849 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -379.0468 , mean = 4.644133e-13 , max = 268.9282 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models Fairness regression object created succesfully Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[31m model type not supported <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -165.8516 , mean = 756.1278 , max = 1708.849 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -379.0468 , mean = 4.644133e-13 , max = 268.9282 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[31m y not equal <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Fairness Metric not given, setting deafult ( TPR ) Performace metric not given, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Creating object with: Fairness metric: FPR Performance metric: f1 Fairness data top rows for FPR group score model 1 African_American 0.35204756 lm 2 Asian 0.04347826 lm 3 Caucasian 0.16393443 lm 4 Hispanic 0.11562500 lm 5 Native_American 0.16666667 lm 6 Other 0.07762557 lm Performance data for f1 : 1 lm 0.6039853 2 ranger 0.6343983 Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: non_existing Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: non_existing Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: auc Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: precision Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: recall Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1144574 , mean = 0.4551199 , max = 0.995477 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9767658 , mean = 5.053909e-13 , max = 0.8822826 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -119.546 , mean = 756.4906 , max = 1594.562 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -302.6659 , mean = 3.478115e-13 , max = 332.7938 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 327.8173 , mean = 756.3617 , max = 1159.246 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -635.2394 , mean = 0.1289064 , max = 608.1891 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m changing protected to factor Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 15 rows 2 cols -> target variable : 15 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 7.884924e-12 , mean = 0.4666667 , max = 1 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -7.884924e-12 , mean = -5.256659e-13 , max = 7.884915e-12 <1b>[32m A new explainer has been created! <1b>[39m [ FAIL 3 | WARN 1 | SKIP 0 | PASS 299 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_heatmap.R:2:3'): Test heatmap ────────────────────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::plot(fairness_heatmap(fobject)) at test_heatmap.R:2:3 2. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(fobject)) 3. └─base::ifelse(...) ── Failure ('test_plot_density.R:14:3'): Test plot_density ───────────────────── plt$labels$x not equal to "probability". target is NULL, current is character ── Error ('test_plot_fairmodels.R:8:3'): Test plot_fairmodels ────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::suppressWarnings(...) at test_plot_fairmodels.R:8:3 2. │ └─base::withCallingHandlers(...) 3. ├─fairmodels:::expect_s3_class(...) 4. │ ├─testthat::expect(...) at tests/testthat/helper_objects.R:70:20 5. │ └─base::class(object) %in% class 6. ├─fairmodels::plot_fairmodels(fc, type = "fairness_heatmap") 7. └─fairmodels:::plot_fairmodels.fairness_object(fc, type = "fairness_heatmap") 8. └─fairmodels:::plot_fairmodels.default(x, type, ...) 9. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(x, ...)) 10. └─base::ifelse(...) [ FAIL 3 | WARN 1 | SKIP 0 | PASS 299 ] Error: Test failures Execution halted Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.2.1
Check: re-building of vignette outputs
Result: ERROR Error(s) in re-building vignettes: --- re-building ‘Advanced_tutorial.Rmd’ using rmarkdown --- finished re-building ‘Advanced_tutorial.Rmd’ --- re-building ‘Basic_tutorial.Rmd’ using rmarkdown Quitting from Basic_tutorial.Rmd:254-257 [unnamed-chunk-19] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <error/rlang_error> Error in `rep()`: ! attempt to replicate an object of type 'object' --- Backtrace: ▆ 1. ├─base::plot(fheatmap, text_size = 3) 2. └─fairmodels:::plot.fairness_heatmap(fheatmap, text_size = 3) 3. └─base::ifelse(...) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'Basic_tutorial.Rmd' failed with diagnostics: attempt to replicate an object of type 'object' --- failed re-building ‘Basic_tutorial.Rmd’ SUMMARY: processing the following file failed: ‘Basic_tutorial.Rmd’ Error: Vignette re-building failed. Execution halted Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-release-windows-x86_64, r-oldrel-windows-x86_64

Version: 1.2.1
Check: examples
Result: ERROR Running examples in ‘fairmodels-Ex.R’ failed The error most likely occurred in: > ### Name: fairness_heatmap > ### Title: Fairness heatmap > ### Aliases: fairness_heatmap > > ### ** Examples > > > data("german") > > y_numeric <- as.numeric(german$Risk) - 1 > > lm_model <- glm(Risk ~ ., + data = german, + family = binomial(link = "logit") + ) > > rf_model <- ranger::ranger(Risk ~ ., + data = german, + probability = TRUE, + num.trees = 200, + num.threads = 1 + ) > > explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1369187 , mean = 0.7 , max = 0.9832426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9572803 , mean = 1.280546e-17 , max = 0.8283475 <1b>[32m A new explainer has been created! <1b>[39m > explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.07287302 , mean = 0.6989152 , max = 0.9974848 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.7219256 , mean = 0.001084826 , max = 0.6142332 <1b>[32m A new explainer has been created! <1b>[39m > > fobject <- fairness_check(explainer_lm, explainer_rf, + protected = german$Sex, + privileged = "male" + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > # same explainers with different cutoffs for female > fobject <- fairness_check(explainer_lm, explainer_rf, fobject, + protected = german$Sex, + privileged = "male", + cutoff = list(female = 0.4), + label = c("lm_2", "rf_2") + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : female: 0.4, male: 0.5 -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > > fh <- fairness_heatmap(fobject) > > plot(fh) Error in rep(yes, length.out = len) : attempt to replicate an object of type 'object' Calls: plot -> plot.fairness_heatmap -> ifelse Execution halted Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 1.2.1
Check: tests
Result: ERROR Running ‘testthat.R’ [55s/65s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(fairmodels) > > > test_check("fairmodels") Welcome to DALEX (version: 2.5.2). Find examples and detailed introduction at: http://ema.drwhy.ai/ Loaded gbm 2.2.2 This version of gbm is no longer under development. Consider transitioning to gbm3, https://github.com/gbm-developers/gbm3 Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.167971 , mean = 0.5447203 , max = 0.8624487 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8480142 , mean = 0.0001598289 , max = 0.7760066 <1b>[32m A new explainer has been created! <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.004522979 , mean = 0.5448801 , max = 0.8855426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8822826 , mean = -5.053611e-13 , max = 0.9767658 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 3 in total ( <1b>[31m model type not supported <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -109.083 , mean = 754.6519 , max = 1726.226 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -349.5516 , mean = 2.612e-14 , max = 310.2391 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models Fairness regression object created succesfully Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[31m model type not supported <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -109.083 , mean = 754.6519 , max = 1726.226 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -349.5516 , mean = 2.612e-14 , max = 310.2391 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[31m y not equal <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Fairness Metric not given, setting deafult ( TPR ) Performace metric not given, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Creating object with: Fairness metric: FPR Performance metric: f1 Fairness data top rows for FPR group score model 1 African_American 0.35204756 lm 2 Asian 0.04347826 lm 3 Caucasian 0.16393443 lm 4 Hispanic 0.11562500 lm 5 Native_American 0.16666667 lm 6 Other 0.07762557 lm Performance data for f1 : 1 lm 0.6039853 2 ranger 0.6333333 Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: non_existing Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: non_existing Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: auc Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: precision Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: recall Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1144574 , mean = 0.4551199 , max = 0.995477 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9767658 , mean = 5.053909e-13 , max = 0.8822826 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -119.546 , mean = 756.4906 , max = 1594.562 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -302.6659 , mean = 3.478115e-13 , max = 332.7938 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 361.6527 , mean = 756.1869 , max = 1136.792 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -669.0748 , mean = 0.3037205 , max = 630.6428 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m changing protected to factor Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 15 rows 2 cols -> target variable : 15 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 7.884924e-12 , mean = 0.4666667 , max = 1 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -7.884924e-12 , mean = -5.256659e-13 , max = 7.884915e-12 <1b>[32m A new explainer has been created! <1b>[39m [ FAIL 3 | WARN 1 | SKIP 0 | PASS 299 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_heatmap.R:2:3'): Test heatmap ────────────────────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::plot(fairness_heatmap(fobject)) at test_heatmap.R:2:3 2. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(fobject)) 3. └─base::ifelse(...) ── Failure ('test_plot_density.R:14:3'): Test plot_density ───────────────────── plt$labels$x not equal to "probability". target is NULL, current is character ── Error ('test_plot_fairmodels.R:8:3'): Test plot_fairmodels ────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::suppressWarnings(...) at test_plot_fairmodels.R:8:3 2. │ └─base::withCallingHandlers(...) 3. ├─fairmodels:::expect_s3_class(...) 4. │ ├─testthat::expect(...) at tests/testthat/helper_objects.R:70:20 5. │ └─base::class(object) %in% class 6. ├─fairmodels::plot_fairmodels(fc, type = "fairness_heatmap") 7. └─fairmodels:::plot_fairmodels.fairness_object(fc, type = "fairness_heatmap") 8. └─fairmodels:::plot_fairmodels.default(x, type, ...) 9. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(x, ...)) 10. └─base::ifelse(...) [ FAIL 3 | WARN 1 | SKIP 0 | PASS 299 ] Error: Test failures Execution halted Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 1.2.1
Check: examples
Result: ERROR Running examples in 'fairmodels-Ex.R' failed The error most likely occurred in: > ### Name: fairness_heatmap > ### Title: Fairness heatmap > ### Aliases: fairness_heatmap > > ### ** Examples > > > data("german") > > y_numeric <- as.numeric(german$Risk) - 1 > > lm_model <- glm(Risk ~ ., + data = german, + family = binomial(link = "logit") + ) > > rf_model <- ranger::ranger(Risk ~ ., + data = german, + probability = TRUE, + num.trees = 200, + num.threads = 1 + ) > > explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1369187 , mean = 0.7 , max = 0.9832426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9572803 , mean = 6.648002e-17 , max = 0.8283475 <1b>[32m A new explainer has been created! <1b>[39m > explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.07287302 , mean = 0.6989152 , max = 0.9974848 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.7219256 , mean = 0.001084826 , max = 0.6142332 <1b>[32m A new explainer has been created! <1b>[39m > > fobject <- fairness_check(explainer_lm, explainer_rf, + protected = german$Sex, + privileged = "male" + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > # same explainers with different cutoffs for female > fobject <- fairness_check(explainer_lm, explainer_rf, fobject, + protected = german$Sex, + privileged = "male", + cutoff = list(female = 0.4), + label = c("lm_2", "rf_2") + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : female: 0.4, male: 0.5 -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > > fh <- fairness_heatmap(fobject) > > plot(fh) Error in rep(yes, length.out = len) : attempt to replicate an object of type 'object' Calls: plot -> plot.fairness_heatmap -> ifelse Execution halted Flavor: r-devel-windows-x86_64

Version: 1.2.1
Check: tests
Result: ERROR Running 'testthat.R' [28s] Running the tests in 'tests/testthat.R' failed. Complete output: > library(testthat) > library(fairmodels) > > > test_check("fairmodels") Welcome to DALEX (version: 2.5.2). Find examples and detailed introduction at: http://ema.drwhy.ai/ Additional features will be available after installation of: ggpubr. Use 'install_dependencies()' to get all suggested dependencies Loaded gbm 2.2.2 This version of gbm is no longer under development. Consider transitioning to gbm3, https://github.com/gbm-developers/gbm3 Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1575998 , mean = 0.5446366 , max = 0.8628671 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8453535 , mean = 0.0002435264 , max = 0.7813384 <1b>[32m A new explainer has been created! <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.004522979 , mean = 0.5448801 , max = 0.8855426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8822826 , mean = -5.053611e-13 , max = 0.9767658 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 3 in total ( <1b>[31m model type not supported <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -54.02757 , mean = 744.8297 , max = 1647.915 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -382.3527 , mean = -1.179752e-13 , max = 283.2315 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models Fairness regression object created succesfully Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[31m model type not supported <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -54.02757 , mean = 744.8297 , max = 1647.915 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -382.3527 , mean = -1.179752e-13 , max = 283.2315 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[31m y not equal <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Fairness Metric not given, setting deafult ( TPR ) Performace metric not given, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Creating object with: Fairness metric: FPR Performance metric: f1 Fairness data top rows for FPR group score model 1 African_American 0.35204756 lm 2 Asian 0.04347826 lm 3 Caucasian 0.16393443 lm 4 Hispanic 0.11562500 lm 5 Native_American 0.16666667 lm 6 Other 0.07762557 lm Performance data for f1 : 1 lm 0.6039853 2 ranger 0.6449945 Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: non_existing Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: non_existing Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: auc Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: precision Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: recall Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1144574 , mean = 0.4551199 , max = 0.995477 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9767658 , mean = 5.053909e-13 , max = 0.8822826 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -119.546 , mean = 756.4906 , max = 1594.562 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -302.6659 , mean = 3.478115e-13 , max = 332.7938 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 361.6527 , mean = 756.1869 , max = 1136.792 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -669.0748 , mean = 0.3037205 , max = 630.6428 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m changing protected to factor Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 15 rows 2 cols -> target variable : 15 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.6.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 7.884924e-12 , mean = 0.4666667 , max = 1 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -7.884924e-12 , mean = -5.256659e-13 , max = 7.884915e-12 <1b>[32m A new explainer has been created! <1b>[39m [ FAIL 3 | WARN 1 | SKIP 0 | PASS 299 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_heatmap.R:2:3'): Test heatmap ────────────────────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::plot(fairness_heatmap(fobject)) at test_heatmap.R:2:3 2. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(fobject)) 3. └─base::ifelse(...) ── Failure ('test_plot_density.R:14:3'): Test plot_density ───────────────────── plt$labels$x not equal to "probability". target is NULL, current is character ── Error ('test_plot_fairmodels.R:8:3'): Test plot_fairmodels ────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::suppressWarnings(...) at test_plot_fairmodels.R:8:3 2. │ └─base::withCallingHandlers(...) 3. ├─fairmodels:::expect_s3_class(...) 4. │ ├─testthat::expect(...) at D:\RCompile\CRANpkg\local\4.6\fairmodels.Rcheck\tests\testthat\helper_objects.R:70:20 5. │ └─base::class(object) %in% class 6. ├─fairmodels::plot_fairmodels(fc, type = "fairness_heatmap") 7. └─fairmodels:::plot_fairmodels.fairness_object(fc, type = "fairness_heatmap") 8. └─fairmodels:::plot_fairmodels.default(x, type, ...) 9. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(x, ...)) 10. └─base::ifelse(...) [ FAIL 3 | WARN 1 | SKIP 0 | PASS 299 ] Error: Test failures Execution halted Flavor: r-devel-windows-x86_64

Version: 1.2.1
Check: examples
Result: ERROR Running examples in ‘fairmodels-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: fairness_heatmap > ### Title: Fairness heatmap > ### Aliases: fairness_heatmap > > ### ** Examples > > > data("german") > > y_numeric <- as.numeric(german$Risk) - 1 > > lm_model <- glm(Risk ~ ., + data = german, + family = binomial(link = "logit") + ) > > rf_model <- ranger::ranger(Risk ~ ., + data = german, + probability = TRUE, + num.trees = 200, + num.threads = 1 + ) > > explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1369187 , mean = 0.7 , max = 0.9832426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9572803 , mean = 4.352454e-17 , max = 0.8283475 <1b>[32m A new explainer has been created! <1b>[39m > explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.07287302 , mean = 0.6989152 , max = 0.9974848 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.7219256 , mean = 0.001084826 , max = 0.6142332 <1b>[32m A new explainer has been created! <1b>[39m > > fobject <- fairness_check(explainer_lm, explainer_rf, + protected = german$Sex, + privileged = "male" + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > # same explainers with different cutoffs for female > fobject <- fairness_check(explainer_lm, explainer_rf, fobject, + protected = german$Sex, + privileged = "male", + cutoff = list(female = 0.4), + label = c("lm_2", "rf_2") + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : female: 0.4, male: 0.5 -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > > fh <- fairness_heatmap(fobject) > > plot(fh) Error in rep(yes, length.out = len) : attempt to replicate an object of type 'object' Calls: plot -> plot.fairness_heatmap -> ifelse Execution halted Flavors: r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.2.1
Check: tests
Result: ERROR Running ‘testthat.R’ [35s/38s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(fairmodels) > > > test_check("fairmodels") Welcome to DALEX (version: 2.5.2). Find examples and detailed introduction at: http://ema.drwhy.ai/ Additional features will be available after installation of: ggpubr. Use 'install_dependencies()' to get all suggested dependencies Loaded gbm 2.2.2 This version of gbm is no longer under development. Consider transitioning to gbm3, https://github.com/gbm-developers/gbm3 Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1567624 , mean = 0.5446174 , max = 0.8662601 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8478945 , mean = 0.0002626889 , max = 0.7727163 <1b>[32m A new explainer has been created! <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.004522979 , mean = 0.5448801 , max = 0.8855426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8822826 , mean = -5.053611e-13 , max = 0.9767658 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 3 in total ( <1b>[31m model type not supported <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -282.7001 , mean = 754.0042 , max = 1623.082 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -369.9672 , mean = 7.268287e-14 , max = 342.6273 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models Fairness regression object created succesfully Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[31m model type not supported <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -282.7001 , mean = 754.0042 , max = 1623.082 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -369.9672 , mean = 7.268287e-14 , max = 342.6273 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[31m y not equal <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Fairness Metric not given, setting deafult ( TPR ) Performace metric not given, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Creating object with: Fairness metric: FPR Performance metric: f1 Fairness data top rows for FPR group score model 1 African_American 0.35204756 lm 2 Asian 0.04347826 lm 3 Caucasian 0.16393443 lm 4 Hispanic 0.11562500 lm 5 Native_American 0.16666667 lm 6 Other 0.07762557 lm Performance data for f1 : 1 lm 0.6039853 2 ranger 0.6451136 Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: non_existing Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: non_existing Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: auc Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: precision Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: recall Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1144574 , mean = 0.4551199 , max = 0.995477 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9767658 , mean = 5.053909e-13 , max = 0.8822826 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -119.546 , mean = 756.4906 , max = 1594.562 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -302.6659 , mean = 3.478115e-13 , max = 332.7938 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 361.6527 , mean = 756.1869 , max = 1136.792 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -669.0748 , mean = 0.3037205 , max = 630.6428 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m changing protected to factor Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 15 rows 2 cols -> target variable : 15 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 7.884924e-12 , mean = 0.4666667 , max = 1 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -7.884924e-12 , mean = -5.256659e-13 , max = 7.884915e-12 <1b>[32m A new explainer has been created! <1b>[39m [ FAIL 3 | WARN 1 | SKIP 0 | PASS 299 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_heatmap.R:2:3'): Test heatmap ────────────────────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::plot(fairness_heatmap(fobject)) at test_heatmap.R:2:3 2. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(fobject)) 3. └─base::ifelse(...) ── Failure ('test_plot_density.R:14:3'): Test plot_density ───────────────────── plt$labels$x not equal to "probability". target is NULL, current is character ── Error ('test_plot_fairmodels.R:8:3'): Test plot_fairmodels ────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::suppressWarnings(...) at test_plot_fairmodels.R:8:3 2. │ └─base::withCallingHandlers(...) 3. ├─fairmodels:::expect_s3_class(...) 4. │ ├─testthat::expect(...) at tests/testthat/helper_objects.R:70:20 5. │ └─base::class(object) %in% class 6. ├─fairmodels::plot_fairmodels(fc, type = "fairness_heatmap") 7. └─fairmodels:::plot_fairmodels.fairness_object(fc, type = "fairness_heatmap") 8. └─fairmodels:::plot_fairmodels.default(x, type, ...) 9. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(x, ...)) 10. └─base::ifelse(...) [ FAIL 3 | WARN 1 | SKIP 0 | PASS 299 ] Error: Test failures Execution halted Flavor: r-patched-linux-x86_64

Version: 1.2.1
Check: tests
Result: ERROR Running ‘testthat.R’ [35s/43s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(fairmodels) > > > test_check("fairmodels") Welcome to DALEX (version: 2.5.2). Find examples and detailed introduction at: http://ema.drwhy.ai/ Additional features will be available after installation of: ggpubr. Use 'install_dependencies()' to get all suggested dependencies Loaded gbm 2.2.2 This version of gbm is no longer under development. Consider transitioning to gbm3, https://github.com/gbm-developers/gbm3 Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1569291 , mean = 0.5449904 , max = 0.868545 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8542404 , mean = -0.0001103223 , max = 0.7871844 <1b>[32m A new explainer has been created! <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.004522979 , mean = 0.5448801 , max = 0.8855426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8822826 , mean = -5.053611e-13 , max = 0.9767658 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 3 in total ( <1b>[31m model type not supported <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -363.7743 , mean = 750.5251 , max = 1616.219 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -323.1543 , mean = -5.81169e-14 , max = 358.6111 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models Fairness regression object created succesfully Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[31m model type not supported <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -363.7743 , mean = 750.5251 , max = 1616.219 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -323.1543 , mean = -5.81169e-14 , max = 358.6111 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[31m y not equal <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 6/13 metrics calculated for all models ( <1b>[33m7 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Fairness Metric not given, setting deafult ( TPR ) Performace metric not given, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Creating object with: Fairness metric: FPR Performance metric: f1 Fairness data top rows for FPR group score model 1 African_American 0.35204756 lm 2 Asian 0.04347826 lm 3 Caucasian 0.16393443 lm 4 Hispanic 0.11562500 lm 5 Native_American 0.16666667 lm 6 Other 0.07762557 lm Performance data for f1 : 1 lm 0.6039853 2 ranger 0.6338001 Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: non_existing Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: non_existing Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: auc Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: precision Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: recall Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1144574 , mean = 0.4551199 , max = 0.995477 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9767658 , mean = 5.053909e-13 , max = 0.8822826 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -119.546 , mean = 756.4906 , max = 1594.562 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -302.6659 , mean = 3.478115e-13 , max = 332.7938 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 361.6527 , mean = 756.1869 , max = 1136.792 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -669.0748 , mean = 0.3037205 , max = 630.6428 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m changing protected to factor Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 15 rows 2 cols -> target variable : 15 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 7.884924e-12 , mean = 0.4666667 , max = 1 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -7.884924e-12 , mean = -5.256659e-13 , max = 7.884915e-12 <1b>[32m A new explainer has been created! <1b>[39m [ FAIL 3 | WARN 2 | SKIP 0 | PASS 299 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_heatmap.R:2:3'): Test heatmap ────────────────────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::plot(fairness_heatmap(fobject)) at test_heatmap.R:2:3 2. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(fobject)) 3. └─base::ifelse(...) ── Failure ('test_plot_density.R:14:3'): Test plot_density ───────────────────── plt$labels$x not equal to "probability". target is NULL, current is character ── Error ('test_plot_fairmodels.R:8:3'): Test plot_fairmodels ────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::suppressWarnings(...) at test_plot_fairmodels.R:8:3 2. │ └─base::withCallingHandlers(...) 3. ├─fairmodels:::expect_s3_class(...) 4. │ ├─testthat::expect(...) at tests/testthat/helper_objects.R:70:20 5. │ └─base::class(object) %in% class 6. ├─fairmodels::plot_fairmodels(fc, type = "fairness_heatmap") 7. └─fairmodels:::plot_fairmodels.fairness_object(fc, type = "fairness_heatmap") 8. └─fairmodels:::plot_fairmodels.default(x, type, ...) 9. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(x, ...)) 10. └─base::ifelse(...) [ FAIL 3 | WARN 2 | SKIP 0 | PASS 299 ] Error: Test failures Execution halted Flavor: r-release-linux-x86_64

Version: 1.2.1
Check: examples
Result: ERROR Running examples in 'fairmodels-Ex.R' failed The error most likely occurred in: > ### Name: fairness_heatmap > ### Title: Fairness heatmap > ### Aliases: fairness_heatmap > > ### ** Examples > > > data("german") > > y_numeric <- as.numeric(german$Risk) - 1 > > lm_model <- glm(Risk ~ ., + data = german, + family = binomial(link = "logit") + ) > > rf_model <- ranger::ranger(Risk ~ ., + data = german, + probability = TRUE, + num.trees = 200, + num.threads = 1 + ) > > explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1369187 , mean = 0.7 , max = 0.9832426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9572803 , mean = 6.648002e-17 , max = 0.8283475 <1b>[32m A new explainer has been created! <1b>[39m > explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.07287302 , mean = 0.6989152 , max = 0.9974848 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.7219256 , mean = 0.001084826 , max = 0.6142332 <1b>[32m A new explainer has been created! <1b>[39m > > fobject <- fairness_check(explainer_lm, explainer_rf, + protected = german$Sex, + privileged = "male" + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > # same explainers with different cutoffs for female > fobject <- fairness_check(explainer_lm, explainer_rf, fobject, + protected = german$Sex, + privileged = "male", + cutoff = list(female = 0.4), + label = c("lm_2", "rf_2") + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : female: 0.4, male: 0.5 -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > > fh <- fairness_heatmap(fobject) > > plot(fh) Error in rep(yes, length.out = len) : attempt to replicate an object of type 'object' Calls: plot -> plot.fairness_heatmap -> ifelse Execution halted Flavor: r-release-windows-x86_64

Version: 1.2.1
Check: tests
Result: ERROR Running 'testthat.R' [29s] Running the tests in 'tests/testthat.R' failed. Complete output: > library(testthat) > library(fairmodels) > > > test_check("fairmodels") Welcome to DALEX (version: 2.5.2). Find examples and detailed introduction at: http://ema.drwhy.ai/ Additional features will be available after installation of: ggpubr. Use 'install_dependencies()' to get all suggested dependencies Loaded gbm 2.2.2 This version of gbm is no longer under development. Consider transitioning to gbm3, https://github.com/gbm-developers/gbm3 Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1550912 , mean = 0.5446626 , max = 0.8681592 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8485889 , mean = 0.0002174809 , max = 0.7761809 <1b>[32m A new explainer has been created! <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.004522979 , mean = 0.5448801 , max = 0.8855426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8822826 , mean = -5.053611e-13 , max = 0.9767658 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 3 in total ( <1b>[31m model type not supported <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -165.7584 , mean = 748.8188 , max = 1712.873 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -287.1747 , mean = -1.477078e-12 , max = 292.0534 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models Fairness regression object created succesfully Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[31m model type not supported <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -165.7584 , mean = 748.8188 , max = 1712.873 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -287.1747 , mean = -1.477078e-12 , max = 292.0534 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[31m y not equal <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 6/13 metrics calculated for all models ( <1b>[33m7 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Fairness Metric not given, setting deafult ( TPR ) Performace metric not given, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Creating object with: Fairness metric: FPR Performance metric: f1 Fairness data top rows for FPR group score model 1 African_American 0.35204756 lm 2 Asian 0.04347826 lm 3 Caucasian 0.16393443 lm 4 Hispanic 0.11562500 lm 5 Native_American 0.16666667 lm 6 Other 0.07762557 lm Performance data for f1 : 1 lm 0.6039853 2 ranger 0.6342211 Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: non_existing Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: non_existing Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: auc Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: precision Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: recall Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1144574 , mean = 0.4551199 , max = 0.995477 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9767658 , mean = 5.053909e-13 , max = 0.8822826 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -119.546 , mean = 756.4906 , max = 1594.562 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -302.6659 , mean = 3.478115e-13 , max = 332.7938 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 361.6527 , mean = 756.1869 , max = 1136.792 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -669.0748 , mean = 0.3037205 , max = 630.6428 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m changing protected to factor Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 15 rows 2 cols -> target variable : 15 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.5.1 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 7.884924e-12 , mean = 0.4666667 , max = 1 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -7.884924e-12 , mean = -5.256659e-13 , max = 7.884915e-12 <1b>[32m A new explainer has been created! <1b>[39m [ FAIL 3 | WARN 2 | SKIP 0 | PASS 299 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_heatmap.R:2:3'): Test heatmap ────────────────────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::plot(fairness_heatmap(fobject)) at test_heatmap.R:2:3 2. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(fobject)) 3. └─base::ifelse(...) ── Failure ('test_plot_density.R:14:3'): Test plot_density ───────────────────── plt$labels$x not equal to "probability". target is NULL, current is character ── Error ('test_plot_fairmodels.R:8:3'): Test plot_fairmodels ────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::suppressWarnings(...) at test_plot_fairmodels.R:8:3 2. │ └─base::withCallingHandlers(...) 3. ├─fairmodels:::expect_s3_class(...) 4. │ ├─testthat::expect(...) at D:\RCompile\CRANpkg\local\4.5\fairmodels.Rcheck\tests\testthat\helper_objects.R:70:20 5. │ └─base::class(object) %in% class 6. ├─fairmodels::plot_fairmodels(fc, type = "fairness_heatmap") 7. └─fairmodels:::plot_fairmodels.fairness_object(fc, type = "fairness_heatmap") 8. └─fairmodels:::plot_fairmodels.default(x, type, ...) 9. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(x, ...)) 10. └─base::ifelse(...) [ FAIL 3 | WARN 2 | SKIP 0 | PASS 299 ] Error: Test failures Execution halted Flavor: r-release-windows-x86_64

Version: 1.2.1
Check: examples
Result: ERROR Running examples in 'fairmodels-Ex.R' failed The error most likely occurred in: > ### Name: fairness_heatmap > ### Title: Fairness heatmap > ### Aliases: fairness_heatmap > > ### ** Examples > > > data("german") > > y_numeric <- as.numeric(german$Risk) - 1 > > lm_model <- glm(Risk ~ ., + data = german, + family = binomial(link = "logit") + ) > > rf_model <- ranger::ranger(Risk ~ ., + data = german, + probability = TRUE, + num.trees = 200, + num.threads = 1 + ) > > explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.4.3 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1369187 , mean = 0.7 , max = 0.9832426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9572803 , mean = 6.648002e-17 , max = 0.8283475 <1b>[32m A new explainer has been created! <1b>[39m > explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric) Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 9 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.07287302 , mean = 0.6989152 , max = 0.9974848 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.7219256 , mean = 0.001084826 , max = 0.6142332 <1b>[32m A new explainer has been created! <1b>[39m > > fobject <- fairness_check(explainer_lm, explainer_rf, + protected = german$Sex, + privileged = "male" + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > # same explainers with different cutoffs for female > fobject <- fairness_check(explainer_lm, explainer_rf, fobject, + protected = german$Sex, + privileged = "male", + cutoff = list(female = 0.4), + label = c("lm_2", "rf_2") + ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : female: 0.4, male: 0.5 -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 10/13 metrics calculated for all models ( <1b>[33m3 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m > > > fh <- fairness_heatmap(fobject) > > plot(fh) Error in rep(yes, length.out = len) : attempt to replicate an object of type 'object' Calls: plot -> plot.fairness_heatmap -> ifelse Execution halted Flavor: r-oldrel-windows-x86_64

Version: 1.2.1
Check: tests
Result: ERROR Running 'testthat.R' [44s] Running the tests in 'tests/testthat.R' failed. Complete output: > library(testthat) > library(fairmodels) > > > test_check("fairmodels") Welcome to DALEX (version: 2.5.2). Find examples and detailed introduction at: http://ema.drwhy.ai/ Additional features will be available after installation of: ggpubr. Use 'install_dependencies()' to get all suggested dependencies Loaded gbm 2.2.2 This version of gbm is no longer under development. Consider transitioning to gbm3, https://github.com/gbm-developers/gbm3 Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1587974 , mean = 0.5448854 , max = 0.8728624 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8513548 , mean = -5.30918e-06 , max = 0.781337 <1b>[32m A new explainer has been created! <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.4.3 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.004522979 , mean = 0.5448801 , max = 0.8855426 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.8822826 , mean = -5.053611e-13 , max = 0.9767658 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models Fairness object created succesfully Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 4 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 3 in total ( <1b>[31m model type not supported <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.4.3 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -59.53575 , mean = 756.8124 , max = 1513.47 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -367.1213 , mean = 1.359858e-13 , max = 323.5572 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models Fairness regression object created succesfully Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from numeric <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[31m model type not supported <1b>[39m ) Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.4.3 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -59.53575 , mean = 756.8124 , max = 1513.47 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -367.1213 , mean = 1.359858e-13 , max = 323.5572 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[31m not compatible <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[31m y not equal <1b>[39m ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 11/13 metrics calculated for all models ( <1b>[33m2 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 2 objects ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Performace metric not given, setting deafult ( accuracy ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Fairness Metric not given, setting deafult ( TPR ) Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 6/13 metrics calculated for all models ( <1b>[33m7 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Fairness Metric not given, setting deafult ( TPR ) Performace metric not given, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Creating object with: Fairness metric: FPR Performance metric: f1 Fairness data top rows for FPR group score model 1 African_American 0.35204756 lm 2 Asian 0.04347826 lm 3 Caucasian 0.16393443 lm 4 Hispanic 0.11562500 lm 5 Native_American 0.16666667 lm 6 Other 0.07762557 lm Performance data for f1 : 1 lm 0.6039853 2 ranger 0.6443375 Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: TPR Performance metric: accuracy Performace metric is NULL, setting deafult ( accuracy ) Creating object with: Fairness metric: non_existing Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: non_existing Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: auc Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: accuracy Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: precision Fairness Metric is NULL, setting deafult parity loss metric ( TPR ) Creating object with: Fairness metric: TPR Performance metric: recall Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 6172 rows 7 cols -> target variable : 6172 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.4.3 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 0.1144574 , mean = 0.4551199 , max = 0.995477 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -0.9767658 , mean = 5.053909e-13 , max = 0.8822826 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 8/13 metrics calculated for all models ( <1b>[33m5 NA created<1b>[39m ) <1b>[32m Fairness object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.lm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.4.3 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = -119.546 , mean = 756.4906 , max = 1594.562 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -302.6659 , mean = 3.478115e-13 , max = 332.7938 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[33m changed from character <1b>[39m ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Preparation of a new explainer is initiated -> model label : ranger ( <1b>[33m default <1b>[39m ) -> data : 1000 rows 3 cols -> target variable : 1000 values -> predict function : yhat.ranger will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package ranger , ver. 0.17.0 , task regression ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 361.6527 , mean = 756.1869 , max = 1136.792 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -669.0748 , mean = 0.3037205 , max = 630.6428 <1b>[32m A new explainer has been created! <1b>[39m Creating fairness regression object -> Privileged subgroup : character (<1b>[33m from first fairness object <1b>[39m ) -> Protected variable : factor (<1b>[33m from first fairness object <1b>[39m ) -> Fairness objects : 1 object ( <1b>[32m compatible <1b>[39m ) -> Checking explainers : 2 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 3/3 metrics calculated for all models <1b>[32m Fairness regression object created succesfully <1b>[39m Creating fairness classification object -> Privileged subgroup : character (<1b>[32m Ok <1b>[39m ) -> Protected variable : factor (<1b>[32m Ok <1b>[39m ) -> Cutoff values for explainers : 0.5 ( for all subgroups ) -> Fairness objects : 0 objects -> Checking explainers : 1 in total ( <1b>[32m compatible <1b>[39m ) -> Metric calculation : 13/13 metrics calculated for all models <1b>[32m Fairness object created succesfully <1b>[39m changing protected to factor Preparation of a new explainer is initiated -> model label : lm ( <1b>[33m default <1b>[39m ) -> data : 15 rows 2 cols -> target variable : 15 values -> predict function : yhat.glm will be used ( <1b>[33m default <1b>[39m ) -> predicted values : No value for predict function target column. ( <1b>[33m default <1b>[39m ) -> model_info : package stats , ver. 4.4.3 , task classification ( <1b>[33m default <1b>[39m ) -> predicted values : numerical, min = 7.884924e-12 , mean = 0.4666667 , max = 1 -> residual function : difference between y and yhat ( <1b>[33m default <1b>[39m ) -> residuals : numerical, min = -7.884924e-12 , mean = -5.256659e-13 , max = 7.884915e-12 <1b>[32m A new explainer has been created! <1b>[39m [ FAIL 3 | WARN 2 | SKIP 0 | PASS 299 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_heatmap.R:2:3'): Test heatmap ────────────────────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::plot(fairness_heatmap(fobject)) at test_heatmap.R:2:3 2. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(fobject)) 3. └─base::ifelse(...) ── Failure ('test_plot_density.R:14:3'): Test plot_density ───────────────────── plt$labels$x not equal to "probability". target is NULL, current is character ── Error ('test_plot_fairmodels.R:8:3'): Test plot_fairmodels ────────────────── Error in `rep(yes, length.out = len)`: attempt to replicate an object of type 'object' Backtrace: ▆ 1. ├─base::suppressWarnings(...) at test_plot_fairmodels.R:8:3 2. │ └─base::withCallingHandlers(...) 3. ├─fairmodels:::expect_s3_class(...) 4. │ ├─testthat::expect(...) at D:\RCompile\CRANpkg\local\4.4\fairmodels.Rcheck\tests\testthat\helper_objects.R:70:20 5. │ └─base::class(object) %in% class 6. ├─fairmodels::plot_fairmodels(fc, type = "fairness_heatmap") 7. └─fairmodels:::plot_fairmodels.fairness_object(fc, type = "fairness_heatmap") 8. └─fairmodels:::plot_fairmodels.default(x, type, ...) 9. └─fairmodels:::plot.fairness_heatmap(fairness_heatmap(x, ...)) 10. └─base::ifelse(...) [ FAIL 3 | WARN 2 | SKIP 0 | PASS 299 ] Error: Test failures Execution halted Flavor: r-oldrel-windows-x86_64

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