Unfold fairness object to 3 columns (metrics, label, score) to construct better base for visualization.
expand_fairness_object(
x,
scale = FALSE,
drop_metrics_with_na = FALSE,
fairness_metrics = NULL
)| x | object of class |
|---|---|
| scale | logical, if |
| drop_metrics_with_na | logical, if |
| fairness_metrics | character, vector of fairness metrics names indicating from which expand. |
object of class expand_fairness_object. It is a data.frame with scores for each metric and model.
data("german")
y_numeric <- as.numeric(german$Risk) - 1
lm_model <- glm(Risk ~ .,
data = german,
family = binomial(link = "logit")
)
explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric)
#> Preparation of a new explainer is initiated
#> -> model label : lm ( default )
#> -> data : 1000 rows 9 cols
#> -> target variable : 1000 values
#> -> predict function : yhat.glm will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package stats , ver. 4.1.1 , task classification ( default )
#> -> predicted values : numerical, min = 0.1369187 , mean = 0.7 , max = 0.9832426
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -0.9572803 , mean = 1.940006e-17 , max = 0.8283475
#> A new explainer has been created!
fobject <- fairness_check(explainer_lm,
protected = german$Sex,
privileged = "male"
)
#> Creating fairness classification object
#> -> Privileged subgroup : character ( Ok )
#> -> Protected variable : factor ( Ok )
#> -> Cutoff values for explainers : 0.5 ( for all subgroups )
#> -> Fairness objects : 0 objects
#> -> Checking explainers : 1 in total ( compatible )
#> -> Metric calculation : 13/13 metrics calculated for all models
#> Fairness object created succesfully
expand_fairness_object(fobject, drop_metrics_with_na = TRUE)
#> metric model score
#> 1 TPR lm 0.096364988
#> 2 TNR lm 0.433770370
#> 3 PPV lm 0.021418116
#> 4 NPV lm 0.006376217
#> 5 FNR lm 0.764119178
#> 6 FPR lm 0.347333014
#> 7 FDR lm 0.075989500
#> 8 FOR lm 0.011650617
#> 9 TS lm 0.096572597
#> 10 STP lm 0.184128760
#> 11 ACC lm 0.041523503
#> 12 F1 lm 0.056585621
#> 13 NEW_METRIC lm 0.860484166
# \donttest{
rf_model <- ranger::ranger(Risk ~ .,
data = german,
probability = TRUE,
num.trees = 200
)
explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric)
#> Preparation of a new explainer is initiated
#> -> model label : ranger ( default )
#> -> data : 1000 rows 9 cols
#> -> target variable : 1000 values
#> -> predict function : yhat.ranger will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package ranger , ver. 0.13.1 , task classification ( default )
#> -> predicted values : numerical, min = 0.04864286 , mean = 0.6963437 , max = 0.9955556
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -0.7585476 , mean = 0.003656338 , max = 0.6813942
#> A new explainer has been created!
fobject <- fairness_check(explainer_rf, fobject)
#> Creating fairness classification object
#> -> Privileged subgroup : character ( from first fairness object )
#> -> Protected variable : factor ( from first fairness object )
#> -> Cutoff values for explainers : 0.5 ( for all subgroups )
#> -> Fairness objects : 1 object ( compatible )
#> -> Checking explainers : 2 in total ( compatible )
#> -> Metric calculation : 10/13 metrics calculated for all models ( 3 NA created )
#> Fairness object created succesfully
expand_fairness_object(fobject, drop_metrics_with_na = TRUE)
#> Warning: Found metric with NA: FNR, FOR, NEW_METRIC, omiting it
#> metric model score
#> 1 TPR ranger 0.0080483332
#> 2 TPR lm 0.0963649885
#> 3 TNR ranger 0.0545052155
#> 4 TNR lm 0.4337703703
#> 5 PPV ranger 0.0080578374
#> 6 PPV lm 0.0214181164
#> 7 NPV ranger 0.0261452801
#> 8 NPV lm 0.0063762171
#> 9 FPR ranger 0.2375821504
#> 10 FPR lm 0.3473330144
#> 11 FDR ranger 0.0946873209
#> 12 FDR lm 0.0759894996
#> 13 TS ranger 0.0006090134
#> 14 TS lm 0.0965725965
#> 15 STP ranger 0.0930757170
#> 16 STP lm 0.1841287596
#> 17 ACC ranger 0.0060735986
#> 18 ACC lm 0.0415235028
#> 19 F1 ranger 0.0003175107
#> 20 F1 lm 0.0565856215
# }