Easier access to all plots in fairmodels. Provide plot type (that matches to function name), pass additional parameters and plot.
plot_fairmodels(x, type, ...)
# S3 method for explainer
plot_fairmodels(x, type = "fairness_check", ..., protected, privileged)
# S3 method for fairness_object
plot_fairmodels(x, type = "fairness_check", ...)
# S3 method for default
plot_fairmodels(x, type = "fairness_check", ...)Arguments
| x | object created with |
|---|---|
| type | character, type of plot. Should match function name in fairmodels. Default is fairness_check. |
| ... | other parameters passed to fairmodels functions. |
| protected | factor, vector containing sensitive attributes such as gender, race, etc... |
| privileged | character/factor, level in factor denoting privileged subgroup |
Value
ggplot2 object
Details
types (function names) available:
fairness_check
stack_metrics
fairness_heatmap
fairness_pca
fairness_radar
group_metric
choose_metric
metric_scores
performance_and_fairness
all_cutoffs
ceteris_paribus_cutoff
Examples
data("german")
y_numeric <- as.numeric(german$Risk) - 1
lm_model <- glm(Risk ~ .,
data = german,
family = binomial(link = "logit")
)
explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric)
#> Preparation of a new explainer is initiated
#> -> model label : lm ( default )
#> -> data : 1000 rows 9 cols
#> -> target variable : 1000 values
#> -> predict function : yhat.glm will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package stats , ver. 4.1.1 , task classification ( default )
#> -> predicted values : numerical, min = 0.1369187 , mean = 0.7 , max = 0.9832426
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -0.9572803 , mean = 1.940006e-17 , max = 0.8283475
#> A new explainer has been created!
# works with explainer when protected and privileged are passed
plot_fairmodels(explainer_lm,
type = "fairness_radar",
protected = german$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
# or with fairness_object
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
plot_fairmodels(fobject, type = "fairness_radar")
