Make fairness_radar object with chosen fairness_metrics. Note that there must be at least three metrics that does not contain NA.

fairness_radar(x, fairness_metrics = c("ACC", "TPR", "PPV", "FPR", "STP"))

Arguments

x

object of class fairness_object

fairness_metrics

character, vector of metric names, at least 3 metrics without NA needed. Full names of metrics can be found in fairness_check documentation.

Value

fairness_radar object. It is a list containing:

  • radar_data - data.frame containing scores for each model and parity loss metric

  • label - model labels

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. 4.1.0 , 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		: 12/12 metrics calculated for all models
#>  Fairness object created succesfully  

fradar <- fairness_radar(fobject, fairness_metrics = c("ACC", "STP", "TNR",
                                                       "TPR", "PPV"))

plot(fradar)


# \donttest{

rf_model <- ranger::ranger(Risk ~.,
                           data = german,
                           probability = TRUE,
                           num.trees = 200,
                           num.threads = 1)


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. 0.12.1 , task classification (  default  ) 
#>   -> predicted values  :  numerical, min =  0.08029365 , mean =  0.6967018 , max =  0.9975  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.6935774 , mean =  0.003298243 , max =  0.6411429  
#>   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		: 12/12 metrics calculated for all models
#>  Fairness object created succesfully  


fradar <- fairness_radar(fobject, fairness_metrics = c("ACC",
                                                       "STP",
                                                       "TNR",
                                                       "TPR",
                                                       "PPV"))

plot(fradar)

# }