Fairness radar

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. 4.1.1 , 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  

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. 0.13.1 , 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		: 13/13 metrics calculated for all models
#>  Fairness object created succesfully  


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

plot(fradar)

# }