Plot chosen metric in group. Notice how models are treating different subgroups. Compare models both in fairness metrics and in performance. Parity loss can be enabled when creating group_metric object.

# S3 method for group_metric
plot(x, ...)

Arguments

x

object of class group_metric

...

other group_metric objects and other parameters

Value

list of ggplot2 objects

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  

gm <- group_metric(fobject, "TPR", "f1", parity_loss = TRUE)
#> 
#> Creating object with: 
#> Fairness metric:  TPR 
#> Performance metric:  f1 
#> 
plot(gm)

# \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 =  0.05022024 , mean =  0.6969863 , max =  0.9981746  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.7025734 , mean =  0.003013703 , max =  0.6601389  
#>   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  

gm <- group_metric(fobject, "TPR", "f1", parity_loss = TRUE)
#> 
#> Creating object with: 
#> Fairness metric:  TPR 
#> Performance metric:  f1 
#> 

plot(gm)

# }