Ceteris paribus cutoff is way to check how will parity loss behave if we changed only cutoff in one subgroup. It plots object of class ceteris_paribus_cutoff. It might have two types - default and cumulated. Cumulated sums metrics and plots it all in one plot. When default one is used all chosen metrics will be plotted for each model.

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

Arguments

x

ceteris_paribus_cutoff object

...

other plot parameters

Value

ggplot2 object

Examples


data("compas")

# positive outcome - not being recidivist
two_yr_recidivism <- factor(compas$Two_yr_Recidivism, levels = c(1,0))
y_numeric <- as.numeric(two_yr_recidivism) -1
compas$Two_yr_Recidivism <- two_yr_recidivism


lm_model <- glm(Two_yr_Recidivism~.,
                data=compas,
                family=binomial(link="logit"))

explainer_lm <- DALEX::explain(lm_model, data = compas[,-1], y = y_numeric)
#> Preparation of a new explainer is initiated
#>   -> model label       :  lm  (  default  )
#>   -> data              :  6172  rows  6  cols 
#>   -> target variable   :  6172  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 =  0.004522979 , mean =  0.5448801 , max =  0.8855426  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.8822826 , mean =  -5.07018e-13 , max =  0.9767658  
#>   A new explainer has been created!  

fobject <- fairness_check(explainer_lm,
                          protected = compas$Ethnicity,
                          privileged = "Caucasian")
#> 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		: 10/12 metrics calculated for all models ( 2 NA created )
#>  Fairness object created succesfully  

cpc <- ceteris_paribus_cutoff(fobject, "African_American")
plot(cpc)
#> Warning: Removed 63 row(s) containing missing values (geom_path).


# \donttest{
rf_model <- ranger::ranger(Two_yr_Recidivism ~.,
                           data = compas,
                           probability = TRUE,
                           num.trees = 200)

explainer_rf <- DALEX::explain(rf_model, data = compas[,-1], y = y_numeric)
#> Preparation of a new explainer is initiated
#>   -> model label       :  ranger  (  default  )
#>   -> data              :  6172  rows  6  cols 
#>   -> target variable   :  6172  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 =  0.153945 , mean =  0.5452013 , max =  0.8650518  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.847902 , mean =  -0.0003212319 , max =  0.7775046  
#>   A new explainer has been created!  

fobject <- fairness_check(explainer_lm, explainer_rf,
                          protected = compas$Ethnicity,
                          privileged = "Caucasian")
#> 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		: 2 in total (  compatible  )
#> -> Metric calculation		: 8/12 metrics calculated for all models ( 4 NA created )
#>  Fairness object created succesfully  

cpc <- ceteris_paribus_cutoff(fobject, "African_American")
plot(cpc)
#> Warning: Removed 73 row(s) containing missing values (geom_path).

# }