Plot Local Variable Importance measure — plot.local

Function plot.local_importance plots local importance measure based on Ceteris Paribus profiles.

# S3 method for local_importance
plot(
  x,
  ...,
  variables = NULL,
  color = NULL,
  type = NULL,
  title = "Local variable importance"
)

Arguments

x	object returned from `local_variable_importance()` function
...	other object returned from `local_variable_importance()` function that shall be plotted together
variables	if not `NULL` then only `variables` will be presented
color	a character. How to aggregated measure? Either "_label_method_" or "_label_model_".
type	a character. How variables shall be plotted? Either "bars" (default) or "lines".
title	the plot's title, by default `'Local variable importance'`

Value

a ggplot2 object

Examples


library("DALEX")
data(apartments)

library("randomForest")
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface +
                                    floor + no.rooms, data = apartments)

explainer_rf <- explain(apartments_rf_model, data = apartmentsTest[,2:5],
                        y = apartmentsTest$m2.price)
#> Preparation of a new explainer is initiated
#>   -> model label       :  randomForest  (  default  )
#>   -> data              :  9000  rows  4  cols 
#>   -> target variable   :  9000  values 
#>   -> predict function  :  yhat.randomForest  will be used (  default  )
#>   -> predicted values  :  numerical, min =  2113.505 , mean =  3514.194 , max =  5405.559  
#>   -> model_info        :  package randomForest , ver. 4.6.14 , task regression (  default  ) 
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -1224.547 , mean =  -2.670117 , max =  2116.356  
#>   A new explainer has been created!  

new_apartment <- data.frame(construction.year = 1998, surface = 88, floor = 2L, no.rooms = 3)

profiles <- predict_profile(explainer_rf, new_apartment)

library("vivo")
measure1 <- local_variable_importance(profiles, apartments[,2:5],
                          absolute_deviation = TRUE, point = TRUE, density = FALSE)

plot(measure1)

measure2 <- local_variable_importance(profiles, apartments[,2:5],
                          absolute_deviation = TRUE, point = TRUE, density = TRUE)
plot(measure1, measure2, color = "_label_method_", type = "lines")