Function show_observations adds a layer to a plot created with plot.ceteris_paribus_explainer for selected observations. Various parameters help to decide what should be plotted, profiles, aggregated profiles, points or rugs.

show_observations(
  x,
  ...,
  size = 2,
  alpha = 1,
  color = "#371ea3",
  variable_type = "numerical",
  variables = NULL
)

Arguments

x

a ceteris paribus explainer produced with function ceteris_paribus()

...

other explainers that shall be plotted together

size

a numeric. Size of lines to be plotted

alpha

a numeric between 0 and 1. Opacity of lines

color

a character. Either name of a color or name of a variable that should be used for coloring

variable_type

a character. If numerical then only numerical variables will be plotted. If categorical then only categorical variables will be plotted.

variables

if not NULL then only variables will be presented

Value

a ggplot2 layer

References

Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/

Examples

library("DALEX") library("ingredients") # \donttest{ library("ranger") rf_model <- ranger(survived ~., data = titanic_imputed, probability = TRUE) explainer_rf <- explain(rf_model, data = titanic_imputed[,-8], y = titanic_imputed[,8], label = "ranger forest", verbose = FALSE) selected_passangers <- select_sample(titanic_imputed, n = 100) cp_rf <- ceteris_paribus(explainer_rf, selected_passangers) cp_rf
#> Top profiles : #> gender age class embarked fare sibsp parch _yhat_ #> 515 female 45 2nd Southampton 10.1000 0 0 0.8168505 #> 515.1 male 45 2nd Southampton 10.1000 0 0 0.1059383 #> 604 female 17 3rd Southampton 7.1701 1 0 0.4675747 #> 604.1 male 17 3rd Southampton 7.1701 1 0 0.1167633 #> 1430 female 25 engineering crew Southampton 0.0000 0 0 0.7796172 #> 1430.1 male 25 engineering crew Southampton 0.0000 0 0 0.2402162 #> _vname_ _ids_ _label_ #> 515 gender 515 ranger forest #> 515.1 gender 515 ranger forest #> 604 gender 604 ranger forest #> 604.1 gender 604 ranger forest #> 1430 gender 1430 ranger forest #> 1430.1 gender 1430 ranger forest #> #> #> Top observations: #> gender age class embarked fare sibsp parch _yhat_ #> 515 male 45 2nd Southampton 10.1000 0 0 0.1059383 #> 604 male 17 3rd Southampton 7.1701 1 0 0.1167633 #> 1430 male 25 engineering crew Southampton 0.0000 0 0 0.2402162 #> 865 male 20 3rd Cherbourg 7.0406 0 0 0.1177738 #> 452 female 17 3rd Queenstown 7.1408 0 0 0.6502409 #> 1534 male 38 victualling crew Southampton 0.0000 0 0 0.1697892 #> _label_ _ids_ #> 515 ranger forest 1 #> 604 ranger forest 2 #> 1430 ranger forest 3 #> 865 ranger forest 4 #> 452 ranger forest 5 #> 1534 ranger forest 6
plot(cp_rf, variables = "age", color = "grey") + show_observations(cp_rf, variables = "age", color = "black") + show_rugs(cp_rf, variables = "age", color = "red")
# }