Adds a Layer with Residuals to a Profile Plot

Function show_residuals adds a layer to a plot created with plot.ceteris_paribus_explainer for selected observations. Note that the y argument has to be specified in the ceteris_paribus function.

show_residuals(
  x,
  ...,
  size = 0.75,
  alpha = 1,
  color = c(`TRUE` = "#8bdcbe", `FALSE` = "#f05a71"),
  variables = NULL
)

Arguments

x

a ceteris paribus explainer produced with function ceteris_paribus(). Note that y parameter shall be supplied in this function.

...

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

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")
library("ranger")

johny_d <- data.frame(
  class = factor("1st", levels = c("1st", "2nd", "3rd", "deck crew", "engineering crew",
                                   "restaurant staff", "victualling crew")),
  gender = factor("male", levels = c("female", "male")),
  age = 8,
  sibsp = 0,
  parch = 0,
  fare = 72,
  embarked = factor("Southampton", levels = c("Belfast", "Cherbourg", "Queenstown", "Southampton"))
)

# \donttest{
model_titanic_rf <- ranger(survived ~., data = titanic_imputed, probability = TRUE)

explain_titanic_rf <- explain(model_titanic_rf,
                              data = titanic_imputed[,-8],
                              y = titanic_imputed[,8],
                              label = "ranger forest",
                              verbose = FALSE)

johny_neighbours <- select_neighbours(data = titanic_imputed,
                                      observation = johny_d,
                                      variables = c("age", "gender", "class",
                                                  "fare", "sibsp", "parch"),
                                      n = 10)

cp_neighbours <- ceteris_paribus(explain_titanic_rf,
                                 johny_neighbours,
                                 y = johny_neighbours$survived == "yes",
                                 variable_splits = list(age = seq(0,70, length.out = 1000)))

plot(cp_neighbours, variables = "age") +
  show_observations(cp_neighbours, variables = "age")



cp_johny <- ceteris_paribus(explain_titanic_rf, johny_d,
                            variable_splits = list(age = seq(0,70, length.out = 1000)))

plot(cp_johny, variables = "age", size = 1.5, color = "#8bdcbe") +
  show_profiles(cp_neighbours, variables = "age", color = "#ceced9") +
  show_observations(cp_johny, variables = "age", size = 5, color = "#371ea3") +
  show_residuals(cp_neighbours, variables = "age")


# }