ingredients (version 2.3.0)

show_rugs: Adds a Layer with Rugs to a Profile Plot

Description

Function show_rugs 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.

Usage

show_rugs(
  x,
  ...,
  size = 0.5,
  alpha = 1,
  color = "#371ea3",
  variable_type = "numerical",
  sides = "b",
  variables = NULL
)

Value

a ggplot2 layer

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.

sides

a string containing any of "trbl", for top, right, bottom, and left. Passed to geom rug.

variables

if not NULL then only variables will be presented

References

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

Examples

library("DALEX")
library("ingredients")
titanic_small <- select_sample(titanic_imputed, n = 500, seed = 1313)

# build a model
model_titanic_glm <- glm(survived ~ gender + age + fare,
                         data = titanic_small,
                         family = "binomial")

explain_titanic_glm <- explain(model_titanic_glm,
                               data = titanic_small[,-8],
                               y = titanic_small[,8])

cp_glm <- ceteris_paribus(explain_titanic_glm, titanic_small[1,])
cp_glm

# \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

plot(cp_rf, variables = "age", color = "grey") +
show_observations(cp_rf, variables = "age", color = "black") +
  show_rugs(cp_rf, variables = "age", color = "red")
# }