plotD3_ceteris_paribus: Plots Ceteris Paribus Profiles in D3 with r2d3 Package.

In ModelOriented/ingredients: Effects and Importances of Model Ingredients

Plots Ceteris Paribus Profiles in D3 with r2d3 Package.

Description

Function plotD3.ceteris_paribus_explainer plots Individual Variable Profiles for selected observations. It uses output from ceteris_paribus function. Various parameters help to decide what should be plotted, profiles, aggregated profiles, points or rugs.

Find more details in Ceteris Paribus Chapter.

Usage

plotD3(x, ...)

## S3 method for class 'ceteris_paribus_explainer'
plotD3(
  x,
  ...,
  size = 2,
  alpha = 1,
  color = "#46bac2",
  variable_type = "numerical",
  facet_ncol = 2,
  scale_plot = FALSE,
  variables = NULL,
  chart_title = "Ceteris Paribus Profiles",
  label_margin = 60,
  show_observations = TRUE,
  show_rugs = TRUE
)

Arguments

x	a ceteris paribus explainer produced with function ceteris_paribus()
...	other explainers that shall be plotted together
size	a numeric. Set width of lines
alpha	a numeric between 0 and 1. Opacity of lines
color	a character. Set line color
variable_type	a character. If "numerical" then only numerical variables will be plotted. If "categorical" then only categorical variables will be plotted.
facet_ncol	number of columns for the facet_wrap
scale_plot	a logical. If TRUE, the height of plot scales with window size. By default it's FALSE
variables	if not NULL then only variables will be presented
chart_title	a character. Set custom title
label_margin	a numeric. Set width of label margins in categorical type
show_observations	a logical. Adds observations layer to a plot. By default it's TRUE
show_rugs	a logical. Adds rugs layer to a plot. By default it's TRUE

Value

a r2d3 object.

References

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

Examples

library("DALEX")
library("ingredients")
library("ranger")


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)

selected_passangers <- select_sample(titanic_imputed, n = 10)
cp_rf <- ceteris_paribus(explain_titanic_rf, selected_passangers)

plotD3(cp_rf, variables = c("age","parch","fare","sibsp"),
     facet_ncol = 2, scale_plot = TRUE)

selected_passanger <- select_sample(titanic_imputed, n = 1)
cp_rf <- ceteris_paribus(explain_titanic_rf, selected_passanger)

plotD3(cp_rf, variables = c("class", "embarked", "gender", "sibsp"),
     facet_ncol = 2, variable_type = "categorical", label_margin = 100, scale_plot = TRUE)

ModelOriented/ingredients documentation built on March 16, 2023, 1:53 a.m.