plotD3_aggregated_profiles: Plots Aggregated Ceteris Paribus Profiles in D3 with r2d3...

In ingredients: Effects and Importances of Model Ingredients

Plots Aggregated Ceteris Paribus Profiles in D3 with r2d3 Package.

Description

Function plotD3.aggregated_profiles_explainer plots an aggregate of ceteris paribus profiles. It works in a similar way to plotD3.ceteris_paribus_explainer but, instead of individual profiles, show average profiles for each variable listed in the variables vector.

Find more details in Ceteris Paribus Chapter.

Usage

## S3 method for class 'aggregated_profiles_explainer'
plotD3(
  x,
  ...,
  size = 2,
  alpha = 1,
  color = "#46bac2",
  facet_ncol = 2,
  scale_plot = FALSE,
  variables = NULL,
  chart_title = "Aggregated Profiles",
  label_margin = 60
)

Arguments

x	a aggregated profiles explainer produced with function aggregate_profiles()
...	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/bar color
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

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")

# smaller data, quicker example
titanic_small <- select_sample(titanic_imputed, n = 500, seed = 1313)


# build a model
model_titanic_rf <- ranger(survived ~., data = titanic_small, probability = TRUE)

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

selected_passangers <- select_sample(titanic_small, n = 100)
cp_rf <- ceteris_paribus(explain_titanic_rf, selected_passangers)

pdp_rf_p <- aggregate_profiles(cp_rf, type = "partial", variable_type = "numerical")
pdp_rf_p$`_label_` <- "RF_partial"
pdp_rf_c <- aggregate_profiles(cp_rf, type = "conditional", variable_type = "numerical")
pdp_rf_c$`_label_` <- "RF_conditional"
pdp_rf_a <- aggregate_profiles(cp_rf, type = "accumulated", variable_type = "numerical")
pdp_rf_a$`_label_` <- "RF_accumulated"

plotD3(pdp_rf_p, pdp_rf_c, pdp_rf_a, scale_plot = TRUE)

pdp <- aggregate_profiles(cp_rf, type = "partial", variable_type = "categorical")
pdp$`_label_` <- "RF_partial"

plotD3(pdp, variables = c("gender","class"), label_margin = 70)

ingredients documentation built on Jan. 15, 2023, 5:09 p.m.