calculate_oscillations: Calculate Oscillations for Ceteris Paribus Explainer

In ingredients: Effects and Importances of Model Ingredients

Calculate Oscillations for Ceteris Paribus Explainer

Description

Oscillations are proxies for local feature importance at the instance level. Find more details in Ceteris Paribus Oscillations Chapter.

Usage

calculate_oscillations(x, sort = TRUE, ...)

Arguments

x	a ceteris paribus explainer produced with the ceteris_paribus() function
sort	a logical value. If TRUE then rows are sorted along the oscillations
...	other arguments

Value

an object of the class ceteris_paribus_oscillations

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

calculate_oscillations(cp_rf)


library("ranger")

apartments_rf_model <- ranger(m2.price ~ construction.year + surface + floor +
                                    no.rooms + district, data = apartments)

explainer_rf <- explain(apartments_rf_model,
                        data = apartments_test[,-1],
                        y = apartments_test$m2.price,
                        label = "ranger forest",
                        verbose = FALSE)

apartment <- apartments_test[1,]

cp_rf <- ceteris_paribus(explainer_rf, apartment)

calculate_oscillations(cp_rf)

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