ingredients (version 0.3.1)

calculate_oscillations: Calculate Oscillations for Ceteris Paribus Explainer

Description

Oscillations are proxies for local feature importance at the instance level.

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

References

Predictive Models: Visual Exploration, Explanation and Debugging https://pbiecek.github.io/PM_VEE

Examples

# NOT RUN {
library("DALEX")
# Toy examples, because CRAN angels ask for them
titanic <- na.omit(titanic)
set.seed(1313)
titanic_small <- titanic[sample(1:nrow(titanic), 500), c(1,2,6,9)]
model_titanic_glm <- glm(survived == "yes" ~ gender + age + fare,
                       data = titanic_small, family = "binomial")
explain_titanic_glm <- explain(model_titanic_glm,
                           data = titanic_small[,-9],
                           y = titanic_small$survived == "yes")
cp_rf <- ceteris_paribus(explain_titanic_glm, titanic_small[1, ])
calculate_oscillations(cp_rf)

 
# }
# NOT RUN {
library("randomForest")
set.seed(59)

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

explainer_rf <- explain(apartments_rf_model,
      data = apartmentsTest, y = apartmentsTest$m2.price)

apartment <- apartmentsTest[1,]

cp_rf <- ceteris_paribus(explainer_rf, apartment)
calculate_oscillations(cp_rf)
# }