ingredients (version 2.3.0)

cluster_profiles: Cluster Ceteris Paribus Profiles

Description

This function calculates aggregates of ceteris paribus profiles based on hierarchical clustering.

Usage

cluster_profiles(
  x,
  ...,
  aggregate_function = mean,
  variable_type = "numerical",
  center = FALSE,
  k = 3,
  variables = NULL
)

Value

an object of the class aggregated_profiles_explainer

Arguments

x

a ceteris paribus explainer produced with function ceteris_paribus()

...

other explainers that shall be plotted together

aggregate_function

a function for profile aggregation. By default it's mean

variable_type

a character. If numerical then only numerical variables will be computed. If categorical then only categorical variables will be computed.

center

shall profiles be centered before clustering

k

number of clusters for the hclust function

variables

if not NULL then only variables will be presented

Details

Find more detailes in the Clustering Profiles Chapter.

References

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

Examples

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

selected_passangers <- select_sample(titanic_imputed, n = 100)
model_titanic_glm <- glm(survived ~ gender + age + fare,
                         data = titanic_imputed, family = "binomial")

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

cp_rf <- ceteris_paribus(explain_titanic_glm, selected_passangers)
clust_rf <- cluster_profiles(cp_rf, k = 3, variables = "age")
plot(clust_rf)

# \donttest{
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)

cp_rf <- ceteris_paribus(explain_titanic_rf, selected_passangers)
cp_rf

pdp_rf <- aggregate_profiles(cp_rf, variables = "age")
head(pdp_rf)
clust_rf <- cluster_profiles(cp_rf, k = 3, variables = "age")
head(clust_rf)

plot(clust_rf, color = "_label_") +
  show_aggregated_profiles(pdp_rf, color = "black", size = 3)

plot(cp_rf, color = "grey", variables = "age") +
  show_aggregated_profiles(clust_rf, color = "_label_", size = 2)

clust_rf <- cluster_profiles(cp_rf, k = 3, center = TRUE, variables = "age")
head(clust_rf)
# }