accumulated_dependence: Accumulated Local Effects Profiles aka ALEPlots

In ingredients: Effects and Importances of Model Ingredients

Accumulated Local Effects Profiles aka ALEPlots

Description

Accumulated Local Effects Profiles accumulate local changes in Ceteris Paribus Profiles. Function accumulated_dependence calls ceteris_paribus and then aggregate_profiles.

Usage

accumulated_dependence(x, ...)

## S3 method for class 'explainer'
accumulated_dependence(
  x,
  variables = NULL,
  N = 500,
  variable_splits = NULL,
  grid_points = 101,
  ...,
  variable_type = "numerical"
)

## Default S3 method:
accumulated_dependence(
  x,
  data,
  predict_function = predict,
  label = class(x)[1],
  variables = NULL,
  N = 500,
  variable_splits = NULL,
  grid_points = 101,
  ...,
  variable_type = "numerical"
)

## S3 method for class 'ceteris_paribus_explainer'
accumulated_dependence(x, ..., variables = NULL)

accumulated_dependency(x, ...)

Arguments

x	an explainer created with function DALEX::explain(), an object of the class ceteris_paribus_explainer or a model to be explained.
...	other parameters
variables	names of variables for which profiles shall be calculated. Will be passed to calculate_variable_split. If NULL then all variables from the validation data will be used.
N	number of observations used for calculation of partial dependence profiles. By default, 500 observations will be chosen randomly.
variable_splits	named list of splits for variables, in most cases created with calculate_variable_split. If NULL then it will be calculated based on validation data avaliable in the explainer.
grid_points	number of points for profile. Will be passed tocalculate_variable_split.
variable_type	a character. If "numerical" then only numerical variables will be calculated. If "categorical" then only categorical variables will be calculated.
data	validation dataset Will be extracted from x if it's an explainer NOTE: It is best when target variable is not present in the data
predict_function	predict function Will be extracted from x if it's an explainer
label	name of the model. By default it's extracted from the class attribute of the model

Details

Find more detailes in the Accumulated Local Dependence Chapter.

Value

an object of the class aggregated_profiles_explainer

References

ALEPlot: Accumulated Local Effects (ALE) Plots and Partial Dependence (PD) Plots https://cran.r-project.org/package=ALEPlot, Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/

Examples

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

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],
                               verbose = FALSE)

adp_glm <- accumulated_dependence(explain_titanic_glm,
                                  N = 25, variables = c("age", "fare"))
head(adp_glm)
plot(adp_glm)


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)

adp_rf <- accumulated_dependence(explain_titanic_rf, N = 200, variable_type = "numerical")
plot(adp_rf)

adp_rf <- accumulated_dependence(explain_titanic_rf, N = 200, variable_type = "categorical")
plotD3(adp_rf, label_margin = 80, scale_plot = TRUE)

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