Accumulated Local Effects Profiles aka ALEPlots — accumulated

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

accumulated_dependence(x, ...)

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

# S3 method for default
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 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

Value

an object of the class aggregated_profiles_explainer

Details

Find more detailes in the Accumulated Local Dependence Chapter.

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")
#> Welcome to DALEX (version: 2.4.2).
#> Find examples and detailed introduction at: http://ema.drwhy.ai/
#> Additional features will be available after installation of: ggpubr.
#> Use 'install_dependencies()' to get all suggested dependencies
#> 
#> Attaching package: ‘DALEX’
#> The following object is masked from ‘package:ingredients’:
#> 
#>     feature_importance
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)
#> Top profiles    : 
#>   _vname_ _label_        _x_       _yhat_ _ids_
#> 1     age      lm  0.1666667  0.000000000     0
#> 2     age      lm  2.0000000 -0.002538769     0
#> 3     age      lm  4.0000000 -0.005291217     0
#> 4     age      lm  7.0000000 -0.009385779     0
#> 5     age      lm  9.0000000 -0.012095000     0
#> 6     age      lm 13.0000000 -0.017457612     0
plot(adp_glm)


# \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)

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)

# }