ASML (version 1.1.0)

ASexplainer: Create DALEX explainers for multiple ASML-trained models

Description

This function simplifies the use of DALEX with models trained using AStrain from ASML. It automatically creates DALEX explainers for all trained models (one per algorithm in the portfolio), allowing users to easily apply DALEX functions to analyze model performance, evaluate feature importance, and generate partial dependence plots (PDPs), among other analyses.

Usage

ASexplainer(training, data, y, labels = NULL, ...)

Value

A named list of DALEX explainer objects, one per trained model. Names are taken from labels or names(training).

Arguments

training

An object of class as_train containing models trained with AStrain.

data

A data.frame or matrix with predictor variables. Must not include the target columns.

y

A matrix or data.frame containing the target variables. Each column corresponds to the output for the model at the same position in training.

labels

Optional character vector of labels for the explainers. If NULL, names(training) are used. Must have the same length as training.

...

Additional arguments passed to DALEX::explain.

References

Biecek, P. (2018). DALEX: Explainers for Complex Predictive Models in R. Journal of Machine Learning Research, 19(84), 1--5. http://jmlr.org/papers/v19/18-416.html

Examples

if (FALSE) {
library(ASML)
library(DALEX)
data(branching)
features <- branching$x
KPI <- branching$y
lab_rules <- c("max", "sum", "dual", "range", "eig-VI", "eig-CMI")

# Preprocess data
data_obj <- partition_and_normalize(
  features,
  KPI,
  family_column = 1,
  split_by_family = TRUE,
  better_smaller = TRUE
)

# Train models
training <- AStrain(data_obj, method = "rf", parallel = TRUE)

# Create explainers
out <- ASexplainer(
  training,
  data = data_obj$x.test,
  y = data_obj$y.test,
  labels = lab_rules,
  verbose = FALSE
)

# Model performance
mp_regr_rf <- lapply(out, DALEX::model_performance)
do.call(plot, unname(mp_regr_rf))
do.call(plot, c(unname(mp_regr_rf), list(geom = "boxplot")))

# Variable importance
vi_regr_rf <- lapply(out, DALEX::model_parts)
do.call(plot, c(unname(vi_regr_rf), list(max_vars = 5)))

# Partial dependence plots
pdp_regr_rf <- lapply(out, DALEX::model_profile, variable = "degree", type = "partial")
do.call(plot, unname(pdp_regr_rf))
}