4. Advanced Features & Customization

library(E2E)
# Initialize systems for the examples
initialize_modeling_system_dia()
#> Diagnostic modeling system initialized and default models registered.
initialize_modeling_system_pro()
#> Prognosis modeling system initialized.

Advanced Features

This guide covers advanced topics such as extending the E2E framework with custom models and interpreting model predictions using SHAP.

1. Extending the Framework: Adding New Models

The E2E framework is fully extensible. You can register your own custom models. For example, to add Adaboost for diagnosis:

# 1. Define the model function (must accept X, y, and other standard args)
ab_dia <- function(X, y, tune = FALSE, cv_folds = 5) {
  # Ensure caret is available
  if (!requireNamespace("caret", quietly = TRUE)) {
    stop("Package 'caret' is required for this custom model.")
  }
  ctrl <- caret::trainControl(method = "cv", number = cv_folds,
                              classProbs = TRUE, summaryFunction = caret::twoClassSummary)
  grid <- if (tune) {
    expand.grid(iter = c(50, 100), maxdepth = c(1, 2), nu = 0.1)
  } else {
    expand.grid(iter = 50, maxdepth = 1, nu = 0.1)
  }
  caret::train(x = X, y = y, method = "ada", metric = "ROC", trControl = ctrl, tuneGrid = grid)
}

# 2. Register the model with a unique name
register_model_dia("ab", ab_dia)

# 3. Now you can use "ab" in any diagnostic function
results_ab <- models_dia(train_dia, model = "ab")
#> Running model: ab
print_model_summary_dia("ab", results_ab$ab)
#> 
#> --- ab Model (on Training Data) Metrics ---
#> Threshold Strategy: default (0.5000)
#> AUROC: 0.9994 (95% CI: 0.9987 - 1.0000)
#> AUPRC: 0.9999
#> Accuracy: 0.9919
#> F1: 0.9955
#> Precision: 0.9936
#> Recall: 0.9974
#> Specificity: 0.9375
#> --------------------------------------------------

# and also you can add coxboost as a prognosis model like
#' @title Train CoxBoost
#your_model_pro <- function(X, y_surv, tune = FALSE) {
#  if (!requireNamespace("CoxBoost", quietly = TRUE)) {
#    stop("Package 'CoxBoost' is required for this function but is not installed.
#          Please install it from GitHub using: remotes::install_github('binderh/CoxBoost')")}
#  X_matrix <- as.matrix(X)
#  stepno_val <- 100
#  if (tune) stepno_val <- 100
#  fit <- CoxBoost::CoxBoost(time = y_surv[,1], status = y_surv[,2], x = X_matrix, stepno = stepno_val, penalty = 100)
#  fit$fitted_scores <- as.vector(predict(fit, newdata = X_matrix, type = "lp"))
#  structure(
#    list(finalModel = fit, X_train_cols = colnames(X), model_type = "survival_coxboost"),
#    class = c("survival_coxboost", "pro_model")
#  )
#}
#
#' @export
#predict_pro.survival_coxboost <- function(object, newdata, ...) {
#  if (!requireNamespace("CoxBoost", quietly = TRUE)) {
#    stop("Package 'CoxBoost' is needed for prediction.")
#  }
#  newdata <- .ensure_features(object, newdata)
#  X_matrix <- as.matrix(newdata)
#  as.vector(predict(object$finalModel, newdata = X_matrix, type = "lp"))
#}

#register_model_pro("cb_pro", cb_pro)

2. Model Explanation with SHAP (figure_shap)

The figure_shap function provides model-agnostic explanations by calculating SHAP values. This reveals which features had the most impact on the model’s output.

Explaining a Diagnostic Model

# First, we need a model result object
bagging_rf_results <- bagging_dia(train_dia, base_model_name = "rf", n_estimators = 10)
#> Running Bagging model: Bagging_dia (base: rf)

# Now, generate the SHAP explanation plot
p6 <- figure_shap(
  data = bagging_rf_results,
  raw_data = train_dia,
  target_type = "diagnosis"
)
#> Training 'xgboost' surrogate model and calculating SHAP values...
#plot(p6)

Explaining a Prognostic Model

# First, we need a model result object
stacking_stepcox_pro_results <- stacking_pro(
  results_all_models = models_pro(train_pro, model = c("lasso_pro", "rsf_pro")),
  data = train_pro,
  meta_model_name = "stepcox_pro"
)
#> Running model: lasso_pro
#> Running model: rsf_pro

# Generate the SHAP explanation plot
p7 <- figure_shap(
  data = stacking_stepcox_pro_results,
  raw_data = train_pro,
  target_type = "prognosis"
)
#> Training 'xgboost' surrogate model and calculating SHAP values...
#plot(p7)