R/dal_tune.R

In daltoolbox: Leveraging Experiment Lines to Data Analytics

#'@title DAL Tune (base for hyperparameter search)
#'@description Base class for hyperparameter optimization that stores a base model, a fold count,
#' and a parameter grid. Specializations (classification/regression/clustering) implement the evaluation logic.
#'@details Ranges are expanded via `expand.grid`, and selection is delegated to `select_hyper()` which can be
#' overridden by subclasses to implement custom criteria.
#'@param base_model base model for tuning
#'@param folds number of folds for cross-validation
#'@param ranges a list of hyperparameter ranges to explore
#'@return returns a `dal_tune` object
#'@examples
#'#See ?cla_tune for classification tuning
#'#See ?reg_tune for regression tuning
#'#See ?ts_tune for time series tuning
#'@export
dal_tune <- function(base_model, folds=10, ranges) {
  obj <- dal_base()
  obj$base_model <- base_model
  obj$folds <- folds
  obj$ranges <- ranges
  class(obj) <- append("dal_tune", class(obj))
  return(obj)
}

#'@title Selection of hyperparameters
#'@description Generic to select the best hyperparameters from cross‑validation results; subclasses can override.
#'@param obj the object or model used for hyperparameter selection.
#'@param hyperparameters data set with hyper parameters and quality measure from execution
#'@return returns the index of selected hyper parameter
#'@export
select_hyper <- function(obj, hyperparameters) {
  UseMethod("select_hyper")
}

#'@exportS3Method select_hyper default
select_hyper.default <- function(obj, hyperparameters) {
  # default: choose last row (useful when ranges has a single configuration)
  return(length(hyperparameters))
}