R/WCC.NNLS.R

In frbl/OnlineSuperLearner: Online SuperLearner package

#' WCC.NNLS
#'
#' @docType class
#' @importFrom R6 R6Class
#' @importFrom nnls nnls
#' @include WeightedCombinationComputer.R
#'
#' @section Methods:
#' \describe{
#'   \item{\code{new()}}{
#'    Creates a new computer for determining the best weighted combination of the ML libraries.
#'    currently this is merely the NNLS version from the general superlearner, but in the future
#'    it should be updated to be 1. online, and 2. using better algorithm (like NLopts)
#'    \code{weights.initial} vector containing the initial weights
#'   }
#'   \item{\code{getWeigths()}}{
#'    Returns the current list of optimal weights (or the initial weights, if not yet fitted)
#'   }
#'   \item{\code{compute(Z, Y, libraryNames, weights.initial)}}{
#'    Method to compute the best weighted combination of the underlying estimators
#'    \code{Z} matrix containing the outcomes of each of the estimators
#'    \code{Y} vector containing the actual outcome
#'    \code{libraryNames} vector containing the names of the estimators
#'   }
#' }
WCC.NNLS <- R6Class("WCC.NNLS",
  inherit = WeightedCombinationComputer,
  private =
    list(),
  active =
    list(),
  public =
    list(
        initialize = function(weights.initial) {
          super$initialize(weights.initial)
        },

        compute = function(Z, Y, libraryNames ) {
          # Compute the best convex combination
          weights <- coef(nnls(Z,Y))

          if(all(weights == 0)) {
            warning('All algorithms have zero weight')
            return(weights)
          }

          # Normalize the weights and store them for later use
          private$weights <- weights/sum(weights)
          private$weights
        }
    )
)