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Adds verbose parameter and implements the best layer-specific learner…
… als meta learner
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| #' @title The best layer-specific model is used as meta model. | ||
| #' | ||
| #' @description | ||
| #' The meta learner is the best layer-specific laerner. | ||
| #' | ||
| #' @param x `data.frame(1)` \cr | ||
| #' \code{data.frame} of predictors. | ||
| #' @param y `vector(1)` \cr | ||
| #' Target observations. Either binary or two level factor variable. | ||
| #' @param perf `function(1)` \cr | ||
| #' Function to compute layer-specific performance of learners. If NULL, the Brier Score is used by default. | ||
| #' Otherwise, the performance function must accept two parameters: \code{observed} (observed values) and \code{predicted} (predicted values). | ||
| #' | ||
| #' @return | ||
| #' A model object of class \code{weightedMeanLeaner}. | ||
| #' | ||
| #' @export | ||
| #' | ||
| #' @examples | ||
| #' set.seed(20240624L) | ||
| #' x = data.frame(x1 = runif(n = 50L, min = 0, max = 1)) | ||
| #' y = sample(x = 0L:1L, size = 50L, replace = TRUE) | ||
| #' my_best_model = bestSpecificLearner(x = x, y = y) | ||
| #' | ||
| bestSpecificLearner = function (x, y, perf = NULL) { | ||
| if (is.null(perf)) { | ||
| # y must be binomial for Brier Score estimation. | ||
| # If dichotomy, first category (case) = 1 and | ||
| # second (control) = 0 | ||
| if ((length(unique(y)) > 2) | is.character(y)) { | ||
| stop("y must be either binary or two level factor variable.") | ||
| } else { | ||
| if (!all(y %in% 0:1)) { | ||
| y = 2 - as.integer(y) | ||
| } else { | ||
| if (is.factor(y)) { | ||
| y = as.integer(y) - 1 | ||
| } else { | ||
| y = y | ||
| } | ||
| } | ||
| perf_values = lapply(X = x, FUN = function (predicted) { | ||
| mean(x = (predicted - y)^2L, na.rm = TRUE) | ||
| }) | ||
| perf_values = unlist(perf_values) | ||
| } | ||
| } else { | ||
| if (is.function(perf)) { | ||
| arg_names <- names(formals(perf)) | ||
| if (arg_names %in% c("observed", "predicted")) { | ||
| # Function has been provided to estimated performance of layer-specific learner | ||
| perf_values = lapply(X = x, FUN = function (predicted) { | ||
| perf_estimate = do.call(what = perf, args = list(observed = y, | ||
| predicted = predicted)) | ||
| return(perf_estimate) | ||
| }) | ||
| perf_values = unlist(perf_values) | ||
| } else { | ||
| stop("perf argument must be a function.") | ||
| } | ||
| } else { | ||
| stop("Arguments of the perf function must be 'observed' and 'predicted'.") | ||
| } | ||
| } | ||
| weights_values = (1L / perf_values) / sum((1L / perf_values)) | ||
| max_index = which.max(weights_values) | ||
| weights_values = rep(0L, length(weights_values)) | ||
| weights_values[max_index] = 1L | ||
| names(weights_values) = names(x) | ||
| class(weights_values) = "bestSpecificLearner" | ||
| return(weights_values) | ||
| } |
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