R/CLUSTERING-cvi-evaluators.R

In dtwclust: Time Series Clustering Along with Optimizations for the Dynamic Time Warping Distance

#' Cluster comparison based on CVIs
#'
#' Create evaluation functions for [compare_clusterings()].
#'
#' @export
#'
#' @param type A character vector with options supported by [cvi()].
#' @param fuzzy Logical indicating whether to use fuzzy CVIs or not.
#' @param ground.truth A vector that can be coerced to integers used for the calculation of external
#'   CVIs (passed as `b` to [cvi()]).
#'
#' @details
#'
#' Think of this as a factory for [compare_clusterings()] that creates functions that can be passed
#' as its `score.clus` and `pick.clus` arguments. It is somewhat limited in scope because it depends
#' on the cluster validity indices available in [cvi()] for scoring and performs *majority voting*
#' for picking. They always assume that no errors occurred.
#'
#' The scoring function takes the CVIs that are to be minimized and "inverts" them by taking their
#' reciprocal so that maximization can be considered uniformly for the purpose of majority voting.
#' Its ellipsis (`...`) is passed to [cvi()].
#'
#' The picking function returns the best configuration if `return.objects` is `FALSE`, or a list
#' with the chosen [TSClusters-class] object and the corresponding configuration otherwise.
#'
#' Refer to the examples in [compare_clusterings()].
#'
#' @note
#'
#' To avoid ambiguity, if this function is used, configurations for both fuzzy and crisp clusterings
#' should *not* be provided in the same call to [compare_clusterings()]. In such cases the scoring
#' function may fail entirely, e.g. if it was created with `type = "valid"`.
#'
#' @return A list with two functions: `score` and `pick`.
#'
cvi_evaluators <- function(type = "valid", fuzzy = FALSE, ground.truth = NULL) {
    if (fuzzy) {
        external <- c("RI", "ARI", "VI", "NMIM")
        internal <- c("MPC", "K", "T", "SC", "PBMF")
        minimize <- c("VI", "K", "T")
    }
    else {
        external <- c("RI", "ARI", "J", "FM", "VI")
        internal <- c("Sil", "D", "COP", "DB", "DBstar", "CH", "SF")
        minimize <- c("VI", "COP", "DB", "DBstar")
    }

    if ("valid" %in% type) {
        type <- if (is.null(ground.truth)) internal else c(external, internal)
    }
    else if ("external" %in% type) {
        if (is.null(ground.truth))
            stop("The ground.truth is needed for external CVIs.")
        type <- external
    }
    else if ("internal" %in% type) {
        type <- internal
    }
    else {
        type <- match.arg(type, c(external, internal), several.ok = TRUE)
        if (any(external %in% type) && is.null(ground.truth))
            stop("The ground.truth is needed for external CVIs.")
    }

    internal <- intersect(type, internal)
    external <- intersect(type, external)

    if (any(c(internal, external) %in% minimize)) {
        message("Some of the chosen CVIs are to be minized,",
                " but their values will be inverted by the scoring function.",
                " See this function's documentation for more details,",
                " and use suppressMessages to avoid this message.")
    }

    majority <- function(x) {
        ux <- unique(x)
        ux[which.max(tabulate(match(x, ux)))]
    }

    score <- function(objs, ...) {
        call_rbind(lapply(objs, function(obj) {
            if (length(internal) > 0L)
                cvis <- cvi(a = obj, type = internal, ...)
            else
                cvis <- numeric()

            if (length(external) > 0L) {
                if (fuzzy)
                    cvis <- c(cvis, cvi(obj@fcluster, ground.truth, external, ...))
                else
                    cvis <- c(cvis, cvi(obj@cluster, ground.truth, external, ...))
            }

            minimized <- names(cvis) %in% minimize
            if (length(minimized) > 0L && any(minimized))
                cvis[minimized] <- 1 / cvis[minimized]

            # return
            cvis
        }))
    }

    pick <- function(results, objs, ...) {
        objs_missing <- missing(objs)

        best_by_type <- sapply(results, function(result) {
            score <- result[type]
            best_by_cvi <- apply(score, 2L, which.max)
            if (length(type) > 1L && length(unique(best_by_cvi)) == length(best_by_cvi))
                stop("All votes are distinct, so majority voting is inconclusive.")
            # return
            majority(best_by_cvi)
        })

        best_overall <- Map(results, best_by_type, f = function(result, row_id) {
            result[row_id, type, drop = FALSE]
        })
        best_overall <- call_rbind(best_overall)
        best_overall <- apply(best_overall, 2L, which.max)
        if (length(type) > 1L && length(unique(best_overall)) == length(best_overall))
            stop("All votes are distinct, so majority voting is inconclusive.")
        best_overall <- majority(best_overall)

        if (objs_missing)
            results[[best_overall]][best_by_type[best_overall], , drop = FALSE]
        else
            list(
                object = objs[[best_overall]][[best_by_type[best_overall]]],
                config = results[[best_overall]][best_by_type[best_overall], , drop = FALSE]
            )
    }

    # return
    list(score = score, pick = pick)
}