R/ext_distance.R

In CommKern: Network-Based Communities and Kernel Machine Methods

#' Extrinsic evaluation distance matrix creation
#'
#' Description of the extrinsic evaluation distance matrix creation function.
#'
#' This function creates a distance matrix using the community output values
#' from any community detection algorithm, such as the hierarchical multimodal
#' spinglass algorithm. Because extrinsic evaluation metrics for clustering
#' algorithms use the underlying idea of similarity, distance is calculated as
#' (1-similarity). The use of distance ensures that the distance matrix will be
#' positive and semi-definite, a requirement for its use in the kernel function.
#'
#' @param comm_df a data frame whose columns are different partitions. All
#' partitions must have the same set of nodes in order for this function to work
#' and this data frame should exclude a node ID column for ease of computation.
#' @param variant a string in ('NMI', 'Adj_RI', 'purity') that calculates different
#' extrinsic cluster evaluation metrics.
#'
#' @seealso \code{\link{adj_RI}}, \code{\link{NMI}}, and \code{\link{purity}}
#'
#' @return A m x m (m is the number of partitions) extrinsic evaluation distance
#' matrix to be used as input for the kernel function
#'
#' @examples
#' x <- c(2,2,3,1,3,1,3,3,2,2,1,1)
#' y <- c(3,3,2,1,1,1,1,2,2,3,2,3)
#' z <- c(1,1,2,3,2,3,2,1,1,2,3,3)
#'
#' xyz_comms <- data.frame(x_comm = x, y_comm = y, z_comm = z)
#' ext_distance(xyz_comms, variant = 'NMI')
#' ext_distance(xyz_comms, variant = 'adj_RI')
#' ext_distance(xyz_comms, variant = 'purity')
#'
#' @export

ext_distance <- function(comm_df, variant = c("NMI", "adj_RI", "purity")) {
    variant <- match.arg(variant)

    comm_expand <- expand.grid(1:ncol(comm_df), 1:ncol(comm_df))
    colnames(comm_expand) <- c("id_a", "id_b")
    comm_expand$ext_sim <- NA

    for (i in 1:nrow(comm_expand)) {
        pair_compare <- switch(variant, NMI = NMI(comm_df[, comm_expand$id_a[i]],
            comm_df[, comm_expand$id_b[i]], variant = "max"), adj_RI = adj_RI(comm_df[,
            comm_expand$id_a[i]], comm_df[, comm_expand$id_b[i]]), purity = purity(comm_df[,
            comm_expand$id_a[i]], comm_df[, comm_expand$id_b[i]]))
        comm_expand$ext_sim[i] <- 1 - pair_compare
    }

    comm_dist <- as.matrix(reshape2::dcast(data = comm_expand, formula = id_a ~ id_b,
        value.var = "ext_sim"))[, -1]
    return(comm_dist)
}