R/c_SPEC.R

In egenn/rtemis: Machine Learning and Visualization

# c_SPEC.R
# ::rtemis::
# 2017 E.D. Gennatas www.lambdamd.org

#' Spectral Clustering
#'
#' Perform [Spectral Clustering](https://en.wikipedia.org/wiki/Spectral_clustering) 
#' using `kernlab::specc`
#'
#' @inheritParams c_KMeans
#' @param x Input matrix / data.frame
#' @param k Integer: Number of clusters to get
#' @param kernel Character: Kernel to use: "rbfdot", "polydot", "vanilladot", tanhdot", "laplacedot", "besseldot",
#' "anovadot", "splinedot", "stringdot"
#' @param kpar String OR List: "automatic", "local" OR list with: sigma (for "rbfdor", "laplacedot");
#' degree, scale, offset (for "polydot");
#' scale, offset (for "tanhdot");
#' sigma, order, degree (for "besseldot");
#' sigma, degree (for "anovadot");
#' length, lambda, normalized (for "stringdot")
#' @param nystrom.red Logical: if TRUE, use nystrom method to calculate eigenvectors (Default = FALSE)
#' @param nystrom.sample Integer: Number of points to use for estimating the eigenvalues when `nystrom.red = TRUE`
#'  Default = `dim(x)[1]/6`
#' @param iterations Integer: Number of iterations allowed
#' @param mod.sample Float (0, 1): Proportion of data to use when estimating sigma. Default = .75
#' @param na.action Function: Action to perform on NA (Default = `na.omit`)
#' @param ... Additional parameters to be passed to `flexclust::cclust`
#' @author E.D. Gennatas
#' @family Clustering
#' @export

c_SPEC <- function(x,
                   k = 2,
                   kernel = "rbfdot",
                   kpar = "automatic",
                   nystrom.red = FALSE,
                   nystrom.sample = dim(x)[1] / 6,
                   iterations = 200,
                   mod.sample = 0.75,
                   na.action = na.omit,
                   verbose = TRUE, ...) {

  # Intro ----
  start.time <- intro(verbose = verbose)
  clust.name <- "SPEC"

  # Data ----
  if (is.null(colnames(x))) colnames(x) <- paste0("Feature_", seq_len(NCOL(x)))
  x <- as.data.frame(x)
  xnames <- colnames(x)

  # Dependencies ----
  dependency_check("kernlab")

  # Arguments ----
  if (missing(x)) {
    print(args(c_SPEC))
    stop("x is missing")
  }

  # Spectral clust ----
  if (verbose) msg2("Performing Spectral Clustering with k = ", k, "...", sep = "")
  clust <- kernlab::specc(data.matrix(x),
                          centers = k,
                          kernel = kernel,
                          kpar = kpar,
                          nystrom.red = nystrom.red,
                          nystrom.sample = nystrom.sample,
                          iterations = iterations,
                          mod.sample = mod.sample,
                          na.action = na.action, ...)

  # Clusters ----
  clusters.train <- clust@.Data
  clusters.test <- NULL

  # Outro ----
  cl <- rtClust$new(clust.name = clust.name,
                    k = k,
                    xnames = xnames,
                    clust = clust,
                    clusters.train = clusters.train,
                    clusters.test = clusters.test,
                    parameters = list(k = k,
                                      kernel = kernel,
                                      kpar = kpar,
                                      nystrom.red = nystrom.red,
                                      nystrom.sample = nystrom.sample,
                                      iterations = iterations,
                                      mod.sample = mod.sample),
                    extra = list())
  outro(start.time, verbose = verbose)
  cl

} # rtemis::c_SPEC