R/utilities.R

Defines functions check_clustering get_clustering_stats print.clustering_stats

Documented in check_clustering get_clustering_stats

# ==============================================================================
# scclust for R -- R wrapper for the scclust library
# https://github.com/fsavje/scclust-R
#
# Copyright (C) 2016-2017  Fredrik Savje -- http://fredriksavje.com
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see http://www.gnu.org/licenses/
# ==============================================================================


#' Check clustering constraints
#'
#' \code{check_clustering} checks whether a clustering satisfies constraints on
#' the size and composition of the clusters.
#'
#' @param clustering
#'    a \code{\link{scclust}} object containing a non-empty clustering.
#' @param size_constraint
#'    an integer with the required minimum cluster size. If \code{NULL}, only
#'    the type constraints will be checked.
#' @param type_labels
#'    a vector containing the type of each data point. May be \code{NULL} when
#'    \code{type_constraints} is \code{NULL}.
#' @param type_constraints
#'    a named integer vector containing type-specific size constraints. If
#'    \code{NULL}, only the overall constraint will be checked.
#' @param primary_data_points
#'    a vector specifying primary data points, either by point indices or with
#'    a logical vector of length equal to the number of points.
#'    \code{check_clustering} checks so all primary data points are assigned
#'    to a cluster. \code{NULL} indicates that no such check should be done.
#'
#' @return
#'    Returns \code{TRUE} if \code{clustering} satisfies the constraints, and
#'    \code{FALSE} if it does not. Throws an error if \code{clustering} is an
#'    invalid instance of the \code{\link{scclust}} class.
#'
#' @seealso
#'    See \code{\link{sc_clustering}} for details on how to specify the
#'    \code{type_labels} and \code{type_constraints} parameters.
#'
#' @examples
#' # Example scclust clustering
#' my_scclust <- scclust(c("A", "A", "B", "C", "B",
#'                         "C", "C", "A", "B", "B"))
#'
#'
#' # Check so each cluster contains at least two data points
#' check_clustering(my_scclust, 2)
#' # > TRUE
#'
#'
#' # Check so each cluster contains at least four data points
#' check_clustering(my_scclust, 4)
#' # > FALSE
#'
#'
#' # Data point types
#' my_types <- factor(c("x", "y", "y", "z", "z",
#'                      "x", "y", "z", "x", "x"))
#'
#'
#' # Check so each cluster contains at least one point of each type
#' check_clustering(my_scclust,
#'                  NULL,
#'                  my_types,
#'                  c("x" = 1, "y" = 1, "z" = 1))
#' # > TRUE
#'
#'
#' # Check so each cluster contains one data point of both "x" and "z"
#' # and at least three points in total
#' check_clustering(my_scclust,
#'                  3,
#'                  my_types,
#'                  c("x" = 1, "z" = 1))
#' # > TRUE
#'
#'
#' # Check so each cluster contains five data points of type "y"
#' check_clustering(my_scclust,
#'                  NULL,
#'                  my_types,
#'                  c("y" = 5))
#' # > FALSE
#'
#' @export
check_clustering <- function(clustering,
                             size_constraint = NULL,
                             type_labels = NULL,
                             type_constraints = NULL,
                             primary_data_points = NULL) {
  ensure_scclust(clustering)
  num_data_points <- length(clustering)
  if (is.null(type_constraints)) {
    type_labels <- NULL
    size_constraint <- coerce_size_constraint(size_constraint, num_data_points)
  } else {
    type_labels <- coerce_type_labels(type_labels, num_data_points)
    type_constraints <- coerce_type_constraints(type_constraints)
    type_constraints <- make_type_size_constraints(type_constraints,
                                                   type_labels)
    size_constraint <- coerce_total_size_constraint(size_constraint,
                                                    type_constraints,
                                                    num_data_points)
  }

  primary_data_points <- coerce_data_point_indices(primary_data_points, num_data_points)
  if (is.logical(primary_data_points)) {
    primary_data_points <- which(primary_data_points) - 1L
  } else if (is.integer(primary_data_points)) {
    primary_data_points <- primary_data_points - 1L
  }

  .Call(Rscc_check_clustering,
        clustering,
        size_constraint,
        unclass(type_labels),
        type_constraints,
        primary_data_points)
}


#' Get clustering statistics
#'
#' \code{get_clustering_stats} calculates statistics of a clustering.
#'
#' The function reports the following measures:
#'
#' \tabular{ll}{
#'   \code{num_data_points} \tab total number of data points \cr
#'   \code{num_assigned} \tab number of points assigned to a cluster \cr
#'   \code{num_clusters} \tab number of clusters \cr
#'   \code{min_cluster_size} \tab size of the smallest cluster \cr
#'   \code{max_cluster_size} \tab size of the largest cluster \cr
#'   \code{avg_cluster_size} \tab  average cluster size \cr
#'   \code{sum_dists} \tab sum of all within-cluster distances \cr
#'   \code{min_dist} \tab smallest within-cluster distance \cr
#'   \code{max_dist} \tab largest within-cluster distance \cr
#'   \code{avg_min_dist} \tab average of the clusters' smallest distances \cr
#'   \code{avg_max_dist} \tab average of the clusters' largest distances \cr
#'   \code{avg_dist_weighted} \tab average of the clusters' average distances
#'      weighed by cluster size \cr
#'   \code{avg_dist_unweighted} \tab average of the clusters' average distances
#'      (unweighed)  \cr
#' }
#'
#' Let \eqn{d(i,j)}{d(i,j)} denote the distance between data points \eqn{i}{i}
#' and \eqn{j}{j}. Let \eqn{c}{c} be a cluster containing the indices of points
#' assigned to the cluster. Let \deqn{D(c) = \{d(i,j): i,j \in c \wedge i>j\}}{D(c) = { d(i,j) : i,j in c and i > j }}
#' be a function returning all within-cluster distances in \eqn{c}{c}. Let
#' \eqn{C}{C} be a set containing all clusters.
#'
#' \code{sum_dists} is defined as:
#' \deqn{\sum_{c\in C} sum(D(c))}{\sum_[c in C] sum(D(c))}
#'
#' \code{min_dist} is defined as:
#' \deqn{\min_{c\in C} \min(D(c))}{min_[c in C] min(D(c))}
#'
#' \code{max_dist} is defined as:
#' \deqn{\max_{c\in C} \max(D(c))}{max_[c in C] max(D(c))}
#'
#' \code{avg_min_dist} is defined as:
#' \deqn{\sum_{c\in C} \frac{\min(D(c))}{|C|}}{\sum_[c in C] min(D(c)) / count(C)}
#'
#' \code{avg_max_dist} is defined as:
#' \deqn{\sum_{c\in C} \frac{\max(D(c))}{|C|}}{\sum_[c in C] max(D(c)) / count(C)}
#'
#' Let:
#' \deqn{AD(c) = \frac{sum(D(c))}{|D(c)|}}{AD(c) = sum(D(c)) / count(D(c))}
#' be the average within-cluster distance in cluster \eqn{c}{c}.
#'
#' \code{avg_dist_weighted} is defined as:
#' \deqn{\sum_{c\in C} \frac{|c| AD(c)}{num_assigned}}{\sum_[c in C] count(c) * AD(c) / num_assigned}
#' where \eqn{num_assigned}{num_assigned} is the number of assigned data
#' points (see above).
#'
#' \code{avg_dist_unweighted} is defined as:
#' \deqn{\sum_{c\in C} \frac{AD(c)}{|C|}}{\sum_[c in C] AD(c) / count(C)}
#'
#' @param clustering
#'    a \code{\link{scclust}} object containing a non-empty clustering.
#' @param distances
#'    a \code{\link[distances]{distances}} object describing the distances
#'    between the data points in \code{clustering}.
#'
#' @return
#'    Returns a list of class \code{clustering_stats} containing the statistics.
#'
#' @examples
#' my_data_points <- data.frame(x = c(0.1, 0.2, 0.3, 0.4, 0.5,
#'                                    0.6, 0.7, 0.8, 0.9, 1.0),
#'                              y = c(10, 9, 8, 7, 6,
#'                                    10, 9, 8, 7, 6))
#'
#' my_distances <- distances(my_data_points)
#'
#' my_scclust <- scclust(c("A", "A", "B", "C", "B",
#'                         "C", "C", "A", "B", "B"))
#'
#' get_clustering_stats(my_distances, my_scclust)
#'
#' # >                     Value
#' # > num_data_points     10.0000000
#' # > num_assigned        10.0000000
#' # > num_clusters         3.0000000
#' # > min_cluster_size     3.0000000
#' # > max_cluster_size     4.0000000
#' # > avg_cluster_size     3.3333333
#' # > sum_dists           18.2013097
#' # > min_dist             0.5000000
#' # > max_dist             3.0066593
#' # > avg_min_dist         0.8366584
#' # > avg_max_dist         2.4148611
#' # > avg_dist_weighted    1.5575594
#' # > avg_dist_unweighted  1.5847484
#'
#' @export
get_clustering_stats <- function(distances,
                                 clustering) {
  ensure_scclust(clustering)
  num_data_points <- length(clustering)
  ensure_distances(distances, num_data_points)

  clust_stats <- .Call(Rscc_get_clustering_stats,
                       distances,
                       clustering)
  structure(clust_stats,
            class = c("clustering_stats"))
}


#' @export
print.clustering_stats <- function(x, ...) {
  tmp_table <- as.table(format(as.matrix(unlist(x))))
  colnames(tmp_table) <- "Value"
  print(tmp_table)
}

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scclust documentation built on May 2, 2019, 4:04 p.m.