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#######################################################################
# dbscan - Density Based Clustering of Applications with Noise
# and Related Algorithms
# Copyright (C) 2015 Michael Hahsler, Matt Piekenbrock
# 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 2 of the License, or
# 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, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#' Framework for the Optimal Extraction of Clusters from Hierarchies
#'
#' Generic reimplementation of the _Framework for Optimal Selection of Clusters_
#' (FOSC; Campello et al, 2013) to extract clusterings from hierarchical clustering (i.e.,
#' [hclust] objects).
#' Can be parameterized to perform unsupervised
#' cluster extraction through a stability-based measure, or semisupervised
#' cluster extraction through either a constraint-based extraction (with a
#' stability-based tiebreaker) or a mixed (weighted) constraint and
#' stability-based objective extraction.
#'
#' Campello et al (2013) suggested a _Framework for Optimal Selection of
#' Clusters_ (FOSC) as a framework to make local (non-horizontal) cuts to any
#' cluster tree hierarchy. This function implements the original extraction
#' algorithms as described by the framework for hclust objects. Traditional
#' cluster extraction methods from hierarchical representations (such as
#' [hclust] objects) generally rely on global parameters or cutting values
#' which are used to partition a cluster hierarchy into a set of disjoint, flat
#' clusters. This is implemented in R in function [cutree()].
#' Although such methods are widespread, using global parameter
#' settings are inherently limited in that they cannot capture patterns within
#' the cluster hierarchy at varying _local_ levels of granularity.
#'
#' Rather than partitioning a hierarchy based on the number of the cluster one
#' expects to find (\eqn{k}) or based on some linkage distance threshold
#' (\eqn{H}), the FOSC proposes that the optimal clusters may exist at varying
#' distance thresholds in the hierarchy. To enable this idea, FOSC requires one
#' parameter (minPts) that represents _the minimum number of points that
#' constitute a valid cluster._ The first step of the FOSC algorithm is to
#' traverse the given cluster hierarchy divisively, recording new clusters at
#' each split if both branches represent more than or equal to minPts. Branches
#' that contain less than minPts points at one or both branches inherit the
#' parent clusters identity. Note that using FOSC, due to the constraint that
#' minPts must be greater than or equal to 2, it is possible that the optimal
#' cluster solution chosen makes local cuts that render parent branches of
#' sizes less than minPts as noise, which are denoted as 0 in the final
#' solution.
#'
#' Traversing the original cluster tree using minPts creates a new, simplified
#' cluster tree that is then post-processed recursively to extract clusters
#' that maximize for each cluster \eqn{C_i}{Ci} the cost function
#'
#' \deqn{\max_{\delta_2, \dots, \delta_k} J = \sum\limits_{i=2}^{k} \delta_i
#' S(C_i)}{ J = \sum \delta S(Ci) for all i clusters, } where
#' \eqn{S(C_i)}{S(Ci)} is the stability-based measure as \deqn{ S(C_i) =
#' \sum_{x_j \in C_i}(\frac{1}{h_{min} (x_j, C_i)} - \frac{1}{h_{max} (C_i)})
#' }{ S(Ci) = \sum (1/Hmin(Xj, Ci) - 1/Hmax(Ci)) for all Xj in Ci.}
#'
#' \eqn{\delta_i}{\delta} represents an indicator function, which constrains
#' the solution space such that clusters must be disjoint (cannot assign more
#' than 1 label to each cluster). The measure \eqn{S(C_i)}{S(Ci)} used by FOSC
#' is an unsupervised validation measure based on the assumption that, if you
#' vary the linkage/distance threshold across all possible values, more
#' prominent clusters that survive over many threshold variations should be
#' considered as stronger candidates of the optimal solution. For this reason,
#' using this measure to detect clusters is referred to as an unsupervised,
#' _stability-based_ extraction approach. In some cases it may be useful
#' to enact _instance-level_ constraints that ensure the solution space
#' conforms to linkage expectations known _a priori_. This general idea of
#' using preliminary expectations to augment the clustering solution will be
#' referred to as _semisupervised clustering_. If constraints are given in
#' the call to `extractFOSC()`, the following alternative objective function
#' is maximized:
#'
#' \deqn{J = \frac{1}{2n_c}\sum\limits_{j=1}^n \gamma (x_j)}{J = 1/(2 * nc)
#' \sum \gamma(Xj)}
#'
#' \eqn{n_c}{nc} is the total number of constraints given and
#' \eqn{\gamma(x_j)}{\gamma(Xj)} represents the number of constraints involving
#' object \eqn{x_j}{Xj} that are satisfied. In the case of ties (such as
#' solutions where no constraints were given), the unsupervised solution is
#' used as a tiebreaker. See Campello et al (2013) for more details.
#'
#' As a third option, if one wishes to prioritize the degree at which the
#' unsupervised and semisupervised solutions contribute to the overall optimal
#' solution, the parameter \eqn{\alpha} can be set to enable the extraction of
#' clusters that maximize the `mixed` objective function
#'
#' \deqn{J = \alpha S(C_i) + (1 - \alpha) \gamma(C_i))}{J = \alpha S(Ci) + (1 -
#' \alpha) \gamma(Ci).}
#'
#' FOSC expects the pairwise constraints to be passed as either 1) an
#' \eqn{n(n-1)/2} vector of integers representing the constraints, where 1
#' represents should-link, -1 represents should-not-link, and 0 represents no
#' preference using the unsupervised solution (see below for examples).
#' Alternatively, if only a few constraints are needed, a named list
#' representing the (symmetric) adjacency list can be used, where the names
#' correspond to indices of the points in the original data, and the values
#' correspond to integer vectors of constraints (positive indices for
#' should-link, negative indices for should-not-link). Again, see the examples
#' section for a demonstration of this.
#'
#' The parameters to the input function correspond to the concepts discussed
#' above. The `minPts` parameter to represent the minimum cluster size to
#' extract. The optional `constraints` parameter contains the pairwise,
#' instance-level constraints of the data. The optional `alpha` parameters
#' controls whether the mixed objective function is used (if `alpha` is
#' greater than 0). If the `validate_constraints` parameter is set to
#' true, the constraints are checked (and fixed) for symmetry (if point A has a
#' should-link constraint with point B, point B should also have the same
#' constraint). Asymmetric constraints are not supported.
#'
#' Unstable branch pruning was not discussed by Campello et al (2013), however
#' in some data sets it may be the case that specific subbranches scores are
#' significantly greater than sibling and parent branches, and thus sibling
#' branches should be considered as noise if their scores are cumulatively
#' lower than the parents. This can happen in extremely nonhomogeneous data
#' sets, where there exists locally very stable branches surrounded by unstable
#' branches that contain more than `minPts` points.
#' `prune_unstable = TRUE` will remove the unstable branches.
#'
#' @family clustering functions
#'
#' @param x a valid [hclust] object created via [hclust()] or [hdbscan()].
#' @param constraints Either a list or matrix of pairwise constraints. If
#' missing, an unsupervised measure of stability is used to make local cuts and
#' extract the optimal clusters. See details.
#' @param alpha numeric; weight between \eqn{[0, 1]} for mixed-objective
#' semi-supervised extraction. Defaults to 0.
#' @param minPts numeric; Defaults to 2. Only needed if class-less noise is a
#' valid label in the model.
#' @param prune_unstable logical; should significantly unstable subtrees be
#' pruned? The default is `FALSE` for the original optimal extraction
#' framework (see Campello et al, 2013). See details for what `TRUE`
#' implies.
#' @param validate_constraints logical; should constraints be checked for
#' validity? See details for what are considered valid constraints.
#'
#' @returns A list with the elements:
#'
#' \item{cluster }{A integer vector with cluster assignments. Zero
#' indicates noise points (if any).}
#' \item{hc }{The original [hclust] object with additional list elements
#' `"stability"`, `"constraint"`, and `"total"`
#' for the \eqn{n - 1} cluster-wide objective scores from the extraction.}
#'
#' @author Matt Piekenbrock
#' @seealso [hclust()], [hdbscan()], [stats::cutree()]
#' @references Campello, Ricardo JGB, Davoud Moulavi, Arthur Zimek, and Joerg
#' Sander (2013). A framework for semi-supervised and unsupervised optimal
#' extraction of clusters from hierarchies. _Data Mining and Knowledge
#' Discovery_ 27(3): 344-371.
#' \doi{10.1007/s10618-013-0311-4}
#' @keywords model clustering
#' @examples
#' data("moons")
#'
#' ## Regular HDBSCAN using stability-based extraction (unsupervised)
#' cl <- hdbscan(moons, minPts = 5)
#' cl$cluster
#'
#' ## Constraint-based extraction from the HDBSCAN hierarchy
#' ## (w/ stability-based tiebreaker (semisupervised))
#' cl_con <- extractFOSC(cl$hc, minPts = 5,
#' constraints = list("12" = c(49, -47)))
#' cl_con$cluster
#'
#' ## Alternative formulation: Constraint-based extraction from the HDBSCAN hierarchy
#' ## (w/ stability-based tiebreaker (semisupervised)) using distance thresholds
#' dist_moons <- dist(moons)
#' cl_con2 <- extractFOSC(cl$hc, minPts = 5,
#' constraints = ifelse(dist_moons < 0.1, 1L,
#' ifelse(dist_moons > 1, -1L, 0L)))
#'
#' cl_con2$cluster # same as the second example
#' @export
extractFOSC <-
function(x,
constraints,
alpha = 0,
minPts = 2L,
prune_unstable = FALSE,
validate_constraints = FALSE) {
if (!inherits(x, "hclust"))
stop("extractFOSC expects 'x' to be a valid hclust object.")
# if constraints are given then they need to be a list, a matrix or a vector
if (!(
missing(constraints) ||
is.list(constraints) ||
is.matrix(constraints) ||
is.numeric(constraints)
))
stop("extractFOSC expects constraints to be either an adjacency list or adjacency matrix.")
if (!minPts >= 2)
stop("minPts must be at least 2.")
if (alpha < 0 ||
alpha > 1)
stop("alpha can only takes values between [0, 1].")
n <- nrow(x$merge) + 1L
## First step for both unsupervised and semisupervised - compute stability scores
cl_tree <- computeStability(x, minPts)
## Unsupervised Extraction
if (missing(constraints)) {
cl_tree <- extractUnsupervised(cl_tree, prune_unstable)
}
## Semi-supervised Extraction
else {
## If given as adjacency-list form
if (is.list(constraints)) {
## Checks for proper indexing, symmetry of constraints, etc.
if (validate_constraints) {
is_valid <- max(as.integer(names(constraints))) < n
is_valid <-
is_valid && all(sapply(constraints, function(ilc)
all(ilc <= n)))
if (!is_valid) {
stop("Detected constraint indices not in the interval [1, n]")
}
constraints <- validateConstraintList(constraints, n)
}
cl_tree <-
extractSemiSupervised(cl_tree, constraints, alpha, prune_unstable)
}
## Adjacency matrix given (probably from dist object), retrieve adjacency list form
else if (is.vector(constraints)) {
if (!all(constraints %in% c(-1, 0, 1))) {
stop(
"'extractFOSC' only accepts instance-level constraints. See ?extractFOSC for more details."
)
}
## Checks for proper integer labels, symmetry of constraints, length of vector, etc.
if (validate_constraints) {
is_valid <- length(constraints) == choose(n, 2)
constraints_list <-
validateConstraintList(distToAdjacency(constraints, n), n)
} else {
constraints_list <- distToAdjacency(constraints, n)
}
cl_tree <-
extractSemiSupervised(cl_tree, constraints_list, alpha, prune_unstable)
}
## Full nxn adjacency-matrix given, give warning and retrieve adjacency list form
else if (is.matrix(constraints)) {
if (!all(constraints %in% c(-1, 0, 1))) {
stop(
"'extractFOSC' only accepts instance-level constraints. See ?extractFOSC for more details."
)
}
if (!all(dim(constraints) == c(n, n))) {
stop("Given matrix is not square.")
}
warning(
"Full nxn matrix given; extractFOCS does not support asymmetric relational constraints. Using lower triangular."
)
constraints <- constraints[lower.tri(constraints)]
## Checks for proper integer labels, symmetry of constraints, length of vector, etc.
if (validate_constraints) {
is_valid <- length(constraints) == choose(n, 2)
constraints_list <-
validateConstraintList(distToAdjacency(constraints, n), n)
} else {
constraints_list <- distToAdjacency(constraints, n)
}
cl_tree <-
extractSemiSupervised(cl_tree, constraints_list, alpha, prune_unstable)
} else {
stop(paste(
"'extractFOSC' doesn't know how to handle constraints of type",
class(constraints)
))
}
}
total_stab <-
if (is.null(attr(cl_tree, "total_stability")))
1
else
attr(cl_tree, "total_stability")
cl_track <- attr(cl_tree, "cl_tracker")
stability_score <-
unlist(sapply(cl_track, function(cid)
cl_tree[[as.character(cid)]]$stability))
constraint_score <-
unlist(sapply(cl_track, function(cid)
cl_tree[[as.character(cid)]]$vscore))
total_score <-
unlist(sapply(cl_track, function(cid)
cl_tree[[as.character(cid)]]$vscore))
out <- append(
x,
list(
"cluster" = cl_track,
"stability" = stability_score,
"constraint" = constraint_score,
"total" = total_score
)
)
extraction_type <-
ifelse(
missing(constraints),
"(w/ stability-based extraction)",
ifelse(
alpha == 0,
"(w/ constraint-based extraction)",
"(w/ mixed-objective extraction)"
)
)
substrs <- unlist(strsplit(x$method, split = " \\(w\\/"))
out[["method"]] <-
if (length(substrs) > 1)
paste(substrs[[1]], extraction_type)
else
paste(out[["method"]], extraction_type)
class(out) <- "hclust"
return(list(cluster = attr(cl_tree, "cluster"), hc = out))
}
```

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