Added silhouette plot

Project.toml

@@ -6,6 +6,7 @@ version = "0.12.0"
 Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 DataValues = "e7dc6d0d-1eca-5fa6-8ad6-5aecde8b7ea5"
+Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
 KernelDensity = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b"

src/StatsPlots.jl

@@ -13,7 +13,9 @@ using Widgets, Observables
 import Observables: AbstractObservable, @map, observe
 import Widgets: @nodeps
 import DataStructures: OrderedDict
+using Distances: pairwise, Euclidean
 import Clustering: Hclust, nnodes
+using Clustering: ClusteringResult, silhouettes, assignments, counts
 using Interpolations
 import MultivariateStats: MDS, eigvals, projection, principalvars,
                             principalratio, transform
@@ -40,5 +42,6 @@ include("bar.jl")
 include("dendrogram.jl")
 include("andrews.jl")
 include("ordinations.jl")
+include("silhouetteplot.jl")
 
 end # module

src/silhouetteplot.jl

@@ -0,0 +1,83 @@
+"""
+	silhouetteplot(C,X[,D];...)
+Make a silhouette plot to assess the quality of a clustering. `C` must be a `ClusteringResult` (see the `Clustering` package), and `X` is a matrix in which each column represents a data point. If supplied, `D` should be a distance matrix (as in `Distances`); otherwise, pairwise Euclidean distances are used.
+
+Each data point has a silhouette score between -1 and 1 indicating how unambiguously the point belongs to its assigned cluster. These are sorted within each cluster and portrayed using horizontal bars. Also shown is a dashed line at the average score. Typically a high-quality clustering has significant numbers of bars within each cluster that cross the line, and few negative scores overall.
+
+See also: [`Clustering.silhouettes`](@ref), [`Distances`](@ref).
+
+# Examples
+
+```
+using Clustering, Distances, Plots
+# random dataset with 3-ish clusters in 5 dimensions
+X = hcat([rand(5,1) .+ 0.2*randn(5, 200) for _=1:3]...)
+D = pairwise(Euclidean(),X,dims=2)
+R = kmeans(D, 3; maxiter=200, display=:iter)
+
+silhouetteplot(R,X,D)
+```
+"""
+silhouetteplot
+
+@userplot SilhouettePlot
+@recipe function f(h::SilhouettePlot)#R::ClusteringResult,X::AbstractArray,D::AbstractMatrix=[];distance=Euclidean())
+	narg = length(h.args)
+	@assert narg > 1 "At least two arguments are required."
+	R = h.args[1]
+	@assert R isa ClusteringResult "First argument must be a ClusteringResult."
+	X = h.args[2]
+	@assert X isa AbstractArray "Second argument must be an array."
+	if narg > 2
+		D = h.args[3]
+		@assert D isa AbstractMatrix "Third argument must be a distance matrix."
+	else
+		D = pairwise(Euclidean(),X,dims=2)
+	end
+
+	a = assignments(R)  # assignments to clusters
+	c = counts(R)  # cluster sizes
+	k = length(c)  # number of clusters
+	n = sum(c)     # number of points overall
+
+	s = silhouettes(R,D)
+
+	# Settings for the axes
+	legend --> false
+	yflip := true
+	xlims := [min(-0.1,minimum(s)),1]
+	# y ticks used to show cluster boundaries, and labels to show the sizes
+	yticks := cumsum([0;c]),["0",["+$z" for z in c]...]
+
+	# Generate the polygons for each cluster.
+	offset = 0;
+	plt = plot([],label="")
+	for i in 1:k
+		idx = (a.==i)    # members of cluster i
+		si = sort(s[idx],rev=true)
+		@series begin
+			linealpha --> 0
+			seriestype := :shape
+			label := "$i"
+			x = [0;repeat(si,inner=(2));0]
+			y = offset .+ repeat(0:c[i],inner=(2))
+			x,y
+		end
+		# text label to the left of the bars
+		@series begin
+			linealpha := 0
+			series_annotations := [ Plots.text("$i",:center,:middle,9) ]
+			[-0.04], [offset+c[i]/2]
+		end
+		offset += c[i];
+	end
+
+	# Dashed line for overall average.
+	savg = sum(s)/n
+	@series begin
+		linecolor := :black
+		linestyle := :dash
+		label := ""
+		[savg,savg], [0,n]
+	end
+end