Fix more deprecation warnings

src/Clustering.jl

@@ -7,6 +7,7 @@ module Clustering
 
     using Printf
     using LinearAlgebra
+    using SparseArrays
     if VERSION >= v"0.7.0-beta.85"
         using Statistics
     end

src/dbscan.jl

@@ -163,7 +163,7 @@ function _dbscan(kdtree::KDTree, points::AbstractMatrix, radius::Real;
             visited[current_index] && continue
             visited[current_index] = true
             append!(adj_list, inrange(kdtree, points[:, current_index], radius))
-            cluster_selection[adj_list] = true
+            cluster_selection[adj_list] .= true
             # if a point doesn't have enough neighbors it is not a 'core' point and its neighbors are not added to the to_explore list
             if (length(adj_list) - 1) < min_neighbors
                 empty!(adj_list)

src/hclust.jl

@@ -74,7 +74,7 @@ end
 ##   update D(i,j) and N(i) accordingly
 function hclust_minimum(ds::Symmetric{T}) where T<:Real
     ## For each i < j compute d[i,j] (this is already given)
-    d = full(ds)                #  we need a local copy
+    d = Matrix(ds)                #  we need a local copy
     nc = size(d,1)
     mr = Vector{Int}(undef, nc-1)       # min row
     mc = Vector{Int}(undef, nc-1)       # min col

src/mcl.jl

@@ -21,12 +21,16 @@ end
 # `zero_tol` is a minimal value to consider as an element-to-cluster assignment
 function _mcl_clusters(mcl_adj::AbstractMatrix, allow_singles::Bool, zero_tol::Float64 = 1E-20)
     # remove rows containing only zero elements and convert into a mask of nonzero elements
-    el2clu_mask = mcl_adj[squeeze(sum(mcl_adj, dims=2), 2) .> zero_tol, :] .> zero_tol
+    el2clu_mask = mcl_adj[squeeze(sum(mcl_adj, dims=2), dims=2) .> zero_tol, :] .> zero_tol
 
     # assign cluster indexes to each node
     # cluster index is the index of the first TRUE in a given column
-    clu_ixs = squeeze(mapslices(el_mask -> !isempty(el_mask) ? findmax(el_mask)[2] : 0,
-                                el2clu_mask, 1), 1)
+    @static if VERSION >= v"0.7.0-beta.73"
+        _ms = mapslices(el_mask->isempty(el_mask) ? 0 : argmax(el_mask), el2clu_mask, dims=1)
+    else
+        _ms = mapslices(el_mask->isempty(el_mask) ? 0 : argmax(el_mask), el2clu_mask, 1)
+    end
+    clu_ixs = squeeze(_ms, dims=1)
     clu_sizes = zeros(Int, size(el2clu_mask, 1))
     unassigned_count = 0
     @inbounds for clu_ix in clu_ixs
@@ -151,8 +155,8 @@ function mcl(adj::AbstractMatrix{T};
     # initialize the MCL adjacency matrix by normalized `adj` weights
     mcl_adj = copy(adj)
     # normalize in columns
-    scale!(mcl_adj, map(x -> x != 0.0 ?  1.0/x : x, squeeze(sum(mcl_adj, dims=1), 1)))
-    mcl_norm = vecnorm(mcl_adj)
+    rmul!(mcl_adj, Diagonal(map(x -> x != 0.0 ?  1.0/x : x, squeeze(sum(mcl_adj, dims=1), dims=1))))
+    mcl_norm = norm(mcl_adj)
     if !isfinite(mcl_norm)
         throw(OverflowError("The norm of the input adjacency matrix is not finite"))
     end
@@ -171,10 +175,10 @@ function mcl(adj::AbstractMatrix{T};
         _mcl_prune!(next_mcl_adj, prune_tol)
 
         # normalize in columns
-        scale!(next_mcl_adj, map(x -> x != 0.0 ? 1.0/x : x,
-                                 squeeze(sum(next_mcl_adj, dims=1), 1)))
+        rmul!(next_mcl_adj, Diagonal(map(x -> x != 0.0 ? 1.0/x : x,
+                                         squeeze(sum(next_mcl_adj, dims=1), dims=1))))
 
-        next_mcl_norm = vecnorm(next_mcl_adj)
+        next_mcl_norm = norm(next_mcl_adj)
         if !isfinite(next_mcl_norm)
             @warn("MCL adjacency matrix norm is not finite")
             break

src/randindex.jl

@@ -11,8 +11,8 @@ function randindex(c1,c2)
     c = counts(c1,c2,(1:maximum(c1),1:maximum(c2))) # form contingency matrix
 
     n = round(Int,sum(c))
-    nis = sum(sum(c,2).^2)        # sum of squares of sums of rows
-    njs = sum(sum(c,1).^2)        # sum of squares of sums of columns
+    nis = sum(abs2, sum(c, dims=2))        # sum of squares of sums of rows
+    njs = sum(abs2, sum(c, dims=1))        # sum of squares of sums of columns
 
     t1 = binomial(n,2)            # total number of pairs of entities
     t2 = sum(c.^2)                # sum over rows & columnns of nij^2

test/affprop.jl

@@ -3,6 +3,8 @@
 using Test
 using Distances
 using Clustering
+using LinearAlgebra
+using Random
 
 srand(34568)
 
@@ -12,7 +14,7 @@ x = rand(d, n)
 S = -pairwise(Euclidean(), x, x)
 
 # set diagonal value to median value
-S = S - diagm(diag(S)) + median(S)*eye(size(S,1)) 
+S = S - diagm(0 => diag(S)) + median(S)*I
 
 R = affinityprop(S)
 
@@ -26,6 +28,6 @@ k = length(R.exemplars)
 @test length(R.counts) == k
 @test sum(R.counts) == n
 for i = 1:k
-    @test R.counts[i] == countnz(R.assignments .== i)
+    @test R.counts[i] == count(==(i), R.assignments)
 end
 

test/dbscan.jl

@@ -21,7 +21,7 @@ k = length(R.seeds)
 @test length(R.assignments) == n
 @test length(R.counts) == k
 for c = 1:k
-    @test countnz(R.assignments .== c) == R.counts[c]
+    @test count(==(c), R.assignments) == R.counts[c]
 end
 @test all(R.counts .>= 180)
 

test/kmeans.jl

@@ -87,6 +87,6 @@ r2 = kmeans(x, k; maxiter=50, init=:kmcen)
 @test sum(r.counts) == n
 @test r.cweights == map(Float64, r.counts)
 @test isapprox(sum(r.costs), r.totalcost)
-for fn in fieldnames(r)
+for fn in fieldnames(typeof(r))
     @test getfield(r, fn) == getfield(r2, fn)
 end

test/mcl.jl

@@ -15,8 +15,8 @@ edges = Tuple{Symbol, Symbol, Float64}[(:cat, :hat, 0.2), (:hat, :bat, 0.16),
                                        (:hit, :bit, 0.16)]
 adj_matrix = zeros(Float64, length(nodes), length(nodes))
 for edge in edges
-    n1 = findfirst(nodes, edge[1])
-    n2 = findfirst(nodes, edge[2])
+    n1 = findfirst(isequal(edge[1]), nodes)
+    n2 = findfirst(isequal(edge[2]), nodes)
     adj_matrix[n1, n2] = adj_matrix[n2, n1] = edge[3]
 end
 @assert issymmetric(adj_matrix)
@@ -30,7 +30,7 @@ k = length(res.counts)
 @test length(res.assignments) == length(nodes)
 @test length(res.counts) == k
 for c = 1:k
-    @test countnz(res.assignments .== c) == res.counts[c]
+    @test count(==(c), res.assignments) == res.counts[c]
 end
 @test res.nunassigned == 0
 @test res.assignments == [1, 2, 1, 2, 1, 2]

test/runtests.jl

@@ -1,6 +1,8 @@
 using Clustering
 using Test
 using Random
+using LinearAlgebra
+using SparseArrays
 
 tests = ["seeding",
          "kmeans",