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add queuepenalty #33

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c6f8149
add queuepenalty
Aug 5, 2025
b895242
fix some api
Aug 5, 2025
04bfa31
delete shotpenalty
Aug 5, 2025
84df5e8
update the docstring of queuepenalty
Sep 3, 2025
5b14a34
Merge branch 'main' into yg/add_queuepenalty
youdongguo Sep 3, 2025
da9e6ea
Merge branch 'main' into yg/add_queuepenalty
youdongguo Sep 4, 2025
6350807
consistent_with_dealing with zero
Sep 4, 2025
10c241e
fix bugs
Sep 4, 2025
74856b6
test_customized merge function
Sep 5, 2025
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56 changes: 29 additions & 27 deletions src/NMFMerge.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,8 @@ Performs "NMF-Merge" on data matrix `X`.

Arguments:

-`queuepenalty`: a function of the form `f(λ_min, t1sq, t2sq)` that computes the penalty for merging two components, where `λ_min` is the smaller eigenvalue of the generalized eigenvalue problem.

- `X::AbstractMatrix`: the data matrix to be factorized

- `ncomponents::Pair{Int,Int}`: in the form of `n1 => n2`, merging from `n1` components to `n2`components,
Expand All @@ -35,7 +37,7 @@ Keyword arguments:

Other keywords arguments are passed to `NMF.nnmf`.
"""
function nmfmerge(X, ncomponents::Pair{Int,Int}; tol_final=1e-4, tol_intermediate=sqrt(tol_final), W0=nothing, H0=nothing, kwargs...)
function nmfmerge(queuepenalty, X, ncomponents::Pair{Int,Int}; tol_final=1e-4, tol_intermediate=sqrt(tol_final), W0=nothing, H0=nothing, kwargs...)
n1, n2 = ncomponents
f = tsvd(X, n2)
Un, Sn, Vn = f
Expand All @@ -50,11 +52,13 @@ function nmfmerge(X, ncomponents::Pair{Int,Int}; tol_final=1e-4, tol_intermediat
result_over = nnmf(X, n1; kwargs..., init=:custom, tol=tol_intermediate, W0=W_over_init, H0=H_over_init)
W_over, H_over = result_over.W, result_over.H
W_over_normed, H_over_normed = colnormalize(W_over, H_over)
Wmerge, Hmerge, _ = colmerge2to1pq(W_over_normed, H_over_normed, n2)
Wmerge, Hmerge, _ = colmerge2to1pq(queuepenalty, W_over_normed, H_over_normed, n2)
result_renmf = nnmf(X, n2; kwargs..., init=:custom, tol=tol_final, W0=Wmerge, H0=Hmerge)
return result_renmf
end
nmfmerge(X, ncomponents::Integer; kwargs...) = nmfmerge(X, ncomponents+max(1, round(Int, 0.2*ncomponents)) => Int(ncomponents); kwargs...)
nmfmerge(queuepenalty, X, ncomponents::Integer; kwargs...) = nmfmerge(queuepenalty, X, ncomponents+max(1, round(Int, 0.2*ncomponents)) => Int(ncomponents); kwargs...)
nmfmerge(X, ncomponents::Pair{Int,Int}; kwargs...) = nmfmerge(mergepenalty, X, ncomponents; kwargs...)
nmfmerge(X, ncomponents::Integer; kwargs...) = nmfmerge(mergepenalty, X, ncomponents::Integer; kwargs...)

function colnormalize!(W, H, p::Integer=2)
nonzerocolids = Int[]
Expand Down Expand Up @@ -89,7 +93,7 @@ components remain.
`mergeseq` is the sequence of merge pair ids (id1, id2). Values larger than the
number of columns in `W` indicate the output of previous merge steps.
"""
function colmerge2to1pq(S::AbstractArray, T::AbstractArray, n::Integer)
function colmerge2to1pq(queuepenalty, S::AbstractArray, T::AbstractArray, n::Integer)
mrgseq = Tuple{Int, Int}[]
S = let S = S # julia #15276
[S[:, j] for j in axes(S, 2)]
Expand All @@ -103,7 +107,10 @@ function colmerge2to1pq(S::AbstractArray, T::AbstractArray, n::Integer)
Nt = length(S)
Nt >= 2 || throw(ArgumentError("Cannot do 2 to 1 merge: Matrix size smaller than 2"))
Nt >= n || throw(ArgumentError("Final solution more than original size"))
pq = initialize_pq_2to1(S, T)
pq = PriorityQueue{Tuple{Int,Int},Float64}()
for id0 in length(S):-1:2
pq = pqupdate2to1!(pq, queuepenalty, S, T, id0, 1:id0-1)
end
m = Nt
while m > n
id0, id1 = dequeue!(pq)
Expand All @@ -112,33 +119,28 @@ function colmerge2to1pq(S::AbstractArray, T::AbstractArray, n::Integer)
end
push!(mrgseq, (id0, id1))
S, T, id01, _ = mergecol2to1!(S, T, id0, id1);
pqupdate2to1!(pq, S, T, id01, 1:id01-1);
pqupdate2to1!(pq, queuepenalty, S, T, id01, 1:id01-1);
m -= 1
end
Smtx, Tmtx = reduce(hcat, filter(!isempty, S)), reduce(hcat, filter(!isempty, T))'
return Smtx, Matrix(Tmtx), mrgseq
end
colmerge2to1pq(S::AbstractArray, T::AbstractArray, n::Integer) = colmerge2to1pq(mergepenalty, S, T, n)

function initialize_pq_2to1(S::AbstractVector, T::AbstractVector)
err_pq = PriorityQueue{Tuple{Int, Int},Float64}()
for id0 in length(S):-1:2
err_pq = pqupdate2to1!(err_pq, S, T, id0, 1:id0-1)
end
return err_pq
end

function pqupdate2to1!(pq, S::AbstractVector, T::AbstractVector, id01::Integer, overlapids::AbstractRange{To}) where To
function pqupdate2to1!(pq, queuepenalty::Function, S::AbstractVector, T::AbstractVector, id01::Integer, overlapids::AbstractRange{To}) where To
for id in overlapids
if !isempty(S[id]) && !isempty(S[id01])
loss = solve_remix(S, T, id, id01)[2]
enqueue!(pq, (id, id01), loss)
t1sq, t1t2, t2sq, c = build_tr_det(S, T, id, id01)
loss = solve_remix(t1sq, t1t2, t2sq, c)[2]
enqueue!(pq, (id, id01), queuepenalty(loss, t1sq, t2sq))
end
end
return pq
end

function solve_remix(S, T, id1, id2)
τ, δ, c, h1h1, h1h2, h2h2 = build_tr_det(S, T, id1, id2)
function solve_remix(h1h1::AbstractFloat, h1h2::AbstractFloat, h2h2::AbstractFloat, c::AbstractFloat)
τ = h1h1+2c*h1h2+h2h2
δ = (1-c^2)*(h1h1*h2h2-h1h2^2)
if h1h1 == 0
return c, zero(c), (zero(c), one(c))
end
Expand All @@ -159,13 +161,9 @@ function solve_remix(S, T, id1, id2)
end

function build_tr_det(W::AbstractVector, H::AbstractVector, id1::Integer, id2::Integer)
c = W[id1]'*W[id2]
h1h1 = H[id1]'*H[id1]
h1h2 = H[id1]'*H[id2]
h2h2 = H[id2]'*H[id2]
τ = h1h1+2c*h1h2+h2h2
δ = (1-c^2)*(h1h1*h2h2-h1h2^2)
return τ, δ, c, h1h1, h1h2, h2h2
h1sq, h1h2, h2sq = H[id1]'*H[id1], H[id1]'*H[id2], H[id2]'*H[id2]
c = W[id1]'*W[id2] # assumes normalization
return h1sq, h1h2, h2sq, c
end

function mergecol2to1!(S::AbstractVector, T::AbstractVector, id0::Integer, id1::Integer)
Expand All @@ -178,7 +176,8 @@ function mergecol2to1!(S::AbstractVector, T::AbstractVector, id0::Integer, id1::
end

function mergepair(S::AbstractVector, T::AbstractVector, id1::Integer, id2::Integer)
c, loss, u, = solve_remix(S, T, id1, id2)
t1sq, t1t2, t2sq, c = build_tr_det(S, T, id1, id2)
c, loss, u = solve_remix(t1sq, t1t2, t2sq, c)
S12, T12 = remix_enact(S, T, id1, id2, c, u)
return S12, T12, loss
end
Expand Down Expand Up @@ -221,4 +220,7 @@ function mergecolumns(W::AbstractArray, H::AbstractArray, mergeseq::AbstractArra
return Smtx, Matrix(Tmtx), STstage, Err
end

mergepenalty(λ_min, t1sq, t2sq) = λ_min
shotpenalty(λ_min, t1sq, t2sq) = λ_min / sqrt(min(t1sq, t2sq))
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Should we consider exporting mergepenalty and shotpenalty? If so, they would need docstrings and each to have tests. Alternatively we could keep them private. If so, then since shotpenalty is untested perhaps it should not be in this package?

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Deleted shotpenalty, I think keeping it private at this moment is better as I am not sure how to describe the queuepenalty in a good way. We have a queuepenalty work in a fixed way as: f(E, t1sq, t2sq), users cannot define a customized penalty function.

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Once this merges, they will be able to define their own queuepenalty, but I like the idea of not exporting anything.


end
29 changes: 16 additions & 13 deletions test/runtests.jl
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Expand Up @@ -32,7 +32,7 @@ H_GT = [6 10 8 2 0 1 2 10;
4 9 10 7 7 0 0 0
]

@testset "test top wrapper" begin
@testset "test top wrapper" begin
W = W_GT[:, 3:4]
H = H_GT[3:4, :]
X = W*H
Expand All @@ -51,7 +51,6 @@ H_GT = [6 10 8 2 0 1 2 10;
@test sum(abs2, W_standard - W_renmf) <= 1e-12
@test sum(abs2, H_standard - H_renmf) <= 1e-12


X = rand(30, 20)
result_1 = nmfmerge(X, 10; alg=:cd)
result_2 = nmfmerge(X, 12 => 10; alg=:cd)
Expand All @@ -67,7 +66,7 @@ H_GT = [6 10 8 2 0 1 2 10;
result_2 = nmfmerge(X, 10 => 8; alg=:cd)
@test sum(abs2, result_1.W - result_2.W) <= 1e-12
@test sum(abs2, result_1.H - result_2.H) <= 1e-12

end

@testset "merge coefficients" begin
Expand All @@ -86,8 +85,10 @@ end
idx = argmax(Fvals)
w = Fvecs[:,idx]

τ, δ, c, h1h1, h1h2, h2h2 = NMFMerge.build_tr_det(W_v, H_v, 1, 2)
c, p, u = NMFMerge.solve_remix(W_v, H_v, 1, 2)
h1h1, h1h2, h2h2, c = NMFMerge.build_tr_det(W_v, H_v, 1, 2)
τ = h1h1+2c*h1h2+h2h2
δ = (1-c^2)*(h1h1*h2h2-h1h2^2)
c, p, u = NMFMerge.solve_remix(h1h1, h1h2, h2h2, c)
u = [u[1], u[2]]
b = sqrt(τ^2/4-δ)
λ_max = τ/2+b
Expand All @@ -101,8 +102,8 @@ end
@test norm(u[1].*W_v[1].+u[2].*W_v[2]) ≈ 1
@test norm(Q1*u - maximum(F.values)*Q2*u) <= 1e-10
@test norm(Q1*u - λ_max*Q2*u) <= 1e-10
W12, H12, loss = NMFMerge.mergepair(W_v, H_v, 1, 2)

W12, H12, _ = NMFMerge.mergepair(W_v, H_v, 1, 2)
Err(Hm) = sum(abs2, W12 * Hm' - W * H)
@test norm(ForwardDiff.gradient(Err, H12)) <= 1e-10
end
Expand All @@ -121,7 +122,7 @@ end
imgnf = NMF.solve!(NMF.CoordinateDescent{Float64}(), img, W0, H0)
W1, H1 = imgnf.W, imgnf.H
W1n, H1n = colnormalize(W1, H1)
[@test abs(norm(W1n[:,j], 2)-1) <= 1e-12 for j in axes(W1n, 2)]
[@test abs(norm(W1n[:,j], 2)-1) <= 1e-12 for j in axes(W1n, 2)]

W2 = [W1n[:, j] for j in axes(W1n, 2)];
H2 = [H1n[i, :] for i in axes(H1n, 1)];
Expand All @@ -134,13 +135,15 @@ end
idx = argmax(Fvals)
w = Fvecs[:,idx]

τ, δ, c, h1h1, h1h2, h2h2 = NMFMerge.build_tr_det(W2, H2, 1, 2)
c, p, u = NMFMerge.solve_remix(W2, H2, 1, 2)
h1h1, h1h2, h2h2, c = NMFMerge.build_tr_det(W2, H2, 1, 2)
τ = h1h1+2c*h1h2+h2h2
δ = (1-c^2)*(h1h1*h2h2-h1h2^2)
c, p, u = NMFMerge.solve_remix(h1h1, h1h2, h2h2, c)
u = [u[1], u[2]]
b = sqrt(τ^2/4-δ)
λ_max = τ/2+b
λ_min = δ/λ_max

@test abs(λ_max - maximum(F.values))<=1e-12
@test abs(λ_min - minimum(F.values))<=1e-10

Expand All @@ -150,7 +153,7 @@ end
@test norm(Q1*u - maximum(F.values)*Q2*u) <= 1e-10
@test norm(Q1*u - λ_max*Q2*u) <= 1e-10

W12, H12, loss = NMFMerge.mergepair(W2, H2, 1, 2)
W12, H12, _ = NMFMerge.mergepair(W2, H2, 1, 2)
Err(Hm) = sum(abs2, W12*Hm'-W1*H1)
@test norm(ForwardDiff.gradient(Err, H12)) <= 1e-12

Expand All @@ -169,7 +172,7 @@ end
N1b = randn(length(S1)); N1b = N1b / norm(N1b) * coef
T2 = zero(T1)
T2[15] = 0.25 * sqrt(min(sum(abs2, N1a) * sum(abs2, T1a), sum(abs2, N1b) * sum(abs2, T1b)))

W, H = [S1 S1 S2], [T1a'; T1b'; T2']
W0, H0 = [S1 S2], [T1'; T2']
Wn, Hn = colnormalize(W, H)
Expand Down