remove loops
bug: optimisation yields two identical components
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1 changed files with 206 additions and 136 deletions
342
optim_mixture.jl
342
optim_mixture.jl
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@ -1,40 +1,38 @@
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module OptimMixture
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import GLMakie as GLM
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import DelimitedFiles as DF
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import BenchmarkTools as BT
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import UnicodePlots as UP
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import LinearAlgebra: ⋅
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import NLopt
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import LinearAlgebra: norm, mul!, axpy!, axpby!
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import Logging
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Logging.global_logger(Logging.ConsoleLogger(stderr, Logging.Info))
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const ENERGIES = [2803, 2811, 2819, 2826, 2834, 2842, 2850, 2858, 2866, 2874,
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2882, 2890, 2897, 2905, 2913, 2921, 2929, 2937, 2945, 2953,
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2961, 2969, 2977, 2985, 2993, 3001, 3009, 3018, 3026, 3034,
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3042, 3050]
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@kwdef struct Slab
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@kwdef struct Slab{T<:Real}
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energy::Int
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data::Matrix{Int}
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data::Matrix{T}
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end
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function load_slabs(width::Int=512, height::Int=512)
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slabs = Slab[]()
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function load_slabs(; T::Type=Int, width::Int=512, height::Int=512)
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slabs = Slab[]
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i_min, i_max = typemax(Int), 0
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for e in ENERGIES
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slab = Slab(energy=e, data=DF.readdlm("test_sample/HeLa_F-SRS_512x512_2803cm-1.txt", ',', Int))
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slab::Slab{T} = Slab{T}(energy=e, data=DF.readdlm("test_sample/HeLa_F-SRS_512x512_$(e)cm-1.txt", ',', T))
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push!(slabs, slab)
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i_min, i_max = min(i_min, minimum(slab.data)), max(i_max, maximum(slab.data))
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return slabs, i_min, i_max
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end
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end
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function Q_matrix_transpose(X::Matrix{Float64}, Y::Matrix{Float64}, N::Int)
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return sum(abs2, Y - view(X, 1:N, :) * view(X, (N+1):size(X, 1), :)')
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end
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function Q_matrix_reshape(X::Matrix{Float64}, Y::Matrix{Float64}, N::Int)
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N_plus_M, m = size(X)
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return sum(abs2, Y - view(X, 1:N, :) * reshape(view(X, (N+1):N_plus_M, :), m, N_plus_M - N))
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s = join(size(slabs[1].data), "x")
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@info "Loaded $(length(slabs)) $s slabs"
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return slabs, i_min, i_max
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end
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function E_loop(X::Matrix{Float64}, Y::Matrix{Float64})
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@ -53,8 +51,49 @@ function E_loop(X::Matrix{Float64}, Y::Matrix{Float64})
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return s
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end
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function E!(X::Matrix{Float64}, Y::Matrix{Float64}, tmp::Matrix{Float64})
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N, M = size(Y)
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Q!(tmp, X, N)
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# roughly 6ms with N = 512*512, M = 32, m = 2
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# slighly faster than tmp .= tmp .- Y (7ms)
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# slighly faster than tmp .-= Y (7ms)
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axpy!(-1.0, Y, tmp)
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# roughly 2.5m
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# slightly faster than norm2(tmp)
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s = sum(x -> x^2, tmp)
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return s
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end
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function test_E(N::Int, M::Int, m::Int; benchmark::Bool=true)
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X = rand(N + M, m)
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Y = rand(N, M)
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tmp = zeros(N, M)
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E_from_loop = E_loop(X, Y)
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E_from_mul = E!(X, Y, tmp)
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@show abs(E_from_mul - E_from_loop)
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if benchmark
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@info "Running benchmarks..., this may take a while"
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show(stdout, "text/plain", BT.@benchmark E_loop($X, $Y))
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println()
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show(stdout, "text/plain", BT.@benchmark E!($X, $Y, $tmp))
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end
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end
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function Q!(dst::Matrix{Float64}, X::Matrix{Float64}, N::Int)
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N_plus_M, _ = size(X)
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# roughly 9ms with N = 512*512, M = 32, m = 2
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# could be sped up using StrideArray?
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mul!(dst, view(X, 1:N, :), view(X, N+1:N_plus_M, :)')
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end
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function Q_loop!(dst::Matrix{Float64}, X::Matrix{Float64}, N::Int)
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N_plus_M, m = size(X)
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# roughly 23.5ms with N = 512*512, M = 32, m = 2
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for i in 1:N
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for j in N+1:N_plus_M
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x = 0
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@ -66,58 +105,43 @@ function Q_loop!(dst::Matrix{Float64}, X::Matrix{Float64}, N::Int)
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end
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end
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function test_Q(N::Int, M::Int, m::Int; benchmark::Bool=true)
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X = rand(N + M, m)
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Q_from_loop = zeros(N, M)
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Q_from_mul = zeros(N, M)
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Q!(Q_from_mul, X, N)
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Q_loop!(Q_from_loop, X, N)
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@show norm(Q_from_mul - Q_from_loop)
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if benchmark
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@info "Running benchmarks..., this may take a while"
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show(stdout, "text/plain", BT.@benchmark Q!($Q_from_mul, $X, $N))
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println()
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show(stdout, "text/plain", BT.@benchmark Q_loop!($Q_from_loop, $X, $N))
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end
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end
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function DE_loop!(dst::Matrix{Float64}, X::Matrix{Float64}, Y::Matrix{Float64})
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dst_W = zeros(size(Y))
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DE_loop!(dst, dst_W, X, Y)
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DE_loop!!(dst, dst_W, X, Y)
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end
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function dot_prod_dual_loop(W::Matrix{Float64}, X::Matrix{Float64}, V::Matrix{Float64})
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_, m = size(X)
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N, M = size(W)
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s = 0
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for k in 1:m
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for i in 1:N
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x = 0
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for j in 1:M
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@inbounds x += W[i, j] * X[N+j, k]
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end
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s += x * V[i, k]
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end
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for j in 1:M
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x = 0
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for i in 1:N
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@inbounds x += W[i, j] * X[i, k]
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end
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s += x * V[N+j, k]
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end
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end
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return s
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function DE!!(dst::Matrix{Float64}, dst_W::Matrix{Float64}, X::Matrix{Float64}, Y::Matrix{Float64})
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N_plus_M, m = size(X)
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N, M = size(Y)
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# dst is the same size as X
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# we need storage of size Y
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Q!(dst_W, X, N)
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# compute W = 2(Q(X) - Y)
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axpby!(-2.0, Y, 2.0, dst_W)
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mul!(view(dst, 1:N, :), dst_W, view(X, N+1:N_plus_M, :))
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mul!(view(dst, N+1:N_plus_M, :), dst_W', view(X, 1:N, :))
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end
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function dot_prod_primal_loop(W::Matrix{Float64}, X::Matrix{Float64}, V::Matrix{Float64})
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_, m = size(X)
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N, M = size(W)
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s = 0
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for i in 1:N
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for j in 1:M
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x = 0
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for k in 1:m
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@inbounds x += X[i, k] * V[N+j, k] + X[N+j, k] * V[i, k]
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end
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s += x * W[i, j]
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end
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end
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return s
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end
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function DE_loop!(dst::Matrix{Float64}, dst_W::Matrix{Float64}, X::Matrix{Float64}, Y::Matrix{Float64})
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function DE_loop!!(dst::Matrix{Float64}, dst_W::Matrix{Float64}, X::Matrix{Float64}, Y::Matrix{Float64})
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_, m = size(X)
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N, M = size(Y)
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# dst is the same size as X
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@ -144,116 +168,162 @@ function DE_loop!(dst::Matrix{Float64}, dst_W::Matrix{Float64}, X::Matrix{Float6
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dst .*= 2
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end
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function DQ_V_loop!(dst::Matrix{Float64}, X::Matrix{Float64}, V::Matrix{Float64}, N::Int)
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N_plus_M, m = size(X)
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for i in 1:N
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for j in N+1:N_plus_M
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x = 0
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for k in 1:m
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@inbounds x += (V[i, k] * X[j, k] + X[i, k] * V[j, k])
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end
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@inbounds dst[i, j-N] = x
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end
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end
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end
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function Q_loop_transposed(X::Matrix{Float64}, Y::Matrix{Float64}, N::Int)
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s = 0
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m, N_plus_M = size(X)
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for i in 1:N
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for j in N+1:N_plus_M
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x = 0
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for k in 1:m
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@inbounds x += X[k, i] * X[k, j]
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end
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@inbounds s += (Y[j-N, i] - x)^2
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end
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end
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return s
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end
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function test_dot_prod(N::Int, M::Int, m::Int)
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X = rand(N + M, m)
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W = rand(N, M)
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V = rand(N + M, m) .- 0.5
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@show dot_prod_primal_loop(W, X, V)
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@show dot_prod_dual_loop(W, X, V)
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end
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function test_DE(N::Int, M::Int, m::Int)
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function check_derivative_E(N::Int, M::Int, m::Int; loop::Bool=false)
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X = rand(N + M, m)
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Y = rand(N, M)
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V = rand(N + M, m) .- 0.5
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EX = E_loop(X, Y)
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grad_EX = zeros(size(X))
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DE_loop!(grad_EX, X, Y)
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tmp = zeros(size(Y))
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if loop
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DE_loop!!(grad_EX, tmp, X, Y)
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else
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DE!!(grad_EX, tmp, X, Y)
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end
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eps = [10.0^k for k in 1:-1:-12]
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errors = zeros(size(eps))
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for i in eachindex(eps)
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EX_V_true = E_loop(X + eps[i] * V, Y)
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if loop
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EX_V_true = E_loop(X + eps[i] * V, Y)
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else
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EX_V_true = E!(X + eps[i] * V, Y, tmp)
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end
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EX_V_approx = EX + eps[i] * sum(grad_EX .* V)
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@show EX_V_true
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@show EX_V_approx
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# @show EX_V_true
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# @show EX_V_approx
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errors[i] = abs(EX_V_true - EX_V_approx)
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end
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@show eps
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@show errors
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# @show eps
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# @show errors
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foo = UP.lineplot(eps, errors, xscale=:log10, yscale=:log10)
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UP.lineplot!(foo, eps, eps)
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println(foo)
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end
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function test_DQ(N::Int, M::Int, m::Int)
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X = rand(N + M, m) * 255
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V = rand(N + M, m)
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function test_DE(N::Int, M::Int, m::Int; benchmark::Bool=true)
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X = rand(N + M, m)
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Y = rand(N, M)
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QX = zeros(N, M)
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Q_loop!(QX, X, N)
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tmp_W = zeros(N, M)
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dst_true = zeros(N, M)
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dst_approx = zeros(N, M)
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∇E_loop = zeros(N + M, m)
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∇E = zeros(N + M, m)
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eps = [10.0^k for k in 1:-1:-8]
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errors = zeros(size(eps))
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DE_loop!!(∇E_loop, tmp_W, X, Y)
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DE!!(∇E, tmp_W, X, Y)
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for i in eachindex(eps)
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Q_loop!(dst_true, X + eps[i] * V, N)
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DQ_V_loop!(dst_approx, X, eps[i] * V, N)
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dst_approx .+= QX
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errors[i] = sum(abs, dst_approx - dst_true)
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@show norm(∇E_loop - ∇E)
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if benchmark
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@info "Running benchmarks..., this may take a while"
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show(stdout, "text/plain", BT.@benchmark DE_loop!!($∇E_loop, $tmp_W, $X, $Y))
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println()
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show(stdout, "text/plain", BT.@benchmark DE!!($∇E, $tmp_W, $X, $Y))
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end
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end
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function benchmark()
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slabs, _, _ = load_slabs(T=Float64)
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Y::Matrix{Float64} = hcat([vec(slab.data) for slab in slabs]...)
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@show size(Y)
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N::Int, M::Int = size(Y)
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m::Int = 2
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X::Vector{Float64} = fill(0.5, (N + M) * m)
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∇E::Vector{Float64} = zeros((N + M) * m)
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tmp = zeros(size(Y))
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show(stdout, "text/plain", BT.@benchmark DE_loop!!(reshape($∇E, $N + $M, $m), $tmp, reshape($X, $N + $M, $m), reshape($Y, $N, $M)))
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println()
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show(stdout, "text/plain", BT.@benchmark E_loop(reshape($X, $N + $M, $m), reshape($Y, $N, $M)))
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end
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function plot_result_mixture_1(X::Vector{Float64}, N::Int, M::Int, m::Int)
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X_ = reshape(X, N + M, m)
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λ = view(X_, 1:N, :)
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s = view(X_, N+1:N+M, :)
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@show size(λ)
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@show size(s)
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fig = GLM.Figure()
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axes = map(1:m+1) do i
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aspect = if i < m + 1
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GLM.DataAspect()
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else
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nothing
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end
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GLM.Axis(fig[1, i], aspect=aspect)
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end
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@show eps
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@show errors
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println(UP.lineplot(eps, errors, xscale=:log10, yscale=:log10))
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n = Int(sqrt(N))
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# Component density
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GLM.image!(axes[1], reshape(view(λ, :, 1), n, n))
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GLM.image!(axes[2], reshape(view(λ, :, 2), n, n))
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# Spectra
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GLM.lines!(axes[3], s[:, 1])
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GLM.lines!(axes[3], s[:, 2])
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@show norm(λ[:, 1] - λ[:, 2])
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@show norm(s[:, 1] - s[:, 2])
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display(fig)
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end
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function test(N::Int, M::Int, m::Int)
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Y = rand(N, M) * 255
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X = rand(N + M, m) * 255
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function optim(algo::Symbol=:LD_MMA)
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slabs, _, _ = load_slabs(T=Float64)
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Y::Matrix{Float64} = hcat([vec(slab.data) for slab in slabs]...)
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@show size(Y)
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X_pre = copy(X)
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X_pre[N+1:N+M] .= vec(X_pre[N+1:N+M]')
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N::Int, M::Int = size(Y)
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m::Int = 2
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r_matrix = Q_matrix_transpose(X, Y, N)
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r_loop = Q_loop(X, Y, N)
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X::Vector{Float64} = fill(0.5, (N + M) * m)
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∇E::Vector{Float64} = zeros((N + M) * m)
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tmp = zeros(size(Y))
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@show r_matrix, r_loop, abs(r_matrix - r_loop) / r_loop
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function objective(X::Vector{Float64}, grad::Vector{Float64})
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if length(grad) > 0
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DE!!(reshape(grad, N + M, m), tmp, reshape(X, N + M, m), reshape(Y, N, M))
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else
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@debug "No gradient requested!"
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end
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# @show "|∇E| = $(norm(grad))"
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max_Σ_λ = maximum(sum(view(reshape(X, N + M, m), 1:N, :); dims=2))
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value = E!(reshape(X, N + M, m), reshape(Y, N, M), tmp)
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@info "E = $value, |∇E| = $(norm(grad)), max_Σ_λ = $max_Σ_λ"
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return value
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end
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opt = NLopt.Opt(algo, (N + M) * m)
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NLopt.lower_bounds!(opt, fill(0.0, (N + M) * m))
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NLopt.upper_bounds!(opt, vec([fill(1.0, N, m); fill(Inf, M, m)]))
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NLopt.min_objective!(opt, objective)
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res = NLopt.optimize(opt, X)
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return res
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end
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end # module
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# OptimMixture.test(512 * 512, 20, 2)
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# OptimMixture.test_DQ(512 * 512, 20, 2)
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OptimMixture.test_DE(512 * 512, 20, 2)
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# OptimMixture.test_DE(20, 2, 2)
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# OptimMixture.test_dot_prod(512^2, 20, 2)
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res = OptimMixture.optim(:LD_LBFGS);
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OptimMixture.plot_result_mixture_1(res[2], 512 * 512, 32, 2)
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# OptimMixture.benchmark();
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# OptimMixture.test_E(512 * 512, 32, 2)
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# OptimMixture.test_DE(512 * 512, 32, 2)
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# OptimMixture.test_Q(512 * 512, 32, 2)
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# OptimMixture.check_derivative_E(512 * 512, 32, 2)
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# OptimMixture.check_derivative_E(512 * 512, 32, 2; loop=true)
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# vim: ts=2:sw=2:sts=2
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Loading…
Reference in a new issue