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v0.3.0
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PharmCat committed Mar 26, 2021
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4 changes: 2 additions & 2 deletions Project.toml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
name = "MetidaNLopt"
uuid = "4e1362ca-e5a0-4237-9ffd-bca39e5440e3"
authors = ["Vladimir Arnautov <[email protected]>"]
version = "0.2.1"
version = "0.3.0"

[deps]

Expand All @@ -13,7 +13,7 @@ Reexport = "189a3867-3050-52da-a836-e630ba90ab69"

[compat]

Metida = "0.6"
Metida = "0.9"
NLopt = "0.6"
ForwardDiff = "0.10"
Reexport = "0.2, 1"
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182 changes: 94 additions & 88 deletions src/MetidaNLopt.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -4,31 +4,11 @@ module MetidaNLopt
@reexport using Metida
import Metida: LMM, AbstractLMMDataBlocks, LMMDataViews, initvar, thetalength,
varlinkrvecapply!, varlinkvecapply,
lmmlog!, LMMLogMsg, fit_nlopt!, rmat_base_inc!, zgz_base_inc!
lmmlog!, LMMLogMsg, fit_nlopt!, rmat_base_inc!, zgz_base_inc!, logerror!, reml_sweep_β

reml_sweep_β_cuda(args...) = error("MetidaCu not found. \n - Run `using MetidaCu` before.")
cudata(args...) = error("MetidaCu not found. \n - Run `using MetidaCu` before.")

#=
struct LMMDataBlocks{T1, T2} <: AbstractLMMDataBlocks
# Fixed effect matrix views
xv::T1
# Responce vector views
yv::T2
function LMMDataBlocks(xv::Matrix, yv::Vector, vcovblock::Vector)
x = Vector{typeof(xv)}(undef, length(vcovblock))
y = Vector{typeof(yv)}(undef, length(vcovblock))
for i = 1:length(vcovblock)
x[i] = Matrix(view(xv, vcovblock[i],:))
y[i] = Vector(view(yv, vcovblock[i]))
end
new{typeof(x), typeof(y)}(x, y)
end
function LMMDataBlocks(lmm)
return LMMDataBlocks(lmm.data.xv, lmm.data.yv, lmm.covstr.vcovblock)
end
end
=#
function Metida.fit_nlopt!(lmm::LMM{T};
solver = :nlopt,
verbose = :auto,
Expand All @@ -37,77 +17,104 @@ module MetidaNLopt
aifirst = false,
g_tol = 1e-16,
x_tol = 1e-16,
x_rtol = 0.0,
x_rtol = -Inf,
f_tol = 1e-16,
f_rtol = 0.0,
f_rtol = -Inf,
hes::Bool = false,
init = nothing,
io = stdout) where T

if lmm.result.fit lmmlog!(io, lmm, verbose, LMMLogMsg(:INFO, "Refit model...")) end
lmm.result.fit = false

# Optimization function
if solver == :nlopt
optfunc = reml_sweep_β_nlopt
data = LMMDataViews(lmm)
elseif solver == :cuda
optfunc = reml_sweep_β_cuda
data = cudata(lmm)
elseif solver == :nloptsw
optfunc = reml_sweep_β
data = LMMDataViews(lmm)
else
error("Unknown solver!")
end
if verbose == :auto
verbose = 1
end
############################################################################
#Initial variance
# Initial variance
θ = zeros(T, lmm.covstr.tl)
if isa(init, Vector{T}) && length(θ) == length(init)
copyto!(θ, init)
lb = similar(θ)
ub = similar(θ)
lb .= eps() * 1e4
ub .= Inf
if isa(init, Vector{T})
if length(θ) == length(init)
copyto!(θ, init)
lmmlog!(io, lmm, verbose, LMMLogMsg(:INFO, "Using provided θ: "*string(θ)))
else
error("init length $(length(init)) != θ length $(length(θ))")
end
else
initθ = sqrt(initvar(lmm.data.yv, lmm.mm.m)[1]/4)
initθ = sqrt(initvar(lmm.data.yv, lmm.mm.m)[1])/(length(lmm.covstr.random)+1)
θ .= initθ
for i = 1:length(θ)
if lmm.covstr.ct[i] == :rho
θ[i] = 0.0
lb[i] = -1.0 + eps() * 1e4
ub[i] = 1.0 - eps() * 1e4
end
end
lmmlog!(lmm, verbose, LMMLogMsg(:INFO, "Initial θ: "*string(θ)))
lmmlog!(io, lmm, verbose, LMMLogMsg(:INFO, "Initial θ: "*string(θ)))
end
############################################################################
varlinkrvecapply!(θ, lmm.covstr.ct)
#varlinkrvecapply!(θ, lmm.covstr.ct; rholinkf = rholinkf)
############################################################################
#COBYLA
# COBYLA BOBYQA
opt = NLopt.Opt(:LN_BOBYQA, thetalength(lmm))
NLopt.ftol_rel!(opt, f_rtol)
NLopt.ftol_abs!(opt, f_tol)
NLopt.xtol_rel!(opt, x_rtol)
NLopt.xtol_abs!(opt, x_tol)
#opt.lower_bounds = lb::Union{AbstractVector,Real}
#opt.upper_bounds = ub::Union{AbstractVector,Real}
opt.lower_bounds = lb
opt.upper_bounds = ub
#-----------------------------------------------------------------------
obj = (x,y) -> optfunc(lmm, data, varlinkvecapply(x, lmm.covstr.ct; rholinkf = rholinkf))[1]
#obj = (x,y) -> optfunc(lmm, data, varlinkvecapply(x, lmm.covstr.ct; rholinkf = rholinkf))[1]
obj = (x,y) -> optfunc(lmm, data, x)[1]
NLopt.min_objective!(opt, obj)
#Optimization object
# Optimization object
lmm.result.optim = NLopt.optimize!(opt, θ)
#Theta (θ) vector
lmm.result.theta = varlinkvecapply(lmm.result.optim[2], lmm.covstr.ct)
# Theta (θ) vector
#lmm.result.theta = varlinkvecapply(lmm.result.optim[2], lmm.covstr.ct)
lmm.result.theta = lmm.result.optim[2]
lmmlog!(io, lmm, verbose, LMMLogMsg(:INFO, "Resulting θ: "*string(lmm.result.theta)))

#-2 LogREML, β, iC
lmm.result.reml, lmm.result.beta, iC, θ₃ = optfunc(lmm, data, lmm.result.theta)
if !isnan(lmm.result.reml) && !isinf(lmm.result.reml)
#Variance-vovariance matrix of β
lmm.result.c = pinv(iC)
#SE
lmm.result.se = sqrt.(diag(lmm.result.c))
lmmlog!(io, lmm, verbose, LMMLogMsg(:INFO, "Model fitted."))
lmm.result.fit = true
else
lmmlog!(io, lmm, verbose, LMMLogMsg(:INFO, "Model NOT fitted."))
lmm.result.fit = false
# -2 LogREML, β, iC
lmm.result.reml, lmm.result.beta, iC, θ₃, noerrors = optfunc(lmm, data, lmm.result.theta)
if !isnan(lmm.result.reml) && !isinf(lmm.result.reml) && noerrors
# Variance-vovariance matrix of β
lmm.result.c = inv(iC)
# SE
if !any(x-> x < 0.0, diag(lmm.result.c))
lmm.result.se = sqrt.(diag(lmm.result.c)) #ERROR: DomainError with -1.9121111845919027e-54
if any(x-> x < 1e-8, lmm.result.se) && minimum(lmm.result.se)/maximum(lmm.result.se) < 1e-8 lmmlog!(io, lmm, verbose, LMMLogMsg(:WARN, "Some of the SE parameters is suspicious.")) end
lmmlog!(io, lmm, verbose, LMMLogMsg(:INFO, "Model fitted."))
lmm.result.fit = true
end
end
# Check G
if lmm.covstr.random[1].covtype.s != :ZERO
for i = 1:length(lmm.covstr.random)
dg = det(gmatrix(lmm, i))
if dg < 1e-8 lmmlog!(io, lmm, verbose, LMMLogMsg(:WARN, "det(G) of random effect $(i) is less 1e-08.")) end
end
end
# Check Hessian
if hes && lmm.result.fit
#Hessian
# Hessian
lmm.result.h = hessian(lmm, lmm.result.theta)
#H positive definite check
# H positive definite check
if !isposdef(Symmetric(lmm.result.h))
lmmlog!(io, lmm, verbose, LMMLogMsg(:WARN, "Hessian is not positive definite."))
end
Expand All @@ -122,9 +129,15 @@ module MetidaNLopt
end
end
end
#
if !lmm.result.fit
lmmlog!(io, lmm, verbose, LMMLogMsg(:INFO, "Model NOT fitted."))
end
lmm
end



function reml_sweep_β_nlopt(lmm, θ::Vector{T}) where T
data = LMMDataViews(lmm)
reml_sweep_β_nlopt(lmm, data, θ)
Expand All @@ -139,55 +152,48 @@ module MetidaNLopt
θ₂ = zeros(T, lmm.rankx, lmm.rankx)
θ₂tc = zeros(T, lmm.rankx, lmm.rankx)
θ₃ = zero(T)
#βm = zeros(T, lmm.rankx)
βtc = zeros(T, lmm.rankx)
β = Vector{T}(undef, lmm.rankx)
A = Vector{Matrix{T}}(undef, n)
#X = Vector{Matrix}(undef, n)
#y = Vector{Vector}(undef, n)
q = zero(Int)
qswm = zero(Int)
logdetθ₂ = zero(T)
@inbounds for i = 1:n
q = length(lmm.covstr.vcovblock[i])
qswm = q + lmm.rankx
V = zeros(T, q, q)
zgz_base_inc!(V, θ, lmm.covstr, lmm.covstr.vcovblock[i], lmm.covstr.sblock[i])
rmat_base_inc!(V, θ[lmm.covstr.tr[end]], lmm.covstr, lmm.covstr.vcovblock[i], lmm.covstr.sblock[i])
#-------------------------------------------------------------------
#X[i] = view(lmm.data.xv, lmm.covstr.vcovblock[i], :)
#y[i] = view(lmm.data.yv, lmm.covstr.vcovblock[i])
try
@inbounds @simd for i = 1:n
q = length(lmm.covstr.vcovblock[i])
qswm = q + lmm.rankx
V = zeros(T, q, q)
zgz_base_inc!(V, θ, lmm.covstr, lmm.covstr.vcovblock[i], lmm.covstr.sblock[i])
rmat_base_inc!(V, θ[lmm.covstr.tr[end]], lmm.covstr, lmm.covstr.vcovblock[i], lmm.covstr.sblock[i])
#-------------------------------------------------------------------
#Cholesky
A[i] = LinearAlgebra.LAPACK.potrf!('L', V)[1]
try
θ₁ += logdet(Cholesky(A[i], 'L', 0))
# Cholesky
A[i] = LinearAlgebra.LAPACK.potrf!('U', V)[1]

θ₁ += logdet(Cholesky(A[i], 'U', 0))
#θ₁ += sum(log.(diag(A[i])))*2
catch
lmmlog!(lmm, LMMLogMsg(:ERROR, "θ₁ not estimated during REML calculation, V isn't positive definite or |V| less zero."))
return (1e100, nothing, nothing, 1e100)
end
vX = LinearAlgebra.LAPACK.potrs!('L', A[i], copy(data.xv[i]))
vy = LinearAlgebra.LAPACK.potrs!('L', A[i], copy(data.yv[i]))
#LinearAlgebra.BLAS.gemm!('T', 'N', one(T), data.xv[i], vX, one(T), θ₂tc)
mul!(θ₂tc, data.xv[i]', vX, one(T), one(T))
#LinearAlgebra.BLAS.gemv!('T', one(T), data.xv[i], vy, one(T), βtc)
mul!(βtc, data.xv[i]', vy, one(T), one(T))
vX = LinearAlgebra.LAPACK.potrs!('U', A[i], copy(data.xv[i]))
vy = LinearAlgebra.LAPACK.potrs!('U', A[i], copy(data.yv[i]))
mul!(θ₂tc, data.xv[i]', vX, one(T), one(T))
mul!(βtc, data.xv[i]', vy, one(T), one(T))
#-------------------------------------------------------------------
end
#Beta calculation
copyto!(θ₂, θ₂tc)
LinearAlgebra.LAPACK.potrf!('L', θ₂tc)
copyto!(β, LinearAlgebra.LAPACK.potrs!('L', θ₂tc, βtc))
end
# Beta calculation
copyto!(θ₂, θ₂tc)
LinearAlgebra.LAPACK.potrf!('U', θ₂tc)
copyto!(β, LinearAlgebra.LAPACK.potrs!('U', θ₂tc, βtc))
# θ₃ calculation
@simd for i = 1:n
@inbounds @simd for i = 1:n
#r = LinearAlgebra.BLAS.gemv!('N', -one(T), data.xv[i], βtc, one(T), copy(data.yv[i]))
r = mul!(copy(data.yv[i]), data.xv[i], βtc, -one(T), one(T))
vr = LinearAlgebra.LAPACK.potrs!('L', A[i], copy(r))
vr = LinearAlgebra.LAPACK.potrs!('U', A[i], copy(r))
θ₃ += r'*vr
#θ₃ += BLAS.dot(length(r), r, 1, vr, 1)
end
logdetθ₂ = logdet(θ₂)
return θ₁ + logdetθ₂ + θ₃ + c, β, θ₂, θ₃
ldθ₂ = LinearAlgebra.LAPACK.potrf!('U', copy(θ₂))[1]
logdetθ₂ = logdet(Cholesky(ldθ₂, 'U', 0))
catch e
logerror!(e, lmm)
return (Inf, nothing, nothing, nothing, false)
end
return θ₁ + logdetθ₂ + θ₃ + c, β, θ₂, θ₃, true
end

end # module
4 changes: 2 additions & 2 deletions test/runtests.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -14,10 +14,10 @@ transform!(df0, :formulation=> categorical, renamecols=false)
random = VarEffect(@covstr(formulation|subject), CSH),
repeated = VarEffect(@covstr(formulation|subject), DIAG),
)
fit!(lmm; solver = :nlopt, rholinkf = :sigm, f_tol=1e-16, x_tol=1e-16)
fit!(lmm; solver = :nlopt, f_tol=1e-16, x_tol=1e-16)
#Metida.fit_nlopt!(lmm; solver = :nlopt, rholinkf = :sigm, f_tol=0.0, x_tol = 0.0, f_rtol =0.0, x_rtol =1e-18)
#Metida.m2logreml(lmm) ≈ 10.065239006121315
#10.065239006121315
#10.065456008797781
@test Metida.m2logreml(lmm) ≈ 10.065241064600908 atol=1E-6
@test Metida.m2logreml(lmm) ≈ 10.065238620486195 atol=1E-6
end

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Registration pull request created: JuliaRegistries/General/32880

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v0.3.0 -m "<description of version>" 7c49110a135561150162fbe84a4a380f44172a8c
git push origin v0.3.0

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