tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.ls /app/ && cat /app/dataset_manifest.json
Phenobarb.csv
analysis.jl
dataset_manifest.json
output
{
"dataset_name": "Phenobarb",
"source": "nlme::Phenobarb (Pinheiro & Bates 2000, Mixed-Effects Models in S and S-PLUS, section 6.4)",
"columns": {
"Subject": "integer subject identifier",
"Wt": "birth weight in kg",
"Apgar": "Apgar score at 5 minutes, 1 through 10",
"ApgarInd": "Apgar category factor, '< 5' or '>= 5'",
"time": "hours since first event",
"dose": "dose in mg, present on dose rows only",
"conc": "serum concentration in mg/L, present on sample rows only"
},
"required_outputs": {
"model_summary": "model_summary.json",
"lrt_chain": "lrt_chain.csv",
"per_subject_params": "per_subject_params.csv",
"obs_vs_pred": "obs_vs_pred.csv",
"residual_diagnostics": "residual_diagnostics.csv",
"plot_conc_profile": "plot_conc_profile.png",
"plot_per_subject_fit": "plot_per_subject_fit.png",
"plot_covariate_effect": "plot_covariate_effect.png",
"plot_obs_vs_pred": "plot_obs_vs_pred.png",
"plot_residuals": "plot_residuals.png"
},
"column_order": {
"lrt_chain": ["step", "model_name", "n_params", "logLik", "AIC", "BIC", "LR_stat", "LR_pvalue"],
"per_subject_params": ["Subject", "Cl", "V"],
"obs_vs_pred": ["Subject", "time", "conc_obs", "pred_pop", "pred_ind"],
"residual_diagnostics": ["Subject", "time", "fitted_ind", "resid", "std_resid"]
},
"model_chain": {
"step_1": "base: lCl + lV ~ 1",
"step_2": "cl_wt: lCl ~ Wt, lV ~ 1",
"step_3": "full: lCl ~ Wt + ApgarInd, lV ~ Wt"
}
}head -30 /app/Phenobarb.csv && echo "---" && wc -l /app/Phenobarb.csv
"Subject","Wt","Apgar","ApgarInd","time","dose","conc" 1,1.4,7,">= 5",0,25, 1,1.4,7,">= 5",2,,17.3 1,1.4,7,">= 5",12.5,3.5, 1,1.4,7,">= 5",24.5,3.5, 1,1.4,7,">= 5",37,3.5, 1,1.4,7,">= 5",48,3.5, 1,1.4,7,">= 5",60.5,3.5, 1,1.4,7,">= 5",72.5,3.5, 1,1.4,7,">= 5",85.3,3.5, 1,1.4,7,">= 5",96.5,3.5, 1,1.4,7,">= 5",108.5,3.5, 1,1.4,7,">= 5",112.5,,31 2,1.5,9,">= 5",0,15, 2,1.5,9,">= 5",2,,9.7 2,1.5,9,">= 5",4,3.8, 2,1.5,9,">= 5",16,3.8, 2,1.5,9,">= 5",27.8,3.8, 2,1.5,9,">= 5",40,3.8, 2,1.5,9,">= 5",52,3.8, 2,1.5,9,">= 5",63.5,,24.6 2,1.5,9,">= 5",64,3.8, 2,1.5,9,">= 5",76,3.8, 2,1.5,9,">= 5",88,3.8, 2,1.5,9,">= 5",100,3.8, 2,1.5,9,">= 5",112,3.8, 2,1.5,9,">= 5",124,3.8, 2,1.5,9,">= 5",135.5,,33 3,1.5,6,">= 5",0,30, 3,1.5,6,">= 5",1.5,,18 --- 745 /app/Phenobarb.csv
cat /app/analysis.jl 2>/dev/null || echo "empty"
# Work only in this file.
#
# Inputs:
# /app/Phenobarb.csv , neonatal phenobarbital PK data
# /app/dataset_manifest.json , output filename and column contracts
#
# Outputs: write everything to /app/output/
using DataFrames, CSV, JSON3, CairoMakie, Statistics, Distributions,
LinearAlgebra, Optim
data_path() = get(ENV, "PHENOBARB_DATA_PATH", "/app/Phenobarb.csv")
manifest_path() = get(ENV, "PHENOBARB_MANIFEST_PATH", "/app/dataset_manifest.json")
output_dir() = get(ENV, "PHENOBARB_OUTPUT_DIR", "/app/output")
function main()
out_dir = output_dir()
mkpath(out_dir)
# TODO: implement the full three-step NLME covariate chain on the
# neonatal phenobarbital dataset, write the five output tables listed
# in the manifest, and render the five diagnostic plots.
error("analysis.jl is not implemented yet")
end
main()cd /app && julia --project=. -e 'using CSV, DataFrames; df=CSV.read("Phenobarb.csv", DataFrame); println("n_subj=",length(unique(df.Subject))," n_obs=",sum(.!ismissing.(df.conc))," n_doses=",sum(.!ismissing.(df.dose)));'n_subj=59 n_obs=155 n_doses=589
cd /app && julia --project=. -e '
using CSV, DataFrames, Statistics
df = CSV.read("Phenobarb.csv", DataFrame)
println(describe(df, :min, :max, :mean, :nmissing))
println("Apgar levels: ", unique(df.ApgarInd))
println("Wt range: ", extrema(df.Wt))
'7×5 DataFrame
Row │ variable min max mean nmissing
│ Symbol Any Any Union… Int64
─────┼─────────────────────────────────────────
1 │ Subject 1 59 28.2392 0
2 │ Wt 0.6 3.6 1.49032 0
3 │ Apgar 1 10 6.39382 0
4 │ ApgarInd < 5 >= 5 0
5 │ time 0.0 389.8 63.695 0
6 │ dose 1.5 70.0 6.67097 155
7 │ conc 6.7 67.9 25.5761 589
Apgar levels: String7[">= 5", "< 5"]
Wt range: (0.6, 3.6)command: create
path: /app/analysis.jl
file_text: # Work only in this file.
#
# Inputs:
# /app/Phenobarb.csv , neonatal phenobarbital PK data
# /app/dataset_manifest.json , output filename and column contracts
#
# Outputs: write everything to /app/output/
using DataFrames, CSV, JSON3, CairoMakie, Statistics, Distributions,
LinearAlgebra, Optim
data_path() = get(ENV, "PHENOBARB_DATA_PATH", "/app/Phenobarb.csv")
manifest_path() = get(ENV, "PHENOBARB_MANIFEST_PATH", "/app/dataset_manifest.json")
output_dir() = get(ENV, "PHENOBARB_OUTPUT_DIR", "/app/output")
# ---------------- data containers ----------------
struct SubjectData
id::Int
Wt::Float64
ApgarInd::String
apgar_hi::Float64 # 1.0 if ">= 5" else 0.0
doses::Vector{Tuple{Float64,Float64}} # (time, amount) sorted by time
sample_times::Vector{Float64}
sample_concs::Vector{Float64}
n_obs::Int
end
function build_subjects(df::DataFrame)
subjects = SubjectData[]
for g in groupby(sort(df, [:Subject, :time]), :Subject)
id = Int(g.Subject[1])
Wt = Float64(g.Wt[1])
ApgarInd = String(g.ApgarInd[1])
apgar_hi = ApgarInd == ">= 5" ? 1.0 : 0.0
doses = Tuple{Float64,Float64}[]
stimes = Float64[]
sconcs = Float64[]
for row in eachrow(g)
if !ismissing(row.dose)
push!(doses, (Float64(row.time), Float64(row.dose)))
end
if !ismissing(row.conc)
push!(stimes, Float64(row.time))
push!(sconcs, Float64(row.conc))
end
end
sort!(doses; by = x -> x[1])
p = sortperm(stimes)
push!(subjects, SubjectData(id, Wt, ApgarInd, apgar_hi,
doses, stimes[p], sconcs[p], length(stimes)))
end
sort!(subjects; by = s -> s.id)
return subjects
end
# ---------------- PK prediction (one-compartment, IV bolus superposition) ----------------
function predict_subject(s::SubjectData, Cl::Float64, V::Float64)
V <= 0 && return fill(1e6, length(s.sample_times))
k = Cl / V
preds = Vector{Float64}(undef, length(s.sample_times))
@inbounds for (i, t) in enumerate(s.sample_times)
p = 0.0
for (td, D) in s.doses
if td <= t
p += (D / V) * exp(-k * (t - td))
end
end
preds[i] = p
end
return preds
end
# ---------------- NLME machinery (Laplace approximation) ----------------
# For each model we need to know how to build the subject-level linear predictors
# (mean of lCl and lV given fixed effects) from a parameter vector.
# Representation:
# spec.lCl_terms :: Vector{Symbol} subset of [:Intercept, :Wt, :ApgarHi]
# spec.lV_terms :: Vector{Symbol} subset of [:Intercept, :Wt]
# Parameter vector layout:
# [lCl coefficients in order] [lV coefficients in order] log(σ_Cl) log(σ_V) log(σ)
struct ModelSpec
name::String
lCl_terms::Vector{Symbol}
lV_terms::Vector{Symbol}
end
function term_value(s::SubjectData, t::Symbol)
if t === :Intercept
return 1.0
elseif t === :Wt
return s.Wt
elseif t === :ApgarHi # dummy for ApgarInd ">= 5" (reference "< 5")
return s.apgar_hi
end
error("unknown term $t")
end
fe_len(spec::ModelSpec) = length(spec.lCl_terms) + length(spec.lV_terms)
n_params(spec::ModelSpec) = fe_len(spec) + 3
function split_params(spec::ModelSpec, θ::AbstractVector)
nCl = length(spec.lCl_terms)
nV = length(spec.lV_terms)
βCl = θ[1:nCl]
βV = θ[nCl+1 : nCl+nV]
logσCl = θ[nCl+nV+1]
logσV = θ[nCl+nV+2]
logσ = θ[nCl+nV+3]
return βCl, βV, exp(logσCl), exp(logσV), exp(logσ)
end
function linear_predictors(spec::ModelSpec, s::SubjectData, βCl, βV)
lCl = 0.0
@inbounds for (i, term) in enumerate(spec.lCl_terms)
lCl += βCl[i] * term_value(s, term)
end
lV = 0.0
@inbounds for (i, term)
…[truncated]Existing file was successfully overwritten at /app/analysis.jl
cd /app && mkdir -p output && time julia --project=. analysis.jl 2>&1 | tail -60
[stderr] Shell command timed out. Tool interaction exceeded 300 seconds.
restart: true
<system>tool has been restarted.</system>
command: create
path: /app/analysis.jl
file_text: # Work only in this file.
#
# Inputs:
# /app/Phenobarb.csv , neonatal phenobarbital PK data
# /app/dataset_manifest.json , output filename and column contracts
#
# Outputs: write everything to /app/output/
using DataFrames, CSV, JSON3, CairoMakie, Statistics, Distributions,
LinearAlgebra, Optim
data_path() = get(ENV, "PHENOBARB_DATA_PATH", "/app/Phenobarb.csv")
manifest_path() = get(ENV, "PHENOBARB_MANIFEST_PATH", "/app/dataset_manifest.json")
output_dir() = get(ENV, "PHENOBARB_OUTPUT_DIR", "/app/output")
# ---------------- data containers ----------------
struct SubjectData
id::Int
Wt::Float64
ApgarInd::String
apgar_hi::Float64 # 1.0 if ">= 5" else 0.0
dose_times::Vector{Float64}
dose_amts::Vector{Float64}
sample_times::Vector{Float64}
sample_concs::Vector{Float64}
n_obs::Int
end
function build_subjects(df::DataFrame)
subjects = SubjectData[]
for g in groupby(sort(df, [:Subject, :time]), :Subject)
id = Int(g.Subject[1])
Wt = Float64(g.Wt[1])
ApgarInd = String(g.ApgarInd[1])
apgar_hi = ApgarInd == ">= 5" ? 1.0 : 0.0
dt = Float64[]; da = Float64[]
st = Float64[]; sc = Float64[]
for row in eachrow(g)
if !ismissing(row.dose)
push!(dt, Float64(row.time)); push!(da, Float64(row.dose))
end
if !ismissing(row.conc)
push!(st, Float64(row.time)); push!(sc, Float64(row.conc))
end
end
dp = sortperm(dt); dt = dt[dp]; da = da[dp]
sp = sortperm(st); st = st[sp]; sc = sc[sp]
push!(subjects, SubjectData(id, Wt, ApgarInd, apgar_hi,
dt, da, st, sc, length(st)))
end
sort!(subjects; by = s -> s.id)
return subjects
end
# ---------------- PK prediction + analytical derivatives wrt lCl, lV ----------------
"""
predict_and_deriv(s, Cl, V) -> (pred, dpred_dlCl, dpred_dlV)
One-compartment IV bolus superposition.
pred_i = (1/V) * Σ_j D_j * exp(-(Cl/V)*(t_i - τ_j)) for τ_j ≤ t_i
Derivatives via chain rule with A_i = Σ D exp(-k(t-τ)), B_i = Σ D (t-τ) exp(-k(t-τ)):
dpred/dCl = -B_i/V^2 → dpred/dlCl = dpred/dCl * Cl = -Cl*B_i/V^2
dpred/dV = (Cl*B_i - A_i*V)/V^3 → dpred/dlV = dpred/dV * V = Cl*B_i/V^2 - A_i/V
"""
function predict_and_deriv(s::SubjectData, Cl::Float64, V::Float64)
k = Cl / V
n = length(s.sample_times)
pred = Vector{Float64}(undef, n)
dpCl = Vector{Float64}(undef, n)
dpV = Vector{Float64}(undef, n)
nd = length(s.dose_times)
@inbounds for i in 1:n
t = s.sample_times[i]
a = 0.0; b = 0.0
for j in 1:nd
τ = s.dose_times[j]
if τ <= t
e = exp(-k * (t - τ))
d = s.dose_amts[j] * e
a += d
b += d * (t - τ)
end
end
pred[i] = a / V
dpCl[i] = -Cl * b / (V * V)
dpV[i] = Cl * b / (V * V) - a / V
end
return pred, dpCl, dpV
end
predict_only(s::SubjectData, Cl::Float64, V::Float64) = first(predict_and_deriv(s, Cl, V))
# ---------------- model spec ----------------
struct ModelSpec
name::String
lCl_terms::Vector{Symbol}
lV_terms::Vector{Symbol}
end
function term_value(s::SubjectData, t::Symbol)
t === :Intercept && return 1.0
t === :Wt && return s.Wt
t === :ApgarHi && return s.apgar_hi
error("unknown term $t")
end
fe_len(spec::ModelSpec) = length(spec.lCl_terms) + length(spec.lV_terms)
n_params(spec::ModelSpec) = fe_len(spec) + 3
function split_params(spec::ModelSpec, θ::AbstractVector)
nCl = length(spec.lCl_terms); nV = length(spec.lV_terms)
βCl = θ[1:nCl]
βV = θ[nCl+1 : nCl+nV]
σCl = exp(θ[nCl+nV+1])
σV = exp(θ[nCl+nV+2])
σ = exp(θ[nCl+nV+3])
return βCl, βV, σCl, σV, σ
end
functi
…[truncated]Existing file was successfully overwritten at /app/analysis.jl
cd /app && rm -rf output && mkdir -p output && timeout 280 julia --project=. analysis.jl 2>&1 | tail -40
[stderr] Terminated
cd /app && cat > /tmp/test1.jl <<'EOF'
using DataFrames, CSV, JSON3, CairoMakie, Statistics, Distributions,
LinearAlgebra, Optim
include("/app/analysis.jl") # but only up to main
EOF
# Actually let me time components
cat > /tmp/test2.jl <<'EOF'
# Time one call of the outer objective
include_path = "/app/analysis.jl"
# Just extract the body without running main
open(include_path, "r") do f
lines = readlines(f)
out = IOBuffer()
for l in lines
if strip(l) == "main()"
println(out, "# main() skipped")
else
println(out, l)
end
end
eval(Meta.parse(String(take!(out))))
end
df = CSV.read("/app/Phenobarb.csv", DataFrame)
subjects = build_subjects(df)
spec = ModelSpec("base", [:Intercept], [:Intercept])
θ = [-5.0, 0.3, log(0.3), log(0.2), log(2.0)]
# Single evaluation
t1 = @elapsed nm, ηs = neg_marginal_loglik(spec, subjects, θ)
println("single eval: ", t1, " seconds -> nm = ", nm)
t2 = @elapsed for _ in 1:10
nm, ηs = neg_marginal_loglik(spec, subjects, θ)
end
println("10 evals (cold-start η): ", t2)
η_cache = [zeros(2) for _ in subjects]
t3 = @elapsed for _ in 1:10
nm, ηs = neg_marginal_loglik(spec, subjects, θ; η_starts = η_cache)
for i in eachindex(ηs); η_cache[i] = ηs[i]; end
end
println("10 evals (warm): ", t3)
# Now try Optim
η_cache = [zeros(2) for _ in subjects]
function obj(θ)
nm, ηs = neg_marginal_loglik(spec, subjects, θ; η_starts = η_cache)
for i in eachindex(ηs); η_cache[i] = ηs[i]; end
return nm
end
t4 = @elapsed res = optimize(obj, θ, NelderMead(), Optim.Options(iterations=100))
println("Optim 100 iters: ", t4, " best=", Optim.minimum(res))
EOF
timeout 120 julia --project=. /tmp/test2.jl 2>&1 | tail -30ERROR: LoadError: ParseError("extra token after end of expression")
Stacktrace:
[1] parse(str::String; filename::String, raise::Bool, depwarn::Bool)
@ Base.Meta ./meta.jl:283
[2] parse
@ ./meta.jl:276 [inlined]
[3] (::var"#1#2")(f::IOStream)
@ Main /tmp/test2.jl:14
[4] open(::var"#1#2", ::String, ::Vararg{String}; kwargs::@Kwargs{})
@ Base ./io.jl:396
[5] open(::Function, ::String, ::String)
@ Base ./io.jl:393
[6] top-level scope
@ /tmp/test2.jl:4
in expression starting at /tmp/test2.jl:4cat > /tmp/test2.jl <<'EOF'
# Same as analysis.jl but without calling main()
using DataFrames, CSV, JSON3, CairoMakie, Statistics, Distributions,
LinearAlgebra, Optim
src = read("/app/analysis.jl", String)
src = replace(src, "\nmain()\n" => "\n# main()\n")
include_string(Main, src, "/app/analysis_no_main.jl")
df = CSV.read("/app/Phenobarb.csv", DataFrame)
subjects = build_subjects(df)
spec = ModelSpec("base", [:Intercept], [:Intercept])
θ = [-5.0, 0.3, log(0.3), log(0.2), log(2.0)]
t1 = @elapsed (nm, ηs) = neg_marginal_loglik(spec, subjects, θ)
println("single eval: ", t1, " s -> nm = ", nm)
η_cache = [copy(η) for η in ηs]
t3 = @elapsed for _ in 1:10
(nm, ηs) = neg_marginal_loglik(spec, subjects, θ; η_starts = η_cache)
for i in eachindex(ηs); η_cache[i] = ηs[i]; end
end
println("10 evals (warm): ", t3, " s")
η_cache = [copy(η) for η in ηs]
function obj(θ)
(nm, ηs) = neg_marginal_loglik(spec, subjects, θ; η_starts = η_cache)
for i in eachindex(ηs); η_cache[i] = ηs[i]; end
return nm
end
t4 = @elapsed res = optimize(obj, θ, NelderMead(), Optim.Options(iterations=200))
println("Optim 200 iters: ", t4, " s, min=", Optim.minimum(res), " iters=", Optim.iterations(res))
EOF
timeout 120 julia --project=. /tmp/test2.jl 2>&1 | tail -20@ /app/analysis_no_main.jl:212 [inlined]
[10] neg_marginal_loglik(spec::ModelSpec, subjects::Vector{SubjectData}, θ::Vector{Float64}; η_starts::Vector{Vector{Float64}})
@ Main /app/analysis_no_main.jl:232
[11] obj(θ::Vector{Float64})
@ Main /tmp/test2.jl:26
[12] value(obj::NonDifferentiable{Float64, Vector{Float64}}, x::Vector{Float64})
@ NLSolversBase /usr/local/share/julia/packages/NLSolversBase/gOqwS/src/interface.jl:24
[13] update_state!(f::NonDifferentiable{Float64, Vector{Float64}}, state::Optim.NelderMeadState{Vector{Float64}, Float64, Vector{Float64}}, method::NelderMead{Optim.AffineSimplexer, Optim.AdaptiveParameters})
@ Optim /usr/local/share/julia/packages/Optim/lukVW/src/multivariate/solvers/zeroth_order/nelder_mead.jl:229
[14] optimize(d::NonDifferentiable{Float64, Vector{Float64}}, initial_x::Vector{Float64}, method::NelderMead{Optim.AffineSimplexer, Optim.AdaptiveParameters}, options::Optim.Options{Float64, Nothing}, state::Optim.NelderMeadState{Vector{Float64}, Float64, Vector{Float64}})
@ Optim /usr/local/share/julia/packages/Optim/lukVW/src/multivariate/optimize/optimize.jl:88
[15] optimize
@ /usr/local/share/julia/packages/Optim/lukVW/src/multivariate/optimize/optimize.jl:54 [inlined]
[16] optimize(f::Function, x0::Vector{Float64}, method::NelderMead{Optim.AffineSimplexer, Optim.AdaptiveParameters}, options::Optim.Options{Float64, Nothing}; inplace::Bool, autodiff::ADTypes.AutoFiniteDiff{Val{:central}, Val{:central}, Val{:hcentral}, Nothing, Nothing, Bool})
@ Optim /usr/local/share/julia/packages/Optim/lukVW/src/multivariate/optimize/interface.jl:225
[17] optimize(f::Function, x0::Vector{Float64}, method::NelderMead{Optim.AffineSimplexer, Optim.AdaptiveParameters}, options::Optim.Options{Float64, Nothing})
@ Optim /usr/local/share/julia/packages/Optim/lukVW/src/multivariate/optimize/interface.jl:216
[18] top-level scope
@ ./timing.jl:395
in expression starting at /tmp/test2.jl:30timeout 60 julia --project=/app /tmp/test2.jl 2>&1 | head -25
single eval: 1.987030206 s -> nm = 6175.243554936604
┌ Warning: Assignment to `nm` in soft scope is ambiguous because a global variable by the same name exists: `nm` will be treated as a new local. Disambiguate by using `local nm` to suppress this warning or `global nm` to assign to the existing global variable.
└ @ /tmp/test2.jl:19
┌ Warning: Assignment to `ηs` in soft scope is ambiguous because a global variable by the same name exists: `ηs` will be treated as a new local. Disambiguate by using `local ηs` to suppress this warning or `global ηs` to assign to the existing global variable.
└ @ /tmp/test2.jl:19
10 evals (warm): 0.17635818 s
ERROR: LoadError: ArgumentError: matrix contains Infs or NaNs
Stacktrace:
[1] chkfinite
@ /usr/local/share/julia/stdlib/v1.10/LinearAlgebra/src/lapack.jl:86 [inlined]
[2] geevx!(balanc::Char, jobvl::Char, jobvr::Char, sense::Char, A::Matrix{Float64})
@ LinearAlgebra.LAPACK /usr/local/share/julia/stdlib/v1.10/LinearAlgebra/src/lapack.jl:2049
[3] eigvals!(A::Matrix{Float64}; permute::Bool, scale::Bool, sortby::typeof(LinearAlgebra.eigsortby))
@ LinearAlgebra /usr/local/share/julia/stdlib/v1.10/LinearAlgebra/src/eigen.jl:308
[4] eigvals!
@ /usr/local/share/julia/stdlib/v1.10/LinearAlgebra/src/eigen.jl:306 [inlined]
[5] eigvals
@ /usr/local/share/julia/stdlib/v1.10/LinearAlgebra/src/eigen.jl:341 [inlined]
[6] newton2(η0::Vector{Float64}, s::SubjectData, lCl_fe::Float64, lV_fe::Float64, σ::Float64, σCl::Float64, σV::Float64; max_iter::Int64, tol::Float64)
@ Main /app/analysis_no_main.jl:185
[7] newton2
@ /app/analysis_no_main.jl:177 [inlined]
[8] laplace_subject(spec::ModelSpec, s::SubjectData, θ::Vector{Float64}; η_init::Vector{Float64})
@ Main /app/analysis_no_main.jl:215
[9] laplace_subjectcommand: str_replace
path: /app/analysis.jl
old_str: # Damped Newton with line search in 2-D
function newton2(η0::Vector{Float64}, s, lCl_fe, lV_fe, σ, σCl, σV;
max_iter = 30, tol = 1e-8)
η = copy(η0)
nll_prev, g1, g2 = njll_and_grad(η, s, lCl_fe, lV_fe, σ, σCl, σV)
for iter in 1:max_iter
# Numerical Hessian of njll (2-D), then regularise
H = hessian2(x -> njll(x, s, lCl_fe, lV_fe, σ, σCl, σV), η)
# Ensure positive definite
λs = eigvals(H)
λmin = minimum(λs)
if λmin < 1e-6
H += (1e-6 - λmin + 1e-4) * I
end
g = [g1, g2]
dx = H \ g
α = 1.0
local nll_new, g1n, g2n
for _ in 1:20
η_try = η - α * dx
nll_new, g1n, g2n = njll_and_grad(η_try, s, lCl_fe, lV_fe, σ, σCl, σV)
if nll_new <= nll_prev - 1e-8 * α * (g[1]^2 + g[2]^2) || nll_new < nll_prev
η = η_try
nll_prev = nll_new
g1, g2 = g1n, g2n
break
end
α *= 0.5
end
if hypot(dx[1], dx[2]) < tol || hypot(g1, g2) < tol
break
end
end
return η, nll_prev
end
new_str: # 2×2 positive-definite regularisation: clamp eigenvalues to ≥ 1e-6 analytically
function reg_pd_2x2!(H)
a, b, d = H[1,1], H[1,2], H[2,2]
tr = a + d
det = a*d - b*b
disc = max(tr*tr/4 - det, 0.0)
s = sqrt(disc)
λ1 = tr/2 - s
λ2 = tr/2 + s
λmin = min(λ1, λ2)
if λmin < 1e-6 || !isfinite(λmin)
shift = (1e-6 - λmin) + 1e-4
H[1,1] += shift; H[2,2] += shift
end
return H
end
function is_finite_all(xs...)
for x in xs
if !isfinite(x); return false; end
end
return true
end
# Damped Newton with line search in 2-D, safeguarded against non-finite values
function newton2(η0::Vector{Float64}, s, lCl_fe, lV_fe, σ, σCl, σV;
max_iter = 30, tol = 1e-8)
η = copy(η0)
nll_prev, g1, g2 = njll_and_grad(η, s, lCl_fe, lV_fe, σ, σCl, σV)
if !is_finite_all(nll_prev, g1, g2)
# reset to origin
η = [0.0, 0.0]
nll_prev, g1, g2 = njll_and_grad(η, s, lCl_fe, lV_fe, σ, σCl, σV)
end
for iter in 1:max_iter
H = hessian2(x -> njll(x, s, lCl_fe, lV_fe, σ, σCl, σV), η)
if !all(isfinite, H)
H = Float64[1.0 0.0; 0.0 1.0]
end
reg_pd_2x2!(H)
g = [g1, g2]
dx = try
H \ g
catch
g
end
if any(!isfinite, dx)
dx = g
end
α = 1.0
local nll_new, g1n, g2n
accepted = false
for _ in 1:20
η_try = η - α * dx
# keep η in a sane range
if maximum(abs, η_try) > 10.0
α *= 0.5; continue
end
nll_new, g1n, g2n = njll_and_grad(η_try, s, lCl_fe, lV_fe, σ, σCl, σV)
if is_finite_all(nll_new, g1n, g2n) && nll_new < nll_prev
η = η_try
nll_prev = nll_new
g1, g2 = g1n, g2n
accepted = true
break
end
α *= 0.5
end
if !accepted
break
end
if hypot(α*dx[1], α*dx[2]) < tol || hypot(g1, g2) < tol
break
end
end
return η, nll_prev
endThe file /app/analysis.jl has been edited successfully.
command: str_replace
path: /app/analysis.jl
old_str: function laplace_subject(spec, s, θ; η_init = [0.0, 0.0])
βCl, βV, σCl, σV, σ = split_params(spec, θ)
lCl_fe, lV_fe = linear_predictors(spec, s, βCl, βV)
η_hat, nll_hat = newton2(copy(η_init), s, lCl_fe, lV_fe, σ, σCl, σV)
H = hessian2(x -> njll(x, s, lCl_fe, lV_fe, σ, σCl, σV), η_hat)
# safeguard
dH = det(H)
if dH <= 0
dH = 1e-10
end
logdetH = log(dH)
neg_marg = nll_hat - log(2π) + 0.5 * logdetH # d = 2
return neg_marg, η_hat
end
new_str: function laplace_subject(spec, s, θ; η_init = [0.0, 0.0])
βCl, βV, σCl, σV, σ = split_params(spec, θ)
# Clamp σ values for numerical safety
σCl = max(σCl, 1e-4); σV = max(σV, 1e-4); σ = max(σ, 1e-4)
lCl_fe, lV_fe = linear_predictors(spec, s, βCl, βV)
# guard against extreme fixed-effect predictions
lCl_fe = clamp(lCl_fe, -15.0, 10.0)
lV_fe = clamp(lV_fe, -10.0, 10.0)
η_hat, nll_hat = newton2(copy(η_init), s, lCl_fe, lV_fe, σ, σCl, σV)
H = hessian2(x -> njll(x, s, lCl_fe, lV_fe, σ, σCl, σV), η_hat)
if !all(isfinite, H)
H = Float64[1.0 0.0; 0.0 1.0]
end
reg_pd_2x2!(H)
dH = det(H)
dH = dH > 0 ? dH : 1e-10
neg_marg = nll_hat - log(2π) + 0.5 * log(dH) # d = 2
return neg_marg, η_hat
endThe file /app/analysis.jl has been edited successfully.
timeout 60 julia --project=/app /tmp/test2.jl 2>&1 | tail -15
single eval: 1.407889582 s -> nm = 6175.243554936604 ┌ Warning: Assignment to `nm` in soft scope is ambiguous because a global variable by the same name exists: `nm` will be treated as a new local. Disambiguate by using `local nm` to suppress this warning or `global nm` to assign to the existing global variable. └ @ /tmp/test2.jl:19 ┌ Warning: Assignment to `ηs` in soft scope is ambiguous because a global variable by the same name exists: `ηs` will be treated as a new local. Disambiguate by using `local ηs` to suppress this warning or `global ηs` to assign to the existing global variable. └ @ /tmp/test2.jl:19 10 evals (warm): 0.116086292 s Optim 200 iters: 1.459592561 s, min=365.3488422677459 iters=200
cat > /tmp/test3.jl <<'EOF'
using DataFrames, CSV, JSON3, CairoMakie, Statistics, Distributions,
LinearAlgebra, Optim
src = read("/app/analysis.jl", String)
src = replace(src, "\nmain()\n" => "\n# main()\n")
include_string(Main, src, "/app/analysis_no_main.jl")
df = CSV.read("/app/Phenobarb.csv", DataFrame)
subjects = build_subjects(df)
# Test each model fit and how long
println("Fitting base:")
spec = ModelSpec("base", [:Intercept], [:Intercept])
θ0 = [-5.0, 0.3, log(0.3), log(0.2), log(2.0)]
tb = @elapsed (θb, ηb, llb) = fit_model(spec, subjects, θ0)
println(" t = $tb s, logLik = $llb, θ = ", θb)
spec2 = ModelSpec("cl_wt", [:Intercept, :Wt], [:Intercept])
θ02 = [θb[1], 0.0, θb[2], θb[3], θb[4], θb[5]]
t2 = @elapsed (θ2, η2, ll2) = fit_model(spec2, subjects, θ02)
println("cl_wt t=$t2 s, ll=$ll2, θ=", θ2)
spec3 = ModelSpec("full", [:Intercept, :Wt, :ApgarHi], [:Intercept, :Wt])
θ03 = [θ2[1], θ2[2], 0.0, θ2[3], 0.0, θ2[4], θ2[5], θ2[6]]
t3 = @elapsed (θ3, η3, ll3) = fit_model(spec3, subjects, θ03)
println("full t=$t3 s, ll=$ll3, θ=", θ3)
println("AICs: base=", -2llb + 2*5, " clwt=", -2ll2+2*6, " full=", -2ll3+2*8)
println("LR 1->2: ", 2*(ll2-llb), " p=", 1-cdf(Chisq(1), 2*(ll2-llb)))
println("LR 2->3: ", 2*(ll3-ll2), " p=", 1-cdf(Chisq(2), 2*(ll3-ll2)))
EOF
timeout 200 julia --project=/app /tmp/test3.jl 2>&1 | tail -15Fitting base: t = 6.011476758 s, logLik = -318.68590166514764, θ = [-4.872304019910516, 0.6575291639132796, -4.290721079232927, -24.644051747868684, 2.6509564363724714] cl_wt t=7.217493158 s, ll=909.4023268426763, θ=[-4.542137244175368, -0.029227993148342606, 0.797163495296133, -11.306074667993347, -13.000947682576316, 2.7786157115917947] full t=9.651091183 s, ll=962.5158668151337, θ=[-4.542581866708794, -0.02920765301466776, 0.00304196391886807, 0.9766592276944012, -2.186658636540968e-5, -11.514606936933076, -14.785623979796881, 2.778542460471842] AICs: base=647.3718033302953 clwt=-1806.8046536853526 full=-1909.0317336302674 LR 1->2: 2456.176457015648 p=0.0 LR 2->3: 106.22707994491475 p=0.0
command: str_replace
path: /app/analysis.jl
old_str: function split_params(spec::ModelSpec, θ::AbstractVector)
nCl = length(spec.lCl_terms); nV = length(spec.lV_terms)
βCl = θ[1:nCl]
βV = θ[nCl+1 : nCl+nV]
σCl = exp(θ[nCl+nV+1])
σV = exp(θ[nCl+nV+2])
σ = exp(θ[nCl+nV+3])
return βCl, βV, σCl, σV, σ
end
new_str: function split_params(spec::ModelSpec, θ::AbstractVector)
nCl = length(spec.lCl_terms); nV = length(spec.lV_terms)
βCl = θ[1:nCl]
βV = θ[nCl+1 : nCl+nV]
# Guard log-σ parameters to a sensible range so variances stay away from 0 or ∞.
# log σ in (-4, 4) ⇒ σ ∈ (0.018, 54.6), broad enough for these data.
lσCl = clamp(θ[nCl+nV+1], -4.0, 4.0)
lσV = clamp(θ[nCl+nV+2], -4.0, 4.0)
lσ = clamp(θ[nCl+nV+3], -4.0, 4.0)
σCl = exp(lσCl); σV = exp(lσV); σ = exp(lσ)
return βCl, βV, σCl, σV, σ
endThe file /app/analysis.jl has been edited successfully.
timeout 200 julia --project=/app /tmp/test3.jl 2>&1 | tail -15
Fitting base: t = 7.930437213 s, logLik = -858.6313759112962, θ = [-4.947390353144572, 0.5189958492816323, -4.64815793059011, -5.982861968046709, 2.4568994145258816] cl_wt t=5.451265623 s, ll=-793.3051201314142, θ=[-4.960102472911416, 0.03197783063468555, 0.5160316502801536, -5.995313157053546, -6.622655268877459, 2.4512596569776726] full t=10.438636092 s, ll=-518.490841652701, θ=[-6.086517875379176, 0.6960978324402382, -0.013626774295008451, -0.412826175869857, 0.46500661672491517, -4.789913389398804, -8.293452982928716, 1.7391153041818166] AICs: base=1727.2627518225925 clwt=1598.6102402628285 full=1052.981683305402 LR 1->2: 130.65251155976398 p=0.0 LR 2->3: 549.6285569574266 p=0.0
cat > /tmp/test4.jl <<'EOF'
using DataFrames, CSV, JSON3, CairoMakie, Statistics, Distributions,
LinearAlgebra, Optim
src = read("/app/analysis.jl", String)
src = replace(src, "\nmain()\n" => "\n# main()\n")
include_string(Main, src, "/app/analysis_no_main.jl")
df = CSV.read("/app/Phenobarb.csv", DataFrame)
subjects = build_subjects(df)
spec = ModelSpec("base", [:Intercept], [:Intercept])
# Grid search over σ_Cl and σ_V and σ
println("Grid search:")
best_ll = -Inf
best_θ = nothing
for lσCl in [log(0.1), log(0.3), log(0.5), log(0.7), log(1.0)]
for lσV in [log(0.05), log(0.1), log(0.2), log(0.3), log(0.5)]
for lσ in [log(1.0), log(2.0), log(3.0)]
θ = [-4.9, 0.5, lσCl, lσV, lσ]
η_cache = [zeros(2) for _ in subjects]
try
nm, ηs = neg_marginal_loglik(spec, subjects, θ; η_starts = η_cache)
if -nm > best_ll
best_ll = -nm
best_θ = θ
end
catch e
end
end
end
end
println("best init ll=$best_ll θ=", best_θ)
# Fit from this start
tb = @elapsed (θb, ηb, llb) = fit_model(spec, subjects, best_θ)
println("Fit from best init: t = $tb s, ll = $llb, θ = ", θb)
println("σCl = ", exp(θb[3]), " σV = ", exp(θb[4]), " σ = ", exp(θb[5]))
EOF
timeout 120 julia --project=/app /tmp/test4.jl 2>&1 | tail -10optimize(::Any, ::Any, ::Any, !Matched::AbstractArray, !Matched::AbstractArray, !Matched::AbstractArray, !Matched::IPNewton, !Matched::Optim.Options)
@ Optim /usr/local/share/julia/packages/Optim/lukVW/src/multivariate/solvers/constrained/ipnewton/ipnewton.jl:454
...
Stacktrace:
[1] fit_model(spec::ModelSpec, subjects::Vector{SubjectData}, θ0::Nothing)
@ Main /app/analysis_no_main.jl:303
[2] top-level scope
@ ./timing.jl:395
in expression starting at /tmp/test4.jl:36# best_θ is nothing because all init tries failed. Let me debug one call
cat > /tmp/test5.jl <<'EOF'
using DataFrames, CSV, JSON3, CairoMakie, Statistics, Distributions,
LinearAlgebra, Optim
src = read("/app/analysis.jl", String)
src = replace(src, "\nmain()\n" => "\n# main()\n")
include_string(Main, src, "/app/analysis_no_main.jl")
df = CSV.read("/app/Phenobarb.csv", DataFrame)
subjects = build_subjects(df)
spec = ModelSpec("base", [:Intercept], [:Intercept])
θ = [-4.9, 0.5, log(0.3), log(0.1), log(3.0)]
η_cache = [zeros(2) for _ in subjects]
t = @elapsed (nm, ηs) = neg_marginal_loglik(spec, subjects, θ; η_starts = η_cache)
println("eval 1: t=$t nm=$nm (-> ll=$(-nm))")
# Inspect η_hat distribution
println("η ranges: η1 ∈ [$(minimum(x->x[1], ηs)), $(maximum(x->x[1], ηs))]")
println(" η2 ∈ [$(minimum(x->x[2], ηs)), $(maximum(x->x[2], ηs))]")
βCl, βV, σCl, σV, σ = split_params(spec, θ)
println("σCl=$σCl σV=$σV σ=$σ")
# Subject 1 , manual check
s = subjects[1]
println("Subject $(s.id): Wt=$(s.Wt) n_obs=$(s.n_obs) n_doses=$(length(s.dose_times))")
lCl_fe, lV_fe = linear_predictors(spec, s, βCl, βV)
η_hat, nll_hat = newton2([0.0, 0.0], s, lCl_fe, lV_fe, σ, σCl, σV)
println("η_hat=$η_hat nll_hat=$nll_hat")
Cl = exp(lCl_fe + η_hat[1]); V = exp(lV_fe + η_hat[2])
preds = predict_only(s, Cl, V)
println("obs: ", s.sample_concs)
println("pred: ", preds)
println("η for all subjects (η1): ", [η[1] for η in ηs])
EOF
timeout 60 julia --project=/app /tmp/test5.jl 2>&1 | tail -30eval 1: t=1.373665702 nm=2946.74666053329 (-> ll=-2946.74666053329)
η ranges: η1 ∈ [-2.7642401151852877e-7, 3.2952512671391306e-6]
η2 ∈ [-9.738126265397205e-7, 1.246392024533403e-5]
σCl=0.3 σV=0.10000000000000002 σ=3.0000000000000004
Subject 1: Wt=1.4 n_obs=2 n_doses=10
η_hat=[-2.1489740160419474e-7, -5.826256792172574e-7] nll_hat=5.085142335022724
obs: [17.3, 31.0]
pred: [15.026919744663616, 24.38410296695376]
η for all subjects (η1): [-2.1489740160419474e-7, -1.5160883539423592e-7, 1.7682433718534386e-7, -2.663207604989301e-9, 1.6734148352451845e-7, -1.5149669771768332e-7, -1.2355403997340466e-7, -1.0232006907952128e-7, -2.167582353232164e-7, -1.8897027695095248e-7, 3.2952512671391306e-6, -2.7642401151852877e-7, -4.07019858404548e-9, -3.126678709153448e-9, -2.6922452513329056e-7, -1.5754333884545737e-7, -1.3120130992725697e-7, -6.734401058422754e-8, -1.4324356864074406e-7, -1.1829123781387019e-7, 2.9244845816458706e-9, -8.892533689610675e-8, 1.8879797534838805e-7, 1.7424537250789666e-7, -3.1806817516375363e-9, 1.6506879229054206e-7, -1.3708639180024529e-7, 5.267775350535039e-9, -9.821238632035877e-8, 3.4624439342705195e-7, -4.418274922263333e-9, 7.15497090362643e-8, -1.45207847553698e-7, -1.4339795014923855e-7, 1.7981231750333594e-7, 4.18992147409631e-9, -2.1141617111502737e-7, -4.836942180498626e-9, 4.902747028526197e-7, -6.868822080156023e-9, 9.816435016016283e-8, 9.460280580024496e-8, -2.803124281584997e-9, -1.7343151749590384e-7, -2.2356992933141988e-9, -6.090671344419913e-9, 1.4036470603905298e-7, -1.896129865053773e-9, -2.1976884467530594e-7, -2.6260975893721117e-9, -1.1992093247162887e-7, -2.9244633213076186e-9, -1.2379891209766084e-7, -1.644495098846106e-7, 0.0, -6.155447951580723e-10, 2.785537995874985e-7, -1.3407468190016716e-7, -3.4754323114650064e-9]cat > /tmp/test6.jl <<'EOF'
using DataFrames, CSV, JSON3, CairoMakie, Statistics, Distributions,
LinearAlgebra, Optim
src = read("/app/analysis.jl", String)
src = replace(src, "\nmain()\n" => "\n# main()\n")
include_string(Main, src, "/app/analysis_no_main.jl")
df = CSV.read("/app/Phenobarb.csv", DataFrame)
subjects = build_subjects(df)
spec = ModelSpec("base", [:Intercept], [:Intercept])
θ = [-4.9, 0.5, log(0.3), log(0.1), log(3.0)]
βCl, βV, σCl, σV, σ = split_params(spec, θ)
s = subjects[1]
lCl_fe, lV_fe = linear_predictors(spec, s, βCl, βV)
println("lCl_fe=$lCl_fe lV_fe=$lV_fe σ=$σ σCl=$σCl σV=$σV")
# Manual Newton , verbose
η = [0.0, 0.0]
nll, g1, g2 = njll_and_grad(η, s, lCl_fe, lV_fe, σ, σCl, σV)
println("init η=$η nll=$nll g=[$g1, $g2]")
for iter in 1:15
H = hessian2(x -> njll(x, s, lCl_fe, lV_fe, σ, σCl, σV), η)
println("H = $H")
g = [g1, g2]
dx = H \ g
println("dx = $dx")
η_new = η - dx
println("try η_new = $η_new")
nll_new, g1n, g2n = njll_and_grad(η_new, s, lCl_fe, lV_fe, σ, σCl, σV)
println("nll_new = $nll_new (was $nll)")
if nll_new < nll
η = η_new
nll, g1, g2 = nll_new, g1n, g2n
else
println("step not accepted; halving")
break
end
if hypot(dx...) < 1e-8; break; end
end
println("final η=$η nll=$nll")
EOF
timeout 30 julia --project=/app /tmp/test6.jl 2>&1 | tail -30lCl_fe=-4.9 lV_fe=0.5 σ=3.0000000000000004 σCl=0.3 σV=0.10000000000000002 init η=[0.0, 0.0] nll=5.085152837491174 g=[5.852588289831345, 15.86745846355781] ┌ Warning: Assignment to `η` in soft scope is ambiguous because a global variable by the same name exists: `η` will be treated as a new local. Disambiguate by using `local η` to suppress this warning or `global η` to assign to the existing global variable. └ @ /tmp/test6.jl:34 ┌ Warning: Assignment to `nll` in soft scope is ambiguous because a global variable by the same name exists: `nll` will be treated as a new local. Disambiguate by using `local nll` to suppress this warning or `global nll` to assign to the existing global variable. └ @ /tmp/test6.jl:35 ┌ Warning: Assignment to `g1` in soft scope is ambiguous because a global variable by the same name exists: `g1` will be treated as a new local. Disambiguate by using `local g1` to suppress this warning or `global g1` to assign to the existing global variable. └ @ /tmp/test6.jl:35 ┌ Warning: Assignment to `g2` in soft scope is ambiguous because a global variable by the same name exists: `g2` will be treated as a new local. Disambiguate by using `local g2` to suppress this warning or `global g2` to assign to the existing global variable. └ @ /tmp/test6.jl:35 ERROR: LoadError: UndefVarError: `η` not defined Stacktrace: [1] top-level scope @ /tmp/test6.jl:24 in expression starting at /tmp/test6.jl:23
cat > /tmp/test6.jl <<'EOF'
using DataFrames, CSV, JSON3, CairoMakie, Statistics, Distributions,
LinearAlgebra, Optim
src = read("/app/analysis.jl", String)
src = replace(src, "\nmain()\n" => "\n# main()\n")
include_string(Main, src, "/app/analysis_no_main.jl")
function test()
df = CSV.read("/app/Phenobarb.csv", DataFrame)
subjects = build_subjects(df)
spec = ModelSpec("base", [:Intercept], [:Intercept])
θ = [-4.9, 0.5, log(0.3), log(0.1), log(3.0)]
βCl, βV, σCl, σV, σ = split_params(spec, θ)
s = subjects[1]
lCl_fe, lV_fe = linear_predictors(spec, s, βCl, βV)
println("σ=$σ σCl=$σCl σV=$σV lCl_fe=$lCl_fe lV_fe=$lV_fe")
η = [0.0, 0.0]
nll, g1, g2 = njll_and_grad(η, s, lCl_fe, lV_fe, σ, σCl, σV)
println("init nll=$nll g=[$g1, $g2]")
for iter in 1:15
H = hessian2(x -> njll(x, s, lCl_fe, lV_fe, σ, σCl, σV), η)
println("iter=$iter H = $H det=$(det(H))")
g = [g1, g2]
dx = H \ g
println(" dx = $dx")
α = 1.0
accepted = false
for _ in 1:20
η_try = η - α * dx
nll_new, g1n, g2n = njll_and_grad(η_try, s, lCl_fe, lV_fe, σ, σCl, σV)
println(" α=$α try η=$η_try nll=$nll_new")
if isfinite(nll_new) && nll_new < nll
η = η_try
nll, g1, g2 = nll_new, g1n, g2n
accepted = true
break
end
α *= 0.5
end
if !accepted; println("failed"); break; end
if hypot(dx...) < 1e-8; println("converged"); break; end
end
println("final η=$η nll=$nll")
end
test()
EOF
timeout 30 julia --project=/app /tmp/test6.jl 2>&1 | tail -50σ=3.0000000000000004 σCl=0.3 σV=0.10000000000000002 lCl_fe=-4.9 lV_fe=0.5
init nll=5.085152837491174 g=[5.852588289831345, 15.86745846355781]
iter=1 H = [8.170305889572546e8 5.470527675299763; 5.470527675299763 8.170307156438894e8] det=6.675390867986943e17
dx = [7.163242432646617e-9, 1.9420883597829235e-8]
α=1.0 try η=[-7.163242432646617e-9, -1.9420883597829235e-8] nll=5.085152487407639
iter=2 H = [8.17030518940546e8 5.470529185203077; 5.470529185203077 8.170306456271826e8] det=6.675389723871064e17
dx = [7.163242728336921e-9, 1.942088169273554e-8]
α=1.0 try η=[-1.432648516098354e-8, -3.884176529056477e-8] nll=5.085152137324177
iter=3 H = [8.170304489238546e8 5.470529829132431; 5.470529829132431 8.170305756104943e8] det=6.675388579755575e17
dx = [7.1632430240271e-9, 1.9420879787641346e-8]
α=1.0 try η=[-2.148972818501064e-8, -5.826264507820612e-8] nll=5.085151787240806
iter=4 H = [8.17030378907182e8 5.470530428652864; 5.470530428652864 8.170305055938227e8] det=6.675387435640475e17
dx = [7.163243319717156e-9, 1.9420877882546722e-8]
α=1.0 try η=[-2.8652971504727795e-8, -7.768352296075285e-8] nll=5.0851514371575215
iter=5 H = [8.170303088905251e8 5.470531294626824; 5.470531294626824 8.17030435577168e8] det=6.675386291525738e17
dx = [7.163243615407102e-9, 1.9420875977451638e-8]
α=1.0 try η=[-3.5816215120134895e-8, -9.710439893820449e-8] nll=5.085151087074329
iter=6 H = [8.170302388738858e8 5.470532271623085; 5.470532271623085 8.170303655605315e8] det=6.675385147411392e17
dx = [7.163243911096932e-9, 1.9420874072356094e-8]
α=1.0 try η=[-4.2979459031231827e-8, -1.1652527301056057e-7] nll=5.0851507369912206
iter=7 H = [8.170301688572648e8 5.470532871143519; 5.470532871143519 8.17030295543912e8] det=6.675384003297434e17
dx = [7.163244206786635e-9, 1.9420872167260103e-8]
α=1.0 try η=[-5.0142703238018464e-8, -1.3594614517782069e-7] nll=5.0851503869082
iter=8 H = [8.170300988406609e8 5.470533959162083; 5.470533959162083 8.170302255273094e8] det=6.675382859183852e17
dx = [7.1632445024762135e-9, 1.942087026216367e-8]
α=1.0 try η=[-5.7305947740494675e-8, -1.5536701543998435e-7] nll=5.085150036825263
iter=9 H = [8.170300288240746e8 5.470534558682516; 5.470534558682516 8.170301555107253e8] det=6.67538171507066e17
dx = [7.163244798165679e-9, 1.9420868357066742e-8]
α=1.0 try η=[-6.446919253866036e-8, -1.7478788379705108e-7] nll=5.085149686742421
iter=10 H = [8.170299588075054e8 5.470535802132304; 5.470535802132304 8.170300854941573e8] det=6.67538057095784e17
dx = [7.163245093855022e-9, 1.9420866451969395e-8]
α=1.0 try η=[-7.163243763251538e-8, -1.9420875024902047e-7] nll=5.085149336659657
iter=11 H = [8.170298887909533e8 5.4705365348795; 5.4705365348795 8.170300154776075e8] det=6.675379426845405e17
dx = [7.163245389544247e-9, 1.9420864546871565e-8]
α=1.0 try η=[-7.879568302205962e-8, -2.1362961479589203e-7] nll=5.085148986576984
iter=12 H = [8.170298187744188e8 5.470537245422236; 5.470537245422236 8.170299454610755e8] det=6.675378282733358e17
dx = [7.16324568523336e-9, 1.9420862641773274e-8]
α=1.0 try η=[-8.595892870729298e-8, -2.330504774376653e-7] nll=5.085148636494399
iter=13 H = [8.17029748757902e8 5.470538400054181; 5.470538400054181 8.170298754445603e8] det=6.675377138621691e17
dx = [7.16324598092234e-9, 1.942086073667454e-8]
α=1.0 try η=[-9.312217468821533e-8, -2.5247133817433983e-7] nll=5.085148286411894
iter=14 H = [8.17029678741402e8 5.4705389995746145; 5.4705389995746145 8.170298054280626e8] det=6.675375994510401e17
dx = [7.16324627661121e-9, 1.942085883157534e-8]
α=1.0 try η=[-1.0028542096482654e-7, -2.7189219700591516e-7] nll=5.085147936329492
iter=15 H = [8.170296087249205e8 5.470540043184258; 5.470540043184258 8.170297354115825e8] det=6.675374850399506e17
dx = [7.163246572299966e-9, 1.9420856926475707e-8]
α=1.0 try η=[-1.0744866753712651e-7, -2.913130539323909e-7] nll=5.08514758624716
final η=[-1.0744866753712651e-7, -2.913130539323909e-7] nll=
…[truncated]cat > /tmp/test7.jl <<'EOF'
using DataFrames, CSV
src = read("/app/analysis.jl", String)
src = replace(src, "\nmain()\n" => "\n# main()\n")
include_string(Main, src, "/app/analysis_no_main.jl")
function test()
df = CSV.read("/app/Phenobarb.csv", DataFrame)
subjects = build_subjects(df)
spec = ModelSpec("base", [:Intercept], [:Intercept])
θ = [-4.9, 0.5, log(0.3), log(0.1), log(3.0)]
βCl, βV, σCl, σV, σ = split_params(spec, θ)
s = subjects[1]
lCl_fe, lV_fe = linear_predictors(spec, s, βCl, βV)
f = η -> njll(η, s, lCl_fe, lV_fe, σ, σCl, σV)
# Check f at various η
for η in ([0.0,0.0], [0.01,0], [-0.01,0], [0,0.01], [0,-0.01], [-0.5,-0.1], [0.5,0.1])
println("η=$η f=$(f(η))")
end
# Check gradient
η = [0.0,0.0]
_, g1, g2 = njll_and_grad(η, s, lCl_fe, lV_fe, σ, σCl, σV)
println("analytical g at [0,0]: ($g1, $g2)")
h = 1e-5
g1fd = (f([h,0]) - f([-h,0]))/(2h)
g2fd = (f([0,h]) - f([0,-h]))/(2h)
println("numerical g: ($g1fd, $g2fd)")
# Hessian check
H = hessian2(f, η)
println("H from hessian2: $H")
# Manual Hessian around 0 with small h
for hh in (1e-2, 1e-3, 1e-4, 1e-5)
H11 = (f([hh,0]) - 2*f([0,0]) + f([-hh,0])) / hh^2
println("h=$hh H11=$H11")
end
end
test()
EOF
timeout 30 julia --project=/app /tmp/test7.jl 2>&1 | tail -20η=[0.0, 0.0] f=5.085152837491174 η=[0.01, 0.0] f=5.144753607910554 η=[-0.01, 0.0] f=5.027697968727345 η=[0.0, 0.01] f=5.25121806267359 η=[0.0, -0.01] f=4.933902256057686 η=[-0.5, -0.1] f=4.328998304035687 η=[0.5, 0.1] f=13.199688912357304 analytical g at [0,0]: (5.852588289831345, 15.86745846355781) numerical g: (5.852588290089499, 15.867458461560345) H from hessian2: [8.170305889572546e8 5.470527675299763; 5.470527675299763 8.170307156438894e8] h=0.01 H11=21.459016555507304 h=0.001 H11=21.45901935701744 h=0.0001 H11=21.45901980554754 h=1.0e-5 H11=21.459101162690782
cat > /tmp/test8.jl <<'EOF'
using DataFrames, CSV
src = read("/app/analysis.jl", String)
src = replace(src, "\nmain()\n" => "\n# main()\n")
include_string(Main, src, "/app/analysis_no_main.jl")
function test()
df = CSV.read("/app/Phenobarb.csv", DataFrame)
subjects = build_subjects(df)
spec = ModelSpec("base", [:Intercept], [:Intercept])
θ = [-4.9, 0.5, log(0.3), log(0.1), log(3.0)]
βCl, βV, σCl, σV, σ = split_params(spec, θ)
s = subjects[1]
lCl_fe, lV_fe = linear_predictors(spec, s, βCl, βV)
f = η -> njll(η, s, lCl_fe, lV_fe, σ, σCl, σV)
println("Calling hessian2 at eta=[0,0], h default 1e-4")
η = [0.0,0.0]
H = hessian2(f, η)
println("H = $H")
println("\nCalling hessian2 with h=1e-4 explicitly")
H2 = hessian2(f, η; h=1e-4)
println("H2 = $H2")
println("\nCalling hessian2 with h=1e-3 explicitly")
H3 = hessian2(f, η; h=1e-3)
println("H3 = $H3")
end
test()
EOF
timeout 30 julia --project=/app /tmp/test8.jl 2>&1 | tail -15Calling hessian2 at eta=[0,0], h default 1e-4 H = [8.170305889572546e8 5.470527675299763; 5.470527675299763 8.170307156438894e8] Calling hessian2 with h=1e-4 explicitly H2 = [8.170305889572546e8 5.470527675299763; 5.470527675299763 8.170307156438894e8] Calling hessian2 with h=1e-3 explicitly H3 = [8.170327134001707e6 5.470538765539601; 5.470538765539601 8.170453820644199e6]
cat > /tmp/test9.jl <<'EOF'
using DataFrames, CSV
src = read("/app/analysis.jl", String)
src = replace(src, "\nmain()\n" => "\n# main()\n")
include_string(Main, src, "/app/analysis_no_main.jl")
function test()
df = CSV.read("/app/Phenobarb.csv", DataFrame)
subjects = build_subjects(df)
spec = ModelSpec("base", [:Intercept], [:Intercept])
θ = [-4.9, 0.5, log(0.3), log(0.1), log(3.0)]
βCl, βV, σCl, σV, σ = split_params(spec, θ)
s = subjects[1]
lCl_fe, lV_fe = linear_predictors(spec, s, βCl, βV)
f = η -> njll(η, s, lCl_fe, lV_fe, σ, σCl, σV)
η = [0.0,0.0]
h = 1e-4
f0 = f(η)
f1p = f([η[1] + h, η[2]])
f1m = f([η[1] - h, η[2]])
println("f0=$f0 f1p=$f1p f1m=$f1m")
println("f1p-2f0+f1m = ", f1p - 2*f0 + f1m)
println("H11 = ", (f1p - 2*f0 + f1m) / h^2)
end
test()
EOF
timeout 20 julia --project=/app /tmp/test9.jl 2>&1 | tail -10f0=5.085152837491174 f1p=5.085738203617191 f1m=5.0845676859553555 f1p-2f0+f1m = 2.145901980554754e-7 H11 = 21.45901980554754
cat > /tmp/testA.jl <<'EOF'
using DataFrames, CSV
src = read("/app/analysis.jl", String)
src = replace(src, "\nmain()\n" => "\n# main()\n")
include_string(Main, src, "/app/analysis_no_main.jl")
function test()
df = CSV.read("/app/Phenobarb.csv", DataFrame)
subjects = build_subjects(df)
spec = ModelSpec("base", [:Intercept], [:Intercept])
θ = [-4.9, 0.5, log(0.3), log(0.1), log(3.0)]
βCl, βV, σCl, σV, σ = split_params(spec, θ)
s = subjects[1]
lCl_fe, lV_fe = linear_predictors(spec, s, βCl, βV)
# Use closure just like newton2 does
f = x -> njll(x, s, lCl_fe, lV_fe, σ, σCl, σV)
η = [0.0,0.0]
println("direct call via closure:")
println(" f(η) = ", f(η))
println(" f([1e-4, 0]) = ", f([1e-4, 0]))
println(" f([-1e-4, 0]) = ", f([-1e-4, 0]))
println("now calling hessian2:")
H = hessian2(f, η; h=1e-4)
println(" H = $H")
# Try with explicit function
g = x -> njll(x, subjects[1], -4.9, 0.5, 3.0, 0.3, 0.1)
println(" g(η) = ", g(η))
println(" hessian2(g, η) = ", hessian2(g, η; h=1e-4))
end
test()
EOF
timeout 20 julia --project=/app /tmp/testA.jl 2>&1 | tail -15direct call via closure: f(η) = 5.085152837491174 f([1e-4, 0]) = 5.085738203617191 f([-1e-4, 0]) = 5.0845676859553555 now calling hessian2: H = [8.170305889572546e8 5.470527675299763; 5.470527675299763 8.170307156438894e8] g(η) = 5.085152837491174 hessian2(g, η) = [8.170305889572546e8 5.470527675299763; 5.470527675299763 8.170307156438893e8]
grep -n "hessian2" /app/analysis.jl
163:function hessian2(f, η; h = 1e-4) 214: H = hessian2(x -> njll(x, s, lCl_fe, lV_fe, σ, σCl, σV), η) 266: H = hessian2(x -> njll(x, s, lCl_fe, lV_fe, σ, σCl, σV), η_hat)
sed -n '160,185p' /app/analysis.jl
njll_and_grad(η, s, lCl_fe, lV_fe, σ, σCl, σV)[1]
# Finite-difference 2×2 Hessian at η of scalar f(η)
function hessian2(f, η; h = 1e-4)
f0 = f(η)
f1p = f([η[1] + h, η[2]])
f1m = f([η[1] - h, η[2]])
f2p = f([η[1], η[2] + h])
f2m = f([η[1], η[2] - h])
fpp = f([η[1] + h, η[2] + h])
fpm = f([η[1] + h, η[2] - h])
fmp = f([η[1] - h, η[2] + h])
fmm = f([η[1] - h, η[2] - h])
H11 = (f1p - 2f0 + f1m) / h^2
H22 = (f2p - 2f0 + f2m) / h^2
H12 = (fpp - fpm - fmp + fmm) / (4h^2)
return [H11 H12; H12 H22]
end
# 2×2 positive-definite regularisation: clamp eigenvalues to ≥ 1e-6 analytically
function reg_pd_2x2!(H)
a, b, d = H[1,1], H[1,2], H[2,2]
tr = a + d
det = a*d - b*b
disc = max(tr*tr/4 - det, 0.0)
s = sqrt(disc)cat > /tmp/testB.jl <<'EOF'
using DataFrames, CSV
src = read("/app/analysis.jl", String)
src = replace(src, "\nmain()\n" => "\n# main()\n")
include_string(Main, src, "/app/analysis_no_main.jl")
function test()
df = CSV.read("/app/Phenobarb.csv", DataFrame)
subjects = build_subjects(df)
spec = ModelSpec("base", [:Intercept], [:Intercept])
θ = [-4.9, 0.5, log(0.3), log(0.1), log(3.0)]
βCl, βV, σCl, σV, σ = split_params(spec, θ)
s = subjects[1]
lCl_fe, lV_fe = linear_predictors(spec, s, βCl, βV)
f = x -> njll(x, s, lCl_fe, lV_fe, σ, σCl, σV)
η = [0.0,0.0]
h = 1e-4
f0 = f(η)
f1p = f([η[1]+h, η[2]])
f1m = f([η[1]-h, η[2]])
println("directly: f0=$f0 f1p=$f1p f1m=$f1m")
println("H11 = ", (f1p - 2*f0 + f1m)/h^2)
# Try a simple function to test hessian2
g = x -> x[1]^2 + 2*x[2]^2
println("\nHessian of x^2 + 2y^2 at [0,0] should be [2 0;0 4]")
println(hessian2(g, [0.0, 0.0]))
end
test()
EOF
timeout 20 julia --project=/app /tmp/testB.jl 2>&1 | tail -15directly: f0=5.085152837491174 f1p=5.085738203617191 f1m=5.0845676859553555 H11 = 21.45901980554754 Hessian of x^2 + 2y^2 at [0,0] should be [2 0;0 4] [-1.99999998e8 0.0; 0.0 -1.9999999599999997e8]
command: str_replace
path: /app/analysis.jl
old_str: H11 = (f1p - 2f0 + f1m) / h^2
H22 = (f2p - 2f0 + f2m) / h^2
H12 = (fpp - fpm - fmp + fmm) / (4h^2)
new_str: H11 = (f1p - 2*f0 + f1m) / h^2
H22 = (f2p - 2*f0 + f2m) / h^2
H12 = (fpp - fpm - fmp + fmm) / (4*h^2)The file /app/analysis.jl has been edited successfully.
grep -n "2f0\|2f[a-zA-Z]\|2[a-z]0\|4h\^2" /app/analysis.jl
325: H[i,i] = (fp[i] - 2f0 + fm[i]) / h^2 334: H[i,j] = (fpp - fpm - fmp + fmm) / (4h^2)
command: str_replace path: /app/analysis.jl old_str: H[i,i] = (fp[i] - 2f0 + fm[i]) / h^2 new_str: H[i,i] = (fp[i] - 2*f0 + fm[i]) / h^2
The file /app/analysis.jl has been edited successfully.
command: str_replace path: /app/analysis.jl old_str: H[i,j] = (fpp - fpm - fmp + fmm) / (4h^2) new_str: H[i,j] = (fpp - fpm - fmp + fmm) / (4*h^2)
The file /app/analysis.jl has been edited successfully.
timeout 120 julia --project=/app /tmp/test3.jl 2>&1 | tail -10
Fitting base: t = 4.952413508 s, logLik = -505.0183602810384, θ = [-5.174444171315125, 0.3674708781885502, -0.8025880690244473, -0.803974386197206, 1.0320552369216087] cl_wt t=2.151932989 s, ll=-479.3799215828116, θ=[-6.089114523760178, 0.6902496828543799, 0.3443619449604075, -1.6760570715316228, -0.9038479385394916, 1.0287055747939164] full t=2.131626963 s, ll=-437.56366657913935, θ=[-5.937959580849297, 0.6137466532959094, -0.044502409073016155, -0.47365746399871717, 0.5334003155187331, -1.484164506862632, -1.7685485114928858, 1.0025635029362678] AICs: base=1020.0367205620768 clwt=970.7598431656232 full=891.1273331582787 LR 1->2: 51.27687739645353 p=8.021361352916756e-13 LR 2->3: 83.63251000734454 p=0.0
cd /app && rm -rf output && mkdir -p output && timeout 200 julia --project=. analysis.jl 2>&1 | tail -25
Fitting base model ... logLik(base) = -505.0183602810384 AIC = 1020.0367205620768 Fitting cl_wt model ... logLik(cl_wt) = -479.3799215828116 AIC = 970.7598431656232 Fitting full model ... logLik(full) = -437.56366657913935 AIC = 891.1273331582787 LR 1->2: stat=51.27687739645353 p=8.021361352916756e-13 LR 2->3: stat=83.63251000734454 p=0.0 cor(obs, pred_ind) = 0.9711286224347995 cor(obs, pred_pop) = 0.8143954336957241 DONE
ls -la /app/output/
total 1289 drwxr-sr-x 2 h2tagent h2tagent 4096 May 31 10:19 . drwxrws--- 3 h2tagent h2tagent 4096 May 31 10:18 .. -rw-r--r-- 1 h2tagent h2tagent 317 May 31 10:19 lrt_chain.csv -rw-r--r-- 1 h2tagent h2tagent 1371 May 31 10:19 model_summary.json -rw-r--r-- 1 h2tagent h2tagent 7760 May 31 10:19 obs_vs_pred.csv -rw-r--r-- 1 h2tagent h2tagent 2517 May 31 10:19 per_subject_params.csv -rw-r--r-- 1 h2tagent h2tagent 385390 May 31 10:19 plot_conc_profile.png -rw-r--r-- 1 h2tagent h2tagent 112567 May 31 10:19 plot_covariate_effect.png -rw-r--r-- 1 h2tagent h2tagent 173579 May 31 10:19 plot_obs_vs_pred.png -rw-r--r-- 1 h2tagent h2tagent 459933 May 31 10:19 plot_per_subject_fit.png -rw-r--r-- 1 h2tagent h2tagent 155738 May 31 10:19 plot_residuals.png -rw-r--r-- 1 h2tagent h2tagent 10189 May 31 10:19 residual_diagnostics.csv
cat /app/output/lrt_chain.csv echo "---" cat /app/output/model_summary.json
step,model_name,n_params,logLik,AIC,BIC,LR_stat,LR_pvalue
1,base,5,-505.0183602810384,1020.0367205620768,1035.253846146673,,
2,cl_wt,6,-479.3799215828116,970.7598431656232,989.0203938671388,51.27687739645353,8.021361352916756e-13
3,full,8,-437.56366657913935,891.1273331582787,915.4747340936327,83.63251000734454,0.0
---
{
"bic": 915.4747340936327,
"logLik": -437.56366657913935,
"fixed_effects": [
{
"term": "lCl.(Intercept)",
"t_value": -26.330405995062243,
"df": 150,
"std_error": 0.22551720554414717,
"estimate": -5.937959580849297
},
{
"term": "lCl.Wt",
"t_value": 7.466731404070454,
"df": 150,
"std_error": 0.08219749982721064,
"estimate": 0.6137466532959094
},
{
"term": "lCl.ApgarInd>= 5",
"t_value": -0.2786980688561577,
"df": 150,
"std_error": 0.1596796463486973,
"estimate": -0.044502409073016155
},
{
"term": "lV.(Intercept)",
"t_value": -6.986890032147021,
"df": 150,
"std_error": 0.06779231701363499,
"estimate": -0.47365746399871717
},
{
"term": "lV.Wt",
"t_value": 13.157612852562773,
"df": 150,
"std_error": 0.04053929246100596,
"estimate": 0.5334003155187331
}
],
"aic": 891.1273331582787,
"n_subjects": 59,
"n_obs": 155,
"random_effects_sd": {
"lCl": 0.22669166090456455,
"lV": 0.17058040471829605,
"Residual": 2.7252590912037196
}
}head -5 /app/output/per_subject_params.csv echo "---" head -5 /app/output/obs_vs_pred.csv echo "---" head -5 /app/output/residual_diagnostics.csv
Subject,Cl,V 1,0.005731495213441933,1.348868162923784 2,0.0054384825932727865,1.22613248258178 3,0.00802030614885737,1.6622485112585779 4,0.004052070453972518,0.914746781947537 --- Subject,time,conc_obs,pred_pop,pred_ind 1,2.0,17.3,18.853647166898092,18.377218141323752 1,112.5,31.0,30.558147817396634,30.38483969499013 2,2.0,9.7,10.723821596587234,12.12554463686612 2,63.5,24.6,19.78477230626546,22.50417101854639 --- Subject,time,fitted_ind,resid,std_resid 1,2.0,18.377218141323752,-1.0772181413237512,-0.39527182747529327 1,112.5,30.38483969499013,0.6151603050098693,0.22572543909509873 2,2.0,12.12554463686612,-2.425544636866121,-0.890023500772832 2,63.5,22.50417101854639,2.095828981453611,0.7690384331597273
command: view path: /app/output/plot_conc_profile.png
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command: view path: /app/output/plot_per_subject_fit.png
<system>Image resized from 2200x1720 to 1232x963 to fit display limits</system> <output_image> …[truncated]
command: view path: /app/output/plot_covariate_effect.png
<system>Image resized from 2000x1200 to 1400x840 to fit display limits</system> <output_image> …[truncated]
command: view path: /app/output/plot_obs_vs_pred.png
<system>Image resized from 2200x1200 to 1489x812 to fit display limits</system> <output_image> …[truncated]
command: view path: /app/output/plot_residuals.png
<system>Image resized from 2200x1200 to 1489x812 to fit display limits</system> <output_image> …[truncated]
cd /app && head -1 output/obs_vs_pred.csv && head -1 output/lrt_chain.csv && head -1 output/per_subject_params.csv && head -1 output/residual_diagnostics.csv
Subject,time,conc_obs,pred_pop,pred_ind step,model_name,n_params,logLik,AIC,BIC,LR_stat,LR_pvalue Subject,Cl,V Subject,time,fitted_ind,resid,std_resid
command: str_replace
path: /app/analysis.jl
old_str: function split_params(spec::ModelSpec, θ::AbstractVector)
nCl = length(spec.lCl_terms); nV = length(spec.lV_terms)
βCl = θ[1:nCl]
βV = θ[nCl+1 : nCl+nV]
# Guard log-σ parameters to a sensible range so variances stay away from 0 or ∞.
# log σ in (-4, 4) ⇒ σ ∈ (0.018, 54.6), broad enough for these data.
lσCl = clamp(θ[nCl+nV+1], -4.0, 4.0)
lσV = clamp(θ[nCl+nV+2], -4.0, 4.0)
lσ = clamp(θ[nCl+nV+3], -4.0, 4.0)
σCl = exp(lσCl); σV = exp(lσV); σ = exp(lσ)
return βCl, βV, σCl, σV, σ
end
new_str: function split_params(spec::ModelSpec, θ::AbstractVector)
nCl = length(spec.lCl_terms); nV = length(spec.lV_terms)
βCl = θ[1:nCl]
βV = θ[nCl+1 : nCl+nV]
# Guard log-σ parameters to a sensible range so variances stay away from 0 or ∞.
# log σ in (-5, 5) ⇒ σ ∈ (0.007, 148), broad enough for these data.
lσCl = clamp(θ[nCl+nV+1], -5.0, 5.0)
lσV = clamp(θ[nCl+nV+2], -5.0, 5.0)
lσ = clamp(θ[nCl+nV+3], -5.0, 5.0)
σCl = exp(lσCl); σV = exp(lσV); σ = exp(lσ)
return βCl, βV, σCl, σV, σ
endThe file /app/analysis.jl has been edited successfully.
command: str_replace
path: /app/analysis.jl
old_str: spec_base = ModelSpec("base", [:Intercept], [:Intercept])
spec_clwt = ModelSpec("cl_wt", [:Intercept, :Wt], [:Intercept])
spec_full = ModelSpec("full", [:Intercept, :Wt, :ApgarHi], [:Intercept, :Wt])
# Starting values from neonatal PK priors
θ0_base = [-5.0, 0.3, log(0.3), log(0.2), log(2.0)]
new_str: spec_base = ModelSpec("base", [:Intercept], [:Intercept])
spec_clwt = ModelSpec("cl_wt", [:Intercept, :Wt], [:Intercept])
spec_full = ModelSpec("full", [:Intercept, :Wt, :ApgarHi], [:Intercept, :Wt])
# Data-driven starting values for lCl, lV:
# approximate V from (first dose / first concentration) per subject
V_hat = Float64[]
for s in subjects
if s.n_obs > 0 && !isempty(s.dose_times)
D = s.dose_amts[1]
c1 = s.sample_concs[1]
if c1 > 0
push!(V_hat, D / c1)
end
end
end
lV0 = isempty(V_hat) ? 0.3 : log(max(median(V_hat), 0.1))
# lCl0 ≈ log(k * V) where k ≈ 0.005/h as a prior guess
lCl0 = -5.0 + lV0
# residual SD start near data std
σres0 = std(vcat([s.sample_concs for s in subjects]...)) / 2
θ0_base = [lCl0, lV0, log(0.3), log(0.2), log(max(σres0, 1.0))]The file /app/analysis.jl has been edited successfully.
cd /app && rm -rf output && mkdir -p output && timeout 200 julia --project=. analysis.jl 2>&1 | tail -15
Fitting base model ... logLik(base) = -505.0183600970788 AIC = 1020.0367201941576 Fitting cl_wt model ... logLik(cl_wt) = -479.379921532333 AIC = 970.759843064666 Fitting full model ... logLik(full) = -437.56366673241405 AIC = 891.1273334648281 LR 1->2: stat=51.276877129491595 p=8.021361352916756e-13 LR 2->3: stat=83.63250959983793 p=0.0 cor(obs, pred_ind) = 0.9711276217139927 cor(obs, pred_pop) = 0.8143952346953517 DONE
cd /app && ls -la output/ | wc -l ls output/ | sort
13 lrt_chain.csv model_summary.json obs_vs_pred.csv per_subject_params.csv plot_conc_profile.png plot_covariate_effect.png plot_obs_vs_pred.png plot_per_subject_fit.png plot_residuals.png residual_diagnostics.csv
cat > /tmp/perturb_test.jl <<'EOF'
using DataFrames, CSV, Random
Random.seed!(42)
df = CSV.read("/app/Phenobarb.csv", DataFrame)
# Keep a random 45 subjects, slightly perturb concentrations
subjs = sample(unique(df.Subject), 45; replace=false)
df2 = df[in.(df.Subject, Ref(subjs)), :]
# Renumber subjects
id_map = Dict(old => i for (i, old) in enumerate(sort(unique(df2.Subject))))
df2.Subject = [id_map[s] for s in df2.Subject]
# Perturb conc by 5% multiplicative noise
for i in 1:nrow(df2)
if !ismissing(df2.conc[i])
df2.conc[i] = df2.conc[i] * (1 + 0.05*randn())
end
end
CSV.write("/tmp/Phenobarb_perturbed.csv", df2)
println("Saved perturbed with ", length(unique(df2.Subject)), " subjects, ",
nrow(df2), " rows, ", sum(.!ismissing.(df2.conc)), " obs")
EOF
julia --project=/app /tmp/perturb_test.jl
echo "---"
# Now run analysis on perturbed data
PHENOBARB_DATA_PATH=/tmp/Phenobarb_perturbed.csv PHENOBARB_OUTPUT_DIR=/tmp/out_perturb timeout 200 julia --project=/app /app/analysis.jl 2>&1 | tail -15--- [1] CSV.Context(source::CSV.Arg, header::CSV.Arg, normalizenames::CSV.Arg, datarow::CSV.Arg, skipto::CSV.Arg, footerskip::CSV.Arg, transpose::CSV.Arg, comment::CSV.Arg, ignoreemptyrows::CSV.Arg, ignoreemptylines::CSV.Arg, select::CSV.Arg, drop::CSV.Arg, limit::CSV.Arg, buffer_in_memory::CSV.Arg, threaded::CSV.Arg, ntasks::CSV.Arg, tasks::CSV.Arg, rows_to_check::CSV.Arg, lines_to_check::CSV.Arg, missingstrings::CSV.Arg, missingstring::CSV.Arg, delim::CSV.Arg, ignorerepeated::CSV.Arg, quoted::CSV.Arg, quotechar::CSV.Arg, openquotechar::CSV.Arg, closequotechar::CSV.Arg, escapechar::CSV.Arg, dateformat::CSV.Arg, dateformats::CSV.Arg, decimal::CSV.Arg, groupmark::CSV.Arg, truestrings::CSV.Arg, falsestrings::CSV.Arg, stripwhitespace::CSV.Arg, type::CSV.Arg, types::CSV.Arg, typemap::CSV.Arg, pool::CSV.Arg, downcast::CSV.Arg, lazystrings::CSV.Arg, stringtype::CSV.Arg, strict::CSV.Arg, silencewarnings::CSV.Arg, maxwarnings::CSV.Arg, debug::CSV.Arg, parsingdebug::CSV.Arg, validate::CSV.Arg, streaming::CSV.Arg) @ CSV /usr/local/share/julia/packages/CSV/LiiJM/src/context.jl:314 [2] #File#32 @ /usr/local/share/julia/packages/CSV/LiiJM/src/file.jl:222 [inlined] [3] CSV.File(source::String) @ CSV /usr/local/share/julia/packages/CSV/LiiJM/src/file.jl:162 [4] #read#118 @ /usr/local/share/julia/packages/CSV/LiiJM/src/CSV.jl:117 [inlined] [5] read @ /usr/local/share/julia/packages/CSV/LiiJM/src/CSV.jl:113 [inlined] [6] main() @ Main /app/analysis.jl:347 [7] top-level scope @ /app/analysis.jl:635 in expression starting at /app/analysis.jl:635 [stderr] ERROR: LoadError: UndefVarError: `sample` not defined Stacktrace: [1] top-level scope @ /tmp/perturb_test.jl:5 in expression starting at /tmp/perturb_test.jl:5
cat > /tmp/perturb_test.jl <<'EOF'
using DataFrames, CSV, Random
Random.seed!(42)
df = CSV.read("/app/Phenobarb.csv", DataFrame)
uniq_subj = unique(df.Subject)
subjs = shuffle(uniq_subj)[1:45]
df2 = df[in.(df.Subject, Ref(subjs)), :]
id_map = Dict(old => i for (i, old) in enumerate(sort(unique(df2.Subject))))
df2.Subject = [id_map[s] for s in df2.Subject]
for i in 1:nrow(df2)
if !ismissing(df2.conc[i])
df2.conc[i] = df2.conc[i] * (1 + 0.05*randn())
end
end
CSV.write("/tmp/Phenobarb_perturbed.csv", df2)
println("Saved perturbed with ", length(unique(df2.Subject)), " subjects, ",
nrow(df2), " rows, ", sum(.!ismissing.(df2.conc)), " obs")
EOF
julia --project=/app /tmp/perturb_test.jl
echo "---"
PHENOBARB_DATA_PATH=/tmp/Phenobarb_perturbed.csv PHENOBARB_OUTPUT_DIR=/tmp/out_perturb timeout 200 julia --project=/app /app/analysis.jl 2>&1 | tail -15Saved perturbed with 45 subjects, 581 rows, 121 obs --- Fitting base model ... logLik(base) = -396.1585538901713 AIC = 802.3171077803426 Fitting cl_wt model ... logLik(cl_wt) = -377.0760742481869 AIC = 766.1521484963738 Fitting full model ... logLik(full) = -340.0953394011032 AIC = 696.1906788022064 LR 1->2: stat=38.164959283968756 p=6.500943117160318e-10 LR 2->3: stat=73.96146969416736 p=1.1102230246251565e-16 cor(obs, pred_ind) = 0.9611780478390989 cor(obs, pred_pop) = 0.780554164828787 DONE
cat > /tmp/perturb_test2.jl <<'EOF'
using DataFrames, CSV, Random
Random.seed!(1234)
df = CSV.read("/app/Phenobarb.csv", DataFrame)
uniq_subj = unique(df.Subject)
# Smaller: 30 subjects, heavier noise
subjs = shuffle(uniq_subj)[1:30]
df2 = df[in.(df.Subject, Ref(subjs)), :]
id_map = Dict(old => i for (i, old) in enumerate(sort(unique(df2.Subject))))
df2.Subject = [id_map[s] for s in df2.Subject]
for i in 1:nrow(df2)
if !ismissing(df2.conc[i])
df2.conc[i] = df2.conc[i] * (1 + 0.1*randn())
end
if !ismissing(df2.dose[i])
df2.dose[i] = df2.dose[i] * (1 + 0.05*randn())
end
end
CSV.write("/tmp/Phenobarb_perturbed2.csv", df2)
println("Saved 2nd perturbed with ", length(unique(df2.Subject)), " subjects, ",
nrow(df2), " rows, ", sum(.!ismissing.(df2.conc)), " obs")
EOF
julia --project=/app /tmp/perturb_test2.jl
echo "---"
PHENOBARB_DATA_PATH=/tmp/Phenobarb_perturbed2.csv PHENOBARB_OUTPUT_DIR=/tmp/out_perturb2 timeout 200 julia --project=/app /app/analysis.jl 2>&1 | tail -15Saved 2nd perturbed with 30 subjects, 392 rows, 84 obs --- Fitting base model ... logLik(base) = -283.3427334875678 AIC = 576.6854669751356 Fitting cl_wt model ... logLik(cl_wt) = -272.8636455683098 AIC = 557.7272911366196 Fitting full model ... logLik(full) = -253.067984536768 AIC = 522.135969073536 LR 1->2: stat=20.958175838516013 p=4.694201896526806e-6 LR 2->3: stat=39.59132206308357 p=2.528445786076361e-9 cor(obs, pred_ind) = 0.9071716410396623 cor(obs, pred_pop) = 0.7714432728122821 DONE
command: str_replace
path: /app/analysis.jl
old_str: function fit_model(spec::ModelSpec, subjects, θ0)
η_cache = [zeros(2) for _ in subjects]
function obj(θ)
try
nm, ηs = neg_marginal_loglik(spec, subjects, θ; η_starts = η_cache)
# warm-start cache
for i in eachindex(ηs); η_cache[i] = ηs[i]; end
return nm
catch
return 1e10
end
end
res = optimize(obj, θ0, NelderMead(),
Optim.Options(iterations = 1500, g_tol = 1e-6))
θ_hat = Optim.minimizer(res)
# One refinement pass
res2 = optimize(obj, θ_hat, NelderMead(),
Optim.Options(iterations = 800, g_tol = 1e-8))
if Optim.minimum(res2) < Optim.minimum(res)
θ_hat = Optim.minimizer(res2)
end
_, ηs = neg_marginal_loglik(spec, subjects, θ_hat; η_starts = η_cache)
loglik = -obj(θ_hat)
return θ_hat, ηs, loglik
end
new_str: function fit_model(spec::ModelSpec, subjects, θ0)
η_cache = [zeros(2) for _ in subjects]
function obj(θ)
try
nm, ηs = neg_marginal_loglik(spec, subjects, θ; η_starts = η_cache)
# warm-start cache
for i in eachindex(ηs); η_cache[i] = ηs[i]; end
return nm
catch
return 1e10
end
end
res = optimize(obj, θ0, NelderMead(),
Optim.Options(iterations = 2000, g_tol = 1e-7))
θ_hat = Optim.minimizer(res)
best_val = Optim.minimum(res)
# Multiple refinement passes with slight perturbation restarts to escape
# shallow local minima in the finite-difference NelderMead search.
for trial in 1:3
res2 = optimize(obj, θ_hat, NelderMead(),
Optim.Options(iterations = 1500, g_tol = 1e-9))
if Optim.minimum(res2) < best_val
θ_hat = Optim.minimizer(res2)
best_val = Optim.minimum(res2)
end
end
_, ηs = neg_marginal_loglik(spec, subjects, θ_hat; η_starts = η_cache)
loglik = -obj(θ_hat)
return θ_hat, ηs, loglik
endThe file /app/analysis.jl has been edited successfully.
PHENOBARB_DATA_PATH=/tmp/Phenobarb_perturbed2.csv PHENOBARB_OUTPUT_DIR=/tmp/out_perturb2 timeout 250 julia --project=/app /app/analysis.jl 2>&1 | tail -10
logLik(base) = -283.3427335842998 AIC = 576.6854671685996 Fitting cl_wt model ... logLik(cl_wt) = -272.86364550910235 AIC = 557.7272910182047 Fitting full model ... logLik(full) = -253.06798442713026 AIC = 522.1359688542605 LR 1->2: stat=20.958176150394934 p=4.694201132360298e-6 LR 2->3: stat=39.5913221639442 p=2.5284456750540585e-9 cor(obs, pred_ind) = 0.9071715384358348 cor(obs, pred_pop) = 0.7714434783563987 DONE
PHENOBARB_DATA_PATH=/tmp/Phenobarb_perturbed.csv PHENOBARB_OUTPUT_DIR=/tmp/out_perturb timeout 250 julia --project=/app /app/analysis.jl 2>&1 | tail -8
logLik(cl_wt) = -377.0760740635555 AIC = 766.152148127111 Fitting full model ... logLik(full) = -340.09533877995113 AIC = 696.1906775599023 LR 1->2: stat=38.16495988506176 p=6.500940896714269e-10 LR 2->3: stat=73.96147056720872 p=1.1102230246251565e-16 cor(obs, pred_ind) = 0.9611771846294819 cor(obs, pred_pop) = 0.7805539576072078 DONE
cd /app && rm -rf output && mkdir -p output && timeout 250 julia --project=. analysis.jl 2>&1 | tail -10
logLik(base) = -505.01836030377626 AIC = 1020.0367206075525 Fitting cl_wt model ... logLik(cl_wt) = -479.3799214553498 AIC = 970.7598429106996 Fitting full model ... logLik(full) = -437.5636663247922 AIC = 891.1273326495844 LR 1->2: stat=51.27687769685292 p=8.021361352916756e-13 LR 2->3: stat=83.63251026111516 p=0.0 cor(obs, pred_ind) = 0.9711285330017053 cor(obs, pred_pop) = 0.8143953914354679 DONE
ls -la /app/output/; echo "---"; python3 -c "
import json
with open('/app/output/model_summary.json') as f:
d = json.load(f)
print('Keys:', sorted(d.keys()))
print('fixed_effects count:', len(d['fixed_effects']))
for fe in d['fixed_effects']:
print(' ', fe['term'], '->', fe)
print('random_effects_sd:', d['random_effects_sd'])
"total 1289
drwxr-sr-x 2 h2tagent h2tagent 4096 May 31 10:27 .
drwxrws--- 3 h2tagent h2tagent 4096 May 31 10:26 ..
-rw-r--r-- 1 h2tagent h2tagent 318 May 31 10:27 lrt_chain.csv
-rw-r--r-- 1 h2tagent h2tagent 1370 May 31 10:27 model_summary.json
-rw-r--r-- 1 h2tagent h2tagent 7792 May 31 10:27 obs_vs_pred.csv
-rw-r--r-- 1 h2tagent h2tagent 2529 May 31 10:27 per_subject_params.csv
-rw-r--r-- 1 h2tagent h2tagent 385390 May 31 10:27 plot_conc_profile.png
-rw-r--r-- 1 h2tagent h2tagent 112504 May 31 10:27 plot_covariate_effect.png
-rw-r--r-- 1 h2tagent h2tagent 173571 May 31 10:27 plot_obs_vs_pred.png
-rw-r--r-- 1 h2tagent h2tagent 459883 May 31 10:27 plot_per_subject_fit.png
-rw-r--r-- 1 h2tagent h2tagent 155735 May 31 10:27 plot_residuals.png
-rw-r--r-- 1 h2tagent h2tagent 10181 May 31 10:27 residual_diagnostics.csv
---
Keys: ['aic', 'bic', 'fixed_effects', 'logLik', 'n_obs', 'n_subjects', 'random_effects_sd']
fixed_effects count: 5
lCl.(Intercept) -> {'term': 'lCl.(Intercept)', 't_value': -26.32749040004183, 'df': 150, 'std_error': 0.22554241951516632, 'estimate': -5.937965884587749}
lCl.Wt -> {'term': 'lCl.Wt', 't_value': 7.466215725635717, 'df': 150, 'std_error': 0.08220409090001549, 'estimate': 0.6137534761892836}
lCl.ApgarInd>= 5 -> {'term': 'lCl.ApgarInd>= 5', 't_value': -0.27872139092242953, 'df': 150, 'std_error': 0.15970176563093236, 'estimate': -0.04451229824942132}
lV.(Intercept) -> {'term': 'lV.(Intercept)', 't_value': -6.987025532092601, 'df': 150, 'std_error': 0.06779063204682713, 'estimate': -0.47365487694787606}
lV.Wt -> {'term': 'lV.Wt', 't_value': 13.157934186796147, 'df': 150, 'std_error': 0.04053824353771037, 'estimate': 0.5333995405175073}
random_effects_sd: {'lCl': 0.22670781741598445, 'lV': 0.17057266864168671, 'Residual': 2.7252748242628946}python3 -c "import json; json.load(open('/app/output/model_summary.json')); print('JSON valid')"
cat /app/output/lrt_chain.csvJSON valid step,model_name,n_params,logLik,AIC,BIC,LR_stat,LR_pvalue 1,base,5,-505.01836030377626,1020.0367206075525,1035.2538461921488,, 2,cl_wt,6,-479.3799214553498,970.7598429106996,989.0203936122151,51.27687769685292,8.021361352916756e-13 3,full,8,-437.5636663247922,891.1273326495844,915.4747335849385,83.63251026111516,0.0
wc -l /app/analysis.jl grep -c "^function" /app/analysis.jl
640 /app/analysis.jl 16
cd /app && time (rm -rf output && mkdir -p output && julia --project=. analysis.jl 2>&1 | tail -3)
cor(obs, pred_ind) = 0.9711285330017053 cor(obs, pred_pop) = 0.8143953914354679 DONE [stderr] real 1m36.665s user 1m26.910s sys 0m5.150s
ls /app/output/
lrt_chain.csv model_summary.json obs_vs_pred.csv per_subject_params.csv plot_conc_profile.png plot_covariate_effect.png plot_obs_vs_pred.png plot_per_subject_fit.png plot_residuals.png residual_diagnostics.csv
sed -n '1,50p' /app/analysis.jl
# Work only in this file.
#
# Inputs:
# /app/Phenobarb.csv , neonatal phenobarbital PK data
# /app/dataset_manifest.json , output filename and column contracts
#
# Outputs: write everything to /app/output/
using DataFrames, CSV, JSON3, CairoMakie, Statistics, Distributions,
LinearAlgebra, Optim
data_path() = get(ENV, "PHENOBARB_DATA_PATH", "/app/Phenobarb.csv")
manifest_path() = get(ENV, "PHENOBARB_MANIFEST_PATH", "/app/dataset_manifest.json")
output_dir() = get(ENV, "PHENOBARB_OUTPUT_DIR", "/app/output")
# ---------------- data containers ----------------
struct SubjectData
id::Int
Wt::Float64
ApgarInd::String
apgar_hi::Float64 # 1.0 if ">= 5" else 0.0
dose_times::Vector{Float64}
dose_amts::Vector{Float64}
sample_times::Vector{Float64}
sample_concs::Vector{Float64}
n_obs::Int
end
function build_subjects(df::DataFrame)
subjects = SubjectData[]
for g in groupby(sort(df, [:Subject, :time]), :Subject)
id = Int(g.Subject[1])
Wt = Float64(g.Wt[1])
ApgarInd = String(g.ApgarInd[1])
apgar_hi = ApgarInd == ">= 5" ? 1.0 : 0.0
dt = Float64[]; da = Float64[]
st = Float64[]; sc = Float64[]
for row in eachrow(g)
if !ismissing(row.dose)
push!(dt, Float64(row.time)); push!(da, Float64(row.dose))
end
if !ismissing(row.conc)
push!(st, Float64(row.time)); push!(sc, Float64(row.conc))
end
end
dp = sortperm(dt); dt = dt[dp]; da = da[dp]
sp = sortperm(st); st = st[sp]; sc = sc[sp]
push!(subjects, SubjectData(id, Wt, ApgarInd, apgar_hi,
dt, da, st, sc, length(st)))
endsed -n '280,330p' /app/analysis.jl
for (i, s) in enumerate(subjects)
η0 = η_starts === nothing ? [0.0, 0.0] : η_starts[i]
nm, η_hat = laplace_subject(spec, s, θ; η_init = η0)
total += nm
ηs[i] = η_hat
end
return total, ηs
end
# ---------------- outer optimisation ----------------
function fit_model(spec::ModelSpec, subjects, θ0)
η_cache = [zeros(2) for _ in subjects]
function obj(θ)
try
nm, ηs = neg_marginal_loglik(spec, subjects, θ; η_starts = η_cache)
# warm-start cache
for i in eachindex(ηs); η_cache[i] = ηs[i]; end
return nm
catch
return 1e10
end
end
res = optimize(obj, θ0, NelderMead(),
Optim.Options(iterations = 2000, g_tol = 1e-7))
θ_hat = Optim.minimizer(res)
best_val = Optim.minimum(res)
# Multiple refinement passes with slight perturbation restarts to escape
# shallow local minima in the finite-difference NelderMead search.
for trial in 1:3
res2 = optimize(obj, θ_hat, NelderMead(),
Optim.Options(iterations = 1500, g_tol = 1e-9))
if Optim.minimum(res2) < best_val
θ_hat = Optim.minimizer(res2)
best_val = Optim.minimum(res2)
end
end
_, ηs = neg_marginal_loglik(spec, subjects, θ_hat; η_starts = η_cache)
loglik = -obj(θ_hat)
return θ_hat, ηs, loglik
end
# Numerical Hessian of the outer objective (all parameters) at optimum
function outer_hessian(obj, θ; h = 5e-3)
n = length(θ)
H = zeros(n, n)
f0 = obj(θ)
fp = [obj(θ .+ [i == k ? h : 0.0 for k in 1:n]) for i in 1:n]
fm = [obj(θ .- [i == k ? h : 0.0 for k in 1:n]) for i in 1:n]
for i in 1:n
H[i,i] = (fp[i] - 2*f0 + fm[i]) / h^2command: str_replace
path: /app/analysis.jl
old_str: res = optimize(obj, θ0, NelderMead(),
Optim.Options(iterations = 2000, g_tol = 1e-7))
θ_hat = Optim.minimizer(res)
best_val = Optim.minimum(res)
# Multiple refinement passes with slight perturbation restarts to escape
# shallow local minima in the finite-difference NelderMead search.
for trial in 1:3
res2 = optimize(obj, θ_hat, NelderMead(),
Optim.Options(iterations = 1500, g_tol = 1e-9))
if Optim.minimum(res2) < best_val
θ_hat = Optim.minimizer(res2)
best_val = Optim.minimum(res2)
end
end
new_str: res = optimize(obj, θ0, NelderMead(),
Optim.Options(iterations = 2000, g_tol = 1e-7))
θ_hat = Optim.minimizer(res)
best_val = Optim.minimum(res)
# Refinement pass (restart NelderMead from the optimum to shrink the simplex).
res2 = optimize(obj, θ_hat, NelderMead(),
Optim.Options(iterations = 1500, g_tol = 1e-9))
if Optim.minimum(res2) < best_val
θ_hat = Optim.minimizer(res2)
best_val = Optim.minimum(res2)
endThe file /app/analysis.jl has been edited successfully.
cd /app && time (rm -rf output && mkdir -p output && julia --project=. analysis.jl 2>&1 | tail -8)
logLik(cl_wt) = -479.37992163975565 AIC = 970.7598432795113 Fitting full model ... logLik(full) = -437.5636665181407 AIC = 891.1273330362814 LR 1->2: stat=51.27687662360722 p=8.021361352916756e-13 LR 2->3: stat=83.63251024322994 p=0.0 cor(obs, pred_ind) = 0.9711284011654077 cor(obs, pred_pop) = 0.8143954277039701 DONE [stderr] real 0m54.974s user 0m49.580s sys 0m2.640s
cd /app && ls output/ | sort
echo "---"
# Validate JSON and CSVs
python3 <<'EOF'
import json, csv
# JSON
d = json.load(open('/app/output/model_summary.json'))
assert set(d.keys()) == {'n_obs','n_subjects','fixed_effects','random_effects_sd','logLik','aic','bic'}
assert d['n_obs'] == 155
assert d['n_subjects'] == 59
assert len(d['fixed_effects']) == 5
expected_terms = ['lCl.(Intercept)', 'lCl.Wt', 'lCl.ApgarInd>= 5', 'lV.(Intercept)', 'lV.Wt']
for fe, et in zip(d['fixed_effects'], expected_terms):
assert fe['term'] == et, f"got {fe['term']} expected {et}"
assert set(fe.keys()) == {'term', 'estimate', 'std_error', 't_value', 'df'}
re_sd = d['random_effects_sd']
assert set(re_sd.keys()) == {'lCl', 'lV', 'Residual'}
assert all(re_sd[k] > 0 for k in re_sd)
print('JSON ok; logLik=%.3f aic=%.3f bic=%.3f' % (d['logLik'], d['aic'], d['bic']))
# LRT CSV
with open('/app/output/lrt_chain.csv') as f:
rows = list(csv.DictReader(f))
assert len(rows) == 3
assert rows[0]['model_name'] == 'base' and rows[0]['LR_stat'] == ''
assert rows[1]['model_name'] == 'cl_wt'
assert rows[2]['model_name'] == 'full'
aics = [float(r['AIC']) for r in rows]
assert aics[0] > aics[1] > aics[2], f"AICs not decreasing: {aics}"
print('LRT ok; AICs', aics)
# Per-subject params
with open('/app/output/per_subject_params.csv') as f:
rows = list(csv.DictReader(f))
assert all(float(r['Cl']) > 0 and float(r['V']) > 0 for r in rows)
subs = [int(r['Subject']) for r in rows]
assert subs == sorted(subs)
print('Per-subject ok; n=%d' % len(rows))
# Obs vs pred: correlations
import statistics
with open('/app/output/obs_vs_pred.csv') as f:
rows = list(csv.DictReader(f))
oc = [float(r['conc_obs']) for r in rows]
pp = [float(r['pred_pop']) for r in rows]
pi = [float(r['pred_ind']) for r in rows]
def corr(a,b):
ma, mb = statistics.mean(a), statistics.mean(b)
sa, sb = statistics.stdev(a), statistics.stdev(b)
return sum((x-ma)*(y-mb) for x,y in zip(a,b))/((len(a)-1)*sa*sb)
cp = corr(oc, pp); ci = corr(oc, pi)
print(f'cor_pop={cp:.4f} cor_ind={ci:.4f}')
assert cp > 0.55 and ci > 0.75
# Residual diagnostics
with open('/app/output/residual_diagnostics.csv') as f:
rows = list(csv.DictReader(f))
for r in rows[:3]:
resid = float(r['resid']); std = float(r['std_resid'])
# std_resid should be resid/σ for the SAME σ
assert abs(sum(float(r['resid']) for r in rows) / len(rows)) < 5
print('Residual diag ok; n=%d' % len(rows))
print('\nAll checks passed.')
EOFlrt_chain.csv model_summary.json obs_vs_pred.csv per_subject_params.csv plot_conc_profile.png plot_covariate_effect.png plot_obs_vs_pred.png plot_per_subject_fit.png plot_residuals.png residual_diagnostics.csv --- JSON ok; logLik=-437.564 aic=891.127 bic=915.475 LRT ok; AICs [1020.0367199031185, 970.7598432795113, 891.1273330362814] Per-subject ok; n=59 cor_pop=0.8144 cor_ind=0.9711 Residual diag ok; n=155 All checks passed.
cat > /tmp/perturb3.jl <<'EOF'
using DataFrames, CSV, Random
Random.seed!(777)
df = CSV.read("/app/Phenobarb.csv", DataFrame)
uniq_subj = unique(df.Subject)
subjs = shuffle(uniq_subj)[1:55] # 55 of 59 subjects
df2 = df[in.(df.Subject, Ref(subjs)), :]
id_map = Dict(old => i for (i, old) in enumerate(sort(unique(df2.Subject))))
df2.Subject = [id_map[s] for s in df2.Subject]
# Small perturbation to conc
for i in 1:nrow(df2)
if !ismissing(df2.conc[i])
df2.conc[i] = max(df2.conc[i] + 0.5*randn(), 0.5)
end
end
CSV.write("/tmp/Phenobarb_perturbed3.csv", df2)
println("55-subject perturbation: ", nrow(df2), " rows, ", sum(.!ismissing.(df2.conc)), " obs")
EOF
julia --project=/app /tmp/perturb3.jl
PHENOBARB_DATA_PATH=/tmp/Phenobarb_perturbed3.csv PHENOBARB_OUTPUT_DIR=/tmp/out_perturb3 timeout 180 julia --project=/app /app/analysis.jl 2>&1 | tail -1055-subject perturbation: 682 rows, 145 obs logLik(base) = -473.70677882833513 AIC = 957.4135576566703 Fitting cl_wt model ... logLik(cl_wt) = -450.1273045807418 AIC = 912.2546091614836 Fitting full model ... logLik(full) = -409.62034914898067 AIC = 835.2406982979613 LR 1->2: stat=47.15894849518668 p=6.5457639308874604e-12 LR 2->3: stat=81.01391086352226 p=0.0 cor(obs, pred_ind) = 0.9712067644800492 cor(obs, pred_pop) = 0.807706037178943 DONE
cd /app && rm -rf output && mkdir -p output && time julia --project=. analysis.jl 2>&1 | tail -5 echo "---" ls output/
LR 1->2: stat=51.27687662360722 p=8.021361352916756e-13 LR 2->3: stat=83.63251024322994 p=0.0 cor(obs, pred_ind) = 0.9711284011654077 cor(obs, pred_pop) = 0.8143954277039701 DONE --- lrt_chain.csv model_summary.json obs_vs_pred.csv per_subject_params.csv plot_conc_profile.png plot_covariate_effect.png plot_obs_vs_pred.png plot_per_subject_fit.png plot_residuals.png residual_diagnostics.csv [stderr] real 0m54.959s user 0m49.380s sys 0m2.750s
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: neonatal-drug-exposure-nlme # attempt: 10 status: completed raw_score: 0.0 # reward rule: binary: score 0.0 != 1.0 # => reward = 0.0 (FAIL) # # Note: these tasks score via an external hidden grader; no pytest/verifier # block is embedded in the agent transcript. The block below is the last # sizable tool-output captured in the run (supporting evidence). ---------------------------------------------------------------------- LR 1->2: stat=51.27687662360722 p=8.021361352916756e-13 LR 2->3: stat=83.63251024322994 p=0.0 cor(obs, pred_ind) = 0.9711284011654077 cor(obs, pred_pop) = 0.8143954277039701 DONE --- lrt_chain.csv model_summary.json obs_vs_pred.csv per_subject_params.csv plot_conc_profile.png plot_covariate_effect.png plot_obs_vs_pred.png plot_per_subject_fit.png plot_residuals.png residual_diagnostics.csv [stderr] real 0m54.959s user 0m49.380s sys 0m2.750s
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_78a63048bb2547de. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_78a63048bb2547de · verifier authoritative; classifier explanatory.