/
stan_inference.jl
178 lines (171 loc) · 5.93 KB
/
stan_inference.jl
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struct StanModel{M,R,C,N}
model::M
return_code::R
chains::C
cnames::N
end
struct StanODEData
end
function generate_priors(n,priors)
priors_string = ""
if priors==nothing
for i in 1:n
priors_string = string(priors_string,"theta[$i] ~ normal(0, 1)", " ; ")
end
else
for i in 1:n
priors_string = string(priors_string,"theta[$i] ~",stan_string(priors[i]),";")
end
end
priors_string
end
function generate_theta(n,priors)
theta = ""
for i in 1:n
upper_bound = ""
lower_bound = ""
if !isnothing(priors) && maximum(priors[i]) != Inf
upper_bound = string("upper=",maximum(priors[i]))
end
if !isnothing(priors) && minimum(priors[i]) != -Inf
lower_bound = string("lower=",minimum(priors[i]))
end
if lower_bound != "" && upper_bound != ""
theta = string(theta,"real","<$lower_bound",",","$upper_bound>"," theta$i",";")
elseif lower_bound != ""
theta = string(theta,"real","<$lower_bound",">"," theta$i",";")
elseif upper_bound != ""
theta = string(theta,"real","<","$upper_bound>"," theta$i",";")
else
theta = string(theta,"real"," theta$i",";")
end
end
return theta
end
function stan_inference(prob::DiffEqBase.DEProblem,t,data,priors = nothing,
stanmodel = nothing;alg=:rk45,
num_samples=1000, num_warmup=1000, reltol=1e-3,
abstol=1e-6, maxiter=Int(1e5),likelihood=Normal,
vars=(StanODEData(),InverseGamma(3,3)),nchains=1,
sample_u0 = false, save_idxs = nothing, diffeq_string = nothing, printsummary = true)
save_idxs !== nothing && length(save_idxs) == 1 ? save_idxs = save_idxs[1] : save_idxs = save_idxs
length_of_y = length(prob.u0)
save_idxs = something(save_idxs, 1:length_of_y)
length_of_params = length(vars)
if isnothing(diffeq_string)
sys = first(ModelingToolkit.modelingtoolkitize(prob))
length_of_parameter = length(ModelingToolkit.parameters(sys)) + sample_u0 * length(save_idxs)
else
length_of_parameter = length(prob.p) + sample_u0 * length(save_idxs)
end
if stanmodel === nothing
if alg ==:adams
algorithm = "integrate_ode_adams"
elseif alg ==:rk45
algorithm = "integrate_ode_rk45"
elseif alg == :bdf
algorithm = "integrate_ode_bdf"
else
error("The choices for alg are :adams, :rk45, or :bdf")
end
hyper_params = ""
tuple_hyper_params = ""
setup_params = ""
thetas = ""
theta_string = generate_theta(length_of_parameter,priors)
for i in 1:length_of_parameter
thetas = string(thetas,"theta[$i] = theta$i",";")
end
for i in 1:length_of_params
if isa(vars[i],StanODEData)
tuple_hyper_params = string(tuple_hyper_params,"u_hat[t,$save_idxs]",",")
else
dist = stan_string(vars[i])
hyper_params = string(hyper_params,"sigma$(i-1) ~ $dist;")
tuple_hyper_params = string(tuple_hyper_params,"sigma$(i-1)",",")
setup_params = string(setup_params,"row_vector<lower=0>[$(length(save_idxs))] sigma$(i-1);")
end
end
tuple_hyper_params = tuple_hyper_params[1:length(tuple_hyper_params)-1]
priors_string = string(generate_priors(length_of_parameter,priors))
stan_likelihood = stan_string(likelihood)
if sample_u0
nu = length(save_idxs)
if nu < length(prob.u0)
u0 = "{"
for u_ in prob.u0[nu+1:length(prob.u0)]
u0 = u0*string(u_)
end
u0 = u0*"}"
integral_string = "u_hat = $algorithm(sho, append_array(theta[1:$nu],$u0), t0, ts, theta[$(nu+1):$length_of_parameter], x_r, x_i, $reltol, $abstol, $maxiter);"
else
integral_string = "u_hat = $algorithm(sho, theta[1:$nu], t0, ts, theta[$(nu+1):$length_of_parameter], x_r, x_i, $reltol, $abstol, $maxiter);"
end
else
integral_string = "u_hat = $algorithm(sho, u0, t0, ts, theta, x_r, x_i, $reltol, $abstol, $maxiter);"
end
binsearch_string = """
int bin_search(real x, int min_val, int max_val){
int range = (max_val - min_val + 1) / 2;
int mid_pt = min_val + range;
int out;
while (range > 0) {
if (x == mid_pt) {
out = mid_pt;
range = 0;
} else {
range = (range + 1) / 2;
mid_pt = x > mid_pt ? mid_pt + range: mid_pt - range;
}
}
return out;
}
"""
if isnothing(diffeq_string)
diffeq_string = ModelingToolkit.build_function(
ModelingToolkit.equations(sys),ModelingToolkit.states(sys),
ModelingToolkit.parameters(sys),ModelingToolkit.independent_variable(sys),
fname = :sho,
target = ModelingToolkit.StanTarget()
)
end
parameter_estimation_model = "
functions {
$binsearch_string
$diffeq_string
}
data {
real u0[$length_of_y];
int<lower=1> T;
real internal_var___u[T,$(length(save_idxs))];
real t0;
real ts[T];
}
transformed data {
real x_r[0];
int x_i[0];
}
parameters {
$setup_params
$theta_string
}
transformed parameters{
real theta[$length_of_parameter];
$thetas
}
model{
real u_hat[T,$length_of_y];
$hyper_params
$priors_string
$integral_string
for (t in 1:T){
internal_var___u[t,:] ~ $stan_likelihood($tuple_hyper_params);
}
}
"
stanmodel = CmdStan.Stanmodel(num_samples=num_samples, num_warmup=num_warmup, name="parameter_estimation_model", model=parameter_estimation_model, nchains=nchains, printsummary = printsummary)
end
parameter_estimation_data = Dict("u0"=>prob.u0, "T" => length(t), "internal_var___u" => view(data, :, 1:length(t))', "t0" => prob.tspan[1], "ts" => t)
return_code, chains, cnames = CmdStan.stan(stanmodel, [parameter_estimation_data]; CmdStanDir=CMDSTAN_HOME)
return StanModel(stanmodel, return_code, chains, cnames)
end