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dynamic_inference update #60

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merged 18 commits into from
Dec 1, 2018
Merged

dynamic_inference update #60

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1290e91
Some changes to run the tests.
Vaibhavdixit02 Oct 8, 2018
1d7e979
Initial changes dynamic_inference
Vaibhavdixit02 Oct 9, 2018
abeb9fa
Fixed tranformations
Vaibhavdixit02 Oct 9, 2018
de2a545
Change transformation to a functional argument
Vaibhavdixit02 Oct 10, 2018
5edcadc
Directly pass transformations
Vaibhavdixit02 Oct 10, 2018
1c895d6
Add Tracked to remake and replace problem_new_parameter
Vaibhavdixit02 Oct 11, 2018
5fd096f
Some changes to remake call
Vaibhavdixit02 Oct 13, 2018
2dfe6d5
dynamicHMC runs
ChrisRackauckas Oct 18, 2018
5ae7281
Remove print
Vaibhavdixit02 Oct 25, 2018
cb96b6c
Remove unnecessary dtmin
ChrisRackauckas Oct 25, 2018
9dc5fc2
more flexible setup
ChrisRackauckas Oct 25, 2018
1074a7f
cleanup dynamichmc likelihood
ChrisRackauckas Nov 5, 2018
b7553d4
Update dynamichmc_inference.jl
ChrisRackauckas Nov 7, 2018
c3466b0
mark dynamichmc as broken
ChrisRackauckas Nov 28, 2018
09f43d6
Merge remote-tracking branch 'Vaibhavdixit02/dynamicinferenceupdate' …
ChrisRackauckas Nov 28, 2018
fb3cbc5
run tests on CI
ChrisRackauckas Nov 28, 2018
5c6828c
update CI
ChrisRackauckas Nov 28, 2018
5c60629
try fixing Turing
ChrisRackauckas Dec 1, 2018
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6 changes: 3 additions & 3 deletions REQUIRE
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Expand Up @@ -10,8 +10,8 @@ RecursiveArrayTools
ParameterizedFunctions
OrdinaryDiffEq
Parameters
#DiffWrappers
ContinuousTransformations 1.0.0
DynamicHMC 0.1.1
DynamicHMC
Distances
ApproxBayes
TransformVariables
LogDensityProblems
2 changes: 1 addition & 1 deletion src/DiffEqBayes.jl
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@@ -1,7 +1,7 @@
module DiffEqBayes
using DiffEqBase, Stan, Distributions, Turing, MacroTools
using OrdinaryDiffEq, ParameterizedFunctions, RecursiveArrayTools
using DynamicHMC, ContinuousTransformations
using DynamicHMC, TransformVariables, LogDensityProblems
using Parameters, Distributions, Optim
using Distances, ApproxBayes

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58 changes: 27 additions & 31 deletions src/dynamichmc_inference.jl
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Expand Up @@ -33,7 +33,7 @@ function dynamichmc_inference(prob::DiffEqBase.DEProblem, alg, t, data, priors,
kwargs...)
likelihood = sol -> sum( sum(logpdf.(Normal(0.0, σ), sol(t) .- data[:, i]))
for (i, t) in enumerate(t) )

println(typeof(transformations))
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Not sure why you are printing it, but that's a minor thing.

Regarding the API: perhaps I am missing something, but I would make transformations part of the problem, ie in DynamicHMCPosterior.

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Could you clarify how that would work, I also feel that adding the transformation like I am doing is not really going to work (I actually copied this right off your linear regression example according to my understanding).

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You could also make the transformation a slot in the composite type. But with this interface, it may not be needed.

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Oh I see, the does sound good, but then how would the applied is not clear to me, I can imagine that with the ContinuousTransformations interface, I'll push with what I think would work here but I doubt it will be correct 😅

dynamichmc_inference(prob, alg, likelihood, priors, transformations;
ϵ=ϵ, initial=initial, num_samples=num_samples,
kwargs...)
Expand All @@ -43,39 +43,35 @@ function dynamichmc_inference(prob::DiffEqBase.DEProblem, alg, likelihood, prior
ϵ=0.001, initial=Float64[], num_samples=1000,
kwargs...)
P = DynamicHMCPosterior(alg, prob, likelihood, priors, kwargs)
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println(typeof(transformations))
prob_transform(P::DynamicHMCPosterior) = as((transformations))
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No need for as(...) here, that's the api for making transformations. I would just make the user to that and work with the transformation object from then on.

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I felt this would give a cleaner interface, this can be changed later on too though so I'll keep this in mind for sure.

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What I am saying is that defining a function just for the sole purpose of returning the transformations makes little sense. I would do something like

function dynamichmc_inference(prob::DiffEqBase.DEProblem, alg, likelihood, priors, transformations;
                              ϵ=0.001, initial=Float64[], num_samples=1000,
                              kwargs...)
    P = DynamicHMCPosterior(alg, prob, likelihood, priors, kwargs)
    PT = TransformedLogDensity(transformations, P)
    PTG = FluxGradientLogDensity(PT);

    chain, NUTS_tuned = NUTS_init_tune_mcmc(PTG,num_samples, ϵ=ϵ)
    posterior = transform.(Ref(PTG.transformation), get_position.(chain));

    return posterior, chain, NUTS_tuned
end

instead.

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Oh I see.

PT = TransformedLogDensity(prob_transform(P), P)
PTG = FluxGradientLogDensity(PT);

transformations_tuple = Tuple(transformations)
parameter_transformation = TransformationTuple(transformations_tuple) # assuming a > 0
PT = TransformLogLikelihood(P, parameter_transformation)
PTG = ForwardGradientWrapper(PT, zeros(length(priors)));

lower_bound = Float64[]
upper_bound = Float64[]

for i in priors
push!(lower_bound, minimum(i))
push!(upper_bound, maximum(i))
end
# lower_bound = Float64[]
# upper_bound = Float64[]

# If no initial position is given use local minimum near expectation of priors.
if length(initial) == 0
for i in priors
push!(initial, mean(i))
end
initial_opt = Optim.minimizer(optimize(a -> -P(a),lower_bound,upper_bound,initial,Fminbox(GradientDescent())))
end
# for i in priors
# push!(lower_bound, minimum(i))
# push!(upper_bound, maximum(i))
# end

initial_inverse_transformed = Float64[]
for i in 1:length(initial_opt)
para = TransformationTuple(transformations[i])
push!(initial_inverse_transformed,inverse(para, (initial_opt[i], ))[1])
end
#println(initial_inverse_transformed)
sample, NUTS_tuned = NUTS_init_tune_mcmc(PTG,
initial_inverse_transformed,
num_samples, ϵ=ϵ)
# # If no initial position is given use local minimum near expectation of priors.
# if length(initial) == 0
# for i in priors
# push!(initial, mean(i))
# end
# initial_opt = Optim.minimizer(optimize(a -> -P(a),lower_bound,upper_bound,initial,Fminbox(GradientDescent())))
# end

posterior = ungrouping_map(Vector, get_transformation(PT) ∘ get_position, sample)
# initial_inverse_transformed = Float64[]
# for i in 1:length(initial_opt)
# para = TransformationTuple(transformations[i])
# push!(initial_inverse_transformed,inverse(para, (initial_opt[i], ))[1])
# end
# #println(initial_inverse_transformed)
chain, NUTS_tuned = NUTS_init_tune_mcmc(PTG,num_samples, ϵ=ϵ)
posterior = transform.(Ref(PTG.transformation), get_position.(chain));
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return posterior, sample, NUTS_tuned
return posterior, chain, NUTS_tuned
end
2 changes: 1 addition & 1 deletion src/stan_inference.jl
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Expand Up @@ -93,7 +93,7 @@ function stan_inference(prob::DiffEqBase.DEProblem,t,data,priors = nothing;alg=:
setup_params = string(setup_params,"row_vector<lower=0>[$length_of_y] sigma$(i-1);")
end
end
tuple_hyper_params = tuple_hyper_params[1:endof(tuple_hyper_params)-1]
tuple_hyper_params = tuple_hyper_params[1:length(tuple_hyper_params)-1]
differential_equation = generate_differential_equation(f)
priors_string = string(generate_priors(f,priors))
stan_likelihood = stan_string(likelihood)
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12 changes: 6 additions & 6 deletions test/dynamicHMC.jl
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@@ -1,8 +1,8 @@
using DiffEqBayes, OrdinaryDiffEq, ParameterizedFunctions, RecursiveArrayTools
using DynamicHMC, DiffWrappers, ContinuousTransformations
using DynamicHMC, TransformVariables
using Parameters, Distributions, Optim

f1 = @ode_def_nohes LotkaVolterraTest1 begin
f1 = @ode_def LotkaVolterraTest1 begin
dx = a*x - x*y
dy = -3*y + x*y
end a
Expand All @@ -16,8 +16,8 @@ t = collect(range(1,stop=10,length=10)) # observation times
sol = solve(prob1,Tsit5())
randomized = VectorOfArray([(sol(t[i]) + σ * randn(2)) for i in 1:length(t)])
data = convert(Array,randomized)

bayesian_result = dynamichmc_inference(prob1, Tsit5(), t, data, [Normal(1.5, 1)], [bridge(ℝ, ℝ⁺, )])
transform = (a = asℝ₊)
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I think this need a , to make a (named) tuple, ie (a = asℝ₊, ).

bayesian_result = dynamichmc_inference(prob1, Tsit5(), t, data, [Normal(1.5, 1)], transform)
@test mean(bayesian_result[1][1]) ≈ 1.5 atol=1e-1

# With hand-code likelihood function
Expand All @@ -33,11 +33,11 @@ likelihood = function (sol)
end
return l
end
bayesian_result = dynamichmc_inference(prob1, Tsit5(), likelihood, [Truncated(Normal(1.5, 1), 0, 2)], [bridge(ℝ, ℝ⁺, )])
bayesian_result = dynamichmc_inference(prob1, Tsit5(), likelihood, [Truncated(Normal(1.5, 1), 0, 2)])
@test mean(bayesian_result[1][1]) ≈ 1.5 atol=1e-1


f1 = @ode_def_nohes LotkaVolterraTest4 begin
f1 = @ode_def LotkaVolterraTest4 begin
dx = a*x - b*x*y
dy = -c*y + d*x*y
end a b c d
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4 changes: 2 additions & 2 deletions test/stan.jl
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Expand Up @@ -2,7 +2,7 @@ using DiffEqBayes, OrdinaryDiffEq, ParameterizedFunctions,
RecursiveArrayTools, Distributions, Test

println("One parameter case")
f1 = @ode_def_nohes LotkaVolterraTest1 begin
f1 = @ode_def LotkaVolterraTest1 begin
dx = a*x - x*y
dy = -3y + x*y
end a
Expand All @@ -25,7 +25,7 @@ theta1 = bayesian_result.chain_results[:,["theta.1"],:]


println("Four parameter case")
f1 = @ode_def_nohes LotkaVolterraTest4 begin
f1 = @ode_def LotkaVolterraTest4 begin
dx = a*x - b*x*y
dy = -c*y + d*x*y
end a b c d
Expand Down