diff --git a/Project.toml b/Project.toml index d5ca22f..d0096fa 100644 --- a/Project.toml +++ b/Project.toml @@ -1,33 +1,29 @@ name = "SimulatedNeuralMoments" uuid = "43b7c8c0-5622-45b8-90a3-338fc50d2232" authors = ["Michael Creel and contributors"] -version = "1.0.7" +version = "2.0.0" [deps] -AdvancedMH = "5b7e9947-ddc0-4b3f-9b55-0d8042f74170" BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0" DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab" +Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d" -PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" -Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" [compat] -AdvancedMH = "0.6, 0.7" BSON = "0.3" +Distributions = "0.25" Flux = "0.13" MCMCChains = "6.0" -PrettyTables = "2.0" -StatsPlots = "0.15" StatsBase = "0.33" -Turing = "0.22, 0.23, 0.24" +StatsPlots = "0.15" julia = "1.5 - 1.9" [extras] diff --git a/README.md b/README.md index b2977de..d112b3b 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,6 @@ With a trained net, we can feed in statistics computed from real data, and obtai ABC/MSM draws from the posterior are done using Turing.jl, concretely MCMC using Metropolis-Hastings sampling. The likelihood is an estimate of the asymptotic Gaussian distribution of the transformed statistics, which is justified due to the central limit theorem applying to the statistics. The proposal density is also the same Gaussian density, estimated at the transformed statistics corresponding to the real data. Because the proposal and the likelihood are identical, at the true parameter values, random walk MH sampling is effective, because MCMC concentrates around the true parameter values, as the sample size becomes large. -Please see the docs, in the link at the top, and the READMEs for the two examples to see how this all works. To run the two examples, using small training and testing samples, do ``using Pkg; Pkg.test("SimulatedNeuralMoments")`` -* [Stochastic volatility model](https://github.com/mcreel/SimulatedNeuralMoments.jl/blob/main/examples/SV/README.md) -* [Mixture of normals model](https://github.com/mcreel/SimulatedNeuralMoments.jl/blob/main/examples/MN/README.md) +Please see the docs, in the link at the top, and the README for the two examples to see how this all works. +* [example models](https://github.com/mcreel/SimulatedNeuralMoments.jl/blob/main/examples/README.md) diff --git a/docs/API.md b/docs/API.md index 4066103..2f0cf64 100644 --- a/docs/API.md +++ b/docs/API.md @@ -8,9 +8,11 @@ three functions. # the type that holds the model specifics struct SNMmodel modelname::String # name of model + samplesize::Int64 # number of observations the model generates lb # vector of lower bounds. All parameters must have a finite lower bound. ub # vector of upper bounds. All parameters must have a finite upper bound. insupport::Function # function that checks if a draw is valid (in bounds) + prior:: Function # the prior, used to do MCMC priordraw::Function # function that returns a draw from the prior auxstat::Function # function that returns an array of draws of statistic, or the statistic corresponding to the real data end @@ -18,27 +20,28 @@ end One uses this to define the statistical model, the parameters of which we wish to estimate. This is done as ``` -model = SNMmodel(modelname, lb, ub, insupport, priordraw, auxstat) +model = SNMmodel(modelname, samplesize, lb, ub, insupport, prior, priordraw, auxstat) ``` ### The elements of a SNMmodel type must follow: -* modelname is a string variable, which describes the model. +* modelname is a string variable, which describes the model. +* sample size is and integer, the number of data points the model generates * lb and ub are vectors of Float64. These are the lower and upper bounds of the support of the parameters (the parameter space). At present, only bounded parameter spaces are supported. * insupport, prior and priordraw and auxstat are all strings, names of functions. * insupport(theta) returns a boolean, indicating if the theta is in the support. + * prior(theta) computes the prior density at theta * priordraw() returns a vector of Float64, a draw from the prior. - * auxstat is called as +* auxstat is called as * ```auxstat(theta, reps)```, where reps is Int64. It returns reps draws of the statistics drawn at theta, in a reps dimensional vector of vectors. * or as ```auxstat(data)``` where data is an array. This returns the statistics computed at the real data. -The files MNexample.jl and MNlib.jl in the examples/MN directory show how to use this type. +The file example.jl the examples directory show how to use this type. ## The function MakeNeuralMoments -```MakeNeuralMoments(model::SNMmodel, transf; TrainTestSize=1, Epochs=1000)``` +```MakeNeuralMoments(model::SNMmodel; TrainTestSize=1, Epochs=1000)``` The function generates simulated data from the model and trains a neural net to recognize transformed parameters, given the statistic. It returns the trained net, and an array of information for transforming a raw statistic vector from auxstat to use it as an input to the net. ### This function has two required arguments: * model, which is of type SNMmodel, as discussed above. -* tranfs: This function transforms draws from the prior to lie in ℛⁿ. It is expected that this tranformation will be computed using Bijectors.jl, as in ```transf = bijector(@Prior)```, for example. See the MN or SV examples. ### optional arguments * TrainTestSize is an optional argument. If omitted, then 20000 samples per parameter will be used to train and test the net. The default gives fairly quick training for @@ -53,24 +56,53 @@ The files MNexample.jl and MNlib.jl in the examples/MN directory show how to use * nninfo: the infomation needed to transform a raw vector of statistics, from auxstat, to be used as an input to the neural net. ## The function NeuralMoments -```NeuralMoments(z, nnmodel, nninfo``` -This function takes a raw statistic, the trained neural net, and the array of transforming information, and returns a predicted transformed parameter vector. +```NeuralMoments(z, model, nnmodel, nninfo``` +This function takes a raw statistic, the SNM model structure, the trained neural net, and the array of transforming information, and returns a predicted transformed parameter vector. ### The inputs are * z: a vector of raw statistics from the model, the output of auxstat(y), for example. +* model: the SNMmodel structure that describes the statistical model being estimated * nnmodel: the trained neural net, a Flux.jl chain. It is the first of the outputs of MakeNeuralMoments. * nninfo: a tuple of vectors, used to transform raw statistics, z, to then pass as inputs to nnmodel. nninfo is the second output of MakeNeuralMoments. ### Output -a vector which is the predicted value, from the neural net, of the transformed parameters that generated the inputted z. +a vector which is the predicted value, from the neural net, of the transformed parameters that generated the argument z. ## The function mΣ ```mΣ(θ, reps, model::SNMmodel, nnmodel, nninfo)``` This function computes the mean and covariance matrix of ```reps``` evaluations of ```NeuralMoments```, where the inputs, ```z```, to ```NeuralMoments``` are drawn at the trial parameter value ```θ```. ### The inputs are -* ```θ```: a trial parameter value (untransformed) +* ```θ```: a trial parameter value * reps: Int64. The number of simulation draws to use to compute the mean vector and the covariance matrix * model: an SNMmodel structure that describes the statistical model being estimated * nnmodel: the trained neural net, a Flux.jl chain. It is the first of the outputs of MakeNeuralMoments. * nninfo: a tuple of vectors, used to transform raw statistics, z, to then pass as inputs to nnmodel. nninfo is the second output of MakeNeuralMoments. ### Outputs * mbar: the mean vector of the reps draws from the NeuralMoments function -* Σ: the covariance matrix of the reps draws from the NeuralMoments function \ No newline at end of file +* Σ: the covariance matrix of the reps draws from the NeuralMoments function + +## the function snmobj +```snmobj(θ, m, reps, model::SNMmodel, nnmodel, nninfo)``` +This function computes the MSM-CUE log likelihood at the parameter value θ. +### The inputs are +* ```θ```: a trial parameter value +* m: the output of NeuralMoments, when the input z is computed using the real (not simulated) data +* reps: an integer number of replications of the simulated statistics. +* model: an SNMmodel structure that describes the statistical model being estimated +* nnmodel: the trained neural net, a Flux.jl chain. It is the first of the outputs of MakeNeuralMoments. +* nninfo: a tuple of vectors, used to transform raw statistics, z, to then pass as inputs to nnmodel. nninfo is the second output of MakeNeuralMoments. +### Outputs +the log likelihood, a Float64 + +## the function mcmc +```mcmc(θ, length, lnL, model::SNMmodel, nnmodel, nninfo, proposal, burnin=100, verbosity=10)``` +### the inputs are +* θ: the parameter vector used to initialize the chain +* length: the desired length of chain, not counting burnin +* lnL: the MSM-CUE log likelihood, as a closure (see the example) +* model: the SNMmodel type that describes the statistical model being estimated +* nnmodel: the trained neural net, a Flux.jl chain. It is the first of the outputs of MakeNeuralMoments. +* nninfo: a tuple of vectors, used to transform raw statistics, z, to then pass as inputs to nnmodel. nninfo is the second output of MakeNeuralMoments. +* proposal: a function that returns a proposed parameter vector +* burnin: the number of burnin draws to be discarded before saving draws to the chain +* verbosity: an integer: report intermediate results every so often +### Outputs +* chain: a reps X k+2 array. The first k columns hold the k parameters for each draw, the penultimate column holds the log-likelihood value, and the last column holds an indicator as to whether the value is a new accepted value (1) or a copy of the previous value (0). This is simple to facilitate computing the acceptance rate diff --git a/examples/.gitignore b/examples/.gitignore new file mode 100644 index 0000000..ba39cc5 --- /dev/null +++ b/examples/.gitignore @@ -0,0 +1 @@ +Manifest.toml diff --git a/examples/MN/Analyze.jl b/examples/MN/Analyze.jl deleted file mode 100644 index 2361f42..0000000 --- a/examples/MN/Analyze.jl +++ /dev/null @@ -1,28 +0,0 @@ -using Statistics -include("MNlib.jl") -function Analyze(chain) - lb,ub = PriorSupport() - θtrue = TrueParameters() - nParams = size(lb,1) - #posmean = vec(mean(chain,dims=1)) - #posmedian = vec(median(chain,dims=1)) - inci01 = zeros(nParams) - inci05 = zeros(nParams) - inci10 = zeros(nParams) - lower = zeros(nParams) - upper = zeros(nParams) - for i = 1:nParams - lower[i] = quantile(chain[:,i],0.005) - upper[i] = quantile(chain[:,i],0.995) - inci01[i] = θtrue[i] >= lower[i] && θtrue[i] <= upper[i] - lower[i] = quantile(chain[:,i],0.025) - upper[i] = quantile(chain[:,i],0.975) - inci05[i] = θtrue[i] >= lower[i] && θtrue[i] <= upper[i] - lower[i] = quantile(chain[:,i],0.05) - upper[i] = quantile(chain[:,i],0.95) - inci10[i] = θtrue[i] >= lower[i] && θtrue[i] <= upper[i] - end - #return vcat(posmean[:], posmedian[:], inci01[:], inci05[:], inci10[:]) - return vcat(inci01[:], inci05[:], inci10[:]) -end - diff --git a/examples/MN/MN_MonteCarlo.jl b/examples/MN/MN_MonteCarlo.jl deleted file mode 100644 index 6eb9cc5..0000000 --- a/examples/MN/MN_MonteCarlo.jl +++ /dev/null @@ -1,45 +0,0 @@ -# This still needs to be updated, it doesn't work with the code in main. -# this replicates the results in the paper- -# it can use threads, so set JULIA_NUM_THREADS -# appropriately for your system. -using SimulatedNeuralMoments, Flux -using BSON:@save -using BSON:@load -include("MNlib.jl") -include("Analyze.jl") - -function main() - lb, ub = PriorSupport() - model = SNMmodel("Mixture of Normals example model", lb, ub, InSupport, Prior, PriorDraw, auxstat) - # train the net, and save it and the transformation info - # nnmodel, nninfo = MakeNeuralMoments(model) - # @save "neuralmodel.bson" nnmodel nninfo # use this line to save the trained neural net - @load "neuralmodel.bson" nnmodel nninfo # use this to load a trained net - R = 1000 - results = zeros(R, 20) - Threads.@threads for r = 1:R - m = NeuralMoments(TrueParameters(), 1, model, nnmodel, nninfo) # the estimate - chain, junk, junk = MCMC(m, 10500, model, nnmodel, nninfo, do_cue=true) - println("r: ", r) - results[r,:] = vcat(median(chain[501:end,:], dims=1)[:], Analyze(chain)) - end - return results -end - -@time results = main() - -theta = TrueParameters() -nParams = size(theta,1) -println("Results") -println("parameter estimates (pos. median)") -est = results[:,1:nParams] -err = est .- TrueParameters()' -b = mean(err, dims=1) -s = std(err, dims=1) -rmse = sqrt.(b.^2 + s.^2) -prettyprint([b' rmse'], ["bias", "rmse"]) -dstats(est; short=true) -println("CI coverage") -clabels = ["99%","95%","90%"] -prettyprint(reshape(mean(results[:,nParams+1:end],dims=1),nParams,3),clabels) - diff --git a/examples/MN/MNexample.jl b/examples/MN/MNexample.jl deleted file mode 100644 index 285cd0d..0000000 --- a/examples/MN/MNexample.jl +++ /dev/null @@ -1,85 +0,0 @@ -using SimulatedNeuralMoments -using Flux, Turing, MCMCChains, AdvancedMH -using StatsPlots, DelimitedFiles, LinearAlgebra -using BSON:@save -using BSON:@load - -# the model-specific code -include("MNlib.jl") - -function MNexample(TrainTestSize=1, Epochs=1000) - - # fill in the structure that defines the model - lb, ub = PriorSupport() # bounds of support - model = SNMmodel("Mixture of normals example", lb, ub, InSupport, PriorDraw, auxstat) - - # train the net, and save it and the transformation info - transf = bijector(@Prior) # transforms draws from prior to draws from ℛⁿ - transformed_prior = transformed(@Prior, transf) # the transformed prior - nnmodel, nninfo = MakeNeuralMoments(model, transf, TrainTestSize=TrainTestSize, Epochs=Epochs) -# @save "neuralmodel.bson" nnmodel nninfo # use this line to save the trained neural net -# @load "neuralmodel.bson" nnmodel nninfo # use this to load a trained net - - # draw a sample at the design parameters, or use an existing data set - y = MNmodel(TrueParameters()) # draw a sample of 500 obsns. at design parameters - #y = readdlm("svdata.txt") # load a data set - n = size(y,1) - p1 = plot(y) - p2 = density(y) - plot(p1, p2, layout=(2,1)) - #savefig("data.png") - - # define the neural moments using the real data - m = Float32.(NeuralMoments(auxstat(y), nnmodel, nninfo)) - # the raw NN parameter estimate - θhat = invlink(@Prior, m) - - # setting for sampling - names = ["μ1", "μ2 ", "σ1", "σ2", "prob"] - S = 100 - covreps = 1000 - length = 1500 - nchains = 4 - burnin = 100 - tuning = 1.0 - # the covariance of the proposal (scaled by tuning) - junk, Σp = mΣ(θhat, covreps, model, nnmodel, nninfo) - - @model function MSM(m, S, model) - θt ~ transformed_prior - if !InSupport(invlink(@Prior, θt)) - Turing.@addlogprob! -Inf - return - end - # sample from the model, at the trial parameter value, and compute statistics - mbar, Σ = mΣ(invlink(@Prior,θt), S, model, nnmodel, nninfo) - m ~ MvNormal(mbar, Symmetric(Σ)) - end - - - chain = sample(MSM(m, S, model), - MH(:θt => AdvancedMH.RandomWalkProposal(MvNormal(zeros(size(m,1)), tuning*Σp))), - MCMCThreads(), length, nchains; init_params=Iterators.repeated(m), discard_initial=burnin) - - # single thread - #= - chain = sample(MSM(m, S, model), - MH(:θt => AdvancedMH.RandomWalkProposal(MvNormal(zeros(size(m,1)), tuning*Σp))), - length*nchains; init_params=m, discard_initial=burnin) - =# - # transform back to original domain - chain = Array(chain) - acceptance = size(unique(chain[:,1]),1)[1] / size(chain,1) - println("acceptance rate: $acceptance") - for i = 1:size(chain,1) - chain[i,:] = invlink(@Prior, chain[i,:]) - end - chain = Chains(chain, names) - - display(chain) - display(plot(chain)) - #savefig("chain.png") - return chain, θhat, Σp -end - - diff --git a/examples/MN/README.md b/examples/MN/README.md deleted file mode 100644 index a78c42f..0000000 --- a/examples/MN/README.md +++ /dev/null @@ -1,106 +0,0 @@ -# MNexample.jl -The main purpose of this example is to show how to use the methods with real data. To run the file, go to its directory, and start Julia using julia --proj, and then instantiate the project to get all needed packages. - -## The first block loads packages: -```julia -using SimulatedNeuralMoments -using Flux, Turing, MCMCChains, AdvancedMH -using StatsPlots, DelimitedFiles, LinearAlgebra -using BSON:@save -using BSON:@load -``` - -## Define the structure for the model -For your own models, you will need to supply the functions found in MNlib.jl, using the same formats -```julia -# fill in the structure that defines the model -lb, ub = PriorSupport() # bounds of support -model = SNMmodel("Mixture of normals example", lb, ub, InSupport, PriorDraw, auxstat) -``` - -## Train the net -or use the pre-trained net which I have kindly provided you. Training the net takes about 10 minute, if you would like to try it. -```julia -# train the net, and save it and the transformation info -transf = bijector(@Prior) # transforms draws from prior to draws from ℛⁿ -transformed_prior = transformed(@Prior, transf) # the transformed prior -#nnmodel, nninfo = MakeNeuralMoments(model, transf) -#@save "neuralmodel.bson" nnmodel nninfo # use this line to save the trained neural net -@load "neuralmodel.bson" nnmodel nninfo # use this to load a trained net -``` - -## Data -Next, we create some new simulated data. -```julia -y = MNmodel(TrueParameters()) # draw a sample at design parameters -n = size(y,1) -p1 = plot(y) -p2 = density(y) -plot(p1, p2, layout=(2,1)) -``` - -## Get moments from the data -Next, we set up sampling. We first get the estimated transformed parameters, and the estimated parameters in untransformed form: -```julia -# define the neural moments using the real data -m = NeuralMoments(auxstat(y), nnmodel, nninfo) -# the raw NN parameter estimate -θhat = invlink(@Prior, m) -``` - -## Set up the controls for MH sampling using Turing: -```julia -# setting for sampling -names = [":μ₁", ":μ₂", ":σ₁" , ":σ₂" , "prob"] -S = 100 -covreps = 1000 -length = 1250 -nchains = 4 -burnin = 0 -tuning = 1.8 -# the covariance of the proposal (scaled by tuning) -junk, Σp = mΣ(θhat, covreps, model, nnmodel, nninfo) -``` - -## Define the likelihood for the Bayesian model -which, in combination with the prior, defines the posterior from which Turing will sample: -```julia -@model function MSM(m, S, model) - θt ~ transformed_prior - if !InSupport(invlink(@Prior, θt)) - Turing.@addlogprob! -Inf - return - end - # sample from the model, at the trial parameter value, and compute statistics - mbar, Σ = mΣ(invlink(@Prior,θt), S, model, nnmodel, nninfo) - m ~ MvNormal(mbar, Symmetric(Σ)) -end -``` - -## Sample from the posterior -using Metropolis-Hasting, and a random walk multivariate normal proposal. This proposal is effective, because it is an estimate of the asymptotic distribution of the estimated neural moments, m, from above: -```julia -chain = sample(MSM(m, S, model), - MH(:θt => AdvancedMH.RandomWalkProposal(MvNormal(zeros(size(m,1)), tuning*Σp))), - MCMCThreads(), length, nchains; init_params=m, discard_initial=burnin) -``` - -## Transform the parameters of the chain back to the original parameter space: -```julia -chain = Array(chain) -acceptance = size(unique(chain[:,1]),1)[1] / size(chain,1) -println("acceptance rate: $acceptance") -for i = 1:size(chain,1) - chain[i,:] = invlink(@Prior, chain[i,:]) -end -chain = Chains(chain, names) -chain -``` - -## Results -Finally, we will see something like -![MNsummary](https://github.com/mcreel/SimulatedNeuralMoments.jl/blob/main/examples/MN/summary.png) -![MNchain](https://github.com/mcreel/SimulatedNeuralMoments.jl/blob/main/examples/MN/chain.png) - - - diff --git a/examples/MN/chain.png b/examples/MN/chain.png deleted file mode 100644 index 6dbc76d..0000000 Binary files a/examples/MN/chain.png and /dev/null differ diff --git a/examples/MN/montecarlo.png b/examples/MN/montecarlo.png deleted file mode 100644 index d6dc724..0000000 Binary files a/examples/MN/montecarlo.png and /dev/null differ diff --git a/examples/MN/neuralmodel.bson b/examples/MN/neuralmodel.bson deleted file mode 100644 index 512fdde..0000000 Binary files a/examples/MN/neuralmodel.bson and /dev/null differ diff --git a/examples/MN/results.png b/examples/MN/results.png deleted file mode 100644 index fe6800c..0000000 Binary files a/examples/MN/results.png and /dev/null differ diff --git a/examples/MN/summary.png b/examples/MN/summary.png deleted file mode 100644 index 77896d2..0000000 Binary files a/examples/MN/summary.png and /dev/null differ diff --git a/examples/MNchain.png b/examples/MNchain.png new file mode 100644 index 0000000..89b8c58 Binary files /dev/null and b/examples/MNchain.png differ diff --git a/examples/MNexample.jl b/examples/MNexample.jl new file mode 100644 index 0000000..4a5f4c9 --- /dev/null +++ b/examples/MNexample.jl @@ -0,0 +1,10 @@ +using SimulatedNeuralMoments +using Flux, MCMCChains +using StatsPlots, Distributions +using DelimitedFiles, LinearAlgebra +using BSON:@save +using BSON:@load + +include("MNlib.jl") +include("runme.jl") + diff --git a/examples/MN/MNlib.jl b/examples/MNlib.jl similarity index 68% rename from examples/MN/MNlib.jl rename to examples/MNlib.jl index fbecf72..29ada5d 100644 --- a/examples/MN/MNlib.jl +++ b/examples/MNlib.jl @@ -1,6 +1,8 @@ using Statistics, StatsBase -function MNmodel(θ, rndseed=1234) +whichdgp = "Mixture of normals model" + +function dgp(θ, rndseed=1234) n = 1000 μ1, μ2, σ1, σ2, prob = θ d1=randn(n).*σ1 .+ μ1 @@ -13,13 +15,13 @@ function MNmodel(θ, rndseed=1234) end function auxstat(data) - r = 0.0 : 0.1 : 1.0 - sqrt(1000.).*vcat(mean(data), std(data), skewness(data), kurtosis(data), + r = 0.1 : 0.1 : 0.9 + vcat(mean(data), std(data), skewness(data), kurtosis(data), quantile.(Ref(data),r)) end function auxstat(θ, reps) - auxstat.([MNmodel(θ, rand(1:Int64(1e12))) for i = 1:reps]) + auxstat.([dgp(θ, rand(1:Int64(1e12))) for i = 1:reps]) end function TrueParameters() @@ -42,10 +44,9 @@ function InSupport(θ) all(θ .>= lb) & all(θ .<= ub) end -# prior should be an array of distributions, one for each parameter -lb, ub = PriorSupport() # need these in Prior -macro Prior() - return :( arraydist([Uniform(lb[i], ub[i]) for i = 1:size(lb,1)]) ) +# prior is flat over support, so return 1 if inside +function Prior(θ) + InSupport(θ) ? 1.0 : 0.0 end diff --git a/examples/MNresults.png b/examples/MNresults.png new file mode 100644 index 0000000..0490224 Binary files /dev/null and b/examples/MNresults.png differ diff --git a/examples/Project.toml b/examples/Project.toml new file mode 100644 index 0000000..0dd564a --- /dev/null +++ b/examples/Project.toml @@ -0,0 +1,9 @@ +[deps] +BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0" +DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab" +Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" +Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d" +SimulatedNeuralMoments = "43b7c8c0-5622-45b8-90a3-338fc50d2232" +StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 0000000..21f0d71 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,20 @@ +# SNM examples +The main purpose of these examples is to show how to use the methods with real data. The examples will run one of two models, a stochastic volatility model with 3 parameters, or a mixture of normals model with five parameters. To run the files, I suggest: +1. download the whole SimulatedNeuralMoments package, and go the the example directory. +2. start Julia using julia --proj -t8 (or the appropriate number of threads for your hardware), and then instantiate the project to get all needed packages. + ```using Pkg; Pkg.instantiate()``` +3. ```include("SVexample.jl"); runme()``` +4. ```include("MNexample.jl"); runme()``` + +You will end up with something like the following: + +## SV: stochastic volatility model +![SVchain](https://github.com/mcreel/SimulatedNeuralMoments.jl/blob/main/example/SVchain.png) +![SVresults](https://github.com/mcreel/SimulatedNeuralMoments.jl/blob/main/example/SVresults.png) + +## MN: mixture of normals model +![MNchain](https://github.com/mcreel/SimulatedNeuralMoments.jl/blob/main/example/MNchain.png) +![MNresults](https://github.com/mcreel/SimulatedNeuralMoments.jl/blob/main/example/MNresults.png) + + + diff --git a/examples/SV/README.md b/examples/SV/README.md deleted file mode 100644 index c5b52da..0000000 --- a/examples/SV/README.md +++ /dev/null @@ -1,108 +0,0 @@ -# SVexample.jl -The main purpose of this example is to show how to use the methods with real data. To run the file, go to its directory, and start Julia using julia --proj, and then instantiate the project to get all needed packages. - -## The first block loads packages: -```julia -using SimulatedNeuralMoments -using Flux, Turing, MCMCChains, AdvancedMH -using StatsPlots, DelimitedFiles, LinearAlgebra -using BSON:@save -using BSON:@load -``` - -## Define the structure for the model -For your own models, you will need to supply the functions found in SVlib.jl, using the same formats -```julia -# fill in the structure that defines the model -lb, ub = PriorSupport() # bounds of support -model = SNMmodel("Stochastic Volatility example", lb, ub, InSupport, PriorDraw, auxstat) -``` - -## Train the net -or use the pre-trained net which I have kindly provided you. Training the net takes about 10 minute, if you would like to try it. -```julia -# train the net, and save it and the transformation info -transf = bijector(@Prior) # transforms draws from prior to draws from ℛⁿ -transformed_prior = transformed(@Prior, transf) # the transformed prior -#nnmodel, nninfo = MakeNeuralMoments(model, transf) -#@save "neuralmodel.bson" nnmodel nninfo # use this line to save the trained neural net -@load "neuralmodel.bson" nnmodel nninfo # use this to load a trained net -``` - -## Data -Next, we load some data, either from a file, or by creating new simulated data. -```julia -# draw a sample at the design parameters, or use an existing data set -y = SVmodel(TrueParameters()) # draw a sample of 500 obsns. at design parameters -#y = readdlm("svdata.txt") # load a data set -n = size(y,1) -p1 = plot(y) -p2 = density(y) -plot(p1, p2, layout=(2,1)) -``` - -## Get moments from the data -Next, we set up sampling. We first get the estimated transformed parameters, and the estimated parameters in untransformed form: -```julia -# define the neural moments using the real data -m = NeuralMoments(auxstat(y), nnmodel, nninfo) -# the raw NN parameter estimate -θhat = invlink(@Prior, m) -``` - -## Set up the controls for MH sampling using Turing: -```julia -# setting for sampling -names = [":α", ":ρ", ":σ"] -S = 100 -covreps = 1000 -length = 1250 -nchains = 4 -burnin = 0 -tuning = 1.8 -# the covariance of the proposal (scaled by tuning) -junk, Σp = mΣ(θhat, covreps, model, nnmodel, nninfo) -``` - -## Define the likelihood for the Bayesian model -which, in combination with the prior, defines the posterior from which Turing will sample: -```julia -@model function MSM(m, S, model) - θt ~ transformed_prior - if !InSupport(invlink(@Prior, θt)) - Turing.@addlogprob! -Inf - return - end - # sample from the model, at the trial parameter value, and compute statistics - mbar, Σ = mΣ(invlink(@Prior,θt), S, model, nnmodel, nninfo) - m ~ MvNormal(mbar, Symmetric(Σ)) -end -``` - -## Sample from the posterior -using Metropolis-Hasting, and a random walk multivariate normal proposal. This proposal is effective, because it is an estimate of the asymptotic distribution of the estimated neural moments, m, from above: -```julia -chain = sample(MSM(m, S, model), - MH(:θt => AdvancedMH.RandomWalkProposal(MvNormal(zeros(size(m,1)), tuning*Σp))), - MCMCThreads(), length, nchains; init_params=m, discard_initial=burnin) -``` - -## Transform the parameters of the chain back to the original parameter space: -```julia -chain = Array(chain) -acceptance = size(unique(chain[:,1]),1)[1] / size(chain,1) -println("acceptance rate: $acceptance") -for i = 1:size(chain,1) - chain[i,:] = invlink(@Prior, chain[i,:]) -end -chain = Chains(chain, names) -chain -``` - -## Results -Finally, we will see something like -![SVsummary](https://github.com/mcreel/SimulatedNeuralMoments.jl/blob/main/examples/SV/summary.png) -![SVchain](https://github.com/mcreel/SimulatedNeuralMoments.jl/blob/main/examples/SV/chain.png) - - - diff --git a/examples/SV/SVexample.jl b/examples/SV/SVexample.jl deleted file mode 100644 index 9528c35..0000000 --- a/examples/SV/SVexample.jl +++ /dev/null @@ -1,83 +0,0 @@ -using SimulatedNeuralMoments -using Flux, Turing, MCMCChains, AdvancedMH -using StatsPlots, DelimitedFiles, LinearAlgebra -using BSON:@save -using BSON:@load - -# the model-specific code -include("SVlib.jl") - -function SVexample(TrainTestSize=1, Epochs=1000) - -# fill in the structure that defines the model -lb, ub = PriorSupport() # bounds of support -model = SNMmodel("Stochastic Volatility example", lb, ub, InSupport, PriorDraw, auxstat) - -# train the net, and save it and the transformation info -transf = bijector(@Prior) # transforms draws from prior to draws from ℛⁿ -transformed_prior = transformed(@Prior, transf) # the transformed prior -nnmodel, nninfo = MakeNeuralMoments(model, transf, TrainTestSize=TrainTestSize, Epochs=Epochs) -#@save "neuralmodel.bson" nnmodel nninfo # use this line to save the trained neural net -#@load "neuralmodel.bson" nnmodel nninfo # use this to load a trained net - -# draw a sample at the design parameters, or use an existing data set -y = SVmodel(TrueParameters()) # draw a sample of 500 obsns. at design parameters -#y = readdlm("svdata.txt") # load a data set -n = size(y,1) -p1 = plot(y) -p2 = density(y) -plot(p1, p2, layout=(2,1)) -#savefig("data.png") - -# define the neural moments using the real data -m = Float32.(NeuralMoments(auxstat(y), nnmodel, nninfo)) -# the raw NN parameter estimate -θhat = invlink(@Prior, m) - -# setting for sampling -names = ["α", "ρ", "σ"] -S = 100 -covreps = 1000 -length = 1250 -nchains = 4 -burnin = 0 -tuning = 1.8 -# the covariance of the proposal (scaled by tuning) -junk, Σp = mΣ(θhat, covreps, model, nnmodel, nninfo) - -@model function MSM(m, S, model) - θt ~ transformed_prior - if !InSupport(invlink(@Prior, θt)) - Turing.@addlogprob! -Inf - return - end - # sample from the model, at the trial parameter value, and compute statistics - mbar, Σ = mΣ(invlink(@Prior,θt), S, model, nnmodel, nninfo) - m ~ MvNormal(mbar, Symmetric(Σ)) -end - -chain = sample(MSM(m, S, model), - MH(:θt => AdvancedMH.RandomWalkProposal(MvNormal(zeros(size(m,1)), tuning*Σp))), - MCMCThreads(), length, nchains; init_params=Iterators.repeated(m), discard_initial=burnin) - -# single thread -#= -chain = sample(MSM(m, S, model), - MH(:θt => AdvancedMH.RandomWalkProposal(MvNormal(zeros(3), tuning*Σp))), - length*nthreads; init_params = m, discard_initial=burnin) -=# - -# transform back to original domain -chain = Array(chain) -acceptance = size(unique(chain[:,1]),1)[1] / size(chain,1) -println("acceptance rate: $acceptance") -for i = 1:size(chain,1) - chain[i,:] = invlink(@Prior, chain[i,:]) -end -chain = Chains(chain, names) -display(chain) -display(plot(chain)) -#savefig("chain.png") -return chain, θhat, Σp -end - diff --git a/examples/SV/chain.png b/examples/SV/chain.png deleted file mode 100644 index 83013a1..0000000 Binary files a/examples/SV/chain.png and /dev/null differ diff --git a/examples/SV/chain.txt 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--1.1884670767029313 --0.1305846271640438 --0.053431074460556276 --1.4831378205959083 -1.5530160547204466 --2.3302696143362382 --0.3466999422865115 -0.9755052601931365 -0.8993482984008018 --1.6912648954533163 -0.20669712921061406 -1.1265105915738023 --0.002569732242534074 --0.28161427765689706 -0.34151345846301967 --0.2127809573094189 --0.1292499482603771 -0.12720119111876843 -0.18853748814188384 --0.38288960202231487 -0.9762980675921956 -1.2484106699072302 -0.30214763612432727 --0.45640075402230246 --0.39336319782828383 -0.9972198191641678 --0.17153372726959634 --0.024646109824911114 -0.9304476129399146 --0.07615007653659368 --0.24621823027354672 --0.6332454223476379 -0.3684167385178226 --0.25188489421152804 -0.5034424396704694 --0.08658515579447916 --0.4425675105858223 --0.22517149528198557 -0.3120275423173054 --0.8028548862887108 -0.27014020312683623 -0.7977584985790923 -0.08540228837186492 -1.166603297410481 -0.5728476576553553 --0.010917329742015093 --0.6006848008797475 -0.6585996386903038 --1.0020794711141814 -0.8668292326450195 --0.9508876989024353 --1.2319725193073516 -0.3329518332788303 -0.14366784834982774 --0.5322762810671702 -0.6937919104592832 -0.05773289850100679 --0.5713092329705038 --0.765197543779831 -1.225558267064647 --1.0112904194413435 -0.22063132565055033 -1.4542143750299703 -0.7137998311572992 -1.543329732448968 -2.1591563434232697 -0.15105468834659486 --0.5705183029619145 --1.9191666902369797 --0.9631442847721401 -0.8767121996207651 -0.24653764670686532 --0.6044262978652795 --0.35952482669326874 --0.9212167215059077 -0.017888964110681475 -0.049473366954307284 --0.23143725254787906 --1.1262585183403468 --0.14022169476396545 -0.7766617178384445 -0.11595597549343907 --0.1255590408792471 -0.10347828144480958 -0.588090716368826 --0.4505819799921988 -0.6999332975947982 --0.4367826431054259 --0.5771977931307161 -0.20983272050174376 --1.1295598852521023 -0.43129480311686197 --0.3223102134486396 --0.28609173514364844 -1.2370425737779838 --0.1612521926253582 -0.8473477853923922 -0.890925396846187 --0.582100717529345 --0.08397742479649274 --0.599555749660249 -0.15987034107512343 -1.2919055314501064 -0.055905548292039 -0.0952812443811459 --0.5154663028361901 -0.06124409117387729 --0.1327920592585841 --0.02600490059858166 -0.5003229492414312 -0.6553065871176736 diff --git a/examples/SVchain.png b/examples/SVchain.png new file mode 100644 index 0000000..44f9cc5 Binary files /dev/null and b/examples/SVchain.png differ diff --git a/examples/SVexample.jl b/examples/SVexample.jl new file mode 100644 index 0000000..2aa2443 --- /dev/null +++ b/examples/SVexample.jl @@ -0,0 +1,10 @@ +using SimulatedNeuralMoments +using Flux, MCMCChains +using StatsPlots, Distributions +using DelimitedFiles, LinearAlgebra +using BSON:@save +using BSON:@load + +include("SVlib.jl") +include("runme.jl") + diff --git a/examples/SV/SVlib.jl b/examples/SVlib.jl similarity index 90% rename from examples/SV/SVlib.jl rename to examples/SVlib.jl index 299a32a..656ad42 100644 --- a/examples/SV/SVlib.jl +++ b/examples/SVlib.jl @@ -2,9 +2,11 @@ using Statistics, Random +whichdgp = "Stochastic volatility model" + # method that generates the sample function auxstat(θ, reps) - auxstat.([SVmodel(θ, rand(1:Int64(1e12))) for i = 1:reps]) # reps draws of data + auxstat.([dgp(θ, rand(1:Int64(1e12))) for i = 1:reps]) # reps draws of data end # method for a given sample @@ -26,7 +28,7 @@ end stats = sqrt(size(y,1)) .* vcat(m, s, s2, k, c, c1, HAR(y)) end -function SVmodel(θ, rndseed=1234) +function dgp(θ, rndseed=1234) Random.seed!(rndseed) n = 500 burnin = 100 @@ -55,11 +57,11 @@ function PriorSupport() lb,ub end -# prior should be an array of distributions, one for each parameter -lb, ub = PriorSupport() # need these in Prior -macro Prior() - return :( arraydist([Uniform(lb[i], ub[i]) for i = 1:size(lb,1)]) ) +# prior is uniform, so just return a 1 if in support +function Prior(θ) + InSupport(θ) ? 1.0 : 0.0 end + # check if parameter is in support. In this case, we require # the bounds, and that the unconditional variance of the volatility # shock be limited diff --git a/examples/SVresults.png b/examples/SVresults.png new file mode 100644 index 0000000..0d35fc3 Binary files /dev/null and b/examples/SVresults.png differ diff --git a/examples/runme.jl b/examples/runme.jl new file mode 100644 index 0000000..670ffd2 --- /dev/null +++ b/examples/runme.jl @@ -0,0 +1,67 @@ +function runme(TrainTestSize=1, Epochs=200, saveplot=false) + +# generate some data, and get sample size +y = dgp(TrueParameters()) # draw a sample at design parameters +n = size(y,1) + +# fill in the structure that defines the model +lb, ub = PriorSupport() # bounds of support +model = SNMmodel(whichdgp, n, lb, ub, InSupport, Prior, PriorDraw, auxstat) + +# train the net, and save it and the transformation info +nnmodel, nninfo = MakeNeuralMoments(model, TrainTestSize=TrainTestSize, Epochs=Epochs) +# @save "neuralmodel.bson" nnmodel nninfo # use this line to save the trained neural net +# @load "neuralmodel.bson" nnmodel nninfo # use this to load a trained net + +# define the neural moments using the data +θnn = NeuralMoments(auxstat(y), model, nnmodel, nninfo)[:] + +# settings +whichdgp == "Stochastic volatility model" ? names = ["α", "ρ", "σ", "lnℒ "] : names = ["μ1", "μ2 ", "σ1", "σ2", "prob", "lnℒ "] +S = 100 +covreps = 500 +length = 5000 +burnin = 1000 +verbosity = 10 # show results every X draws +tuning = 1.0 + +# define the proposal +junk, Σp = mΣ(θnn, covreps, model, nnmodel, nninfo) +proposal(θ) = rand(MvNormal(θ, tuning*Σp)) + +# define the logL +lnL = θ -> snmobj(θ, θnn, S, model, nnmodel, nninfo) + +# run a short chain to improve proposal +# tuning the chain and creating a good proposal may +# need care - this is just an example! +chain = mcmc(θnn, 1000, lnL, model, nnmodel, nninfo, proposal, burnin, verbosity) +Σp = cov(chain[:,1:end-2]) +acceptance = mean(chain[:,end]) +acceptance < 0.2 ? tuning =0.75 : nothing +acceptance > 0.3 ? tuning =1.5 : nothing +proposal2(θ) = rand(MvNormal(θ, tuning*Σp)) + +# final chain using second round proposal +chain = mcmc(θnn, length, lnL, model, nnmodel, nninfo, proposal2, burnin, verbosity) + +# get the summary info +acceptance = mean(chain[:,end]) +println("acceptance rate: $acceptance") +# compute RMSE +cc = chain[:,1:end-2] +tp = TrueParameters() +chain = Chains(chain[:,1:end-1], names) # convert to Chains type, drop acc. rate +display(chain) +t = TrueParameters()' +println() +printstyled("For comparison, the true parameters are $t", color=:green) +println() +display(plot(chain)) +if saveplot + whichdgp == "Stochastic volatility model" ? savefig("SVchain.png") : savefig("MNchain.png") +end +rmse = mean(sqrt.((cc .- tp').^2)) +return acceptance, rmse +end + diff --git a/src/Analyze.jl b/src/Analyze.jl deleted file mode 100644 index 9f9d526..0000000 --- a/src/Analyze.jl +++ /dev/null @@ -1,27 +0,0 @@ -using Statistics, Econometrics -function Analyze(chain) - lb,ub = PriorSupport() - θtrue = TrueParameters() - nParams = size(lb,1) - #posmean = vec(mean(chain,dims=1)) - #posmedian = vec(median(chain,dims=1)) - inci01 = zeros(nParams) - inci05 = zeros(nParams) - inci10 = zeros(nParams) - lower = zeros(nParams) - upper = zeros(nParams) - for i = 1:nParams - lower[i] = quantile(chain[:,i],0.005) - upper[i] = quantile(chain[:,i],0.995) - inci01[i] = θtrue[i] >= lower[i] && θtrue[i] <= upper[i] - lower[i] = quantile(chain[:,i],0.025) - upper[i] = quantile(chain[:,i],0.975) - inci05[i] = θtrue[i] >= lower[i] && θtrue[i] <= upper[i] - lower[i] = quantile(chain[:,i],0.05) - upper[i] = quantile(chain[:,i],0.95) - inci10[i] = θtrue[i] >= lower[i] && θtrue[i] <= upper[i] - end - #return vcat(posmean[:], posmedian[:], inci01[:], inci05[:], inci10[:]) - return vcat(inci01[:], inci05[:], inci10[:]) -end - diff --git a/src/Importance.jl b/src/Importance.jl deleted file mode 100644 index 3b9ec12..0000000 --- a/src/Importance.jl +++ /dev/null @@ -1,11 +0,0 @@ -using BSON: @load -using Flux -using StatsPlots -# get the first layer parameters for influence analysis -@load "neuralmodel.bson" nnmodel -beta = nnmodel.layers[1].weight # get first layer betas -z = maximum(abs.(beta),dims=1); -heatmap(z, xlabel="statistic", title="Importance of inputs, bright=high, dark=low") -savefig("ImportanceOfStatistics.png") - - diff --git a/src/MCMC.jl b/src/MCMC.jl new file mode 100644 index 0000000..116141d --- /dev/null +++ b/src/MCMC.jl @@ -0,0 +1,46 @@ +# simple MH MCMC for symmetric proposal +@views function mcmc( + θ, # the initial value + length, + lnL, + model::SNMmodel, + nnmodel, + nninfo, + proposal, + burnin::Int=100, + verbosity::Int=10 + ) + Lₙθ = lnL(θ) # Objective at current params + pθ = model.prior(θ) + naccept = 0 # Number of acceptance / rejections + accept = false + acceptance_rate = 1f0 + chain = zeros(length, size(θ, 1) + 2) + totreps = length+burnin + @info "starting the MCMC run" + for i ∈ 1:totreps + θᵗ = proposal(θ) # new trial value + pθᵗ = model.prior(θᵗ) # prior at trial + Lₙθᵗ = lnL(θᵗ) # objective trial value + # Accept / reject trial value + accept = rand() < exp(Lₙθᵗ - Lₙθ) * pθᵗ/pθ + if accept + # Replace values + θ = θᵗ + Lₙθ = Lₙθᵗ + pθ = pθ + # Increment number of accepted values + naccept += 1 + end + # Add to chain if burnin is passed + if i > burnin + chain[i-burnin,:] = vcat(θ, Lₙθ, accept) + end + # Report + if verbosity > 0 && mod(i, verbosity) == 0 && i > burnin + acceptance_rate = naccept / i + println("iter $i of $totreps ", round.(θ, digits=3), " acc. rate: ", round(acceptance_rate, digits=3)) + end + end + return chain +end diff --git a/src/MakeNeuralMoments.jl b/src/MakeNeuralMoments.jl new file mode 100644 index 0000000..68e1197 --- /dev/null +++ b/src/MakeNeuralMoments.jl @@ -0,0 +1,85 @@ +# simulates data from prior, trains and tests the net, and returns +# the trained net and the information for transforming the inputs + +using Statistics, Flux +using Base.Iterators + +function MakeNeuralMoments(model::SNMmodel;TrainTestSize=1, Epochs=1000) + data = 0.0 + datadesign = 0.0 + nParams = size(model.lb,1) + # training and testing + if (TrainTestSize == 1) TrainTestSize = Int64(2*nParams*1e4); end # use a default size if none provided + params = zeros(TrainTestSize,nParams) + statistics = zeros(TrainTestSize,size(model.auxstat(model.lb,1)[1],1)) + Threads.@threads for s = 1:TrainTestSize + ok = false + θ = model.priordraw() + W = (model.auxstat(θ,1))[1] + # repeat draw if necessary + while any(isnan.(W)) + θ = model.priordraw() + W = model.auxstat(θ,1)[1] + end + params[s,:] = θ + statistics[s,:] = W + end + # transform stats to robustify against outliers + q50 = zeros(size(statistics,2)) + q01 = similar(q50) + q99 = similar(q50) + iqr = similar(q50) + for i = 1:size(statistics,2) + q = quantile(statistics[:,i],[0.01, 0.25, 0.5, 0.75, 0.99]) + q01[i] = q[1] + q50[i] = q[3] + q99[i] = q[5] + iqr[i] = q[4] - q[2] + end + nninfo = (q01, q50, q99, iqr) + statistics = TransformStats(statistics, nninfo) + # train net + TrainingProportion = 0.5 # size of training/testing + params = Float32.(params) + s = Float32.(std(params, dims=1)') + statistics = Float32.(statistics) + trainsize = Int(TrainingProportion*TrainTestSize) + yin = params[1:trainsize, :]' + yout = params[trainsize+1:end, :]' + xin = statistics[1:trainsize, :]' + xout = statistics[trainsize+1:end, :]' + # define the neural net + nStats = size(xin,1) + NNmodel = Chain( + Dense(nStats, 10*nParams, tanh), + Dense(10*nParams, 3*nParams, tanh), + Dense(3*nParams, nParams) + ) + loss(x,y) = Flux.huber_loss(NNmodel(x)./s, y./s; delta=0.1) # Define the loss function + # monitor training + function monitor(e) + println("epoch $(lpad(e, 4)): training loss = $(round(loss(xin,yin); digits=4)) testing loss = $(round(loss(xout,yout); digits=4)) ") + end + # do the training + bestsofar = 1.0e10 + pred = 0.0 # define it here to have it outside the for loop + batches = [(xin[:,ind],yin[:,ind]) for ind in partition(1:size(yin,2), 50)] + bestmodel = 0.0 + @info "starting training of the net" + for i = 1:Epochs + if i < 20 + opt = Momentum() # the optimizer + else + opt = ADAMW() # the optimizer + end + Flux.train!(loss, Flux.params(NNmodel), batches, opt) + current = loss(xout,yout) + # keep track of best model + if current < bestsofar + bestsofar = current + bestmodel = NNmodel + end + mod(i, 10) == 0 ? monitor(i) : nothing + end + bestmodel, nninfo +end diff --git a/src/SimulatedNeuralMoments.jl b/src/SimulatedNeuralMoments.jl index 6b4a78b..8ec14ab 100644 --- a/src/SimulatedNeuralMoments.jl +++ b/src/SimulatedNeuralMoments.jl @@ -3,125 +3,52 @@ module SimulatedNeuralMoments # the type that holds the model specifics struct SNMmodel modelname::String # name of model + samplesize::Int64 # sample size of model lb # vector of lower bounds. Can be -Inf, if desired ub # vector of upper bounds. Can be inf, if desired insupport::Function # function that checks if a draw is valid + prior::Function # the prior, for MCMC priordraw::Function # function that returns a draw from prior auxstat::Function # function that returns an array of draws of statistic end +# transformation to compactify the statistics, before training the net function TransformStats(data, info) q01,q50,q99,iqr = info - data .= max.(data, q01) - data .= min.(data, q99) - data .= (data .- q50) ./ iqr + data = max.(data, q01') + data = min.(data, q99') + data = (data .- q50') ./ iqr' + return data end # neural moments given statistic -function NeuralMoments(z, nnmodel, nninfo) - nnmodel(TransformStats(z, nninfo)) +function NeuralMoments(z, model::SNMmodel, nnmodel, nninfo) + min.(max.(Float64.(nnmodel(Float32.(TransformStats((z[:])', nninfo))')), model.lb), model.ub) end -# mean and covariance of output of NN, given draw from prior (not transformed) -# remember that the NN outputs estimates of transformed parameters +# mean and covariance of output of NN computed using reps draws at θ using LinearAlgebra function mΣ(θ, reps, model::SNMmodel, nnmodel, nninfo) z = model.auxstat(θ, reps) - Zs = [NeuralMoments(z[i], nnmodel, nninfo) for i = 1:reps] + Zs = [NeuralMoments(z[i], model, nnmodel, nninfo) for i = 1:reps] mean(Zs), Symmetric(cov(Zs)) end -# simulates data from prior, trains and tests the net, and returns -# the trained net and the information for transforming the inputs -using Statistics, Flux -using Base.Iterators -@views function MakeNeuralMoments(model::SNMmodel, transf; TrainTestSize=1, Epochs=1000) - data = 0.0 - datadesign = 0.0 - nParams = size(model.lb,1) - # training and testing - if (TrainTestSize == 1) TrainTestSize = Int64(2*nParams*1e4); end # use a default size if none provided - params = zeros(nParams, TrainTestSize) - nStats = size(model.auxstat(model.lb,1)[1],1) - statistics = zeros(nStats, TrainTestSize) - Threads.@threads for s = 1:TrainTestSize - ok = false - θ = model.priordraw() - W = model.auxstat(θ,1)[1] - # repeat draw if necessary - while any(isnan.(W)) - θ = model.priordraw() - W = model.auxstat(θ,1)[1] - end - params[:,s] = transf(θ) - statistics[:,s] = W - end - # transform stats to robustify against outliers - q50 = zeros(nStats) - q01 = similar(q50) - q99 = similar(q50) - iqr = similar(q50) - for i = 1:nStats - q = quantile(statistics[i,:],[0.01, 0.25, 0.5, 0.75, 0.99]) - q01[i] = q[1] - q50[i] = q[3] - q99[i] = q[5] - iqr[i] = q[4] - q[2] - end - nninfo = (q01, q50, q99, iqr) - for i = 1: nStats - statistics[:,i] .= TransformStats(statistics[:,i], nninfo) - end - # train net - TrainingProportion = 0.5 # size of training/testing - params = Float32.(params) - s = Float32.(std(params, dims=2)) - statistics = Float32.(statistics) - trainsize = Int(TrainingProportion*TrainTestSize) - yin = params[:,1:trainsize] - yout = params[:,trainsize+1:end] - xin = statistics[:,1:trainsize] - xout = statistics[:,trainsize+1:end] - # define the neural net - NNmodel = Chain( - Dense(nStats, 10*nParams, tanh), - Dense(10*nParams, 3*nParams, tanh), - Dense(3*nParams, nParams) - ) - loss(x,y) = Flux.huber_loss(NNmodel(x)./s, y./s; delta=0.1) # Define the loss function - # monitor training - function monitor(e) - println("epoch $(lpad(e, 4)): (training) loss = $(round(loss(xin,yin); digits=4)) (testing) loss = $(round(loss(xout,yout); digits=4))| ") - end - # do the training - bestsofar = 1.0e10 - pred = 0.0 # define it here to have it outside the for loop - batches = [(xin[:,ind],yin[:,ind]) for ind in partition(1:size(yin,2), 50)] - bestmodel = 0.0 - for i = 1:Epochs - if i < 20 - opt = Momentum() # the optimizer - else - opt = Adam() # the optimizer - end - Flux.train!(loss, Flux.params(NNmodel), batches, opt) - current = loss(xout,yout) - if current < bestsofar - bestsofar = current - bestmodel = NNmodel - xx = xout - yy = yout - monitor(i) - pred = NNmodel(xx) - error = yy .- pred - results = [pred;error] - rmse = sqrt.(mean(error.^Float32(2.0),dims=2)) - @info "RMSE for model parameters $rmse" - end - end - bestmodel, nninfo +# MSM-CUE quasi-log-likelihood, following Chernozhukov and Hong, 2003 +using LinearAlgebra +function snmobj(θ, m, reps, model::SNMmodel, nnmodel, nninfo) + model.insupport(θ) || return -Inf + mbar, Σ = mΣ(θ, reps, model, nnmodel, nninfo) + n = model.samplesize + Σ *= n*(1+1/reps) # scale for better numerical accuracy + isposdef(Σ) || return -Inf + x = sqrt(n)*(m - mbar) + LinearAlgebra.inv!(cholesky!(Σ)) + -0.5*dot(x,Σ,x) end -export SNMmodel, MakeNeuralMoments, NeuralMoments, mΣ +include("MakeNeuralMoments.jl") +include("MCMC.jl") +export SNMmodel, MakeNeuralMoments, NeuralMoments, mΣ, mcmc, snmobj end diff --git a/test/neuralmodel.bson b/test/neuralmodel.bson new file mode 100644 index 0000000..2cf0133 Binary files /dev/null and b/test/neuralmodel.bson differ diff --git a/test/runtests.jl b/test/runtests.jl index 6080888..53daa88 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -1,18 +1,21 @@ #using SimulatedNeuralMoments, -using Flux, Test, LinearAlgebra, Statistics -using BSON:@load +using Test cd(@__DIR__) @testset "SimulatedNeuralMoments" begin - @info "running SV model with a small sample" - include("../examples/SV/SVexample.jl") - chain, θhat, Σp = SVexample(1000, 100) # fast run - @test size(θhat,1) == 3 - @test size(Σp) == (3,3) + + @info "running MN example with a small sample" + include("../examples/MNexample.jl") + acceptance, rmse = runme(10000,100,false) # fast run + @test rmse < 0.2 + @test acceptance > 0.1 + @test acceptance < 0.5 + + @info "running MN example with a small sample" + include("../examples/SVexample.jl") + acceptance, rmse = runme(10000,100,false) # fast run + @test rmse < 0.2 + @test acceptance > 0.1 + @test acceptance < 0.5 - @info "running the MN model with a small sample" - include("../examples/MN/MNexample.jl") - chain, θhat, Σp = MNexample(1000, 100) # fast run - @test size(θhat,1) == 5 - @test size(Σp) == (5,5) end