From 677b7b250396232e44d40755e9b2d00da331e38e Mon Sep 17 00:00:00 2001 From: David Widmann Date: Sat, 27 May 2023 00:31:02 +0200 Subject: [PATCH] Support MCMCDiagnosticTools 0.3 and MCMCChains 6 (#79) * Support MCMCDiagnosticTools 0.3 and MCMCChains 6 * Apply suggestions from code review Co-authored-by: Seth Axen * Remove Turing test dependency * Update Project.toml --------- Co-authored-by: Seth Axen --- Project.toml | 9 +- src/ESS.jl | 34 +- test/data/samples_m5_1t.csv | 12001 ++++++++++++++++++++++++++++++++ test/data/samples_m5_2t.csv | 12001 ++++++++++++++++++++++++++++++++ test/data/samples_m5_3t.csv | 12001 ++++++++++++++++++++++++++++++++ test/data/samples_model.csv | 12001 ++++++++++++++++++++++++++++++++ test/tests/ComparisonTests.jl | 32 +- test/tests/TuringTests.jl | 9 +- 8 files changed, 48049 insertions(+), 39 deletions(-) create mode 100644 test/data/samples_m5_1t.csv create mode 100644 test/data/samples_m5_2t.csv create mode 100644 test/data/samples_m5_3t.csv create mode 100644 test/data/samples_model.csv diff --git a/Project.toml b/Project.toml index 8fd6901..b742758 100644 --- a/Project.toml +++ b/Project.toml @@ -1,7 +1,7 @@ name = "ParetoSmooth" uuid = "a68b5a21-f429-434e-8bfa-46b447300aac" authors = ["Carlos Parada "] -version = "0.7.5" +version = "0.7.6" [deps] AxisKeys = "94b1ba4f-4ee9-5380-92f1-94cde586c3c5" @@ -20,8 +20,8 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" AxisKeys = "0.1.18, 0.2" DynamicPPL = "0.21, 0.22" LogExpFunctions = "0.3" -MCMCChains = "5" -MCMCDiagnosticTools = "0.1.0" +MCMCChains = "6" +MCMCDiagnosticTools = "0.3.2" NamedDims = "0.2.35, 1" PrettyTables = "2.1,2.2" Requires = "1.1.3" @@ -42,10 +42,9 @@ RData = "df47a6cb-8c03-5eed-afd8-b6050d6c41da" StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c" Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" TestSetExtensions = "98d24dd4-01ad-11ea-1b02-c9a08f80db04" -Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0" [targets] -test = ["CSV", "DataFrames", "Distributions", "DynamicPPL", "MCMCChains", "RData", "StatsFuns", "Test", "TestSetExtensions", "Turing"] +test = ["CSV", "DataFrames", "Distributions", "DynamicPPL", "MCMCChains", "RData", "StatsFuns", "Test", "TestSetExtensions"] [weakdeps] DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8" diff --git a/src/ESS.jl b/src/ESS.jl index 4215d4f..324fe1e 100644 --- a/src/ESS.jl +++ b/src/ESS.jl @@ -1,33 +1,39 @@ -using MCMCDiagnosticTools +import MCMCDiagnosticTools export relative_eff, psis_ess, sup_ess """ relative_eff( - sample::AbstractArray{<:Real, 3}; - method=MCMCDiagnosticTools.FFTESSMethod() + sample::AbstractArray{<:Real, 3}; + source::Union{AbstractString, Symbol} = "default", + maxlag::Int = typemax(Int), + kwargs..., ) -Calculate the relative efficiency of an MCMC chain, i.e. the effective sample size divided +Calculate the relative efficiency of an MCMC chain, i.e., the effective sample size divided by the nominal sample size. +If `lowercase(String(source))` is `"default"` or `"mcmc"`, the relative effective sample size is computed with `MCMCDiagnosticTools.ess`, using keyword arguments `kind = :basic`, `maxlag = maxlag`, and the remaining keyword arguments `kwargs...`. +Otherwise a vector of ones for each chain is returned. + # Arguments - - `sample::AbstractArray{<:Real, 3}`: An array of log-likelihood values. + - `sample::AbstractArray{<:Real, 3}`: An array of log-likelihood values of the shape `(parameters, draws, chains)`. """ function relative_eff( sample::AbstractArray{<:Real,3}; - source::Union{AbstractString, Symbol}="default", maxlag=size(sample, 2), kwargs... + source::Union{AbstractString, Symbol}="default", + maxlag=typemax(Int), + kwargs..., ) - if lowercase(String(source)) ∉ ["mcmc", "default"] - return ones(size(sample, 1)) + if lowercase(String(source)) ∉ ("mcmc", "default") + # Avoid type instability by computing the return type of `ess` + T = promote_type(eltype(sample), typeof(zero(eltype(sample)) / 1)) + res = similar(sample, T, (axes(sample, 3),)) + return fill!(res, 1) end - - dims = size(sample) - post_sample_size = dims[2] * dims[3] - ess_sample = permutedims(sample, [2, 1, 3]) - ess, = MCMCDiagnosticTools.ess_rhat(ess_sample; maxlag=maxlag, kwargs...) - return r_eff = ess / post_sample_size + ess_sample = PermutedDimsArray(sample, (2, 3, 1)) + return MCMCDiagnosticTools.ess(ess_sample; maxlag, kwargs..., kind=:basic, relative=true) end diff --git a/test/data/samples_m5_1t.csv 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+0.1548432824022825,1.1052682508985323 +0.17185543824689675,0.9485944275501009 +0.3049934015546917,1.0732012539727365 +-0.07716949944346209,1.3599796551817533 +0.17719733321726727,1.1085614088526525 +0.169805540392855,1.1199835106697966 +0.16096790514618664,0.8535417756804892 +0.1445633937335024,0.9354690774315367 diff --git a/test/tests/ComparisonTests.jl b/test/tests/ComparisonTests.jl index 6037c49..82d2f24 100644 --- a/test/tests/ComparisonTests.jl +++ b/test/tests/ComparisonTests.jl @@ -3,7 +3,7 @@ using CSV using DataFrames using StatsBase using NamedDims -using Turing +using DynamicPPL @testset "LooCompare" begin using Random @@ -15,39 +15,26 @@ using Turing df.A = zscore(df.MedianAgeMarriage) data = (D=df.D, A=df.A) - function lin(a, b, c, x...) - result = @. a + b * c - for i in 1:2:length(x) - @. result += x[i] * x[i + 1] - end - return result - end - - @model function m5_1t(A, D) a ~ Normal(0, 0.2) bA ~ Normal(0, 0.5) σ ~ Exponential(1) for i in eachindex(D) - μ = lin(a, A[i], bA) + μ = a + A[i] * bA D[i] ~ Normal(μ, σ) end end - chn5_1t = sample(m5_1t(df.A, df.D), NUTS(1000, 0.9), MCMCThreads(), 1000, 12) - @model function m5_2t(M, D) a ~ Normal(0, 0.2) bM ~ Normal(0, 0.5) σ ~ Exponential(1) for i in eachindex(D) - μ = lin(a, M[i], bM) + μ = a + M[i] * bM D[i] ~ Normal(μ, σ) end end - chn5_2t = sample(m5_2t(df.M, df.D), NUTS(1000, 0.9), MCMCThreads(), 1000, 12) - @model function m5_3t(A, M, D) a ~ Normal(0, 0.2) bA ~ Normal(0, 0.5) @@ -59,15 +46,26 @@ using Turing end end - chn5_3t = sample(m5_3t(df.A, df.M, df.D), NUTS(1000, 0.9), MCMCThreads(), 1000, 12) + # Samples generated by running + # sample(m5_1t(df.A, df.D), Turing.NUTS(1000, 0.9), MCMCThreads(), 1000, 12) + df5_1t = CSV.read(joinpath(@__DIR__, "..", "data", "samples_m5_1t.csv"), DataFrame) + chn5_1t = Chains(permutedims(reshape(Matrix(df5_1t), 1000, 12, 3), (1, 3, 2)), names(df5_1t)) pw_lls5_1t = pointwise_log_likelihoods(m5_1t(df.A, df.D), chn5_1t) psis_loo_output5_1t = psis_loo(m5_1t(df.A, df.D), chn5_1t) psis_loo_output5_1t |> display + # Samples generated by running + # sample(m5_2t(df.M, df.D), Turing.NUTS(1000, 0.9), MCMCThreads(), 1000, 12) + df5_2t = CSV.read(joinpath(@__DIR__, "..", "data", "samples_m5_2t.csv"), DataFrame) + chn5_2t = Chains(permutedims(reshape(Matrix(df5_2t), 1000, 12, 3), (1, 3, 2)), names(df5_2t)) pw_lls5_2t = pointwise_log_likelihoods(m5_2t(df.M, df.D), chn5_2t) psis_loo_output5_2t = psis_loo(m5_2t(df.M, df.D), chn5_2t) psis_loo_output5_2t |> display + # Samples generated by running + # sample(m5_3t(df.A, df.M, df.D), Turing.NUTS(1000, 0.9), MCMCThreads(), 1000, 12) + df5_3t = CSV.read(joinpath(@__DIR__, "..", "data", "samples_m5_3t.csv"), DataFrame) + chn5_3t = Chains(permutedims(reshape(Matrix(df5_3t), 1000, 12, 4), (1, 3, 2)), names(df5_3t)) pw_lls5_3t = pointwise_log_likelihoods(m5_3t(df.A, df.M, df.D), chn5_3t) psis_loo_output5_3t = psis_loo(m5_3t(df.A, df.M, df.D), chn5_3t) psis_loo_output5_3t |> display diff --git a/test/tests/TuringTests.jl b/test/tests/TuringTests.jl index 5e5bf7a..6724ebe 100644 --- a/test/tests/TuringTests.jl +++ b/test/tests/TuringTests.jl @@ -1,4 +1,4 @@ -using Distributions, Random, MCMCChains, Turing +using Distributions, CSV, DataFrames, Random, MCMCChains, DynamicPPL @testset "MCMCChains and Turing utilities" begin Random.seed!(112) @@ -32,13 +32,16 @@ using Distributions, Random, MCMCChains, Turing @model function model(data) μ ~ Normal(0, 1) - σ ~ truncated(Cauchy(0, 1), 0, Inf) + σ ~ truncated(Cauchy(0, 1); lower=0) for i in eachindex(data) data[i] ~ Normal(μ, σ) end end - chain = sample(model(data), NUTS(1000, 0.9), MCMCThreads(), 1000, 12) + # Samples generated by running + # sample(model(data), Turing.NUTS(1000, 0.9), MCMCThreads(), 1000, 12) + df = CSV.read(joinpath(@__DIR__, "..", "data", "samples_model.csv"), DataFrame) + chain = Chains(permutedims(reshape(Matrix(df), 1000, 12, 2), (1, 3, 2)), names(df)) pw_lls_turing = pointwise_log_likelihoods(model(data), chain) pw_lls_loglike = pointwise_log_likelihoods(compute_loglike, chain, data)