Move predict from Turing, implemented using fix

ext/DynamicPPLMCMCChainsExt.jl

@@ -1,10 +1,10 @@
 module DynamicPPLMCMCChainsExt
 
 if isdefined(Base, :get_extension)
-    using DynamicPPL: DynamicPPL
+    using DynamicPPL: DynamicPPL, Random
     using MCMCChains: MCMCChains
 else
-    using ..DynamicPPL: DynamicPPL
+    using ..DynamicPPL: DynamicPPL, Random
     using ..MCMCChains: MCMCChains
 end
 
@@ -190,4 +190,57 @@ function _varname_pairs_without_varname_indexing(
     return varname_pairs
 end
 
+function DynamicPPL.predict(
+    model::DynamicPPL.Model, chain::MCMCChains.Chains; include_all=false
+)
+    return predict(Random.default_rng(), model, chain; include_all=include_all)
+end
+function DynamicPPL.predict(
+    rng::Random.AbstractRNG,
+    model::DynamicPPL.Model,
+    chain::MCMCChains.Chains;
+    include_all=false,
+)
+    params_only_chain = MCMCChains.get_sections(chain, :parameters)
+
+    varname_to_symbol = if :varname_to_symbol in keys(params_only_chain.info)
+        # the mapping is introduced in Turing by
+        # https://github.com/TuringLang/Turing.jl/commit/8d8416ac6c7363c6003ee6ea1fbaac26b4fc8dc3
+        params_only_chain.info[:varname_to_symbol]
+    else
+        # if not using Turing, then we need to construct the mapping ourselves
+        Dict{DynamicPPL.VarName,Symbol}([
+            DynamicPPL.@varname($sym) => sym for
+            sym in params_only_chain.name_map.parameters
+        ])
+    end
+
+    num_of_chains = size(params_only_chain, 3)
+    # num_of_params = 
+    num_of_samples = size(params_only_chain, 1)
+
+    predictions = []
+    for chain_idx in 1:num_of_chains
+        predictions_single_chain = []
+        for sample_idx in 1:num_of_samples
+            d_to_fix = OrderedDict{DynamicPPL.VarName,Any}()
+
+            # construct the dictionary to fix the model
+            for (vn, sym) in varname_to_symbol
+                d_to_fix[vn] = params_only_chain[sample_idx, sym, chain_idx]
+            end
+
+            # fix the model and sample from it
+            fixed_model = DynamicPPL.fix(model, d_to_fix)
+            predictive_sample = rand(rng, fixed_model)
+
+            # TODO: Turing version uses `Transition` and `bundle_samples` to form new chains: is it worth it to move Transition to AbstractMCMC?
+            push!(predictions, predictive_sample)
+        end
+        push!(predictions, predictions_single_chain)
+    end
+
+    return predictions
+end
+
 end

src/DynamicPPL.jl

@@ -152,6 +152,8 @@ end
 # Used here and overloaded in Turing
 function getspace end
 
+function predict end
+
 """
     AbstractVarInfo
 

test/predict.jl

@@ -0,0 +1,57 @@
+module TestPredict
+
+using Test
+using DynamicPPL
+using AbstractMCMC
+using MCMCChains
+using Distributions
+using Random
+using LogDensityProblemsAD
+using AdvancedHMC
+using Tapir
+using ForwardDiff
+
+@model function linear_reg(x, y, σ=0.1)
+    β ~ Normal(0, 1)
+    for i in eachindex(y)
+        y[i] ~ Normal(β * x[i], σ)
+    end
+end
+
+@model function linear_reg_vec(x, y, σ=0.1)
+    β ~ Normal(0, 1)
+    return y ~ MvNormal(β .* x, σ^2 * I)
+end
+
+f(x) = 2 * x + 0.1 * randn()
+
+Δ = 0.1
+xs_train = 0:Δ:10
+ys_train = f.(xs_train)
+xs_test = [10 + Δ, 10 + 2 * Δ]
+ys_test = f.(xs_test)
+
+model = linear_reg(xs_train, ys_train)
+
+m_lin_reg = linear_reg(xs_train, ys_train)
+ldf = DynamicPPL.LogDensityFunction(model, DynamicPPL.VarInfo(model))
+ad_ldf = LogDensityProblemsAD.ADgradient(Val(:Tapir), ldf; safety_on=false)
+chain = AbstractMCMC.sample(
+    ad_ldf, AdvancedHMC.NUTS(0.6), 1000; chain_type=MCMCChains.Chains, param_names=[:β]
+)
+
+DynamicPPL.predict(test_model, chain)
+
+# LKJ example
+@model demo_lkj() = x ~ LKJCholesky(2, 1.0)
+
+model = demo_lkj()
+
+ldf = DynamicPPL.LogDensityFunction(model, DynamicPPL.SimpleVarInfo(model))
+ad_ldf = LogDensityProblemsAD.ADgradient(Val(:ForwardDiff), ldf)
+
+chain = AbstractMCMC.sample(
+    ad_ldf, AdvancedHMC.NUTS(0.6), 1000; chain_type=MCMCChains.Chains, param_names=[:Σ, :x]
+)
+
+end # module