Update for DynamicPPL 0.33

Author: penelopeysm

Project.toml

@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.36.0"
+version = "0.36.1"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -63,7 +63,7 @@ Distributions = "0.23.3, 0.24, 0.25"
 DistributionsAD = "0.6"
 DocStringExtensions = "0.8, 0.9"
 DynamicHMC = "3.4"
-DynamicPPL = "0.32"
+DynamicPPL = "0.33"
 EllipticalSliceSampling = "0.5, 1, 2"
 ForwardDiff = "0.10.3"
 Libtask = "0.8.8"

src/mcmc/Inference.jl

@@ -50,7 +50,6 @@ import LogDensityProblems
 import LogDensityProblemsAD
 import Random
 import MCMCChains
-import StatsBase: predict
 
 export InferenceAlgorithm,
     Hamiltonian,
@@ -78,7 +77,6 @@ export InferenceAlgorithm,
     dot_assume,
     observe,
     dot_observe,
-    predict,
     externalsampler
 
 #######################
@@ -396,7 +394,7 @@ function getparams(model::DynamicPPL.Model, vi::DynamicPPL.VarInfo)
     # this means that the code below will work both of linked and invlinked `vi`.
     # Ref: https://github.com/TuringLang/Turing.jl/issues/2195
     # NOTE: We need to `deepcopy` here to avoid modifying the original `vi`.
-    vals = DynamicPPL.values_as_in_model(model, deepcopy(vi))
+    vals = DynamicPPL.values_as_in_model(model, true, deepcopy(vi))
 
     # Obtain an iterator over the flattened parameter names and values.
     iters = map(DynamicPPL.varname_and_value_leaves, keys(vals), values(vals))
@@ -612,112 +610,6 @@ end
 DynamicPPL.getspace(spl::Sampler) = getspace(spl.alg)
 DynamicPPL.inspace(vn::VarName, spl::Sampler) = inspace(vn, getspace(spl.alg))
 
-"""
-
-    predict([rng::AbstractRNG,] model::Model, chain::MCMCChains.Chains; include_all=false)
-
-Execute `model` conditioned on each sample in `chain`, and return the resulting `Chains`.
-
-If `include_all` is `false`, the returned `Chains` will contain only those variables
-sampled/not present in `chain`.
-
-# Details
-Internally calls `Turing.Inference.transitions_from_chain` to obtained the samples
-and then converts these into a `Chains` object using `AbstractMCMC.bundle_samples`.
-
-# Example
-```jldoctest
-julia> using Turing; Turing.setprogress!(false);
-[ Info: [Turing]: progress logging is disabled globally
-
-julia> @model function linear_reg(x, y, σ = 0.1)
-           β ~ Normal(0, 1)
-
-           for i ∈ eachindex(y)
-               y[i] ~ Normal(β * x[i], σ)
-           end
-       end;
-
-julia> σ = 0.1; f(x) = 2 * x + 0.1 * randn();
-
-julia> Δ = 0.1; xs_train = 0:Δ:10; ys_train = f.(xs_train);
-
-julia> xs_test = [10 + Δ, 10 + 2 * Δ]; ys_test = f.(xs_test);
-
-julia> m_train = linear_reg(xs_train, ys_train, σ);
-
-julia> chain_lin_reg = sample(m_train, NUTS(100, 0.65), 200);
-┌ Info: Found initial step size
-└   ϵ = 0.003125
-
-julia> m_test = linear_reg(xs_test, Vector{Union{Missing, Float64}}(undef, length(ys_test)), σ);
-
-julia> predictions = predict(m_test, chain_lin_reg)
-Object of type Chains, with data of type 100×2×1 Array{Float64,3}
-
-Iterations        = 1:100
-Thinning interval = 1
-Chains            = 1
-Samples per chain = 100
-parameters        = y[1], y[2]
-
-2-element Array{ChainDataFrame,1}
-
-Summary Statistics
-  parameters     mean     std  naive_se     mcse       ess   r_hat
-  ──────────  ───────  ──────  ────────  ───────  ────────  ──────
-        y[1]  20.1974  0.1007    0.0101  missing  101.0711  0.9922
-        y[2]  20.3867  0.1062    0.0106  missing  101.4889  0.9903
-
-Quantiles
-  parameters     2.5%    25.0%    50.0%    75.0%    97.5%
-  ──────────  ───────  ───────  ───────  ───────  ───────
-        y[1]  20.0342  20.1188  20.2135  20.2588  20.4188
-        y[2]  20.1870  20.3178  20.3839  20.4466  20.5895
-
-
-julia> ys_pred = vec(mean(Array(group(predictions, :y)); dims = 1));
-
-julia> sum(abs2, ys_test - ys_pred) ≤ 0.1
-true
-```
-"""
-function predict(model::Model, chain::MCMCChains.Chains; kwargs...)
-    return predict(Random.default_rng(), model, chain; kwargs...)
-end
-function predict(
-    rng::AbstractRNG, model::Model, chain::MCMCChains.Chains; include_all=false
-)
-    # Don't need all the diagnostics
-    chain_parameters = MCMCChains.get_sections(chain, :parameters)
-
-    spl = DynamicPPL.SampleFromPrior()
-
-    # Sample transitions using `spl` conditioned on values in `chain`
-    transitions = transitions_from_chain(rng, model, chain_parameters; sampler=spl)
-
-    # Let the Turing internals handle everything else for you
-    chain_result = reduce(
-        MCMCChains.chainscat,
-        [
-            AbstractMCMC.bundle_samples(
-                transitions[:, chain_idx], model, spl, nothing, MCMCChains.Chains
-            ) for chain_idx in 1:size(transitions, 2)
-        ],
-    )
-
-    parameter_names = if include_all
-        names(chain_result, :parameters)
-    else
-        filter(
-            k -> ∉(k, names(chain_parameters, :parameters)),
-            names(chain_result, :parameters),
-        )
-    end
-
-    return chain_result[parameter_names]
-end
-
 """
 
     transitions_from_chain(

test/Project.toml

@@ -51,7 +51,7 @@ Combinatorics = "1"
 Distributions = "0.25"
 DistributionsAD = "0.6.3"
 DynamicHMC = "2.1.6, 3.0"
-DynamicPPL = "0.32.2"
+DynamicPPL = "0.33"
 FiniteDifferences = "0.10.8, 0.11, 0.12"
 ForwardDiff = "0.10.12 - 0.10.32, 0.10"
 HypothesisTests = "0.11"