Simplify tests with make_chain_from_prior

Author: penelopeysm

test/model.jl

@@ -56,49 +56,7 @@ is_typed_varinfo(varinfo::DynamicPPL.SimpleVarInfo{<:NamedTuple}) = true
 
         #### logprior, logjoint, loglikelihood for MCMC chains ####
         for model in DynamicPPL.TestUtils.DEMO_MODELS # length(DynamicPPL.TestUtils.DEMO_MODELS)=12
-            var_info = VarInfo(model)
-            vns = DynamicPPL.TestUtils.varnames(model)
-            syms = unique(DynamicPPL.getsym.(vns))
-
-            # generate a chain of sample parameter values.
-            N = 200
-            vals_OrderedDict = mapreduce(hcat, 1:N) do _
-                rand(OrderedDict, model)
-            end
-            vals_mat = mapreduce(hcat, 1:N) do i
-                [vals_OrderedDict[i][vn] for vn in vns]
-            end
-            i = 1
-            for col in eachcol(vals_mat)
-                col_flattened = []
-                [push!(col_flattened, x...) for x in col]
-                if i == 1
-                    chain_mat = Matrix(reshape(col_flattened, 1, length(col_flattened)))
-                else
-                    chain_mat = vcat(
-                        chain_mat, reshape(col_flattened, 1, length(col_flattened))
-                    )
-                end
-                i += 1
-            end
-            chain_mat = convert(Matrix{Float64}, chain_mat)
-
-            # devise parameter names for chain
-            sample_values_vec = collect(values(vals_OrderedDict[1]))
-            symbol_names = []
-            chain_sym_map = Dict()
-            for k in 1:length(keys(var_info))
-                vn_parent = keys(var_info)[k]
-                sym = DynamicPPL.getsym(vn_parent)
-                vn_children = DynamicPPL.varname_leaves(vn_parent, sample_values_vec[k]) # `varname_leaves` defined in src/utils.jl
-                for vn_child in vn_children
-                    chain_sym_map[Symbol(vn_child)] = sym
-                    symbol_names = [symbol_names; Symbol(vn_child)]
-                end
-            end
-            chain = Chains(chain_mat, symbol_names)
-
-            # calculate the pointwise loglikelihoods for the whole chain using the newly written functions
+            chain = make_chain_from_prior(chain, 200)
             logpriors = logprior(model, chain)
             loglikelihoods = loglikelihood(model, chain)
             logjoints = logjoint(model, chain)