Turing.jl: Warnings when running generated_quantities

When calling generated_quantities there is always a huge number of warnings with the following message

Warning: the following keys were not found in `vi`, and thus `kernel!` was not applied to these: ["lp"]
└ @ DynamicPPL ~/.julia/packages/DynamicPPL/h8FWT/src/varinfo.jl:1314

Is there some way I can get around this warning messages, alternative to get the same functionality without need to run generated_quantities.

Since Turing.jl now supports the great Dirac function, I don’t have that much need to use generated_quantities, but sometimes it’s nice to be able to work with non parameters or collection of parameters.

Here’s an example where I use generated_quantities to show the probability distributions of the combination of female, recovery, and drug (using my show_var_dist_pct defined below). The summary of the chains is shown first but is then “drowned” in the huge number of warnings from generated_quantities.

using Turing

# Show distribution of a variable in a MCMCChain
# Sort the dictionary in order of decreasing occurrence (percentage)
# Examples:
#  - show_var_dist_pct(chains, :n)       show all entries
#  - show_var_dist_pct(chains, :n, 10)  show first 10 entries (e.g. for large tables)
function show_var_dist_pct(chains::Chains, var, num=0)

    if var in chains.name_map.parameters
        println("Distributions of variable $var (num:$num)")
        len = length(vcat(chains[var]...)) # handle multiple chains
        c = 0
        for kv in sort(collect(make_hash(chains[var])),by=x->x[2],rev=true)
            c += 1
            if (num == 0) || (num > 0 && c <= num)
                @printf "%-3.5f => % 7d  (%2.6f)\n" kv[1] kv[2] kv[2]/len
        # println("Variable $var is not in chains")

# Simple model of Simpson's "paradox"
@model function simpson(problem)
    female ~ flip(0.5)
    drug ~ female ? flip(10/40.0) :  flip(30/40.0)

    recovery ~  if     drug == true  && female==true   flip(0.2)
                elseif drug == true  && female==false  flip(0.6)
                elseif drug == false && female==true   flip(0.3)
                elseif drug == false && female==false  flip(0.7)
                else                                   flip(0.0)

    # 6. prob(recovery|(\+ drug,\+female)): 0.7
    true ~ Dirac(drug == false && female == false)

    return female, recovery, drug

model = simpson(problem)
chains = sample(model, MH(), 10_000)

# This the distributions of the different combinations of
#      female, recovery, group
genq = generated_quantities(model, chains)

Here’s an example of the probability distribution for this problem:

Distributions of variable (num:0)
(false, true, false)	=>	29007 (0.725175)
(false, false, false)	=>	10970 (0.27425)
(false, false, true)	=>	17 (0.000425)
(true, true, false)	=>	4 (0.0001)
(true, true, true)	=>	2 (5.0e-5)

(The full model is here: http://hakank.org/julia/turing/simpson.jl )

generated_quantities and pointwise_loglikelihoods don’t like the internal parameters (i.e. statistics) in Chains. Just extracting the actual sampled parameters should resolve this:

chains_params = Turing.MCMCChains.get_sections(chain, :parameters)
generated_quantities(model, chains_params)
1 Like

@sethaxen Thanks, Seth. This works really great!