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New predict() behaviour and syntax? #2388

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torfjelde opened this issue Nov 1, 2024 Discussed in #2387 · 12 comments
Closed

New predict() behaviour and syntax? #2388

torfjelde opened this issue Nov 1, 2024 Discussed in #2387 · 12 comments

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@torfjelde
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Discussed in #2387

Originally posted by DominiqueMakowski October 30, 2024
It's me again, just wanted some clarification, as it seems like recently the behaviour of predict() changed in the recent updates?
Previously if you ran predict(model([vector_of_missing]), chains) and it would return an array of the size of the vector of missing it's been conditioned on with the draws, but now the same function returns also all the parameters (?)

What is the correct way moving forward of generating predictions on the outcome of a model?

If the current predict() output is here to stay, how can I easily filter out the draws corresponding to my outcome? In my case, I'm interested in predicting a variable called response time rt. And the chains output has the following names:

julia>     names(pred)
3033-element Vector{Symbol}:
 :τ_intercept_random_sd
 :τ_isi1_random_sd
 :τ_isi2_random_sd
 Symbol("τ_intercept_random[1]")
 Symbol("τ_intercept_random[2]")
 Symbol("τ_intercept_random[3]")
 Symbol("τ_intercept_random[4]")
 ⋮
 Symbol("rt[2950]")
 Symbol("rt[2951]")
 Symbol("rt[2952]")
 Symbol("rt[2953]")
 Symbol("rt[2954]")
 Symbol("rt[2955]")

How can I do something like pred[parameters_starting_with_rt] the most Turingy/julian way?
@torfjelde
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@DominiqueMakowski made an issue from this as it's slightly more appropriate 👍

So I can't actually reproduce this on my end. Do you have a MWE maybe? The expected behavior of predict is that all the variables present in the chain you pass to predict do not end up in the resulting pred. If they do, then it's generally an indication that the varialbes in chain are treated as random, which is clearly not what you want.

The following example works fine on my end though:

julia> using Turing

julia> @model function demo()
           x ~ Normal()
           y ~ Normal(x, 1)
       end

demo (generic function with 2 methods)

julia> model = demo() | (y = 1.0,);

julia> chain = sample(model, NUTS(), 1000)
┌ Info: Found initial step size
└   ϵ = 6.4
Sampling 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| Time: 0:00:00
Chains MCMC chain (1000×13×1 Array{Float64, 3}):

Iterations        = 501:1:1500
Number of chains  = 1
Samples per chain = 1000
Wall duration     = 0.61 seconds
Compute duration  = 0.61 seconds
parameters        = x
internals         = lp, n_steps, is_accept, acceptance_rate, log_density, hamiltonian_energy, hamiltonian_energy_error, max_hamiltonian_energy_error, tree_depth, numerical_error, step_size, nom_step_size

Summary Statistics
  parameters      mean       std      mcse   ess_bulk   ess_tail      rhat   ess_per_sec 
      Symbol   Float64   Float64   Float64    Float64    Float64   Float64       Float64 

           x    0.4624    0.6919    0.0278   621.3428   837.0866    0.9999     1023.6290

Quantiles
  parameters      2.5%     25.0%     50.0%     75.0%     97.5% 
      Symbol   Float64   Float64   Float64   Float64   Float64 

           x   -0.8236   -0.0280    0.4350    0.9221    1.8457


julia> predictions = predict(demo(), chain)
Chains MCMC chain (1000×1×1 Array{Float64, 3}):

Iterations        = 1:1:1000
Number of chains  = 1
Samples per chain = 1000
parameters        = y
internals         = 

Summary Statistics
  parameters      mean       std      mcse   ess_bulk   ess_tail      rhat   ess_per_sec 
      Symbol   Float64   Float64   Float64    Float64    Float64   Float64       Missing 

           y    0.4929    1.2464    0.0429   844.6265   876.9116    1.0064       missing

Quantiles
  parameters      2.5%     25.0%     50.0%     75.0%     97.5% 
      Symbol   Float64   Float64   Float64   Float64   Float64 

           y   -1.9575   -0.3699    0.5125    1.3360    2.8975
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@DominiqueMakowski
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Let me try to make an MWE

@DominiqueMakowski
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(On a side note, regarding the new conditioning syntax, I thought I'd bring up Christopher's comment about possible drawbacks)

@DominiqueMakowski
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Alright, I think managed to make it reproducible 😥 , but i didn't manage to narrow it down to a simpler model so I'm not exactly sure what causes the error, it might be me doing something silly.

The script is here (in the folder there's also the manifest).

It takes a few long seconds to sample and compute unfortunately

@DominiqueMakowski
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Very oddly, if I predict on a subset of the data, it then works...

pred2 = predict(model_ExGaussian(fill(missing, length(df.ISI[1:60])), df.ISI[1:60], df.Participant[1:60]; min_rt=minimum(df.RT)), posteriors)
size(Array(pred2))
(200, 60)

@torfjelde
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Looking into it now.

Btw, it's generally not recommend to use the syntax truncated(Normal(), 0, Inf); the preferred syntax is truncated(Normal(), lower=0) :) The former causes some issues with ForwardDiff.jl + misses a few optimizations.

@torfjelde
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Also, for reproducing the issue a bit faster, just use Prior() sampler instead of NUTS() :)

@DominiqueMakowski
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Thanks, don't hesitate to mention any other potential improvements :) (it's my first actual work-related Turing usage)

@torfjelde
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torfjelde commented Nov 5, 2024 •
edited
Loading

So the issue with your model is that you're using different df.Participant:

julia> length(unique(df.Participant))
25

julia> length(unique(df.Participant[1:50]))
1

So you're running the model with 1 participants to get the fit, and then you change it to 25 for the predict which subsequently introduces a bunch of variables not present in your fit

@DominiqueMakowski
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Ah of course, that makes sense. Thanks @torfjelde!

@torfjelde
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Mind me closing the issue then?:)

@DominiqueMakowski
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Nope, cheers

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