sample_smc

[myravian]

Hi there,

I was testing pymc_experimental/inference/smc/sampling.py and noticed the following issues:

• the inference doesn't seem to like pm.Dirichlet, with a shape error at tmp = logp_fn(*[p.squeeze() for p in particles])[0]
• arviz_from_particles doesn't seem to like RVs with shape=(1,)
• the conversion from inferencedata to netCDF fails because the integrations is neither int nor np.array
• the inferencedata doesn't have the marginal likelihood, do you think it will be implemented in the future or it's just not possible?

Thanks a lot for the SMC blackjax implementation, it's very useful!

Cheers, VIan

PS: here's some code that produces the error

` real_a = 0.2 real_b = 2 x = np.linspace(1, 100) y = real_a * x + real_b + np.random.normal(0, 2, len(x))

with pm.Model() as model: a = pm.Normal("a", mu=10, sigma=10) b = pm.Normal("b", mu=10, sigma=10)

either of the following lines produces an error

# c = pm.Normal("c", mu=10, sigma=10, shape=(1,))
# d = pm.Dirichlet("d", [1, 1])

trace = sample_smc(
    n_particles=1000,
    kernel="HMC",
    inner_kernel_params={
        "step_size": 0.01, 
        "integration_steps": 20,
    },
    iterations_to_diagnose=10,
    target_essn=0.5,
    num_mcmc_steps=10,
)

`

[ciguaran]

Hi, I can tackle this could someone assign the issue to me?

[ciguaran]

@myravian could you try your example running it from this branch? I may have a fix https://github.com/ciguaran/pymc-experimental/tree/ciguaran_fix_smc_bj . Also super interested to know what are you using SMC for, it would be great if it would become an example notebook on how to use it!. let me know.

[myravian]

Unfortunately I still have the same error message:

  File "/local/home/vleboute/work/MULTIGRIS/mgris/sampling_smc_ciguaran.py", line 150, in sample_smc_blackjax
    total_iterations, particles, diagnosis = inference_loop(
                                             ^^^^^^^^^^^^^^^
  File "/local/home/vleboute/work/MULTIGRIS/mgris/sampling_smc_ciguaran.py", line 267, in inference_loop
    n_iter, final_state, _, diagnosis = jax.lax.while_loop(
                                        ^^^^^^^^^^^^^^^^^^^
  File "/local/home/vleboute/work/MULTIGRIS/mgris/sampling_smc_ciguaran.py", line 262, in one_step
    state, info = kernel.step(subk, state)
                  ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/local/home/vleboute/miniconda3/lib/python3.11/site-packages/blackjax/smc/adaptive_tempered.py", line 167, in step_fn
    return kernel(
           ^^^^^^^
  File "/local/home/vleboute/miniconda3/lib/python3.11/site-packages/blackjax/smc/adaptive_tempered.py", line 101, in kernel
    return tempered_kernel(rng_key, state, num_mcmc_steps, lmbda, mcmc_parameters)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/local/home/vleboute/miniconda3/lib/python3.11/site-packages/blackjax/smc/tempered.py", line 143, in kernel
    smc_state, info = smc.base.step(
                      ^^^^^^^^^^^^^^
  File "/local/home/vleboute/miniconda3/lib/python3.11/site-packages/blackjax/smc/base.py", line 140, in step
    particles, update_info = update_fn(keys, particles)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/local/home/vleboute/miniconda3/lib/python3.11/site-packages/blackjax/smc/tempered.py", line 131, in mcmc_kernel
    state = mcmc_init_fn(position, tempered_logposterior_fn)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/local/home/vleboute/miniconda3/lib/python3.11/site-packages/blackjax/mcmc/hmc.py", line 89, in init
    logdensity, logdensity_grad = jax.value_and_grad(logdensity_fn)(position)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/local/home/vleboute/miniconda3/lib/python3.11/site-packages/blackjax/smc/tempered.py", line 126, in tempered_logposterior_fn
    logprior = logprior_fn(position)
               ^^^^^^^^^^^^^^^^^^^^^
  File "/local/home/vleboute/work/MULTIGRIS/mgris/sampling_smc_ciguaran.py", line 380, in logp_fn_wrap
    return logp_fn(*particles)[0]
           ^^^^^^^^^^^^^^^^^^^
  File "/tmp/tmpoc516ktt", line 29, in jax_funcified_fgraph
    tensor_variable_13 = dimshuffle_1(d_simplex_)
                         ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/local/home/vleboute/miniconda3/lib/python3.11/site-packages/pytensor/link/jax/dispatch/elemwise.py", line 69, in dimshuffle
    res = jnp.transpose(x, op.transposition)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/local/home/vleboute/miniconda3/lib/python3.11/site-packages/jax/_src/numpy/lax_numpy.py", line 681, in transpose
    return lax.transpose(a, axes_)
           ^^^^^^^^^^^^^^^^^^^^^^^
TypeError: transpose permutation isn't a permutation of operand dimensions, got permutation (0,) for operand shape (1000, 1).
--------------------
For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.

I don't have a straightforward and simple illustration of the way I use SMC, but the gist of it is that I ran an astrophysical code to compute predictions corresponding to several 100 thousand parameter sets. Based on a set of observables I try to infer the parameters. Of course there are various issues such as regularity/completeness of grid and interpolation, but the main issue is the complex, multi-modal posterior distributions that we expect. From all the proof of concept and validation tests we did, SMC has been a great way to probe the prior space and to handle such difficult posteriors (provided the kernel parameters are well tuned). I'm by no means an expert in statistics and I rely a lot on empirical knowledge so I'm sure I'm not doing everything right though...!

[ciguaran]

Could you share a full python file that reproduces the error? I've run the example you posted at the very beginning and it does work for me 🤔 .

[myravian]

Here would be the script: `import pymc as pm

from sampling_smc_ciguaran import sample_smc_blackjax as sample_smc

with pm.Model() as model: c = pm.Normal("c", mu=10, sigma=10, shape=(1,)) d = pm.Dirichlet("d", [1, 1])

trace = sample_smc(
    n_particles=1000,
    kernel="HMC",
    inner_kernel_params={
        "step_size": 0.01,  # small values better
        "integration_steps": 20,
    },
    iterations_to_diagnose=10,
    target_essn=0.5,
    num_mcmc_steps=10,
)

` Maybe it has to do with the blackjax/jax versions (1.1.0/0.4.21 in my system)

[ciguaran]

Hi! so I was able to run the example you just shared via installing pymc-experimental from the branch.

pip install git+https://github.com/ciguaran/pymc-experimental@ciguaran_fix_smc_bj

is it possible that you are still using pymc-experimental from master?

[myravian]

You're right, I was not using the proper versions, just tested it and it seems to work fine, thanks for the modifications!