Nutpie and PYMC Hurdle Gamma Distribution

[zwelitunyiswa]

Pardon my ignorance, but I cannot get nutpie to sample from PYMC's hurdle-gamma likelihood.

I get "TypeError: The fgraph of ScalarLoop must be exclusively composed of scalar operations."

Is this just not possible at this time?

[aseyboldt]

This looks like an issue with the numba backend of pytensor.
Can you provide an example I can run to further debug this?

In the meantime, you can also give the jax backend a go:

compiled = nutpie.compile_pymc_model(model, backend="jax", gradient_backend="jax")
trace = nutpie.sample(compiled)

[zwelitunyiswa]

Sure. Here is a simple model that replicates the error:

x = pm.HurdleGamma.dist(psi=.2, alpha=1, beta=2)
x_draws = pm.draw(x, draws=500, random_seed=1)
data = pd.DataFrame({'variable':x_draws})

with pm.Model() as model:
    
    alpha = pm.HalfNormal("alpha", sigma=2)
    beta = pm.HalfNormal("beta", sigma=2)
    psi = pm.Beta("psi", alpha=2, beta=2)

    obs = pm.HurdleGamma("obs", psi=psi, alpha=alpha, beta=beta, observed=data)

    idata = pm.sample(nuts_sampler='nutpie')

This fails with the same "TypeError: The fgraph of ScalarLoop must be exclusively composed of scalar operations."

[zwelitunyiswa]

compiled = nutpie.compile_pymc_model(model, backend="jax", gradient_backend="jax")
trace = nutpie.sample(compiled)

I tried that with the simple model above and I got this a "RuntimeError: All initialization points failed" error. Image attached.

[aseyboldt]

Sorry for the delay.
I opened a pytensor issue for the problem.

[zwelitunyiswa]

No worries. Thank you for looking into it and opening the issue.