Ah thats too bad, I love how much more clear my code is using pm.Censored - can’t wait until its fixed. To your point, when I dont use a lower/upper bound in pm.censored it fits without an error (but is obviously biased)
I added the full traceback below, its a FloatingPointError - basically it thinks the logp is NaN after a few iterations. Also, unsure how to check the dlogp for a model that failed to run in pymc v4, the developer guide page seems outdated about this topic.
here’s the full traceback
---------------------------------------------------------------------------
FloatingPointError Traceback (most recent call last)
Input In [39], in <cell line: 111>()
143 else:
145 obs = pm.Censored("obs", y_latent,
146 lower=None,
147 upper=cens_,
148 observed=T)
--> 150 idata = pm.sample(init="advi+adapt_diag")
File ~/.pyenv/versions/3.9.7/envs/default_venv/lib/python3.9/site-packages/pymc/sampling.py:533, in sample(draws, step, init, n_init, initvals, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, **kwargs)
531 [kwargs.setdefault(k, v) for k, v in nuts_kwargs.items()]
532 _log.info("Auto-assigning NUTS sampler...")
--> 533 initial_points, step = init_nuts(
534 init=init,
535 chains=chains,
536 n_init=n_init,
537 model=model,
538 random_seed=random_seed_list,
539 progressbar=progressbar,
540 jitter_max_retries=jitter_max_retries,
541 tune=tune,
542 initvals=initvals,
543 **kwargs,
544 )
546 if initial_points is None:
547 # Time to draw/evaluate numeric start points for each chain.
548 ipfns = make_initial_point_fns_per_chain(
549 model=model,
550 overrides=initvals,
551 jitter_rvs=filter_rvs_to_jitter(step),
552 chains=chains,
553 )
File ~/.pyenv/versions/3.9.7/envs/default_venv/lib/python3.9/site-packages/pymc/sampling.py:2527, in init_nuts(init, chains, n_init, model, random_seed, progressbar, jitter_max_retries, tune, initvals, **kwargs)
2519 potential = quadpotential.QuadPotentialDiagAdaptExp(
2520 n,
2521 mean,
(...)
2524 stop_adaptation=stop_adaptation,
2525 )
2526 elif init == "advi+adapt_diag":
-> 2527 approx = pm.fit(
2528 random_seed=random_seed_list[0],
2529 n=n_init,
2530 method="advi",
2531 model=model,
2532 callbacks=cb,
2533 progressbar=progressbar,
2534 obj_optimizer=pm.adagrad_window,
2535 )
2536 approx_sample = approx.sample(
2537 draws=chains, random_seed=random_seed_list[0], return_inferencedata=False
2538 )
2539 initial_points = [approx_sample[i] for i in range(chains)]
File ~/.pyenv/versions/3.9.7/envs/default_venv/lib/python3.9/site-packages/pymc/variational/inference.py:744, in fit(n, method, model, random_seed, start, inf_kwargs, **kwargs)
742 else:
743 raise TypeError(f"method should be one of {set(_select.keys())} or Inference instance")
--> 744 return inference.fit(n, **kwargs)
File ~/.pyenv/versions/3.9.7/envs/default_venv/lib/python3.9/site-packages/pymc/variational/inference.py:144, in Inference.fit(self, n, score, callbacks, progressbar, **kwargs)
142 progress = range(n)
143 if score:
--> 144 state = self._iterate_with_loss(0, n, step_func, progress, callbacks)
145 else:
146 state = self._iterate_without_loss(0, n, step_func, progress, callbacks)
File ~/.pyenv/versions/3.9.7/envs/default_venv/lib/python3.9/site-packages/pymc/variational/inference.py:230, in Inference._iterate_with_loss(self, s, n, step_func, progress, callbacks)
228 except IndexError:
229 pass
--> 230 raise FloatingPointError("\n".join(errmsg))
231 scores[i] = e
232 if i % 10 == 0:
FloatingPointError: NaN occurred in optimization.
The current approximation of RV `mu_log_k`.ravel()[0] is NaN.
The current approximation of RV `mu_log_lambd`.ravel()[0] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[0] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[1] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[2] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[3] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[4] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[5] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[6] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[7] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[8] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[9] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[10] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[11] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[12] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[13] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[14] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[15] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[16] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[17] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[18] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[19] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[20] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[21] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[22] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[23] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[24] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[25] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[26] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[27] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[28] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[29] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[30] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[31] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[32] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[33] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[34] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[35] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[36] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[37] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[38] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[39] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[40] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[41] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[42] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[43] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[44] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[45] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[46] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[47] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[48] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[49] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[50] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[51] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[52] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[53] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[54] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[55] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[56] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[57] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[58] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[59] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[60] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[61] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[62] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[63] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[64] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[65] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[66] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[67] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[68] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[69] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[70] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[71] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[72] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[73] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[74] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[75] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[76] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[77] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[78] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[79] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[80] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[81] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[82] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[83] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[84] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[85] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[86] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[87] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[88] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[89] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[90] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[91] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[92] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[93] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[94] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[95] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[96] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[97] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[98] is NaN.
The current approximation of RV `Δ_log_k`.ravel()[99] is NaN.
The current approximation of RV `σ_log_k_log__`.ravel()[0] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[0] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[1] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[2] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[3] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[4] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[5] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[6] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[7] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[8] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[9] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[10] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[11] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[12] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[13] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[14] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[15] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[16] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[17] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[18] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[19] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[20] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[21] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[22] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[23] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[24] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[25] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[26] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[27] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[28] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[29] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[30] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[31] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[32] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[33] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[34] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[35] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[36] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[37] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[38] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[39] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[40] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[41] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[42] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[43] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[44] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[45] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[46] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[47] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[48] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[49] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[50] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[51] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[52] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[53] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[54] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[55] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[56] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[57] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[58] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[59] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[60] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[61] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[62] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[63] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[64] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[65] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[66] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[67] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[68] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[69] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[70] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[71] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[72] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[73] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[74] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[75] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[76] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[77] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[78] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[79] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[80] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[81] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[82] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[83] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[84] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[85] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[86] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[87] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[88] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[89] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[90] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[91] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[92] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[93] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[94] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[95] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[96] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[97] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[98] is NaN.
The current approximation of RV `Δ_log_lambd`.ravel()[99] is NaN.
The current approximation of RV `σ_log_lambd_log__`.ravel()[0] is NaN.
Try tracking this parameter: http://docs.pymc.io/notebooks/variational_api_quickstart.html#Tracking-parameters