BUG: pm.Beta with certain alpha=beta=X values causes invalid log errors #7678
Description
Describe the issue:
I'm not 100% sure that this is a bug, I might be overlooking something on the math side. That said, I find it odd that the following example works with uniform_prior variable values of 1, 10, 1000, and 10000, but not with the value of 100.
Reproduceable code example:
import pymc as pm
uniform_prior=100
with pm.Model() as model:
p = pm.Beta("p", alpha=uniform_prior, beta=uniform_prior, shape=2)
y = pm.Binomial("y", n=[11618, 13382], p=p, observed=[111, 148], shape=2)
model.debug(verbose=True)
trace = pm.sample(draws=10000)Error message:
/Users/arthur/<proj-dir>/.venv/lib/python3.13/site-packages/pytensor/tensor/elemwise.py:735: RuntimeWarning: invalid value encountered in log
variables = ufunc(*ufunc_args, **ufunc_kwargs)
point={'p_logodds__': array([nan, nan])}
The variable p has the following parameters:
0: [100] [id A] <Vector(int8, shape=(1,))>
1: [100] [id A] <Vector(int8, shape=(1,))>
The parameters evaluate to:
0: [100]
1: [100]
Some of the values of variable p are associated with a non-finite logp:
value = nan -> logp = nan
value = nan -> logp = nan
The variable y has the following parameters:
0: [11618 13382] [id A] <Vector(int64, shape=(2,))>
1: Sigmoid [id B] <Vector(float64, shape=(2,))> 'p'
└─ p_logodds__ [id C] <Vector(float64, shape=(2,))>
Initializing NUTS using jitter+adapt_diag...
The parameters evaluate to:
0: [11618 13382]
1: [nan nan]
This does not respect one of the following constraints: n >= 0, 0 <= p <= 1
n >= 0, 0 <= p <= 1
Apply node that caused the error: Check{n >= 0, 0 <= p <= 1}(Composite{...}.2, All{axes=None}.0)
Toposort index: 5
Inputs types: [TensorType(float64, shape=(2,)), TensorType(bool, shape=())]
Inputs shapes: [(2,), ()]
Inputs strides: [(8,), ()]
Inputs values: [array([nan, nan]), array(False)]
Outputs clients: [[output[0](y_logprob)]]
Backtrace when the node is created (use PyTensor flag traceback__limit=N to make it longer):
File "/Users/arthur/<proj-dir>/.venv/lib/python3.13/site-packages/pymc/logprob/basic.py", line 595, in transformed_conditional_logp
temp_logp_terms = conditional_logp(
File "/Users/arthur/<proj-dir>/.venv/lib/python3.13/site-packages/pymc/logprob/basic.py", line 529, in conditional_logp
node_logprobs = _logprob(
File "/opt/homebrew/Cellar/[email protected]/3.13.2/Frameworks/Python.framework/Versions/3.13/lib/python3.13/functools.py", line 934, in wrapper
return dispatch(args[0].__class__)(*args, **kw)
File "/Users/arthur/<proj-dir>/.venv/lib/python3.13/site-packages/pymc/distributions/distribution.py", line 140, in logp
return class_logp(value, *dist_params)
File "/Users/arthur/<proj-dir>/.venv/lib/python3.13/site-packages/pymc/distributions/discrete.py", line 147, in logp
return check_parameters(
File "/Users/arthur/<proj-dir>/.venv/lib/python3.13/site-packages/pymc/distributions/dist_math.py", line 74, in check_parameters
return CheckParameterValue(msg, can_be_replaced_by_ninf)(expr, all_true_scalar)
File "/Users/arthur/<proj-dir>/.venv/lib/python3.13/site-packages/pytensor/graph/op.py", line 293, in __call__
node = self.make_node(*inputs, **kwargs)
File "/Users/arthur/<proj-dir>/.venv/lib/python3.13/site-packages/pytensor/raise_op.py", line 97, in make_node
[value.type()],
HINT: Use the PyTensor flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.
/Users/arthur/<proj-dir>/.venv/lib/python3.13/site-packages/pytensor/tensor/elemwise.py:735: RuntimeWarning: invalid value encountered in log
variables = ufunc(*ufunc_args, **ufunc_kwargs)
/Users/arthur/<proj-dir>/.venv/lib/python3.13/site-packages/pytensor/tensor/elemwise.py:735: RuntimeWarning: invalid value encountered in log
variables = ufunc(*ufunc_args, **ufunc_kwargs)
---------------------------------------------------------------------------
SamplingError Traceback (most recent call last)
Cell In[2], line 6
4 y = pm.Binomial("y", n=[11618, 13382], p=p, observed=[111, 148], shape=2)
5 model.debug(verbose=True)
----> 6 trace = pm.sample(draws=10000)
File ~/<proj-dir>/.venv/lib/python3.13/site-packages/pymc/sampling/mcmc.py:832, in sample(draws, tune, chains, cores, random_seed, progressbar, progressbar_theme, step, var_names, nuts_sampler, initvals, init, jitter_max_retries, n_init, trace, discard_tuned_samples, compute_convergence_checks, keep_warning_stat, return_inferencedata, idata_kwargs, nuts_sampler_kwargs, callback, mp_ctx, blas_cores, model, compile_kwargs, **kwargs)
830 [kwargs.setdefault(k, v) for k, v in nuts_kwargs.items()]
831 with joined_blas_limiter():
--> 832 initial_points, step = init_nuts(
833 init=init,
834 chains=chains,
835 n_init=n_init,
836 model=model,
837 random_seed=random_seed_list,
838 progressbar=progress_bool,
839 jitter_max_retries=jitter_max_retries,
840 tune=tune,
841 initvals=initvals,
842 compile_kwargs=compile_kwargs,
843 **kwargs,
844 )
845 else:
846 # Get initial points
847 ipfns = make_initial_point_fns_per_chain(
848 model=model,
849 overrides=initvals,
850 jitter_rvs=set(),
851 chains=chains,
852 )
File ~/<proj-dir>/.venv/lib/python3.13/site-packages/pymc/sampling/mcmc.py:1605, in init_nuts(init, chains, n_init, model, random_seed, progressbar, jitter_max_retries, tune, initvals, compile_kwargs, **kwargs)
1602 q, _ = DictToArrayBijection.map(ip)
1603 return logp_dlogp_func([q], extra_vars={})[0]
-> 1605 initial_points = _init_jitter(
1606 model,
1607 initvals,
1608 seeds=random_seed_list,
1609 jitter="jitter" in init,
1610 jitter_max_retries=jitter_max_retries,
1611 logp_fn=model_logp_fn,
1612 )
1614 apoints = [DictToArrayBijection.map(point) for point in initial_points]
1615 apoints_data = [apoint.data for apoint in apoints]
File ~/<proj-dir>/.venv/lib/python3.13/site-packages/pymc/sampling/mcmc.py:1486, in _init_jitter(model, initvals, seeds, jitter, jitter_max_retries, logp_fn)
1483 if not np.isfinite(point_logp):
1484 if i == jitter_max_retries:
1485 # Print informative message on last attempted point
-> 1486 model.check_start_vals(point)
1487 # Retry with a new seed
1488 seed = rng.integers(2**30, dtype=np.int64)
File ~/<proj-dir>/.venv/lib/python3.13/site-packages/pymc/model/core.py:1769, in Model.check_start_vals(self, start, **kwargs)
1766 initial_eval = self.point_logps(point=elem, **kwargs)
1768 if not all(np.isfinite(v) for v in initial_eval.values()):
-> 1769 raise SamplingError(
1770 "Initial evaluation of model at starting point failed!\n"
1771 f"Starting values:\n{elem}\n\n"
1772 f"Logp initial evaluation results:\n{initial_eval}\n"
1773 "You can call `model.debug()` for more details."
1774 )
SamplingError: Initial evaluation of model at starting point failed!
Starting values:
{'p_logodds__': array([nan, nan])}
Logp initial evaluation results:
{'p': nan, 'y': -inf}
You can call `model.debug()` for more details.PyMC version information:
Installed pymc via uv. M1 Mac, Python 3.13.2 (main, Feb 4 2025, 14:51:09) [Clang 16.0.0 (clang-1600.0.26.6)], pymc==5.20.1, pytensor==2.27.1.
Context for the issue:
I was looking to set up some evaluations for data sampled through a TS bandit, that's how I got my initial numbers.