# Getting an error Aesara flag \`exception\_verbosity=high\` error on PyMC model

**URL:** <https://discourse.pymc.io/t/getting-an-error-aesara-flag-exception-verbosity-high-error-on-pymc-model/9593>\
**Category:** v5\
**Tags:** modeling\
**Created:** [June 7, 2022, 8:34pm UTC](https://discourse.pymc.io/t/getting-an-error-aesara-flag-exception-verbosity-high-error-on-pymc-model/9593 "2022-06-07T20:34:44Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![jordan.howell2](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jordan.howell2/32/1742_2.png) [@jordan.howell2](https://discourse.pymc.io/u/jordan.howell2)\
**Post date:** [June 7, 2022, 8:34pm UTC](https://discourse.pymc.io/t/getting-an-error-aesara-flag-exception-verbosity-high-error-on-pymc-model/9593/1 "2022-06-07T20:34:44Z")

</div>

Hello,

I have a model fit, using PyMC and when I tried to sample the ppc, I got the following error:

```auto
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/aesara/compile/function/types.py in __call__ (self, *args, **kwargs)
    975 self.vm()
--> 976 if output_subset is None
    977 else self.vm(output_subset=output_subset)

/opt/conda/lib/python3.7/site-packages/aesara/graph/op.py in rval(p, i, o, n, params)
    523 ):
--> 524 r = p(n, [x[0] for x in i], o)
    525 for o in node.outputs:

/opt/conda/lib/python3.7/site-packages/aesara/tensor/random/op.py in perform(self, node, inputs, outputs)
    367 
--> 368 smpl_val = self.rng_fn(rng, *(args + [size]))
    369 

/opt/conda/lib/python3.7/site-packages/aesara/tensor/random/op.py in rng_fn(self, rng, *args, **kwargs)
    165 """Sample a numeric random variate."""
--> 166 return getattr(rng, self.name)(*args, **kwargs)
    167 

_generator.pyx in numpy.random._generator.Generator.poisson()

_common.pyx in numpy.random._common.disc()

_common.pyx in numpy.random._common.discrete_broadcast_d()

_common.pyx in numpy.random._common.check_array_constraint()

ValueError: lam < 0 or lam contains NaNs

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last)
/tmp/ipykernel_40221/3499126023.py in <module>
      1 with model:
----> 2 ppc = pm.sample_posterior_predictive(trace)
      3 trace.extend(ppc)

/opt/conda/lib/python3.7/site-packages/pymc/sampling.py in sample_posterior_predictive(trace, samples, model, var_names, keep_size, random_seed, progressbar, return_inferencedata, extend_inferencedata, predictions, idata_kwargs, compile_kwargs)
   1955 param = _trace[idx % len_trace]
   1956 
-> 1957 values = sampler_fn(**param)
   1958 
   1959 for k, v in zip(vars_, values):

/opt/conda/lib/python3.7/site-packages/pymc/util.py in wrapped(**kwargs)
    361 def wrapped(**kwargs):
    362 input_point = {k: v for k, v in kwargs.items() if k in ins}
--> 363 return core_function(**input_point)
    364 
    365 return wrapped

/opt/conda/lib/python3.7/site-packages/aesara/compile/function/types.py in __call__ (self, *args, **kwargs)
    990 node=self.vm.nodes[self.vm.position_of_error],
    991 thunk=thunk,
--> 992 storage_map=getattr(self.vm, "storage_map", None),
    993 )
    994 else:

/opt/conda/lib/python3.7/site-packages/aesara/link/utils.py in raise_with_op(fgraph, node, thunk, exc_info, storage_map)
    532 # Some exception need extra parameter in inputs. So forget the
    533 # extra long error message in that case.
--> 534 raise exc_value.with_traceback(exc_trace)
    535 
    536 

/opt/conda/lib/python3.7/site-packages/aesara/compile/function/types.py in __call__ (self, *args, **kwargs)
    974 outputs = (
    975 self.vm()
--> 976 if output_subset is None
    977 else self.vm(output_subset=output_subset)
    978 )

/opt/conda/lib/python3.7/site-packages/aesara/graph/op.py in rval(p, i, o, n, params)
    522 p=p, i=node_input_storage, o=node_output_storage, n=node, params=None
    523 ):
--> 524 r = p(n, [x[0] for x in i], o)
    525 for o in node.outputs:
    526 compute_map[o][0] = True

/opt/conda/lib/python3.7/site-packages/aesara/tensor/random/op.py in perform(self, node, inputs, outputs)
    366 rng_var_out[0] = rng
    367 
--> 368 smpl_val = self.rng_fn(rng, *(args + [size]))
    369 
    370 if (

/opt/conda/lib/python3.7/site-packages/aesara/tensor/random/op.py in rng_fn(self, rng, *args, **kwargs)
    164 def rng_fn(self, rng, *args, **kwargs):
    165 """Sample a numeric random variate."""
--> 166 return getattr(rng, self.name)(*args, **kwargs)
    167 
    168 def __str__ (self):

_generator.pyx in numpy.random._generator.Generator.poisson()

_common.pyx in numpy.random._common.disc()

_common.pyx in numpy.random._common.discrete_broadcast_d()

_common.pyx in numpy.random._common.check_array_constraint()

ValueError: lam < 0 or lam contains NaNs
Apply node that caused the error: poisson_rv{0, (0,), int64, False}(RandomGeneratorSharedVariable(<Generator(PCG64) at 0x7F7CBE6E15F0>), TensorConstant{[]}, TensorConstant{4}, Elemwise{Composite{(i0 + (i1 * i2))}}[(0, 0)].0)
Toposort index: 6
Inputs types: [RandomGeneratorType, TensorType(int64, (0,)), TensorType(int64, ()), TensorType(float64, (None,))]
Inputs shapes: ['No shapes', (0,), (), (493628,)]
Inputs strides: ['No strides', (8,), (), (8,)]
Inputs values: [Generator(PCG64) at 0x7F7CBE6E15F0, array([], dtype=int64), array(4), 'not shown']
Outputs clients: [['output'], []]

Backtrace when the node is created (use Aesara flag traceback__limit=N to make it longer):
  File "/opt/conda/lib/python3.7/site-packages/IPython/core/async_helpers.py", line 78, in _pseudo_sync_runner
    coro.send(None)
  File "/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3257, in run_cell_async
    interactivity=interactivity, compiler=compiler, result=result)
  File "/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3472, in run_ast_nodes
    if (await self.run_code(code, result, async_=asy)):
  File "/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3552, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "/tmp/ipykernel_40221/2033008534.py", line 21, in <module>
    observed = y)
  File "/opt/conda/lib/python3.7/site-packages/pymc/distributions/discrete.py", line 1484, in __new__
    name=name, nonzero_p=psi, nonzero_dist=Poisson.dist(mu=mu), **kwargs
  File "/opt/conda/lib/python3.7/site-packages/pymc/distributions/discrete.py", line 607, in dist
    return super().dist([mu], *args, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/pymc/distributions/distribution.py", line 351, in dist
    rv_out = cls.rv_op(*dist_params, size=create_size, **kwargs)

HINT: Use the Aesara flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.

```

I’m not sure what this means and I tried to set aesara’s exception flag inside the sample, but apparently, that’s not where you set that.

Here is the model I fit:

```auto
items_idx, items = pd.factorize(df1['ITEM_ROOT'])
coords = {'ITEM_ROOT':items,
          'obs_id':np.arange(len(items_idx))}

with pm.Model(coords=coords) as model:
    item_idx = pm.Data('item_idx', items_idx, dims = "obs_id")
    mu_a = pm.Normal("mu_a", mu=0, sigma = 100)
    sigma_a = pm.HalfCauchy("sigma_a", 5)
    
    coef_a = pm.Normal('coef_a', mu=0, sigma = 100)
    sigma_coef = pm.HalfCauchy('sigma_coef', 5)
    
    a = pm.Normal('base_sales', mu = mu_a, sigma = sigma_a, dims='ITEM_ROOT')

    p = pm.Normal('promo_flag',0,1, dims='ITEM_ROOT')

    error = pm.HalfCauchy('error', 1)
    trend = a[item_idx] + (p[item_idx]*np.array(df1['promo_flag']))
    mu_ = trend
    
    likelihood = pm.ZeroInflatedPoisson('y_hat',
                           mu = mu_,
                           psi = .35,
                           observed = y)

    trace = pymc.sampling_jax.sample_numpyro_nuts(tune=2000, draws = 1000)

with model:
    ppc = pm.sample_posterior_predictive(trace)
trace.extend(ppc)

```

This sampled 4 chains in 44 seconds with data the size of `(493628, 2)`. Lightening fast. Am I doing something wrong with