nb: GLM Poisson Regression (error)

Notebook title: GLM: Poisson Regression
Notebook url: https://www.pymc.io/projects/examples/en/latest/generalized_linear_models/GLM-poisson-regression.html

Issue description

# code, cell 18
inf_fish_alt = model.fit()

short Error:

TypeError: You are calling an Aesara function with PyTensor variables.
Starting with PyMC 5.0, Aesara was replaced by PyTensor (see https://www.pymc.io/blog/pytensor_announcement.html).
Replace your import of aesara.tensor with pytensor.tensor.

long Error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[18], line 1
----> 1 inf_fish_alt = model.fit()

File ~/miniforge3/envs/pymc-ex/lib/python3.10/site-packages/bambi/models.py:267, in Model.fit(self, draws, tune, discard_tuned_samples, omit_offsets, include_mean, inference_method, init, n_init, chains, cores, random_seed, **kwargs)
    264         inference_method = method
    266 if not self.built:
--> 267     self.build()
    269 # Tell user which event is being modeled
    270 if isinstance(self.family, univariate.Bernoulli):

File ~/miniforge3/envs/pymc-ex/lib/python3.10/site-packages/bambi/models.py:298, in Model.build(self)
    293 """Set up the model for sampling/fitting.
    294 
    295 Creates an instance of the underlying PyMC model and adds all the necessary terms to it.
    296 """
    297 self.backend = PyMCModel()
--> 298 self.backend.build(self)
    299 self.built = True

File ~/miniforge3/envs/pymc-ex/lib/python3.10/site-packages/bambi/backend/pymc.py:69, in PyMCModel.build(self, spec)
     67 self._build_intercept(spec)
     68 self._build_offsets(spec)
---> 69 self._build_common_terms(spec)
     70 self._build_group_specific_terms(spec)
     71 self._build_response(spec)

File ~/miniforge3/envs/pymc-ex/lib/python3.10/site-packages/bambi/backend/pymc.py:160, in PyMCModel._build_common_terms(self, spec)
    157     columns.append(data)
    159 # Column vector of coefficients and design matrix
--> 160 coefs = at.concatenate(coefs)
    161 data = np.hstack(columns)
    163 # If there's an intercept, center the data
    164 # Also store the design matrix without the intercept to uncenter the intercept later

File ~/miniforge3/envs/pymc-ex/lib/python3.10/site-packages/aesara/tensor/basic.py:2820, in concatenate(tensor_list, axis)
   2813 if not isinstance(tensor_list, (tuple, list)):
   2814     raise TypeError(
   2815         "The 'tensors' argument must be either a tuple "
   2816         "or a list, make sure you did not forget () or [] around "
   2817         "arguments of concatenate.",
   2818         tensor_list,
   2819     )
-> 2820 return join(axis, *tensor_list)

File ~/miniforge3/envs/pymc-ex/lib/python3.10/site-packages/aesara/tensor/basic.py:2645, in join(axis, *tensors_list)
   2643     return tensors_list[0]
   2644 else:
-> 2645     return join_(axis, *tensors_list)

File ~/miniforge3/envs/pymc-ex/lib/python3.10/site-packages/aesara/graph/op.py:297, in Op.__call__(self, *inputs, **kwargs)
    255 r"""Construct an `Apply` node using :meth:`Op.make_node` and return its outputs.
    256 
    257 This method is just a wrapper around :meth:`Op.make_node`.
   (...)
    294 
    295 """
    296 return_list = kwargs.pop("return_list", False)
--> 297 node = self.make_node(*inputs, **kwargs)
    299 if config.compute_test_value != "off":
    300     compute_test_value(node)

File ~/miniforge3/envs/pymc-ex/lib/python3.10/site-packages/aesara/tensor/basic.py:2369, in Join.make_node(self, axis, *tensors)
   2366 if not tensors:
   2367     raise ValueError("Cannot join an empty list of tensors")
-> 2369 tensors = [as_tensor_variable(x) for x in tensors]
   2370 out_dtype = aes.upcast(*[x.type.dtype for x in tensors])
   2372 if not builtins.all(targs.type.ndim for targs in tensors):

File ~/miniforge3/envs/pymc-ex/lib/python3.10/site-packages/aesara/tensor/basic.py:2369, in <listcomp>(.0)
   2366 if not tensors:
   2367     raise ValueError("Cannot join an empty list of tensors")
-> 2369 tensors = [as_tensor_variable(x) for x in tensors]
   2370 out_dtype = aes.upcast(*[x.type.dtype for x in tensors])
   2372 if not builtins.all(targs.type.ndim for targs in tensors):

File ~/miniforge3/envs/pymc-ex/lib/python3.10/site-packages/aesara/tensor/__init__.py:42, in as_tensor_variable(x, name, ndim, **kwargs)
     10 def as_tensor_variable(
     11     x: Any, name: Optional[str] = None, ndim: Optional[int] = None, **kwargs
     12 ) -> "TensorVariable":
     13     """Convert `x` into an equivalent `TensorVariable`.
     14 
     15     This function can be used to turn ndarrays, numbers, `ScalarType` instances,
   (...)
     40 
     41     """
---> 42     return _as_tensor_variable(x, name, ndim, **kwargs)

File ~/miniforge3/envs/pymc-ex/lib/python3.10/functools.py:889, in singledispatch.<locals>.wrapper(*args, **kw)
    885 if not args:
    886     raise TypeError(f'{funcname} requires at least '
    887                     '1 positional argument')
--> 889 return dispatch(args[0].__class__)(*args, **kw)

File ~/miniforge3/envs/pymc-ex/lib/python3.10/site-packages/pymc/pytensorf.py:99, in raise_informative_error(*args, **kwargs)
     97 @_as_tensor_variable_aesara.register(TensorVariable)
     98 def raise_informative_error(*args, **kwargs):
---> 99     raise TypeError(
    100         "You are calling an Aesara function with PyTensor variables.\n"
    101         "Starting with PyMC 5.0, Aesara was replaced by PyTensor (see https://www.pymc.io/blog/pytensor_announcement.html).\n"
    102         "Replace your import of aesara.tensor with pytensor.tensor.",
    103     )

TypeError: You are calling an Aesara function with PyTensor variables.
Starting with PyMC 5.0, Aesara was replaced by PyTensor (see https://www.pymc.io/blog/pytensor_announcement.html).
Replace your import of aesara.tensor with pytensor.tensor.

Expected output

If applicable, describe what should happen instead.

Proposed solution

If applicable, explain possible solutions and workarounds.

Open the GLM: Poisson Regression notebook and inspect the imports and cell 18 where model.fit() fails. Reproduce the example in the current PyMC environment, trace the Aesara/PyTensor mismatch, and verify that the notebook runs through fitting without the TypeError.