Description
While working on #4625 I ran into a failure that is demoed by the following example:
with pm.Model() as model:
pm.Normal("x1", mu=0, sd=1, observed=np.random.normal(size=(3, 4)))
model.logp()
pm.Normal("x2", mu=0, sd=1, observed=np.random.normal(size=(3, 1)))
model.logp()Essentially passing a column vector breaks the model, whereas a matrix is fine.
Traceback
TypeError Traceback (most recent call last)
in
----> 1 test_observed_with_column_vector()
in test_observed_with_column_vector()
4 model.logp()
5 pm.Normal("x2", mu=0, sd=1, observed=np.random.normal(size=(3, 1)))
----> 6 model.logp()
e:\source\repos\pymc3\pymc3\model.py in logp(self)
264 def logp(self):
265 """Compiled log probability density function"""
--> 266 return self.model.fn(self.logpt)
267
268 @Property
e:\source\repos\pymc3\pymc3\model.py in logpt(self)
712 with self:
713 factors = [logpt_sum(var, self.rvs_to_values.get(var, None)) for var in self.free_RVs]
--> 714 factors += [logpt_sum(obs, obs.tag.observations) for obs in self.observed_RVs]
715
716 # Convert random variables into their log-likelihood inputs and
e:\source\repos\pymc3\pymc3\model.py in (.0)
712 with self:
713 factors = [logpt_sum(var, self.rvs_to_values.get(var, None)) for var in self.free_RVs]
--> 714 factors += [logpt_sum(obs, obs.tag.observations) for obs in self.observed_RVs]
715
716 # Convert random variables into their log-likelihood inputs and
e:\source\repos\pymc3\pymc3\distributions\logp.py in logpt_sum(*args, **kwargs)
366 if only the sum of the logp values is needed.
367 """
--> 368 return logpt(*args, sum=True, **kwargs)
e:\source\repos\pymc3\pymc3\distributions\logp.py in logpt(var, rv_values, jacobian, scaling, transformed, cdf, sum, **kwargs)
216 replacements.update({rv_var: rv_value, rv_value_var: rv_value})
217
--> 218 (logp_var,), _ = rvs_to_value_vars(
219 (logp_var,),
220 apply_transforms=transformed and not cdf,
e:\source\repos\pymc3\pymc3\aesaraf.py in rvs_to_value_vars(graphs, apply_transforms, initial_replacements, **kwargs)
352 return [trans_rv_value]
353
--> 354 return replace_rvs_in_graphs(graphs, transform_replacements, initial_replacements, **kwargs)
355
356
e:\source\repos\pymc3\pymc3\aesaraf.py in replace_rvs_in_graphs(graphs, replacement_fn, initial_replacements, **kwargs)
300 )
301
--> 302 fg.replace_all(replacements.items(), import_missing=True)
303
304 graphs = list(fg.outputs)
~\miniconda3\envs\pm3-dev\lib\site-packages\aesara\graph\fg.py in replace_all(self, pairs, **kwargs)
555 """Replace variables in the FunctionGraph according to (var, new_var) pairs in a list."""
556 for var, new_var in pairs:
--> 557 self.replace(var, new_var, **kwargs)
558
559 def attach_feature(self, feature: Feature) -> NoReturn:
~\miniconda3\envs\pm3-dev\lib\site-packages\aesara\graph\fg.py in replace(self, var, new_var, reason, verbose, import_missing)
513 print(reason, var, new_var)
514
--> 515 new_var = var.type.filter_variable(new_var, allow_convert=True)
516
517 if var not in self.variables:
~\miniconda3\envs\pm3-dev\lib\site-packages\aesara\tensor\type.py in filter_variable(self, other, allow_convert)
256 return other2
257
--> 258 raise TypeError(
259 f"Cannot convert Type {other.type} "
260 f"(of Variable {other}) into Type {self}. "
TypeError: Cannot convert Type TensorType(float64, matrix) (of Variable Rebroadcast{?,0}.0) into Type TensorType(float64, col). You can try to manually convert Rebroadcast{?,0}.0 into a TensorType(float64, col).