Hello,
I am trying to run a model from Statistical Rethinking Ch 11. I am using this code but trying to use PyMC v4 and aesara. I am using PyMC 4.1.1 and aesara 2.7.4
Here’s my code:
with pm.Model() as m11_1:
a = pm.Normal('a', 0, 10, shape=6, transform=pm.distributions.transforms.Ordered, initval=np.arange(6)-2.5)
pa = pm.math.sigmoid(a)
p_cum = at.concatenate([[0], pa, [1]])
p = p_cum[1:] - p_cum[:-1]
resp = pm.Categorical('resp', p, observed=data.response-1)
trace = pm.sample()
And the error I am getting is
ERROR (aesara.graph.opt): Optimization failure due to: transform_values
ERROR (aesara.graph.opt): node: normal_rv{0, (0, 0), floatX, False}(RandomGeneratorSharedVariable(<Generator(PCG64) at 0x192823BC900>), TensorConstant{(1,) of 6}, TensorConstant{11}, TensorConstant{0}, TensorConstant{10.0})
ERROR (aesara.graph.opt): TRACEBACK:
ERROR (aesara.graph.opt): Traceback (most recent call last):
File "C:\Users\blake\anaconda3\envs\pymc4\lib\site-packages\aesara\graph\opt.py", line 1861, in process_node
replacements = lopt.transform(fgraph, node)
File "C:\Users\blake\anaconda3\envs\pymc4\lib\site-packages\aesara\graph\opt.py", line 1066, in transform
return self.fn(fgraph, node)
File "C:\Users\blake\anaconda3\envs\pymc4\lib\site-packages\aeppl\transforms.py", line 159, in transform_values
transform.backward(value_var, *trans_node.inputs), value_var
File "C:\Users\blake\anaconda3\envs\pymc4\lib\site-packages\pymc\distributions\transforms.py", line 73, in backward
x = at.zeros(value.shape)
AttributeError: 'RandomGeneratorSharedVariable' object has no attribute 'shape'
ERROR (aesara.graph.opt): Optimization failure due to: transform_values
ERROR (aesara.graph.opt): node: normal_rv{0, (0, 0), floatX, False}(RandomGeneratorSharedVariable(<Generator(PCG64) at 0x192823BC900>), TensorConstant{(1,) of 6}, TensorConstant{11}, TensorConstant{0}, TensorConstant{10.0})
ERROR (aesara.graph.opt): TRACEBACK:
ERROR (aesara.graph.opt): Traceback (most recent call last):
File "C:\Users\blake\anaconda3\envs\pymc4\lib\site-packages\aesara\graph\opt.py", line 1861, in process_node
replacements = lopt.transform(fgraph, node)
File "C:\Users\blake\anaconda3\envs\pymc4\lib\site-packages\aesara\graph\opt.py", line 1066, in transform
return self.fn(fgraph, node)
File "C:\Users\blake\anaconda3\envs\pymc4\lib\site-packages\aeppl\transforms.py", line 159, in transform_values
transform.backward(value_var, *trans_node.inputs), value_var
File "C:\Users\blake\anaconda3\envs\pymc4\lib\site-packages\pymc\distributions\transforms.py", line 73, in backward
x = at.zeros(value.shape)
AttributeError: 'RandomGeneratorSharedVariable' object has no attribute 'shape'
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Input In [8], in <cell line: 1>()
1 with m11_1:
----> 2 trace = pm.sample()
File ~\anaconda3\envs\pymc4\lib\site-packages\pymc\sampling.py:524, 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)
521 auto_nuts_init = False
523 initial_points = None
--> 524 step = assign_step_methods(model, step, methods=pm.STEP_METHODS, step_kwargs=kwargs)
526 if isinstance(step, list):
527 step = CompoundStep(step)
File ~\anaconda3\envs\pymc4\lib\site-packages\pymc\sampling.py:229, in assign_step_methods(model, step, methods, step_kwargs)
221 selected = max(
222 methods,
223 key=lambda method, var=rv_var, has_gradient=has_gradient: method._competence(
224 var, has_gradient
225 ),
226 )
227 selected_steps[selected].append(var)
--> 229 return instantiate_steppers(model, steps, selected_steps, step_kwargs)
File ~\anaconda3\envs\pymc4\lib\site-packages\pymc\sampling.py:147, in instantiate_steppers(model, steps, selected_steps, step_kwargs)
145 args = step_kwargs.get(step_class.name, {})
146 used_keys.add(step_class.name)
--> 147 step = step_class(vars=vars, model=model, **args)
148 steps.append(step)
150 unused_args = set(step_kwargs).difference(used_keys)
File ~\anaconda3\envs\pymc4\lib\site-packages\pymc\step_methods\hmc\nuts.py:178, in NUTS.__init__(self, vars, max_treedepth, early_max_treedepth, **kwargs)
120 def __init__(self, vars=None, max_treedepth=10, early_max_treedepth=8, **kwargs):
121 r"""Set up the No-U-Turn sampler.
122
123 Parameters
(...)
176 `pm.sample` to the desired number of tuning steps.
177 """
--> 178 super().__init__(vars, **kwargs)
180 self.max_treedepth = max_treedepth
181 self.early_max_treedepth = early_max_treedepth
File ~\anaconda3\envs\pymc4\lib\site-packages\pymc\step_methods\hmc\base_hmc.py:95, in BaseHMC.__init__(self, vars, scaling, step_scale, is_cov, model, blocked, potential, dtype, Emax, target_accept, gamma, k, t0, adapt_step_size, step_rand, **aesara_kwargs)
92 else:
93 vars = [self._model.rvs_to_values.get(var, var) for var in vars]
---> 95 super().__init__(vars, blocked=blocked, model=self._model, dtype=dtype, **aesara_kwargs)
97 self.adapt_step_size = adapt_step_size
98 self.Emax = Emax
File ~\anaconda3\envs\pymc4\lib\site-packages\pymc\step_methods\arraystep.py:276, in GradientSharedStep.__init__(self, vars, model, blocked, dtype, logp_dlogp_func, **aesara_kwargs)
273 model = modelcontext(model)
275 if logp_dlogp_func is None:
--> 276 func = model.logp_dlogp_function(vars, dtype=dtype, **aesara_kwargs)
277 else:
278 func = logp_dlogp_func
File ~\anaconda3\envs\pymc4\lib\site-packages\pymc\model.py:637, in Model.logp_dlogp_function(self, grad_vars, tempered, **kwargs)
635 input_vars = {i for i in graph_inputs(costs) if not isinstance(i, Constant)}
636 extra_vars = [self.rvs_to_values.get(var, var) for var in self.free_RVs]
--> 637 ip = self.initial_point(0)
638 extra_vars_and_values = {
639 var: ip[var.name] for var in extra_vars if var in input_vars and var not in grad_vars
640 }
641 return ValueGradFunction(costs, grad_vars, extra_vars_and_values, **kwargs)
File ~\anaconda3\envs\pymc4\lib\site-packages\pymc\model.py:1067, in Model.initial_point(self, seed)
1059 def initial_point(self, seed=None) -> Dict[str, np.ndarray]:
1060 """Computes the initial point of the model.
1061
1062 Returns
(...)
1065 Maps names of transformed variables to numeric initial values in the transformed space.
1066 """
-> 1067 fn = make_initial_point_fn(model=self, return_transformed=True)
1068 return Point(fn(seed), model=self)
File ~\anaconda3\envs\pymc4\lib\site-packages\pymc\initial_point.py:159, in make_initial_point_fn(model, overrides, jitter_rvs, default_strategy, return_transformed)
153 sdict_overrides = convert_str_to_rv_dict(model, overrides or {})
154 initval_strats = {
155 **model.initial_values,
156 **sdict_overrides,
157 }
--> 159 initial_values = make_initial_point_expression(
160 free_rvs=model.free_RVs,
161 rvs_to_values=model.rvs_to_values,
162 initval_strategies=initval_strats,
163 jitter_rvs=jitter_rvs,
164 default_strategy=default_strategy,
165 return_transformed=return_transformed,
166 )
168 # Replace original rng shared variables so that we don't mess with them
169 # when calling the final seeded function
170 graph = FunctionGraph(outputs=initial_values, clone=False)
File ~\anaconda3\envs\pymc4\lib\site-packages\pymc\initial_point.py:281, in make_initial_point_expression(free_rvs, rvs_to_values, initval_strategies, jitter_rvs, default_strategy, return_transformed)
278 transform = getattr(rvs_to_values[variable].tag, "transform", None)
280 if transform is not None:
--> 281 value = transform.forward(value, *variable.owner.inputs)
283 if variable in jitter_rvs:
284 jitter = at.random.uniform(-1, 1, size=value.shape)
File ~\anaconda3\envs\pymc4\lib\site-packages\pymc\distributions\transforms.py:79, in Ordered.forward(self, value, *inputs)
78 def forward(self, value, *inputs):
---> 79 y = at.zeros(value.shape)
80 y = at.inc_subtensor(y[..., 0], value[..., 0])
81 y = at.inc_subtensor(y[..., 1:], at.log(value[..., 1:] - value[..., :-1]))
AttributeError: 'RandomGeneratorSharedVariable' object has no attribute 'shape'
If anyone can point me in that right direction, I would appreciate it.