rethinking chpater5 notebook: 5.34 ValueError: shape mismatch: objects cannot be broadcast to a single shape. about pymc-resources HOT 2 OPEN

Can you try running this with the most recent version of PyMC (v5)?

from pymc-resources.

Can you try running this with the most recent version of PyMC (v5)?

Hello! I have tried the most recent version PyMC (v5). This time, I bumped into another error in an earlier cell so I am not sure if that would fix 5.34
Below is the new error I saw in 5.19

data["Divorce_std"] = standardize(data["Divorce"])
data["Marriage_std"] = standardize(data["Marriage"])
data["MedianAgeMarriage_std"] = standardize(data["MedianAgeMarriage"])

# Use Aesara shared variables so we can change them later
marriage_shared = shared(data["Marriage_std"].values)
age_shared = shared(data["MedianAgeMarriage_std"].values)

with pm.Model() as m5_3_A:
    # A -> D <- M
    sigma = pm.Exponential("sigma", 1)
    bA = pm.Normal("bA", 0, 0.5)
    bM = pm.Normal("bM", 0, 0.5)

    a = pm.Normal("a", 0, 0.2)
    mu = pm.Deterministic("mu", a + bA * age_shared + bM * marriage_shared)
    divorce = pm.Normal("divorce", mu, sigma, observed=data["Divorce_std"].values)

    # A -> M
    sigma_M = pm.Exponential("sigma_m", 1)
    bAM = pm.Normal("bAM", 0, 0.5)
    aM = pm.Normal("aM", 0, 0.2)
    mu_M = pm.Deterministic("mu_m", aM + bAM * age_shared)
    marriage = pm.Normal("marriage", mu_M, sigma_M, observed=data["Marriage_std"].values)

    m5_3_A_trace = pm.sample()

---

NotImplementedError Traceback (most recent call last)
Cell In[33], line 16
13 bM = pm.Normal("bM", 0, 0.5)
15 a = pm.Normal("a", 0, 0.2)
---> 16 mu = pm.Deterministic("mu", a + bA * age_shared + bM * marriage_shared)
17 divorce = pm.Normal("divorce", mu, sigma, observed=data["Divorce_std"].values)
19 # A -> M

File ~\anaconda3\envs\pymc_env\Lib\site-packages\aesara\tensor\var.py:207, in _tensor_py_operators.rmul(self, other)
206 def rmul(self, other):
--> 207 return at.math.mul(other, self)

File ~\anaconda3\envs\pymc_env\Lib\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 ~\anaconda3\envs\pymc_env\Lib\site-packages\aesara\tensor\elemwise.py:484, in Elemwise.make_node(self, *inputs)
478 def make_node(self, *inputs):
479 """
480 If the inputs have different number of dimensions, their shape
481 is left-completed to the greatest number of dimensions with 1s
482 using DimShuffle.
483 """
--> 484 inputs = [as_tensor_variable(i) for i in inputs]
485 out_dtypes, out_shapes, inputs = self.get_output_info(DimShuffle, *inputs)
486 outputs = [
487 TensorType(dtype=dtype, shape=shape)()
488 for dtype, shape in zip(out_dtypes, out_shapes)
489 ]

File ~\anaconda3\envs\pymc_env\Lib\site-packages\aesara\tensor\elemwise.py:484, in (.0)
478 def make_node(self, *inputs):
479 """
480 If the inputs have different number of dimensions, their shape
481 is left-completed to the greatest number of dimensions with 1s
482 using DimShuffle.
483 """
--> 484 inputs = [as_tensor_variable(i) for i in inputs]
485 out_dtypes, out_shapes, inputs = self.get_output_info(DimShuffle, *inputs)
486 outputs = [
487 TensorType(dtype=dtype, shape=shape)()
488 for dtype, shape in zip(out_dtypes, out_shapes)
489 ]

File ~\anaconda3\envs\pymc_env\Lib\site-packages\aesara\tensor_init_.py:49, in as_tensor_variable(x, name, ndim, **kwargs)
17 def as_tensor_variable(
18 x: TensorLike, name: Optional[str] = None, ndim: Optional[int] = None, **kwargs
19 ) -> "TensorVariable":
20 """Convert x into an equivalent TensorVariable.
21
22 This function can be used to turn ndarrays, numbers, ScalarType instances,
(...)
47
48 """
---> 49 return _as_tensor_variable(x, name, ndim, **kwargs)

File ~\anaconda3\envs\pymc_env\Lib\functools.py:909, in singledispatch..wrapper(*args, **kw)
905 if not args:
906 raise TypeError(f'{funcname} requires at least '
907 '1 positional argument')
--> 909 return dispatch(args[0].class)(*args, **kw)

File ~\anaconda3\envs\pymc_env\Lib\site-packages\aesara\tensor_init_.py:56, in _as_tensor_variable(x, name, ndim, **kwargs)
52 @singledispatch
53 def _as_tensor_variable(
54 x: TensorLike, name: Optional[str], ndim: Optional[int], **kwargs
55 ) -> "TensorVariable":
---> 56 raise NotImplementedError(f"Cannot convert {x!r} to a tensor variable.")

NotImplementedError: Cannot convert bA to a tensor variable.

Last but not least, this is my computer setting:
Window11, anaconda, pymc v5
conda create -c conda-forge -n pymc_env "pymc>=5"
Libraries required for this notebook were installed as below:
pip install notebook, patsy, causalgraphicalmodels, daft, theano
conda install -c conda-forge watermark, seaborn, tqdm, aesara==2.8.7
Initially, I was hoping to install all the libraries according to the environment yaml file for pymc v4. However, there was some verion conflicts so I ended up installing some of the libraries via pip.
Please feel free to let me know if you have any thought.

from pymc-resources.