TypeError: ParetoNBDRV.make_node() missing 1 required positional argument: 'T'

Questions v5
1

Hi team,

I am following the “How-to” instruction on pymc-marketing Pareto/NBD, but get the error. Can anyone please help with it? Thank you!

The code:

flat_config = {
    "r_prior": {"dist": "HalfFlat", "kwargs": {}},
    "alpha_prior": {"dist": "HalfFlat", "kwargs": {}},
    "s_prior": {"dist": "HalfFlat", "kwargs": {}},
    "beta_prior": {"dist": "HalfFlat", "kwargs": {}},
    }

pnbd_pymc = clv.ParetoNBDModel(data=rfm_data, model_config=flat_config)
pnbd_pymc.build_model()

The error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[13], line 11
      3 flat_config = {
      4     "r_prior": {"dist": "HalfFlat", "kwargs": {}},
      5     "alpha_prior": {"dist": "HalfFlat", "kwargs": {}},
      6     "s_prior": {"dist": "HalfFlat", "kwargs": {}},
      7     "beta_prior": {"dist": "HalfFlat", "kwargs": {}},
      8     }
     10 pnbd_pymc = clv.ParetoNBDModel(data=rfm_data, model_config=flat_config)
---> 11 pnbd_pymc.build_model()

File ~/miniconda3/envs/pymc_env/lib/python3.12/site-packages/pymc_marketing/clv/models/pareto_nbd.py:313, in ParetoNBDModel.build_model(self)
    310 else:
    311     beta = self.model.register_rv(beta_scale_prior, name="beta")
--> 313 ParetoNBD(
    314     name="recency_frequency",
    315     r=r,
    316     alpha=alpha,
    317     s=s,
    318     beta=beta,
    319     T=self.data["T"],
    320     observed=np.stack(
    321         (self.data["recency"], self.data["frequency"]), axis=1
    322     ),
    323     dims=["customer_id", "obs_var"],
    324 )

File ~/miniconda3/envs/pymc_env/lib/python3.12/site-packages/pymc/distributions/distribution.py:536, in Distribution.__new__(cls, name, rng, dims, initval, observed, total_size, transform, default_transform, *args, **kwargs)
    533     elif observed is not None:
    534         kwargs["shape"] = tuple(observed.shape)
--> 536 rv_out = cls.dist(*args, **kwargs)
    538 rv_out = model.register_rv(
    539     rv_out,
    540     name,
   (...)
    546     initval=initval,
    547 )
    549 # add in pretty-printing support

File ~/miniconda3/envs/pymc_env/lib/python3.12/site-packages/pymc_marketing/clv/distributions.py:413, in ParetoNBD.dist(cls, r, alpha, s, beta, T, **kwargs)
    411 @classmethod
    412 def dist(cls, r, alpha, s, beta, T, **kwargs):
--> 413     return super().dist([r, alpha, s, beta, T], **kwargs)

File ~/miniconda3/envs/pymc_env/lib/python3.12/site-packages/pymc/distributions/distribution.py:618, in Distribution.dist(cls, dist_params, shape, **kwargs)
    615     ndim_supp = cls.rv_op(*dist_params, **kwargs).owner.op.ndim_supp
    617 create_size = find_size(shape=shape, size=size, ndim_supp=ndim_supp)
--> 618 rv_out = cls.rv_op(*dist_params, size=create_size, **kwargs)
    620 rv_out.logp = _make_nice_attr_error("rv.logp(x)", "pm.logp(rv, x)")
    621 rv_out.logcdf = _make_nice_attr_error("rv.logcdf(x)", "pm.logcdf(rv, x)")

File ~/miniconda3/envs/pymc_env/lib/python3.12/site-packages/pymc_marketing/clv/distributions.py:301, in ParetoNBDRV.__call__(self, r, alpha, s, beta, T, size, **kwargs)
    300 def __call__(self, r, alpha, s, beta, T, size=None, **kwargs):
--> 301     return super().__call__(r, alpha, s, beta, T, size=size, **kwargs)

File ~/miniconda3/envs/pymc_env/lib/python3.12/site-packages/pytensor/tensor/random/op.py:311, in RandomVariable.__call__(self, size, name, rng, dtype, *args, **kwargs)
    309     props["dtype"] = dtype
    310     new_op = type(self)(**props)
--> 311     return new_op.__call__(
    312         *args, size=size, name=name, rng=rng, dtype=dtype, **kwargs
    313     )
    315 res = super().__call__(rng, size, *args, **kwargs)
    317 if name is not None:

File ~/miniconda3/envs/pymc_env/lib/python3.12/site-packages/pymc_marketing/clv/distributions.py:301, in ParetoNBDRV.__call__(self, r, alpha, s, beta, T, size, **kwargs)
    300 def __call__(self, r, alpha, s, beta, T, size=None, **kwargs):
--> 301     return super().__call__(r, alpha, s, beta, T, size=size, **kwargs)

File ~/miniconda3/envs/pymc_env/lib/python3.12/site-packages/pytensor/tensor/random/op.py:315, in RandomVariable.__call__(self, size, name, rng, dtype, *args, **kwargs)
    310     new_op = type(self)(**props)
    311     return new_op.__call__(
    312         *args, size=size, name=name, rng=rng, dtype=dtype, **kwargs
    313     )
--> 315 res = super().__call__(rng, size, *args, **kwargs)
    317 if name is not None:
    318     res.name = name

File ~/miniconda3/envs/pymc_env/lib/python3.12/site-packages/pytensor/graph/op.py:293, in Op.__call__(self, name, return_list, *inputs, **kwargs)
    249 def __call__(
    250     self, *inputs: Any, name=None, return_list=False, **kwargs
    251 ) -> Variable | list[Variable]:
    252     r"""Construct an `Apply` node using :meth:`Op.make_node` and return its outputs.
    253 
    254     This method is just a wrapper around :meth:`Op.make_node`.
   (...)
    291 
    292     """
--> 293     node = self.make_node(*inputs, **kwargs)
    294     if name is not None:
    295         if len(node.outputs) == 1:

TypeError: ParetoNBDRV.make_node() missing 1 required positional argument: 'T'
2

I installed pymc on the conda env and was following exactly the steps on the “How-to” webpage: Pareto/NBD Model — pymc-marketing 0.6.0 documentation

3

What version of PyMC is being installed? You can get it by running pymc.__version__ after importing it in whatever environment you’re using

4

The version of PyMC is 5.16.1

5

Try to go down to 5.15.1. The last PyMC release did some breaking API changes that pymc-experimental still is not handling

6

Thank you! I am trying pymc=5.15.1, it works now.