Problem to load trained BART model for prediction (original) (raw)
@aloctavodia Thanks for your fast reply! 
The problem is not so much about saving idata, but to load it later to run predictions. If I train the model and do predictions in one notebook, everything works fine. But if I train the model and save the idata in one notebook, and later in another notebook I load the idata to run predictions, I’m getting the error that I’ve posted above.
I’ve just tried to add shape=data_x.shape but this just gave me an error while building the model:
---------------------------------------------------------------------------
ShapeError Traceback (most recent call last)
Cell In[35], line 1
----> 1 model = build_model()
2 pm.model_to_graphviz(model)
Cell In[34], line 14, in build_model()
11 p1 = pm.Deterministic('p1', pm.invlogit(k1), dims='id')
12 p0 = pm.Deterministic('p0', 1 - p1, dims='id')
---> 14 pm.Bernoulli('class', p=p1, observed=data_class, dims='id', shape=data_x.shape)
15 return model
File /opt/conda/lib/python3.10/site-packages/pymc/distributions/distribution.py:460, in Discrete.__new__(cls, name, *args, **kwargs)
457 if kwargs.get("transform", None):
458 raise ValueError("Transformations for discrete distributions")
--> 460 return super().__new__(cls, name, *args, **kwargs)
File /opt/conda/lib/python3.10/site-packages/pymc/distributions/distribution.py:314, in Distribution.__new__(cls, name, rng, dims, initval, observed, total_size, transform, *args, **kwargs)
310 kwargs["shape"] = tuple(observed.shape)
312 rv_out = cls.dist(*args, **kwargs)
--> 314 rv_out = model.register_rv(
315 rv_out,
316 name,
317 observed,
318 total_size,
319 dims=dims,
320 transform=transform,
321 initval=initval,
322 )
324 # add in pretty-printing support
325 rv_out.str_repr = types.MethodType(str_for_dist, rv_out)
File /opt/conda/lib/python3.10/site-packages/pymc/model.py:1356, in Model.register_rv(self, rv_var, name, observed, total_size, dims, transform, initval)
1349 raise TypeError(
1350 "Variables that depend on other nodes cannot be used for observed data."
1351 f"The data variable was: {observed}"
1352 )
1354 # `rv_var` is potentially changed by `make_obs_var`,
1355 # for example into a new graph for imputation of missing data.
-> 1356 rv_var = self.make_obs_var(rv_var, observed, dims, transform, total_size)
1358 return rv_var
File /opt/conda/lib/python3.10/site-packages/pymc/model.py:1387, in Model.make_obs_var(self, rv_var, data, dims, transform, total_size)
1384 data = convert_observed_data(data).astype(rv_var.dtype)
1386 if data.ndim != rv_var.ndim:
-> 1387 raise ShapeError(
1388 "Dimensionality of data and RV don't match.", actual=data.ndim, expected=rv_var.ndim
1389 )
1391 if pytensor.config.compute_test_value != "off":
1392 test_value = getattr(rv_var.tag, "test_value", None)
ShapeError: Dimensionality of data and RV don't match. (actual 1 != expected 2)
I also tried to add shape=data_class.shape instead which allowed me model building and sampling, but during prediction I’m getting then an error:
ValueError Traceback (most recent call last)
File /opt/conda/lib/python3.10/site-packages/pytensor/compile/function/types.py:970, in Function.__call__(self, *args, **kwargs)
968 try:
969 outputs = (
--> 970 self.vm()
971 if output_subset is None
972 else self.vm(output_subset=output_subset)
973 )
974 except Exception:
File /opt/conda/lib/python3.10/site-packages/pytensor/graph/op.py:543, in Op.make_py_thunk.<locals>.rval(p, i, o, n, params)
539 @is_thunk_type
540 def rval(
541 p=p, i=node_input_storage, o=node_output_storage, n=node, params=None
542 ):
--> 543 r = p(n, [x[0] for x in i], o)
544 for o in node.outputs:
File /opt/conda/lib/python3.10/site-packages/pytensor/tensor/random/op.py:378, in RandomVariable.perform(self, node, inputs, outputs)
376 rng_var_out[0] = rng
--> 378 smpl_val = self.rng_fn(rng, *(args + [size]))
380 if (
381 not isinstance(smpl_val, np.ndarray)
382 or str(smpl_val.dtype) != out_var.type.dtype
383 ):
File /opt/conda/lib/python3.10/site-packages/pytensor/tensor/random/basic.py:55, in ScipyRandomVariable.rng_fn(cls, *args, **kwargs)
54 size = args[-1]
---> 55 res = cls.rng_fn_scipy(*args, **kwargs)
57 if np.ndim(res) == 0:
58 # The sample is an `np.number`, and is not writeable, or non-NumPy
59 # type, so we need to clone/create a usable NumPy result
File /opt/conda/lib/python3.10/site-packages/pytensor/tensor/random/basic.py:1473, in BernoulliRV.rng_fn_scipy(cls, rng, p, size)
1471 @classmethod
1472 def rng_fn_scipy(cls, rng, p, size):
-> 1473 return stats.bernoulli.rvs(p, size=size, random_state=rng)
File /opt/conda/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py:3357, in rv_discrete.rvs(self, *args, **kwargs)
3356 kwargs['discrete'] = True
-> 3357 return super().rvs(*args, **kwargs)
File /opt/conda/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py:1028, in rv_generic.rvs(self, *args, **kwds)
1027 rndm = kwds.pop('random_state', None)
-> 1028 args, loc, scale, size = self._parse_args_rvs(*args, **kwds)
1029 cond = logical_and(self._argcheck(*args), (scale >= 0))
File <string>:6, in _parse_args_rvs(self, p, loc, size)
File /opt/conda/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py:909, in rv_generic._argcheck_rvs(self, *args, **kwargs)
908 if not ok:
--> 909 raise ValueError("size does not match the broadcast shape of "
910 "the parameters. %s, %s, %s" % (size, size_,
911 bcast_shape))
913 param_bcast = all_bcast[:-2]
ValueError: size does not match the broadcast shape of the parameters. (617,), (617,), (5,)
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
Cell In[12], line 5
1 with model:
2 pm.set_data({
3 'data_x': df_test.drop(columns=['Class'], errors='ignore'),
4 }, coords={'id': df_test.index})
----> 5 idata = pm.sample_posterior_predictive(idata, extend_inferencedata=True, predictions=True)
6 idata.to_netcdf(FILE_IDATA)
7 display(idata)
File /opt/conda/lib/python3.10/site-packages/pymc/sampling/forward.py:644, in sample_posterior_predictive(trace, model, var_names, sample_dims, random_seed, progressbar, return_inferencedata, extend_inferencedata, predictions, idata_kwargs, compile_kwargs)
639 # there's only a single chain, but the index might hit it multiple times if
640 # the number of indices is greater than the length of the trace.
641 else:
642 param = _trace[idx % len_trace]
--> 644 values = sampler_fn(**param)
646 for k, v in zip(vars_, values):
647 ppc_trace_t.insert(k.name, v, idx)
File /opt/conda/lib/python3.10/site-packages/pymc/util.py:389, in point_wrapper.<locals>.wrapped(**kwargs)
387 def wrapped(**kwargs):
388 input_point = {k: v for k, v in kwargs.items() if k in ins}
--> 389 return core_function(**input_point)
File /opt/conda/lib/python3.10/site-packages/pytensor/compile/function/types.py:983, in Function.__call__(self, *args, **kwargs)
981 if hasattr(self.vm, "thunks"):
982 thunk = self.vm.thunks[self.vm.position_of_error]
--> 983 raise_with_op(
984 self.maker.fgraph,
985 node=self.vm.nodes[self.vm.position_of_error],
986 thunk=thunk,
987 storage_map=getattr(self.vm, "storage_map", None),
988 )
989 else:
990 # old-style linkers raise their own exceptions
991 raise
File /opt/conda/lib/python3.10/site-packages/pytensor/link/utils.py:535, in raise_with_op(fgraph, node, thunk, exc_info, storage_map)
530 warnings.warn(
531 f"{exc_type} error does not allow us to add an extra error message"
532 )
533 # Some exception need extra parameter in inputs. So forget the
534 # extra long error message in that case.
--> 535 raise exc_value.with_traceback(exc_trace)
File /opt/conda/lib/python3.10/site-packages/pytensor/compile/function/types.py:970, in Function.__call__(self, *args, **kwargs)
967 t0_fn = time.perf_counter()
968 try:
969 outputs = (
--> 970 self.vm()
971 if output_subset is None
972 else self.vm(output_subset=output_subset)
973 )
974 except Exception:
975 restore_defaults()
File /opt/conda/lib/python3.10/site-packages/pytensor/graph/op.py:543, in Op.make_py_thunk.<locals>.rval(p, i, o, n, params)
539 @is_thunk_type
540 def rval(
541 p=p, i=node_input_storage, o=node_output_storage, n=node, params=None
542 ):
--> 543 r = p(n, [x[0] for x in i], o)
544 for o in node.outputs:
545 compute_map[o][0] = True
File /opt/conda/lib/python3.10/site-packages/pytensor/tensor/random/op.py:378, in RandomVariable.perform(self, node, inputs, outputs)
374 rng = copy(rng)
376 rng_var_out[0] = rng
--> 378 smpl_val = self.rng_fn(rng, *(args + [size]))
380 if (
381 not isinstance(smpl_val, np.ndarray)
382 or str(smpl_val.dtype) != out_var.type.dtype
383 ):
384 smpl_val = _asarray(smpl_val, dtype=out_var.type.dtype)
File /opt/conda/lib/python3.10/site-packages/pytensor/tensor/random/basic.py:55, in ScipyRandomVariable.rng_fn(cls, *args, **kwargs)
52 @classmethod
53 def rng_fn(cls, *args, **kwargs):
54 size = args[-1]
---> 55 res = cls.rng_fn_scipy(*args, **kwargs)
57 if np.ndim(res) == 0:
58 # The sample is an `np.number`, and is not writeable, or non-NumPy
59 # type, so we need to clone/create a usable NumPy result
60 res = np.asarray(res)
File /opt/conda/lib/python3.10/site-packages/pytensor/tensor/random/basic.py:1473, in BernoulliRV.rng_fn_scipy(cls, rng, p, size)
1471 @classmethod
1472 def rng_fn_scipy(cls, rng, p, size):
-> 1473 return stats.bernoulli.rvs(p, size=size, random_state=rng)
File /opt/conda/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py:3357, in rv_discrete.rvs(self, *args, **kwargs)
3328 """Random variates of given type.
3329
3330 Parameters
(...)
3354
3355 """
3356 kwargs['discrete'] = True
-> 3357 return super().rvs(*args, **kwargs)
File /opt/conda/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py:1028, in rv_generic.rvs(self, *args, **kwds)
1026 discrete = kwds.pop('discrete', None)
1027 rndm = kwds.pop('random_state', None)
-> 1028 args, loc, scale, size = self._parse_args_rvs(*args, **kwds)
1029 cond = logical_and(self._argcheck(*args), (scale >= 0))
1030 if not np.all(cond):
File <string>:6, in _parse_args_rvs(self, p, loc, size)
File /opt/conda/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py:909, in rv_generic._argcheck_rvs(self, *args, **kwargs)
906 ok = all([bcdim == 1 or bcdim == szdim
907 for (bcdim, szdim) in zip(bcast_shape, size_)])
908 if not ok:
--> 909 raise ValueError("size does not match the broadcast shape of "
910 "the parameters. %s, %s, %s" % (size, size_,
911 bcast_shape))
913 param_bcast = all_bcast[:-2]
914 loc_bcast = all_bcast[-2]
ValueError: size does not match the broadcast shape of the parameters. (617,), (617,), (5,)
Apply node that caused the error: bernoulli_rv{0, (0,), int64, True}(RandomGeneratorSharedVariable(<Generator(PCG64) at 0x79F4A8C210E0>), TensorConstant{(1,) of 617}, TensorConstant{4}, p1)
Toposort index: 2
Inputs types: [RandomGeneratorType, TensorType(int64, (1,)), TensorType(int64, ()), TensorType(float64, (?,))]
Inputs shapes: ['No shapes', (1,), (), (5,)]
Inputs strides: ['No strides', (8,), (), (8,)]
Inputs values: [Generator(PCG64) at 0x79F4A8C210E0, array([617]), array(4), array([0.35075572, 0.35075572, 0.35075572, 0.35075572, 0.35075572])]
Outputs clients: [['output'], ['output']]
HINT: Re-running with most PyTensor optimizations disabled could provide a back-trace showing when this node was created. This can be done by setting the PyTensor flag 'optimizer=fast_compile'. If that does not work, PyTensor optimizations can be disabled with 'optimizer=None'.
HINT: Use the PyTensor flag `exception_verbosity=high` for a debug print-out and storage map footprint of this Apply node.