Thanks so much for the quick reply Ricardo. Full error message pasted below - I ran with 5.0… All help appreciated thank you.
ValueError Traceback (most recent call last)
Input In [9], in <cell line: 1>()
1 with model:
2 #pm.set_data({‘x’: [5., 6., 9., 12., 15.]})
----> 3 y_test = pm.sample_posterior_predictive(idata)
5 print(y_test.posterior_predictive[‘obs’].mean((‘chain’, ‘draw’)))
File ~\Anaconda3\lib\site-packages\pymc\sampling\forward.py:673, in sample_posterior_predictive(trace, model, var_names, sample_dims, random_seed, progressbar, return_inferencedata, extend_inferencedata, predictions, idata_kwargs, compile_kwargs)
671 ikwargs.setdefault(“inplace”, True)
672 return pm.predictions_to_inference_data(ppc_trace, **ikwargs)
→ 673 idata_pp = pm.to_inference_data(posterior_predictive=ppc_trace, **ikwargs)
675 if extend_inferencedata and idata is not None:
676 idata.extend(idata_pp)
File ~\Anaconda3\lib\site-packages\pymc\backends\arviz.py:498, in to_inference_data(trace, prior, posterior_predictive, log_likelihood, coords, dims, sample_dims, model, save_warmup, include_transformed)
495 if isinstance(trace, InferenceData):
496 return trace
→ 498 return InferenceDataConverter(
499 trace=trace,
500 prior=prior,
501 posterior_predictive=posterior_predictive,
502 log_likelihood=log_likelihood,
503 coords=coords,
504 dims=dims,
505 sample_dims=sample_dims,
506 model=model,
507 save_warmup=save_warmup,
508 include_transformed=include_transformed,
509 ).to_inference_data()
File ~\Anaconda3\lib\site-packages\pymc\backends\arviz.py:419, in InferenceDataConverter.to_inference_data(self)
409 def to_inference_data(self):
410 “”“Convert all available data to an InferenceData object.
411
412 Note that if groups can not be created (e.g., there is no trace, so
413 the posterior and sample_stats can not be extracted), then the InferenceData
414 will not have those groups.
415 “””
416 id_dict = {
417 “posterior”: self.posterior_to_xarray(),
418 “sample_stats”: self.sample_stats_to_xarray(),
→ 419 “posterior_predictive”: self.posterior_predictive_to_xarray(),
420 “predictions”: self.predictions_to_xarray(),
421 **self.priors_to_xarray(),
422 “observed_data”: self.observed_data_to_xarray(),
423 }
424 if self.predictions:
425 id_dict[“predictions_constant_data”] = self.constant_data_to_xarray()
File ~\AppData\Roaming\Python\Python39\site-packages\arviz\data\base.py:65, in requires.call..wrapped(cls)
63 if all((getattr(cls, prop_i) is None for prop_i in prop)):
64 return None
—> 65 return func(cls)
File ~\Anaconda3\lib\site-packages\pymc\backends\arviz.py:340, in InferenceDataConverter.posterior_predictive_to_xarray(self)
338 data = self.posterior_predictive
339 dims = {var_name: self.sample_dims + self.dims.get(var_name, ) for var_name in data}
→ 340 return dict_to_dataset(
341 data, library=pymc, coords=self.coords, dims=dims, default_dims=self.sample_dims
342 )
File ~\AppData\Roaming\Python\Python39\site-packages\arviz\data\base.py:307, in dict_to_dataset(data, attrs, library, coords, dims, default_dims, index_origin, skip_event_dims)
305 data_vars = {}
306 for key, values in data.items():
→ 307 data_vars[key] = numpy_to_data_array(
308 values,
309 var_name=key,
310 coords=coords,
311 dims=dims.get(key),
312 default_dims=default_dims,
313 index_origin=index_origin,
314 skip_event_dims=skip_event_dims,
315 )
316 return xr.Dataset(data_vars=data_vars, attrs=make_attrs(attrs=attrs, library=library))
File ~\AppData\Roaming\Python\Python39\site-packages\arviz\data\base.py:254, in numpy_to_data_array(ary, var_name, coords, dims, default_dims, index_origin, skip_event_dims)
252 # filter coords based on the dims
253 coords = {key: xr.IndexVariable((key,), data=np.asarray(coords[key])) for key in dims}
→ 254 return xr.DataArray(ary, coords=coords, dims=dims)
File ~\Anaconda3\lib\site-packages\xarray\core\dataarray.py:422, in DataArray.init(self, data, coords, dims, name, attrs, indexes, fastpath)
420 data = _check_data_shape(data, coords, dims)
421 data = as_compatible_data(data)
→ 422 coords, dims = _infer_coords_and_dims(data.shape, coords, dims)
423 variable = Variable(dims, data, attrs, fastpath=True)
424 indexes, coords = _create_indexes_from_coords(coords)
File ~\Anaconda3\lib\site-packages\xarray\core\dataarray.py:137, in _infer_coords_and_dims(shape, coords, dims)
135 dims = tuple(dims)
136 elif len(dims) != len(shape):
→ 137 raise ValueError(
138 “different number of dimensions on data "
139 f"and dims: {len(shape)} vs {len(dims)}”
140 )
141 else:
142 for d in dims: