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ModelBuilder.load versatility improvements #210

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merged 4 commits into from
Jul 11, 2023
Merged

ModelBuilder.load versatility improvements #210

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8e75284
fixing dims format, enabling input param preservation
michaelraczycki Jul 5, 2023
daaaebe
adding additional test for implemented features
michaelraczycki Jul 6, 2023
7290517
fixing typehinting in linearmodel
michaelraczycki Jul 6, 2023
a0563a6
introducing dims to model_builder tests to check for dim format prese…
michaelraczycki Jul 10, 2023
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17 changes: 15 additions & 2 deletions pymc_experimental/model_builder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -297,7 +297,7 @@ def sample_model(self, **kwargs):
idata.extend(pm.sample_prior_predictive())
idata.extend(pm.sample_posterior_predictive(idata))

self.set_idata_attrs(idata)
idata = self.set_idata_attrs(idata)
return idata

def set_idata_attrs(self, idata=None):
Expand Down Expand Up @@ -338,6 +338,10 @@ def set_idata_attrs(self, idata=None):
idata.attrs["version"] = self.version
idata.attrs["sampler_config"] = json.dumps(self.sampler_config)
idata.attrs["model_config"] = json.dumps(self._serializable_model_config)
# Only classes with non-dataset parameters will implement save_input_params
if hasattr(self, "_save_input_params"):
self._save_input_params(idata)
return idata

def save(self, fname: str) -> None:
"""
Expand Down Expand Up @@ -375,6 +379,12 @@ def save(self, fname: str) -> None:
else:
raise RuntimeError("The model hasn't been fit yet, call .fit() first")

def _convert_dims_to_tuple(model_config: Dict) -> Dict:
for key in model_config:
if "dims" in model_config[key] and isinstance(model_config[key]["dims"], list):
model_config[key]["dims"] = tuple(model_config[key]["dims"])
return model_config

@classmethod
def load(cls, fname: str):
"""
Expand Down Expand Up @@ -403,8 +413,10 @@ def load(cls, fname: str):
"""
filepath = Path(str(fname))
idata = az.from_netcdf(filepath)
# needs to be converted, because json.loads was changing tuple to list
model_config = cls._convert_dims_to_tuple(json.loads(idata.attrs["model_config"]))
model = cls(
model_config=json.loads(idata.attrs["model_config"]),
model_config=model_config,
sampler_config=json.loads(idata.attrs["sampler_config"]),
)
model.idata = idata
Expand Down Expand Up @@ -480,6 +492,7 @@ def fit(
combined_data = pd.concat([X_df, y], axis=1)
assert all(combined_data.columns), "All columns must have non-empty names"
self.idata.add_groups(fit_data=combined_data.to_xarray()) # type: ignore

return self.idata # type: ignore

def predict(
Expand Down