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Fix group selection in sample_posterior_predictive when predictions=True is passed in kwargs #426

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Fix group selection in sample_posterior_predictive when predictions=True is passed in kwargs #426

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34839ad
Fix group selection for posterior predictive samples when predictions…
butterman0 Feb 17, 2025
7cdd900
refactor: make predictions argument explicit
butterman0 Feb 18, 2025
a0501fe
refactor: change default predictions to True
butterman0 Feb 18, 2025
079e131
doc: update docstrings
butterman0 Feb 18, 2025
fb6b1e9
docs: update
butterman0 Mar 6, 2025
ce1b2d5
refactor: pass predictions by keyword
butterman0 Mar 6, 2025
4ea5fbc
test: added test for predictions grouping
butterman0 Apr 9, 2025
7f84f03
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Apr 25, 2025
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15 changes: 10 additions & 5 deletions pymc_extras/model_builder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -530,6 +530,7 @@ def predict(
self,
X_pred: np.ndarray | pd.DataFrame | pd.Series,
extend_idata: bool = True,
predictions: bool = False,
**kwargs,
) -> np.ndarray:
"""
Expand Down Expand Up @@ -559,7 +560,7 @@ def predict(
"""

posterior_predictive_samples = self.sample_posterior_predictive(
X_pred, extend_idata, combined=False, **kwargs
X_pred, extend_idata, predictions, combined=False, **kwargs
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pass by keyword to be on the safe side

)

if self.output_var not in posterior_predictive_samples:
Expand Down Expand Up @@ -624,7 +625,7 @@ def sample_prior_predictive(

return prior_predictive_samples

def sample_posterior_predictive(self, X_pred, extend_idata, combined, **kwargs):
def sample_posterior_predictive(self, X_pred, extend_idata, predictions, combined, **kwargs):
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Provide default

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The other arguments do not have defaults. The sample_posterior_predictive is only called through the predict functions, which do have defaults.

Would you be able to explain why we would want predictions to have a default, when the other arguments do not?

"""
Sample from the model's posterior predictive distribution.

Expand All @@ -646,12 +647,15 @@ def sample_posterior_predictive(self, X_pred, extend_idata, combined, **kwargs):
self._data_setter(X_pred)

with self.model: # sample with new input data
post_pred = pm.sample_posterior_predictive(self.idata, **kwargs)
post_pred = pm.sample_posterior_predictive(self.idata, predictions=predictions, **kwargs)
if extend_idata:
self.idata.extend(post_pred, join="right")

# Determine the correct group
group_name = "predictions" if predictions else "posterior_predictive"

posterior_predictive_samples = az.extract(
post_pred, "posterior_predictive", combined=combined
post_pred, group_name, combined=combined
)

return posterior_predictive_samples
Expand Down Expand Up @@ -700,6 +704,7 @@ def predict_posterior(
X_pred: np.ndarray | pd.DataFrame | pd.Series,
extend_idata: bool = True,
combined: bool = True,
predictions: bool = False,
**kwargs,
) -> xr.DataArray:
"""
Expand All @@ -723,7 +728,7 @@ def predict_posterior(

X_pred = self._validate_data(X_pred)
posterior_predictive_samples = self.sample_posterior_predictive(
X_pred, extend_idata, combined, **kwargs
X_pred, extend_idata, predictions, combined, **kwargs
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pass by keyword argument

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@butterman0 butterman0 Feb 18, 2025 •
edited
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I was aiming to keep it in the same format as current implementation. i.e. x_pred, extend_idata and combined do not use keyword arguments..

Similar question to the one above - should these all be changed to use keyword arguments? Why would we treat predictions differently?

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Hi @ricardoV94, let me know what you think and I can adjust.

)

if self.output_var not in posterior_predictive_samples:
Expand Down