Support PyMC 5.13 and fix bayeux related issues (#803)

CHANGELOG.md

@@ -14,6 +14,8 @@
 
 * Fix bug in predictions with models using HSGP (#780)
 * Fix `get_model_covariates()` utility function (#801)
+* Upgrade PyMC dependency to >= 5.13 (#803)
+* Use `pm.compute_deterministics()` to compute deterministics when bayeux based samplers are used (#803)
 
 ### Documentation
 

bambi/backend/pymc.py

@@ -253,45 +253,55 @@ def _run_mcmc(
         return idata
 
     def _clean_results(self, idata, omit_offsets, include_mean, idata_from):
-        for group in idata.groups():
+        # Before doing anything, make sure we compute deterministics.
+        if idata_from == "bayeux":
+            idata.posterior = pm.compute_deterministics(
+                idata.posterior, model=self.model, merge_dataset=True, progressbar=False
+            )
 
+        for group in idata.groups():
             getattr(idata, group).attrs["modeling_interface"] = "bambi"
             getattr(idata, group).attrs["modeling_interface_version"] = __version__
 
         if omit_offsets:
             offset_vars = [var for var in idata.posterior.data_vars if var.endswith("_offset")]
             idata.posterior = idata.posterior.drop_vars(offset_vars)
 
-        # Drop variables and dimensions associated with LKJ prior
-        vars_to_drop = [var for var in idata.posterior.data_vars if var.startswith("_LKJ")]
-        dims_to_drop = [dim for dim in idata.posterior.dims if dim.startswith("_LKJ")]
+        # NOTE:
+        # This has not had an effect for a while since we haven't been supporting LKJ prior lately.
 
-        idata.posterior = idata.posterior.drop_vars(vars_to_drop)
-        idata.posterior = idata.posterior.drop_dims(dims_to_drop)
+        # Drop variables and dimensions associated with LKJ prior
+        # vars_to_drop = [var for var in idata.posterior.data_vars if var.startswith("_LKJ")]
+        # dims_to_drop = [dim for dim in idata.posterior.dims if dim.startswith("_LKJ")]
+        # idata.posterior = idata.posterior.drop_vars(vars_to_drop)
+        # idata.posterior = idata.posterior.drop_dims(dims_to_drop)
 
         dims_original = list(self.model.coords)
 
         # Identify bayeux idata and rename dims and coordinates to match PyMC model
         if idata_from == "bayeux":
-            pymc_model_dims = [dim for dim in dims_original if "_obs" not in dim]
-            bayeux_dims = [
-                dim for dim in idata.posterior.dims if not dim.startswith(("chain", "draw"))
-            ]
-            cleaned_dims = dict(zip(bayeux_dims, pymc_model_dims))
+            cleaned_dims = {
+                f"{dim}_0": dim
+                for dim in dims_original
+                if not dim.endswith("_obs") and f"{dim}_0" in idata.posterior.dims
+            }
             idata = idata.rename(cleaned_dims)
 
-        # Discard dims that are in the model but unused in the posterior
+        # Don't select dims that are in the model but unused in the posterior
         dims_original = [dim for dim in dims_original if dim in idata.posterior.dims]
 
         # This does not add any new coordinate, it just changes the order so the ones
         # ending in "__factor_dim" are placed after the others.
-        dims_group = [c for c in dims_original if c.endswith("__factor_dim")]
+        dims_group = [dim for dim in dims_original if dim.endswith("__factor_dim")]
 
         # Keep the original order in dims_original
         dims_original_set = set(dims_original) - set(dims_group)
-        dims_original = [c for c in dims_original if c in dims_original_set]
+        dims_original = [dim for dim in dims_original if dim in dims_original_set]
         dims_new = ["chain", "draw"] + dims_original + dims_group
-        idata.posterior = idata.posterior.transpose(*dims_new)
+
+        # Drop unused dimensions before transposing
+        dims_to_drop = [dim for dim in idata.posterior.dims if dim not in dims_new]
+        idata.posterior = idata.posterior.drop_dims(dims_to_drop).transpose(*dims_new)
 
         # Compute the actual intercept in all distributional components that have an intercept
         for pymc_component in self.distributional_components.values():

bambi/backend/terms.py

@@ -110,9 +110,13 @@ def build(self, spec):
             response_dims = list(spec.response_component.response_term.coords)
 
         dims = list(self.coords) + response_dims
+        coef = self.build_distribution(self.term.prior, label, dims=dims, **kwargs)
+
         # Squeeze ensures we don't have a shape of (n, 1) when we mean (n, )
         # This happens with categorical predictors with two levels and intercept.
-        coef = self.build_distribution(self.term.prior, label, dims=dims, **kwargs).squeeze()
+        # See https://github.com/pymc-devs/pymc/issues/7246
+        if len(coef.shape.eval()) == 2 and coef.shape.eval()[-1] == 1:
+            coef = pt.specify_broadcastable(coef, 1).squeeze()
         coef = coef[self.term.group_index]
 
         return coef, predictor

bambi/families/univariate.py

@@ -403,7 +403,7 @@ def transform_backend_eta(eta, kwargs):
     def transform_backend_kwargs(kwargs):
         # P(Y = k) = F(threshold_k - eta) * \prod_{j=1}^{k-1}{1 - F(threshold_j - eta)}
         p = kwargs.pop("p")
-        n_columns = p.type.shape[-1]
+        n_columns = p.shape.eval()[-1]
         p = pt.concatenate(
             [
                 pt.shape_padright(p[..., 0]),

pyproject.toml

@@ -21,7 +21,7 @@ dependencies = [
     "formulae>=0.5.3",
     "graphviz",
     "pandas>=1.0.0",
-    "pymc>=5.12.0,<5.13.0",
+    "pymc>=5.13.0",
 ]
 
 [project.optional-dependencies]