Fix bug in compute_log_likelihood when variable has dims without coords

pymc/backends/arviz.py

@@ -100,6 +100,18 @@ def is_data(name, var, model) -> bool:
     return constant_data
 
 
+def coords_and_dims_for_inferencedata(model: Model) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+    """Parse PyMC model coords and dims format to one accepted by InferenceData."""
+    coords = {
+        cname: np.array(cvals) if isinstance(cvals, tuple) else cvals
+        for cname, cvals in model.coords.items()
+        if cvals is not None
+    }
+    dims = {dname: list(dvals) for dname, dvals in model.named_vars_to_dims.items()}
+
+    return coords, dims
+
+
 class _DefaultTrace:
     """
     Utility for collecting samples into a dictionary.
@@ -216,19 +228,11 @@ def __init__(
                 " one of trace, prior, posterior_predictive or predictions."
             )
 
-        # Make coord types more rigid
-        untyped_coords: Dict[str, Optional[Sequence[Any]]] = {**self.model.coords}
-        if coords:
-            untyped_coords.update(coords)
-        self.coords = {
-            cname: np.array(cvals) if isinstance(cvals, tuple) else cvals
-            for cname, cvals in untyped_coords.items()
-            if cvals is not None
-        }
-
-        self.dims = {} if dims is None else dims
-        model_dims = {k: list(v) for k, v in self.model.named_vars_to_dims.items()}
-        self.dims = {**model_dims, **self.dims}
+        user_coords = {} if coords is None else coords
+        user_dims = {} if dims is None else dims
+        model_coords, model_dims = coords_and_dims_for_inferencedata(self.model)
+        self.coords = {**model_coords, **user_coords}
+        self.dims = {**model_dims, **user_dims}
 
         if sample_dims is None:
             sample_dims = ["chain", "draw"]

pymc/model/core.py

@@ -942,7 +942,7 @@ def RV_dims(self) -> Dict[str, Tuple[Union[str, None], ...]]:
         Entries in the tuples may be ``None``, if the RV dimension was not given a name.
         """
         warnings.warn(
-            "Model.RV_dims is deprecated. User Model.named_vars_to_dims instead.",
+            "Model.RV_dims is deprecated. Use Model.named_vars_to_dims instead.",
             FutureWarning,
         )
         return self.named_vars_to_dims

pymc/sampling/jax.py

@@ -37,7 +37,11 @@
 from pytensor.tensor.random.type import RandomType
 
 from pymc import Model, modelcontext
-from pymc.backends.arviz import find_constants, find_observations
+from pymc.backends.arviz import (
+    coords_and_dims_for_inferencedata,
+    find_constants,
+    find_observations,
+)
 from pymc.distributions.multivariate import PosDefMatrix
 from pymc.initial_point import StartDict
 from pymc.logprob.utils import CheckParameterValue
@@ -392,17 +396,6 @@ def sample_blackjax_nuts(
 
     vars_to_sample = list(get_default_varnames(var_names, include_transformed=keep_untransformed))
 
-    coords = {
-        cname: np.array(cvals) if isinstance(cvals, tuple) else cvals
-        for cname, cvals in model.coords.items()
-        if cvals is not None
-    }
-
-    dims = {
-        var_name: [dim for dim in dims if dim is not None]
-        for var_name, dims in model.named_vars_to_dims.items()
-    }
-
     (random_seed,) = _get_seeds_per_chain(random_seed, 1)
 
     tic1 = datetime.now()
@@ -485,7 +478,7 @@ def sample_blackjax_nuts(
         "sampling_time": (tic3 - tic2).total_seconds(),
     }
 
-    posterior = mcmc_samples
+    coords, dims = coords_and_dims_for_inferencedata(model)
     # Update 'coords' and 'dims' extracted from the model with user 'idata_kwargs'
     # and drop keys 'coords' and 'dims' from 'idata_kwargs' if present.
     _update_coords_and_dims(coords=coords, dims=dims, idata_kwargs=idata_kwargs)
@@ -500,7 +493,7 @@ def sample_blackjax_nuts(
         dims=dims,
         attrs=make_attrs(attrs, library=blackjax),
     )
-    az_trace = to_trace(posterior=posterior, **idata_kwargs)
+    az_trace = to_trace(posterior=mcmc_samples, **idata_kwargs)
 
     return az_trace
 
@@ -613,17 +606,6 @@ def sample_numpyro_nuts(
 
     vars_to_sample = list(get_default_varnames(var_names, include_transformed=keep_untransformed))
 
-    coords = {
-        cname: np.array(cvals) if isinstance(cvals, tuple) else cvals
-        for cname, cvals in model.coords.items()
-        if cvals is not None
-    }
-
-    dims = {
-        var_name: [dim for dim in dims if dim is not None]
-        for var_name, dims in model.named_vars_to_dims.items()
-    }
-
     (random_seed,) = _get_seeds_per_chain(random_seed, 1)
 
     tic1 = datetime.now()
@@ -715,7 +697,7 @@ def sample_numpyro_nuts(
         "sampling_time": (tic3 - tic2).total_seconds(),
     }
 
-    posterior = mcmc_samples
+    coords, dims = coords_and_dims_for_inferencedata(model)
     # Update 'coords' and 'dims' extracted from the model with user 'idata_kwargs'
     # and drop keys 'coords' and 'dims' from 'idata_kwargs' if present.
     _update_coords_and_dims(coords=coords, dims=dims, idata_kwargs=idata_kwargs)
@@ -730,5 +712,5 @@ def sample_numpyro_nuts(
         dims=dims,
         attrs=make_attrs(attrs, library=numpyro),
     )
-    az_trace = to_trace(posterior=posterior, **idata_kwargs)
+    az_trace = to_trace(posterior=mcmc_samples, **idata_kwargs)
     return az_trace

pymc/stats/log_likelihood.py

@@ -13,14 +13,12 @@
 #   limitations under the License.
 from typing import Optional, Sequence, cast
 
-import numpy as np
-
 from arviz import InferenceData, dict_to_dataset
 from fastprogress import progress_bar
 
 import pymc
 
-from pymc.backends.arviz import _DefaultTrace
+from pymc.backends.arviz import _DefaultTrace, coords_and_dims_for_inferencedata
 from pymc.model import Model, modelcontext
 from pymc.pytensorf import PointFunc
 from pymc.util import dataset_to_point_list
@@ -113,14 +111,12 @@ def compute_log_likelihood(
             (*[len(coord) for coord in stacked_dims.values()], *array.shape[1:])
         )
 
+    coords, dims = coords_and_dims_for_inferencedata(model)
     loglike_dataset = dict_to_dataset(
         loglike_trace,
         library=pymc,
-        dims={dname: list(dvals) for dname, dvals in model.named_vars_to_dims.items()},
-        coords={
-            cname: np.array(cvals) if isinstance(cvals, tuple) else cvals
-            for cname, cvals in model.coords.items()
-        },
+        dims=dims,
+        coords=coords,
         default_dims=list(sample_dims),
         skip_event_dims=True,
     )

tests/stats/test_log_likelihood.py

@@ -15,7 +15,7 @@
 import pytest
 import scipy.stats as st
 
-from arviz import InferenceData, dict_to_dataset
+from arviz import InferenceData, dict_to_dataset, from_dict
 
 from pymc.distributions import Dirichlet, Normal
 from pymc.distributions.transforms import log
@@ -117,3 +117,18 @@ def test_invalid_var_names(self):
             idata = InferenceData(posterior=dict_to_dataset({"x": np.arange(100).reshape(4, 25)}))
             with pytest.raises(ValueError, match="var_names must refer to observed_RVs"):
                 compute_log_likelihood(idata, var_names=["x"])
+
+    def test_dims_without_coords(self):
+        # Issues #6820
+        with Model() as m:
+            x = Normal("x")
+            y = Normal("y", x, observed=[0, 0, 0], shape=(3,), dims="obs")
+
+            trace = from_dict({"x": [[0, 1]]})
+            llike = compute_log_likelihood(trace)
+
+        assert len(llike.log_likelihood["obs"]) == 3
+        np.testing.assert_allclose(
+            llike.log_likelihood["y"].values,
+            st.norm.logpdf([[[0, 0, 0], [1, 1, 1]]]),
+        )