Bugfixes to increase robustness against unnamed dims (#6339)

* Extract `ModelGraph._eval` to a function
* More robustness against unlabeled `dims` entries

Closes #6335

pymc/model.py

@@ -1498,6 +1498,8 @@ def add_named_variable(self, var, dims: Optional[Tuple[Union[str, None], ...]] =
         This can include several types of variables such basic_RVs, Data, Deterministics,
         and Potentials.
         """
+        if var.name is None:
+            raise ValueError("Variable is unnamed.")
         if self.named_vars.tree_contains(var.name):
             raise ValueError(f"Variable name {var.name} already exists.")
 
@@ -1507,7 +1509,7 @@ def add_named_variable(self, var, dims: Optional[Tuple[Union[str, None], ...]] =
             for dim in dims:
                 if dim not in self.coords and dim is not None:
                     raise ValueError(f"Dimension {dim} is not specified in `coords`.")
-            if any(var.name == dim for dim in dims):
+            if any(var.name == dim for dim in dims if dim is not None):
                 raise ValueError(f"Variable `{var.name}` has the same name as its dimension label.")
             self.named_vars_to_dims[var.name] = dims
 

pymc/model_graph.py

@@ -14,7 +14,7 @@
 import warnings
 
 from collections import defaultdict
-from typing import Dict, Iterable, List, NewType, Optional, Set
+from typing import Dict, Iterable, List, NewType, Optional, Sequence, Set
 
 from aesara import function
 from aesara.compile.sharedvalue import SharedVariable
@@ -32,6 +32,17 @@
 VarName = NewType("VarName", str)
 
 
+__all__ = (
+    "ModelGraph",
+    "model_to_graphviz",
+    "model_to_networkx",
+)
+
+
+def fast_eval(var):
+    return function([], var, mode="FAST_COMPILE")()
+
+
 class ModelGraph:
     def __init__(self, model):
         self.model = model
@@ -183,9 +194,6 @@ def _make_node(self, var_name, graph, *, nx=False, cluster=False, formatting: st
         else:
             graph.node(var_name.replace(":", "&"), **kwargs)
 
-    def _eval(self, var):
-        return function([], var, mode="FAST_COMPILE")()
-
     def get_plates(self, var_names: Optional[Iterable[VarName]] = None) -> Dict[str, Set[VarName]]:
         """Rough but surprisingly accurate plate detection.
 
@@ -198,18 +206,32 @@ def get_plates(self, var_names: Optional[Iterable[VarName]] = None) -> Dict[str,
         """
         plates = defaultdict(set)
 
+        # TODO: Evaluate all RV shapes and dim_length at once.
+        #       This should help to find discrepancies, and
+        #       avoids unncessary function compiles for deetermining labels.
+
         for var_name in self.vars_to_plot(var_names):
             v = self.model[var_name]
+            shape: Sequence[int] = fast_eval(v.shape)
+            dim_labels = []
             if var_name in self.model.named_vars_to_dims:
-                plate_label = " x ".join(
-                    f"{d} ({self._eval(self.model.dim_lengths[d])})"
-                    for d in self.model.named_vars_to_dims[var_name]
-                )
+                # The RV is associated with `dims` information.
+                for d, dname in enumerate(self.model.named_vars_to_dims[var_name]):
+                    if dname is None:
+                        # Unnamed dimension in a `dims` tuple!
+                        dlen = shape[d]
+                        dname = f"{var_name}_dim{d}"
+                    else:
+                        dlen = fast_eval(self.model.dim_lengths[dname])
+                    dim_labels.append(f"{dname} ({dlen})")
+                plate_label = " x ".join(dim_labels)
             else:
-                plate_label = " x ".join(map(str, self._eval(v.shape)))
+                # The RV has no `dims` information.
+                dim_labels = map(str, shape)
+                plate_label = " x ".join(map(str, shape))
             plates[plate_label].add(var_name)
 
-        return plates
+        return dict(plates)
 
     def make_graph(self, var_names: Optional[Iterable[VarName]] = None, formatting: str = "plain"):
         """Make graphviz Digraph of PyMC model

pymc/tests/test_model.py

@@ -844,6 +844,30 @@ def test_set_dim_with_coords():
     assert pmodel.coords["mdim"] == ("A", "B", "C")
 
 
+def test_add_named_variable_checks_dim_name():
+    with pm.Model() as pmodel:
+        rv = pm.Normal.dist(mu=[1, 2])
+
+        # Checks that vars are named
+        with pytest.raises(ValueError, match="is unnamed"):
+            pmodel.add_named_variable(rv)
+        rv.name = "nomnom"
+
+        # Coords must be available already
+        with pytest.raises(ValueError, match="not specified in `coords`"):
+            pmodel.add_named_variable(rv, dims="nomnom")
+        pmodel.add_coord("nomnom", [1, 2])
+
+        # No name collisions
+        with pytest.raises(ValueError, match="same name as"):
+            pmodel.add_named_variable(rv, dims="nomnom")
+
+        # This should work (regression test against #6335)
+        rv2 = rv[:, None]
+        rv2.name = "yumyum"
+        pmodel.add_named_variable(rv2, dims=("nomnom", None))
+
+
 def test_set_data_indirect_resize():
     with pm.Model() as pmodel:
         pmodel.add_coord("mdim", mutable=True, length=2)

pymc/tests/test_model_graph.py

@@ -14,6 +14,7 @@
 import warnings
 
 import aesara
+import aesara.tensor as at
 import numpy as np
 import pytest
 
@@ -340,6 +341,18 @@ class TestImputationModel(BaseModelGraphTest):
 class TestModelWithDims(BaseModelGraphTest):
     model_func = model_with_dims
 
+    def test_issue_6335_dims_containing_none(self):
+        with pm.Model(coords=dict(time=np.arange(5))) as pmodel:
+            data = at.as_tensor(np.ones((3, 5)))
+            pm.Deterministic("n", data, dims=(None, "time"))
+
+        mg = ModelGraph(pmodel)
+        plates_actual = mg.get_plates()
+        plates_expected = {
+            "n_dim0 (3) x time (5)": {"n"},
+        }
+        assert plates_actual == plates_expected
+
 
 class TestUnnamedObservedNodes(BaseModelGraphTest):
     model_func = model_unnamed_observed_node