Assert ndim and number of dims match

pymc/distributions/discrete.py

@@ -1185,13 +1185,6 @@ def logp(value, p):
         )
 
 
-class _OrderedLogistic(Categorical):
-    r"""
-    Underlying class for ordered logistic distributions.
-    See docs for the OrderedLogistic wrapper class for more details on how to use it in models.
-    """
-
-
 class OrderedLogistic:
     R"""Ordered Logistic distribution.
 
@@ -1263,7 +1256,7 @@ class OrderedLogistic:
     def __new__(cls, name, eta, cutpoints, compute_p=True, **kwargs):
         p = cls.compute_p(eta, cutpoints)
         if compute_p:
-            p = pm.Deterministic(f"{name}_probs", p, dims=kwargs.get("dims"))
+            p = pm.Deterministic(f"{name}_probs", p)
         out_rv = Categorical(name, p=p, **kwargs)
         return out_rv
 
@@ -1367,7 +1360,7 @@ class OrderedProbit:
     def __new__(cls, name, eta, cutpoints, sigma=1, compute_p=True, **kwargs):
         p = cls.compute_p(eta, cutpoints, sigma)
         if compute_p:
-            p = pm.Deterministic(f"{name}_probs", p, dims=kwargs.get("dims"))
+            p = pm.Deterministic(f"{name}_probs", p)
         out_rv = Categorical(name, p=p, **kwargs)
         return out_rv
 

pymc/model/core.py

@@ -1532,6 +1532,11 @@ def add_named_variable(self, var, dims: tuple[str | None, ...] | None = None):
                     raise ValueError(f"Dimension {dim} is not specified in `coords`.")
             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.")
+            # This check implicitly states that only vars with .ndim attribute can have dims
+            if var.ndim != len(dims):
+                raise ValueError(
+                    f"{var} has {var.ndim} dims but {len(dims)} dim labels were provided."
+                )
             self.named_vars_to_dims[var.name] = dims
 
         self.named_vars[var.name] = var

tests/distributions/test_discrete.py

@@ -897,19 +897,25 @@ def test_shape_inputs(self, eta, cutpoints, expected):
         assert p_shape == expected
 
     def test_compute_p(self):
-        with pm.Model() as m:
-            pm.OrderedLogistic("ol_p", cutpoints=np.array([-2, 0, 2]), eta=0)
-            pm.OrderedLogistic("ol_no_p", cutpoints=np.array([-2, 0, 2]), eta=0, compute_p=False)
+        with pm.Model(coords={"test_dim": [0]}) as m:
+            pm.OrderedLogistic("ol_p", cutpoints=np.array([-2, 0, 2]), eta=0, dims="test_dim")
+            pm.OrderedLogistic(
+                "ol_no_p", cutpoints=np.array([-2, 0, 2]), eta=0, compute_p=False, dims="test_dim"
+            )
         assert len(m.deterministics) == 1
 
         x = pm.OrderedLogistic.dist(cutpoints=np.array([-2, 0, 2]), eta=0)
         assert isinstance(x, TensorVariable)
 
         # Test it works with auto-imputation
-        with pm.Model() as m:
+        with pm.Model(coords={"test_dim": [0, 1, 2]}) as m:
             with pytest.warns(ImputationWarning):
                 pm.OrderedLogistic(
-                    "ol", cutpoints=np.array([-2, 0, 2]), eta=0, observed=[0, np.nan, 1]
+                    "ol",
+                    cutpoints=np.array([[-2, 0, 2]]),
+                    eta=0,
+                    observed=[0, np.nan, 1],
+                    dims=["test_dim"],
                 )
         assert len(m.deterministics) == 2  # One from the auto-imputation, the other from compute_p
 

tests/model/test_core.py

@@ -890,7 +890,17 @@ def test_add_named_variable_checks_dim_name(self):
             rv2.name = "yumyum"
             pmodel.add_named_variable(rv2, dims=("nomnom", None))
 
-    def test_dims_type_check(self):
+    def test_add_named_variable_checks_number_of_dims(self):
+        match = "dim labels were provided"
+        with pm.Model(coords={"bad": range(6)}) as m:
+            with pytest.raises(ValueError, match=match):
+                m.add_named_variable(pt.random.normal(size=(6, 6, 6), name="a"), dims=("bad",))
+
+            # "bad" is an iterable with 3 elements, but we treat strings as a single dim, so it's still invalid
+            with pytest.raises(ValueError, match=match):
+                m.add_named_variable(pt.random.normal(size=(6, 6, 6), name="b"), dims="bad")
+
+    def test_rv_dims_type_check(self):
         with pm.Model(coords={"a": range(5)}) as m:
             with pytest.raises(TypeError, match="Dims must be string"):
                 x = pm.Normal("x", shape=(10, 5), dims=(None, "a"))
@@ -899,12 +909,14 @@ def test_none_coords_autonumbering(self):
         # TODO: Either allow dims without coords everywhere or nowhere
         with pm.Model() as m:
             m.add_coord(name="a", values=None, length=3)
-            m.add_coord(name="b", values=range(5))
-            x = pm.Normal("x", dims=("a", "b"))
+            m.add_coord(name="b", values=range(-5, 0))
+            m.add_coord(name="c", values=None, length=7)
+            x = pm.Normal("x", dims=("a", "b", "c"))
             prior = pm.sample_prior_predictive(draws=2).prior
-        assert prior["x"].shape == (1, 2, 3, 5)
+        assert prior["x"].shape == (1, 2, 3, 5, 7)
         assert list(prior.coords["a"].values) == list(range(3))
-        assert list(prior.coords["b"].values) == list(range(5))
+        assert list(prior.coords["b"].values) == list(range(-5, 0))
+        assert list(prior.coords["c"].values) == list(range(7))
 
     def test_set_data_indirect_resize_without_coords(self):
         with pm.Model() as pmodel:
@@ -1068,7 +1080,7 @@ def test_determinsitic_with_dims():
     Test to check the passing of dims to the potential
     """
     with pm.Model(coords={"observed": range(10)}) as model:
-        x = pm.Normal("x", 0, 1)
+        x = pm.Normal("x", 0, 1, shape=(10,))
         y = pm.Deterministic("y", x**2, dims=("observed",))
     assert model.named_vars_to_dims == {"y": ("observed",)}
 
@@ -1078,7 +1090,7 @@ def test_potential_with_dims():
     Test to check the passing of dims to the potential
     """
     with pm.Model(coords={"observed": range(10)}) as model:
-        x = pm.Normal("x", 0, 1)
+        x = pm.Normal("x", 0, 1, shape=(10,))
         y = pm.Potential("y", x**2, dims=("observed",))
     assert model.named_vars_to_dims == {"y": ("observed",)}