Remove ShapeWarning in favor of raising ValueError directly

Author: Luke LB

pymc/model.py

@@ -61,7 +61,7 @@
 from pymc.distributions import joint_logpt
 from pymc.distributions.logprob import _get_scaling
 from pymc.distributions.transforms import _default_transform
-from pymc.exceptions import ImputationWarning, SamplingError, ShapeError, ShapeWarning
+from pymc.exceptions import ImputationWarning, SamplingError, ShapeError
 from pymc.initial_point import make_initial_point_fn
 from pymc.math import flatten_list
 from pymc.util import (
@@ -1180,25 +1180,20 @@ def set_data(
             # NOTE: If there are multiple pm.MutableData containers sharing this dim, but the user only
             #       changes the values for one of them, they will run into shape problems nonetheless.
             if length_changed:
+                if original_coords is not None:
+                    if new_coords is None:
+                        raise ValueError(
+                            f"The '{name}' variable already had {len(original_coords)} coord values defined for "
+                            f"its {dname} dimension. With the new values this dimension changes to length "
+                            f"{new_length}, so new coord values for the {dname} dimension are required."
+                        )
                 if isinstance(length_tensor, TensorConstant):
                     raise ShapeError(
                         f"Resizing dimension '{dname}' is impossible, because "
                         f"a 'TensorConstant' stores its length. To be able "
                         f"to change the dimension length, 'fixed' in "
                         f"'model.add_coord' must be set to `False`."
                     )
-                if length_tensor.owner is None:
-                    # This is the case if the dimension was initialized
-                    # from custom coords, but dimension length was not
-                    # stored in TensorConstant e.g by 'fixed' set to False
-
-                    warnings.warn(
-                        f"You're changing the shape of a variable "
-                        f"in the '{dname}' dimension which was initialized "
-                        f"from coords. Make sure to update the corresponding "
-                        f"coords, otherwise you'll get shape issues.",
-                        ShapeWarning,
-                    )
                 else:
                     length_belongs_to = length_tensor.owner.inputs[0].owner.inputs[0]
                     if not isinstance(length_belongs_to, SharedVariable):
@@ -1210,13 +1205,6 @@ def set_data(
                             actual=new_length,
                             expected=old_length,
                         )
-                if original_coords is not None:
-                    if new_coords is None:
-                        raise ValueError(
-                            f"The '{name}' variable already had {len(original_coords)} coord values defined for "
-                            f"its {dname} dimension. With the new values this dimension changes to length "
-                            f"{new_length}, so new coord values for the {dname} dimension are required."
-                        )
                 if isinstance(length_tensor, ScalarSharedVariable):
                     # Updating the shared variable resizes dependent nodes that use this dimension for their `size`.
                     length_tensor.set_value(new_length)

pymc/tests/test_model.py

@@ -702,6 +702,40 @@ def test_nested_model_coords():
     assert set(m2.RV_dims) < set(m1.RV_dims)
 
 
+def test_shapeerror_from_resize_immutable_dims():
+    """
+    Trying to resize an immutable dimension should raise a ShapeError.
+    Even if the variable being updated is a SharedVariable and has other
+    dimensions that are mutable.
+    """
+    with pm.Model() as pmodel:
+        a = pm.Normal("a", mu=[1, 2, 3], dims="fixed")
+
+        m = pm.MutableData("m", [[1, 2, 3]], dims=("one", "fixed"))
+
+        # This is fine because the "fixed" dim is not resized
+        pm.set_data({"m": [[1, 2, 3], [3, 4, 5]]})
+
+    with pytest.raises(ShapeError, match="was initialized from 'a'"):
+        # Can't work because the "fixed" dimension is linked to a constant shape:
+        # Note that the new data tries to change both dimensions
+        with pmodel:
+            pm.set_data({"m": [[1, 2], [3, 4]]})
+
+
+def test_valueerror_from_resize_without_coords_update():
+    """
+    Resizing a mutable dimension that had coords,
+    without passing new coords raises a ValueError.
+    """
+    with pm.Model() as pmodel:
+        pmodel.add_coord("shared", [1, 2, 3])
+        pm.MutableData("m", [1, 2, 3], dims=("shared"))
+        with pytest.raises(ValueError, match="'m' variable already had 3"):
+            # tries to resize m but without passing coords so raise ValueError
+            pm.set_data({"m": [1, 2, 3, 4]})
+
+
 @pytest.mark.parametrize("jacobian", [True, False])
 def test_model_logp(jacobian):
     with pm.Model() as m: