Add Ellipsis-support for the shape kwarg

Author: michaelosthege

RELEASE-NOTES.md

@@ -11,8 +11,9 @@
 - The `CAR` distribution has been added to allow for use of conditional autoregressions which often are used in spatial and network models.
 - The dimensionality of model variables can now be parametrized through either of `shape`, `dims` or `size` (see [#4696](https://github.com/pymc-devs/pymc3/pull/4696)):
   - With `shape` the length of dimensions must be given numerically or as scalar Aesara `Variables`. Numeric entries in `shape` restrict the model variable to the exact length and re-sizing is no longer possible.
-  - `dims` keeps model variables re-sizeable (for example through `pm.Data`) and leads to well defined coordinates in `InferenceData` objects. An `Ellipsis` (`...`) in the last position of `dims` can be used as short-hand notation for implied dimensions.
+  - `dims` keeps model variables re-sizeable (for example through `pm.Data`) and leads to well defined coordinates in `InferenceData` objects.
   - The `size` kwarg behaves like it does in Aesara/NumPy. For univariate RVs it is the same as `shape`, but for multivariate RVs it depends on how the RV implements broadcasting to dimensionality greater than `RVOp.ndim_supp`.
+  - An `Ellipsis` (`...`) in the last position of `shape` or `dims` can be used as short-hand notation for implied dimensions.
 - Add `logcdf` method to Kumaraswamy distribution (see [#4706](https://github.com/pymc-devs/pymc3/pull/4706)).
 - ...
 

pymc3/distributions/distribution.py

@@ -378,6 +378,9 @@ def dist(
             The inputs to the `RandomVariable` `Op`.
         shape : int, tuple, Variable, optional
             A tuple of sizes for each dimension of the new RV.
+
+            An Ellipsis (...) may be inserted in the last position to short-hand refer to
+            all the dimensions that the RV would get if no shape/size/dims were passed at all.
         size : int, tuple, Variable, optional
             For creating the RV like in Aesara/NumPy.
         testval : optional
@@ -404,9 +407,16 @@ def dist(
         create_size = None
 
         if shape is not None:
-            ndim_expected = len(tuple(shape))
-            ndim_batch = ndim_expected - ndim_supp
-            create_size = tuple(shape)[:ndim_batch]
+            if Ellipsis in shape:
+                # Ellipsis short-hands all implied dimensions. Therefore
+                # we don't know how many dimensions to expect.
+                ndim_expected = ndim_batch = None
+                # Create the RV with its implied shape and resize later
+                create_size = None
+            else:
+                ndim_expected = len(tuple(shape))
+                ndim_batch = ndim_expected - ndim_supp
+                create_size = tuple(shape)[:ndim_batch]
         elif size is not None:
             ndim_expected = ndim_supp + len(tuple(size))
             ndim_batch = ndim_expected - ndim_supp
@@ -419,21 +429,25 @@ def dist(
         ndims_unexpected = ndim_actual != ndim_expected
 
         if shape is not None and ndims_unexpected:
-            # This is rare, but happens, for example, with MvNormal(np.ones((2, 3)), np.eye(3), shape=(2, 3)).
-            # Recreate the RV without passing `size` to created it with just the implied dimensions.
-            rv_out = cls.rv_op(*dist_params, size=None, **kwargs)
-
-            # Now resize by the "extra" dimensions that were not implied from support and parameters
-            if rv_out.ndim < ndim_expected:
-                expand_shape = shape[: ndim_expected - rv_out.ndim]
-                rv_out = change_rv_size(rv_var=rv_out, new_size=expand_shape, expand=True)
-            if not rv_out.ndim == ndim_expected:
-                raise ShapeError(
-                    f"Failed to create the RV with the expected dimensionality. "
-                    f"This indicates a severe problem. Please open an issue.",
-                    actual=ndim_actual,
-                    expected=ndim_batch + ndim_supp,
-                )
+            if Ellipsis in shape:
+                # Resize and we're done!
+                rv_out = change_rv_size(rv_var=rv_out, new_size=shape[:-1], expand=True)
+            else:
+                # This is rare, but happens, for example, with MvNormal(np.ones((2, 3)), np.eye(3), shape=(2, 3)).
+                # Recreate the RV without passing `size` to created it with just the implied dimensions.
+                rv_out = cls.rv_op(*dist_params, size=None, **kwargs)
+
+                # Now resize by any remaining "extra" dimensions that were not implied from support and parameters
+                if rv_out.ndim < ndim_expected:
+                    expand_shape = shape[: ndim_expected - rv_out.ndim]
+                    rv_out = change_rv_size(rv_var=rv_out, new_size=expand_shape, expand=True)
+                if not rv_out.ndim == ndim_expected:
+                    raise ShapeError(
+                        f"Failed to create the RV with the expected dimensionality. "
+                        f"This indicates a severe problem. Please open an issue.",
+                        actual=ndim_actual,
+                        expected=ndim_batch + ndim_supp,
+                    )
 
         # Warn about the edge cases where the RV Op creates more dimensions than
         # it should based on `size` and `RVOp.ndim_supp`.

pymc3/tests/test_shape_handling.py

@@ -236,7 +236,7 @@ class TestShapeDimsSize:
         [
             "implicit",
             "shape",
-            # "shape...",
+            "shape...",
             "dims",
             "dims...",
             "size",
@@ -273,9 +273,9 @@ def test_param_and_batch_shape_combos(
                     if parametrization == "shape":
                         rv = pm.Normal("rv", mu=mu, shape=batch_shape + param_shape)
                         assert rv.eval().shape == expected_shape
-                    # elif parametrization == "shape...":
-                    #     rv = pm.Normal("rv", mu=mu, shape=(*batch_shape, ...))
-                    #     assert rv.eval().shape == batch_shape + param_shape
+                    elif parametrization == "shape...":
+                        rv = pm.Normal("rv", mu=mu, shape=(*batch_shape, ...))
+                        assert rv.eval().shape == batch_shape + param_shape
                     elif parametrization == "dims":
                         rv = pm.Normal("rv", mu=mu, dims=batch_dims + param_dims)
                         assert rv.eval().shape == expected_shape
@@ -376,7 +376,7 @@ def test_dist_api_works(self):
             pm.Normal.dist(mu=mu, dims=("town",))
         assert pm.Normal.dist(mu=mu, shape=(3,)).eval().shape == (3,)
         assert pm.Normal.dist(mu=mu, shape=(5, 3)).eval().shape == (5, 3)
-        # assert pm.Normal.dist(mu=mu, shape=(7, ...)).eval().shape == (7, 3)
+        assert pm.Normal.dist(mu=mu, shape=(7, ...)).eval().shape == (7, 3)
         assert pm.Normal.dist(mu=mu, size=(3,)).eval().shape == (3,)
         assert pm.Normal.dist(mu=mu, size=(4, 3)).eval().shape == (4, 3)
 
@@ -402,9 +402,9 @@ def test_mvnormal_shape_size_difference(self):
         assert rv.ndim == 3
         assert tuple(rv.shape.eval()) == (5, 4, 3)
 
-        # rv = pm.MvNormal.dist(mu=np.ones((4, 3, 2)), cov=np.eye(2), shape=(6, 5, ...))
-        # assert rv.ndim == 5
-        # assert tuple(rv.shape.eval()) == (6, 5, 4, 3, 2)
+        rv = pm.MvNormal.dist(mu=np.ones((4, 3, 2)), cov=np.eye(2), shape=(6, 5, ...))
+        assert rv.ndim == 5
+        assert tuple(rv.shape.eval()) == (6, 5, 4, 3, 2)
 
         with pytest.warns(None):
             rv = pm.MvNormal.dist(mu=[1, 2, 3], cov=np.eye(3), size=(5, 4))