Make `ChoiceRV` behave like NumPy's `choice`

aesara/tensor/random/basic.py

@@ -1759,7 +1759,14 @@ class ChoiceRV(RandomVariable):
         raise NotImplementedError()
 
     def _infer_shape(self, size, dist_params, param_shapes=None):
-        return size
+        (a, p, _) = dist_params
+
+        if isinstance(p.type, aesara.tensor.type_other.NoneTypeT):
+            shape = super()._infer_shape(size, (a,), param_shapes)
+        else:
+            shape = super()._infer_shape(size, (a, p), param_shapes)
+
+        return shape
 
     def __call__(self, a, size=None, replace=True, p=None, **kwargs):
         r"""Generate a random sample from an array.
@@ -1767,7 +1774,8 @@ class ChoiceRV(RandomVariable):
         Parameters
         ----------
         a
-            The array from which to randomly sample an element.
+            The array from which to randomly sample an element. If an int,
+            a sample is generated from `aesara.tensor.arange(a)`.
         size
             Sample shape. If the given size is `(m, n, k)`, then `m * n *
             k` independent samples are returned. Default is `None`, in
@@ -1778,7 +1786,10 @@ class ChoiceRV(RandomVariable):
             The probabilities associated with each entry in `a`. If not
             given, all elements have equal probability.
         """
-        a = as_tensor_variable(a, ndim=1)
+        a = as_tensor_variable(a)
+
+        if a.ndim == 0:
+            a = aesara.tensor.arange(a)
 
         if p is None:
             p = aesara.tensor.type_other.NoneConst.clone()

tests/tensor/random/test_basic.py

@@ -1321,17 +1321,28 @@ def test_choice_samples():
     with pytest.raises(NotImplementedError):
         choice._supp_shape_from_params(np.asarray(5))
 
+    compare_sample_values(choice, np.asarray(5))
     compare_sample_values(choice, np.asarray([5]))
     compare_sample_values(choice, np.array([1.0, 5.0], dtype=config.floatX))
     compare_sample_values(choice, np.asarray([5]), 3)
 
-    with pytest.raises(ValueError):
-        compare_sample_values(choice, np.array([[1, 2], [3, 4]]))
+    compare_sample_values(choice, np.array([[1, 2], [3, 4]]))
+    compare_sample_values(choice, np.array([[1, 2], [3, 4]]), p=[0.4, 0.6])
 
     compare_sample_values(choice, [1, 2, 3], 1)
+
     compare_sample_values(
         choice, [1, 2, 3], 1, p=at.as_tensor([1 / 3.0, 1 / 3.0, 1 / 3.0])
     )
+
+    # p must be 1-dimensional.
+    # TODO: The exception is raised at runtime but could be raised at compile
+    # time in some situations using static shape analysis.
+    with pytest.raises(ValueError):
+        rng = np.random.default_rng()
+        rng_at = shared(rng, borrow=True)
+        choice(a=[1, 2], p=at.as_tensor([[0.1, 0.9], [0.3, 0.7]]), rng=rng_at).eval()
+
     compare_sample_values(choice, [1, 2, 3], (10, 2), replace=True)
     compare_sample_values(choice, at.as_tensor_variable([1, 2, 3]), 2, replace=True)