Assume default_output is the only measurable output in SymbolicRandomVariables

pymc/distributions/distribution.py

@@ -381,6 +381,14 @@ def dist(
 @_get_measurable_outputs.register(SymbolicRandomVariable)
 def _get_measurable_outputs_symbolic_random_variable(op, node):
     # This tells Aeppl that any non RandomType outputs are measurable
+
+    # Assume that if there is one default_output, that's the only one that is measurable
+    # In the rare case this is not what one wants, a specialized _get_measuarable_outputs
+    # can dispatch for a subclassed Op
+    if op.default_output is not None:
+        return [node.default_output()]
+
+    # Otherwise assume that any outputs that are not of RandomType are measurable
     return [out for out in node.outputs if not isinstance(out.type, RandomType)]
 
 

pymc/distributions/timeseries.py

@@ -19,7 +19,6 @@
 import aesara.tensor as at
 import numpy as np
 
-from aeppl.abstract import _get_measurable_outputs
 from aeppl.logprob import _logprob
 from aesara.graph.basic import Node, clone_replace
 from aesara.raise_op import Assert
@@ -203,12 +202,6 @@ def rv_op(cls, init_dist, innovation_dist, steps, size=None):
         )(init_dist, innovation_dist, steps)
 
 
-@_get_measurable_outputs.register(RandomWalkRV)
-def _get_measurable_outputs_random_walk(op, node):
-    # Ignore steps output
-    return [node.default_output()]
-
-
 @_change_dist_size.register(RandomWalkRV)
 def change_random_walk_size(op, dist, new_size, expand):
     init_dist, innovation_dist, steps = dist.owner.inputs

pymc/tests/distributions/test_distribution.py

@@ -339,11 +339,25 @@ class TestInlinedSymbolicRV(SymbolicRandomVariable):
         x_inline = TestInlinedSymbolicRV([], [Flat.dist()], ndim_supp=0)()
         assert np.isclose(logp(x_inline, 0).eval(), 0)
 
-    def test_measurable_outputs(self):
+    def test_measurable_outputs_rng_ignored(self):
+        """Test that any RandomType outputs are ignored as a measurable_outputs"""
+
         class TestSymbolicRV(SymbolicRandomVariable):
             pass
 
         next_rng_, dirac_delta_ = DiracDelta.dist(5).owner.outputs
         next_rng, dirac_delta = TestSymbolicRV([], [next_rng_, dirac_delta_], ndim_supp=0)()
         node = dirac_delta.owner
         assert get_measurable_outputs(node.op, node) == [dirac_delta]
+
+    @pytest.mark.parametrize("default_output_idx", (0, 1))
+    def test_measurable_outputs_default_output(self, default_output_idx):
+        """Test that if provided, a default output is considered the only measurable_output"""
+
+        class TestSymbolicRV(SymbolicRandomVariable):
+            default_output = default_output_idx
+
+        dirac_delta_1_ = DiracDelta.dist(5)
+        dirac_delta_2_ = DiracDelta.dist(10)
+        node = TestSymbolicRV([], [dirac_delta_1_, dirac_delta_2_], ndim_supp=0)().owner
+        assert get_measurable_outputs(node.op, node) == [node.outputs[default_output_idx]]