Support PyTensor deterministic operations as observations

pymc/data.py

@@ -30,14 +30,13 @@
 from pytensor.compile.sharedvalue import SharedVariable
 from pytensor.graph.basic import Variable
 from pytensor.raise_op import Assert
-from pytensor.scalar import Cast
-from pytensor.tensor.elemwise import Elemwise
 from pytensor.tensor.random.basic import IntegersRV
 from pytensor.tensor.type import TensorType
 from pytensor.tensor.variable import TensorConstant, TensorVariable
 
 import pymc as pm
 
+from pymc.logprob.utils import rvs_in_graph
 from pymc.pytensorf import GeneratorOp, convert_data, smarttypeX
 from pymc.vartypes import isgenerator
 
@@ -148,6 +147,10 @@ def __str__(self):
         return "Minibatch"
 
 
+def has_random_ancestor(r):
+    return rvs_in_graph(r) != set()
+
+
 def is_valid_observed(v) -> bool:
     if not isinstance(v, Variable):
         # Non-symbolic constant
@@ -158,13 +161,7 @@ def is_valid_observed(v) -> bool:
         return True
 
     return (
-        # The only PyTensor operation we allow on observed data is type casting
-        # Although we could allow for any graph that does not depend on other RVs
-        (
-            isinstance(v.owner.op, Elemwise)
-            and isinstance(v.owner.op.scalar_op, Cast)
-            and is_valid_observed(v.owner.inputs[0])
-        )
+        not has_random_ancestor(v)
         # Or Minibatch
         or (
             isinstance(v.owner.op, MinibatchOp)

pymc/pytensorf.py

@@ -177,6 +177,11 @@ def extract_obs_data(x: TensorVariable) -> np.ndarray:
             mask[mask_idx] = 1
             return np.ma.MaskedArray(array_data, mask)
 
+    from pymc.data import has_random_ancestor
+
+    if not has_random_ancestor(x):
+        return x.eval()
+
     raise TypeError(f"Data cannot be extracted from {x}")
 
 

tests/test_data.py

@@ -623,3 +623,14 @@ def test_multiple_vars(self):
         [draw_mA, draw_mB] = pm.draw([mA, mB])
         assert draw_mA.shape == (10,)
         np.testing.assert_allclose(draw_mA, -draw_mB)
+
+
+def test_scaling_data_works_in_likelihood() -> None:
+    data = np.array([10, 11, 12, 13, 14, 15])
+
+    with pm.Model() as model:
+        target = pm.Data("target", data)
+        scale = 12
+        scaled_target = target / scale
+        mu = pm.Normal("mu", mu=0, sigma=1)
+        pm.Normal("x", mu=mu, sigma=1, observed=scaled_target)