Added Logprob for discrete minimum

pymc/logprob/order.py

@@ -166,11 +166,11 @@ class MeasurableMaxNeg(Max):
 MeasurableVariable.register(MeasurableMaxNeg)
 
 
-class MeasurableMaxNegDiscrete(Max):
+class DiscreteMeasurableMaxNeg(Max):
     """A placeholder used to specify a log-likelihood for sub-graphs of negative maxima of discrete variables"""
 
 
-MeasurableVariable.register(MeasurableMaxNegDiscrete)
+MeasurableVariable.register(DiscreteMeasurableMaxNeg)
 
 
 @node_rewriter(tracks=[Max])
@@ -215,10 +215,7 @@ def find_measurable_max_neg(fgraph: FunctionGraph, node: Node) -> Optional[List[
 
     # distinguish measurable discrete and continuous (because logprob is different)
     if base_rv.owner.op.dtype.startswith("int"):
-        if isinstance(base_rv.owner.op, RandomVariable):
-            measurable_min = MeasurableMaxNegDiscrete(list(axis))
-        else:
-            return None
+        measurable_min = DiscreteMeasurableMaxNeg(list(axis))
     else:
         measurable_min = MeasurableMaxNeg(list(axis))
 
@@ -253,8 +250,8 @@ def max_neg_logprob(op, values, base_rv, **kwargs):
     return logprob
 
 
-@_logprob.register(MeasurableMaxNegDiscrete)
-def max_neg_logprob_discrete(op, values, base_rv, **kwargs):
+@_logprob.register(DiscreteMeasurableMaxNeg)
+def discrete_max_neg_logprob(op, values, base_rv, **kwargs):
     r"""Compute the log-likelihood graph for the `Max` operation.
 
     The formula that we use here is :

pymc/logprob/transforms.py

@@ -443,7 +443,7 @@ def find_measurable_transforms(fgraph: FunctionGraph, node: Node) -> Optional[Li
     [measurable_input] = measurable_inputs
     [measurable_output] = node.outputs
 
-    # Do not apply rewrite to discrete variables
+    # Do not apply rewrite to discrete variables except for their addition and negation
     if measurable_input.type.dtype.startswith("int"):
         if not (
             find_negated_var(measurable_output) is not None or isinstance(node.op.scalar_op, Add)

pymc/logprob/utils.py

@@ -50,6 +50,7 @@
 from pytensor.raise_op import CheckAndRaise
 from pytensor.scalar.basic import Mul
 from pytensor.tensor.basic import get_underlying_scalar_constant_value
+from pytensor.tensor.elemwise import Elemwise
 from pytensor.tensor.exceptions import NotScalarConstantError
 from pytensor.tensor.random.op import RandomVariable
 from pytensor.tensor.variable import TensorVariable
@@ -320,29 +321,24 @@ def expand(r):
     }
 
 
-def find_negated_var(base_var):
-    """Make sure that the base variable involves a multiplication with -1"""
+def find_negated_var(var):
+    """Return a variable that is being multiplied by -1 or None otherwise."""
 
-    scalar_op = base_var.owner.op.scalar_op
-
-    if len(base_var.owner.inputs) == 2:
-        if base_var.owner.inputs[0] is None:
-            base_rv = base_var.owner.inputs[0]
-            scalar_constant = base_var.owner.inputs[1]
-        else:
-            base_rv = base_var.owner.inputs[1]
-            scalar_constant = base_var.owner.inputs[0]
-    else:
+    if (
+        not (var.owner)
+        and isinstance(var.owner.op, Elemwise)
+        and isinstance(var.owner.op.scalar_op, Mul)
+    ):
         return None
-
-    try:
-        if not (
-            isinstance(scalar_op, Mul)
-            and get_underlying_scalar_constant_value(scalar_constant) == -1
-        ):
-            return None
-
-    except NotScalarConstantError:
+    if len(var.owner.inputs) != 2:
         return None
 
-    return base_rv
+    inputs = var.owner.inputs
+    for mul_var, mul_const in (inputs, reversed(inputs)):
+        try:
+            if get_underlying_scalar_constant_value(mul_const) == -1:
+                return mul_var
+        except NotScalarConstantError:
+            continue
+
+    return None