Add support for power transforms

aeppl/transforms.py

@@ -10,7 +10,7 @@
 from aesara.graph.fg import FunctionGraph
 from aesara.graph.op import Op
 from aesara.graph.rewriting.basic import GraphRewriter, in2out, node_rewriter
-from aesara.tensor.math import add, exp, log, mul, reciprocal, sub, true_div
+from aesara.tensor.math import add, exp, log, mul, pow, reciprocal, sub, true_div
 from aesara.tensor.rewriting.basic import (
     register_specialize,
     register_stabilize,
@@ -422,8 +422,20 @@ def transform(measurable_input, *other_inputs):
 def measurable_reciprocal(fgraph, node):
     """Rewrite a `reciprocal` node to a `MeasurableVariable`."""
 
-    def transform(measurable_input, *other_inputs):
-        return ReciprocalTransform(), (measurable_input,)
+    new_node = at.power(node.inputs[0], at.as_tensor(-1)).owner
+    return measurable_pow.transform(fgraph, new_node)
+
+
+@register_measurable_ir
+@node_rewriter([pow])
+def measurable_pow(fgraph, node):
+    """Rewrite a `pow` node to a `MeasurableVariable`."""
+
+    def transform(measurable_input, *args):
+        return PowerTransform(transform_args_fn=lambda *inputs: inputs[-1]), (
+            measurable_input,
+            *args,
+        )
 
     return construct_elemwise_transform(fgraph, node, transform)
 
@@ -579,17 +591,31 @@ def log_jac_det(self, value, *inputs):
         return -at.log(value)
 
 
-class ReciprocalTransform(RVTransform):
-    name = "reciprocal"
+class PowerTransform(RVTransform):
+    name = "power"
+
+    def __init__(self, transform_args_fn):
+        self.transform_args_fn = transform_args_fn
 
     def forward(self, value, *inputs):
-        return at.reciprocal(value)
+        power = self.transform_args_fn(*inputs)
+        return at.power(value, power)
 
     def backward(self, value, *inputs):
-        return at.reciprocal(value)
+        power = self.transform_args_fn(*inputs)
+
+        inv_power = at.reciprocal(power)
+        return at.switch(
+            at.eq(at.mod(power, 2), 0),
+            at.power(value, inv_power),
+            at.sgn(value) * at.power(at.abs(value), inv_power),
+        )
 
     def log_jac_det(self, value, *inputs):
-        return -2 * at.log(value)
+        from aeppl.logprob import xlogy0
+
+        power = self.transform_args_fn(*inputs)
+        return at.log(at.abs(power)) + xlogy0((power - 1), at.abs(value))
 
 
 class IntervalTransform(RVTransform):

tests/test_transforms.py

@@ -763,3 +763,22 @@ def test_transform_measurable_sub():
 
     with pytest.raises(RuntimeError, match="The logprob terms"):
         joint_logprob(Z_rv, X_rv)
+
+
+@pytest.mark.parametrize(
+    "pow_fn, exp_val_fn",
+    [
+        (lambda x: x**2, lambda z: sp.stats.chi2(df=1).logpdf(z))
+        # TODO: Add more cases.
+    ],
+)
+def test_transform_measurable_pow(pow_fn, exp_val_fn):
+    X_rv = at.random.normal(0, 1, name="X")
+    Z_rv = pow_fn(X_rv)
+    Z_rv.name = "Z"
+
+    z_logp, (z_vv,) = conditional_logprob(Z_rv)
+    z_logp_fn = aesara.function([z_vv], z_logp[Z_rv])
+
+    z_val = 0.5
+    assert np.allclose(z_logp_fn(z_val), exp_val_fn(z_val))