Rename logp_transform to _get_default_transform and move it to tr…

…ansforms.py

pymc/distributions/__init__.py

@@ -15,7 +15,6 @@
 from pymc.distributions.logprob import (  # isort:skip
     logcdf,
     logp,
-    logp_transform,
     joint_logpt,
 )
 
@@ -195,6 +194,5 @@
     "PolyaGamma",
     "joint_logpt",
     "logp",
-    "logp_transform",
     "logcdf",
 ]

pymc/distributions/continuous.py

@@ -73,7 +73,7 @@ def polyagamma_cdf(*args, **kwargs):
 from scipy.special import expit
 
 from pymc.aesaraf import floatX
-from pymc.distributions import logp_transform, transforms
+from pymc.distributions import transforms
 from pymc.distributions.dist_math import (
     SplineWrapper,
     check_parameters,
@@ -87,6 +87,7 @@ def polyagamma_cdf(*args, **kwargs):
 )
 from pymc.distributions.distribution import DIST_PARAMETER_TYPES, Continuous
 from pymc.distributions.shape_utils import rv_size_is_none
+from pymc.distributions.transforms import _get_default_transform
 from pymc.math import invlogit, logdiffexp, logit
 from pymc.util import UNSET
 
@@ -139,17 +140,17 @@ class CircularContinuous(Continuous):
     """Base class for circular continuous distributions"""
 
 
-@logp_transform.register(PositiveContinuous)
+@_get_default_transform.register(PositiveContinuous)
 def pos_cont_transform(op):
     return transforms.log
 
 
-@logp_transform.register(UnitContinuous)
+@_get_default_transform.register(UnitContinuous)
 def unit_cont_transform(op):
     return transforms.logodds
 
 
-@logp_transform.register(CircularContinuous)
+@_get_default_transform.register(CircularContinuous)
 def circ_cont_transform(op):
     return transforms.circular
 

pymc/distributions/logprob.py

@@ -13,7 +13,6 @@
 #   limitations under the License.
 
 from collections.abc import Mapping
-from functools import singledispatch
 from typing import Dict, List, Optional, Sequence, Union
 
 import aesara
@@ -25,7 +24,6 @@
 from aeppl.logprob import logprob as logp_aeppl
 from aeppl.transforms import TransformValuesOpt
 from aesara.graph.basic import graph_inputs, io_toposort
-from aesara.graph.op import Op
 from aesara.tensor.subtensor import (
     AdvancedIncSubtensor,
     AdvancedIncSubtensor1,
@@ -39,11 +37,6 @@
 from pymc.aesaraf import floatX
 
 
-@singledispatch
-def logp_transform(op: Op):
-    return None
-
-
 def _get_scaling(total_size: Optional[Union[int, Sequence[int]]], shape, ndim: int):
     """
     Gets scaling constant for logp.

pymc/distributions/transforms.py

@@ -11,6 +11,7 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
+from functools import singledispatch
 
 import aesara.tensor as at
 
@@ -22,6 +23,7 @@
     RVTransform,
     Simplex,
 )
+from aesara.graph import Op
 
 __all__ = [
     "RVTransform",
@@ -38,6 +40,12 @@
 ]
 
 
+@singledispatch
+def _get_default_transform(op: Op):
+    """Return default transform for a given Distribution `Op`"""
+    return None
+
+
 class LogExpM1(RVTransform):
     name = "log_exp_m1"
 

pymc/model.py

@@ -58,8 +58,9 @@
 )
 from pymc.blocking import DictToArrayBijection, RaveledVars
 from pymc.data import GenTensorVariable, Minibatch
-from pymc.distributions import joint_logpt, logp_transform
+from pymc.distributions import joint_logpt
 from pymc.distributions.logprob import _get_scaling
+from pymc.distributions.transforms import _get_default_transform
 from pymc.exceptions import ImputationWarning, SamplingError, ShapeError
 from pymc.initial_point import make_initial_point_fn
 from pymc.math import flatten_list
@@ -1419,7 +1420,7 @@ def create_value_var(
         # Make the value variable a transformed value variable,
         # if there's an applicable transform
         if transform is UNSET and rv_var.owner:
-            transform = logp_transform(rv_var.owner.op)
+            transform = _get_default_transform(rv_var.owner.op)
 
         if transform is not None and transform is not UNSET:
             value_var.tag.transform = transform