Bump PyMC minimum version requirement

conda-envs/environment-test.yml

@@ -11,6 +11,6 @@ dependencies:
 - xhistogram
 - statsmodels
 - pip:
-  - pymc>=5.9.0  # CI was failing to resolve
+  - pymc>=5.10.0  # CI was failing to resolve
   - blackjax
   - scikit-learn

conda-envs/windows-environment-test.yml

@@ -10,6 +10,6 @@ dependencies:
 - xhistogram
 - statsmodels
 - pip:
-  - pymc>=5.9.0  # CI was failing to resolve
+  - pymc>=5.10.0  # CI was failing to resolve
   - blackjax
   - scikit-learn

pymc_experimental/model/marginal_model.py

@@ -461,8 +461,6 @@ def finite_discrete_marginal_rv_logp(op, values, *inputs, **kwargs):
             for i in range(len(marginalized_rv_domain))
         ]
     else:
-        # Make sure this rewrite is registered
-        from pymc.pytensorf import local_remove_check_parameter
 
         def logp_fn(marginalized_rv_const, *non_sequences):
             return joint_logp_op(marginalized_rv_const, *non_sequences)

pymc_experimental/utils/prior.py

@@ -19,12 +19,12 @@
 import numpy as np
 import pymc as pm
 import pytensor.tensor as pt
-from pymc.logprob.transforms import RVTransform
+from pymc.logprob.transforms import Transform
 
 
 class ParamCfg(TypedDict):
     name: str
-    transform: Optional[RVTransform]
+    transform: Optional[Transform]
     dims: Optional[Union[str, Tuple[str]]]
 
 
@@ -44,14 +44,14 @@ class FlatInfo(TypedDict):
     info: List[VarInfo]
 
 
-def _arg_to_param_cfg(key, value: Optional[Union[ParamCfg, RVTransform, str, Tuple]] = None):
+def _arg_to_param_cfg(key, value: Optional[Union[ParamCfg, Transform, str, Tuple]] = None):
     if value is None:
         cfg = ParamCfg(name=key, transform=None, dims=None)
     elif isinstance(value, Tuple):
         cfg = ParamCfg(name=key, transform=None, dims=value)
     elif isinstance(value, str):
         cfg = ParamCfg(name=value, transform=None, dims=None)
-    elif isinstance(value, RVTransform):
+    elif isinstance(value, Transform):
         cfg = ParamCfg(name=key, transform=value, dims=None)
     else:
         cfg = value.copy()
@@ -62,7 +62,7 @@ def _arg_to_param_cfg(key, value: Optional[Union[ParamCfg, RVTransform, str, Tup
 
 
 def _parse_args(
-    var_names: Sequence[str], **kwargs: Union[ParamCfg, RVTransform, str, Tuple]
+    var_names: Sequence[str], **kwargs: Union[ParamCfg, Transform, str, Tuple]
 ) -> Dict[str, ParamCfg]:
     results = dict()
     for var in var_names:
@@ -133,7 +133,7 @@ def prior_from_idata(
     name="trace_prior_",
     *,
     var_names: Sequence[str] = (),
-    **kwargs: Union[ParamCfg, RVTransform, str, Tuple]
+    **kwargs: Union[ParamCfg, Transform, str, Tuple]
 ) -> Dict[str, pt.TensorVariable]:
     """
     Create a prior from posterior using MvNormal approximation.
@@ -153,7 +153,7 @@ def prior_from_idata(
         Inference data with posterior group
     var_names: Sequence[str]
         names of variables to take as is from the posterior
-    kwargs: Union[ParamCfg, RVTransform, str, Tuple]
+    kwargs: Union[ParamCfg, Transform, str, Tuple]
         names of variables with additional configuration, see more in Examples
 
     Examples

requirements.txt

@@ -1,2 +1,2 @@
-pymc>=5.8.2
+pymc>=5.10.0
 scikit-learn