Do not require class_name for CustomDist and Simulator dists

* Also remove the experimental warning when using CustomSymbolicDists

Author: ricardoV94

pymc/distributions/distribution.py

@@ -488,14 +488,14 @@ class _CustomDist(Distribution):
     def dist(
         cls,
         *dist_params,
-        class_name: str,
         logp: Optional[Callable] = None,
         logcdf: Optional[Callable] = None,
         random: Optional[Callable] = None,
         moment: Optional[Callable] = None,
         ndim_supp: int = 0,
         ndims_params: Optional[Sequence[int]] = None,
         dtype: str = "floatX",
+        class_name: str = "CustomDist",
         **kwargs,
     ):
         dist_params = [as_tensor_variable(param) for param in dist_params]
@@ -523,36 +523,36 @@ def dist(
 
         return super().dist(
             dist_params,
-            class_name=class_name,
             logp=logp,
             logcdf=logcdf,
             random=random,
             moment=moment,
             ndim_supp=ndim_supp,
             ndims_params=ndims_params,
             dtype=dtype,
+            class_name=class_name,
             **kwargs,
         )
 
     @classmethod
     def rv_op(
         cls,
         *dist_params,
-        class_name: str,
         logp: Optional[Callable],
         logcdf: Optional[Callable],
         random: Optional[Callable],
         moment: Optional[Callable],
         ndim_supp: int,
         ndims_params: Optional[Sequence[int]],
         dtype: str,
+        class_name: str,
         **kwargs,
     ):
         rv_type = type(
-            f"CustomDistRV_{class_name}",
+            class_name,
             (CustomDistRV,),
             dict(
-                name=f"CustomDist_{class_name}",
+                name=class_name,
                 inplace=False,
                 ndim_supp=ndim_supp,
                 ndims_params=ndims_params,
@@ -613,20 +613,15 @@ class _CustomSymbolicDist(Distribution):
     def dist(
         cls,
         *dist_params,
-        class_name: str,
         dist: Callable,
         logp: Optional[Callable] = None,
         logcdf: Optional[Callable] = None,
         moment: Optional[Callable] = None,
         ndim_supp: int = 0,
         dtype: str = "floatX",
+        class_name: str = "CustomSymbolicDist",
         **kwargs,
     ):
-        warnings.warn(
-            "CustomDist with dist function is still experimental. Expect bugs!",
-            UserWarning,
-        )
-
         dist_params = [as_tensor_variable(param) for param in dist_params]
 
         if logcdf is None:
@@ -655,13 +650,13 @@ def dist(
     def rv_op(
         cls,
         *dist_params,
-        class_name: str,
         dist: Callable,
         logp: Optional[Callable],
         logcdf: Optional[Callable],
         moment: Optional[Callable],
         size=None,
         ndim_supp: int,
+        class_name: str,
     ):
         size = normalize_size_param(size)
         dummy_size_param = size.type()
@@ -674,7 +669,7 @@ def rv_op(
         dummy_updates_dict = collect_default_updates(dummy_params, (dummy_rv,))
 
         rv_type = type(
-            f"CustomSymbolicDistRV_{class_name}",
+            class_name,
             (CustomSymbolicDistRV,),
             # If logp is not provided, we try to infer it from the dist graph
             dict(
@@ -758,15 +753,6 @@ class CustomDist:
     dist_params : Tuple
         A sequence of the distribution's parameter. These will be converted into
         Pytensor tensor variables internally.
-    class_name : str
-        Name for the class which will wrap the CustomDist methods. When not specified,
-        it will be given the name of the model variable.
-
-        .. warning:: New CustomDists created with the same class_name will override the
-            methods dispatched onto the previous classes. If using CustomDists with
-            different methods across separate models, be sure to use distinct
-            class_names.
-
     dist: Optional[Callable]
         A callable that returns a PyTensor graph built from simpler PyMC distributions
         which represents the distribution. This can be used by PyMC to take random draws
@@ -831,6 +817,9 @@ class CustomDist:
         The dtype of the distribution. All draws and observations passed into the
         distribution will be cast onto this dtype. This is not needed if an PyTensor
         dist function is provided, which should already return the right dtype!
+    class_name : str
+        Name for the class which will wrap the CustomDist methods. When not specified,
+        it will be given the name of the model variable.
     kwargs :
         Extra keyword arguments are passed to the parent's class ``__new__`` method.
 
@@ -979,36 +968,36 @@ def __new__(
         dist_params = cls.parse_dist_params(dist_params)
         cls.check_valid_dist_random(dist, random, dist_params)
         if dist is not None:
+            kwargs.setdefault("class_name", f"CustomSymbolicDist_{name}")
             return _CustomSymbolicDist(
                 name,
                 *dist_params,
-                class_name=name,
                 dist=dist,
                 logp=logp,
                 logcdf=logcdf,
                 moment=moment,
                 ndim_supp=ndim_supp,
                 **kwargs,
             )
-        return _CustomDist(
-            name,
-            *dist_params,
-            class_name=name,
-            random=random,
-            logp=logp,
-            logcdf=logcdf,
-            moment=moment,
-            ndim_supp=ndim_supp,
-            ndims_params=ndims_params,
-            dtype=dtype,
-            **kwargs,
-        )
+        else:
+            kwargs.setdefault("class_name", f"CustomDist_{name}")
+            return _CustomDist(
+                name,
+                *dist_params,
+                random=random,
+                logp=logp,
+                logcdf=logcdf,
+                moment=moment,
+                ndim_supp=ndim_supp,
+                ndims_params=ndims_params,
+                dtype=dtype,
+                **kwargs,
+            )
 
     @classmethod
     def dist(
         cls,
         *dist_params,
-        class_name: str,
         dist: Optional[Callable] = None,
         random: Optional[Callable] = None,
         logp: Optional[Callable] = None,
@@ -1024,7 +1013,6 @@ def dist(
         if dist is not None:
             return _CustomSymbolicDist.dist(
                 *dist_params,
-                class_name=class_name,
                 dist=dist,
                 logp=logp,
                 logcdf=logcdf,
@@ -1035,7 +1023,6 @@ def dist(
         else:
             return _CustomDist.dist(
                 *dist_params,
-                class_name=class_name,
                 random=random,
                 logp=logp,
                 logcdf=logcdf,

pymc/distributions/simulator.py

@@ -86,15 +86,6 @@ class Simulator(Distribution):
         Keyword form of ''unnamed_params''.
         One of unnamed_params or params must be provided.
         If passed both unnamed_params and params, an error is raised.
-    class_name : str
-        Name for the RandomVariable class which will wrap the Simulator methods.
-        When not specified, it will be given the name of the variable.
-
-        .. warning:: New Simulators created with the same class_name will override the
-            methods dispatched onto the previous classes. If using Simulators with
-            different methods across separate models, be sure to use distinct
-            class_names.
-
     distance : PyTensor_Op, callable or str, default "gaussian"
         Distance function. Available options are ``"gaussian"``, ``"laplace"``,
         ``"kullback_leibler"`` or a user defined function (or PyTensor_Op) that takes
@@ -123,6 +114,8 @@ class Simulator(Distribution):
         Number of minimum dimensions of each parameter of the RV. For example,
         if the Simulator accepts two scalar inputs, it should be ``[0, 0]``.
         Default to list of 0 with length equal to the number of parameters.
+    class_name : str, optional
+        Suffix name for the RandomVariable class which will wrap the Simulator methods.
 
     Examples
     --------
@@ -149,7 +142,7 @@ def simulator_fn(rng, loc, scale, size):
     rv_type = SimulatorRV
 
     def __new__(cls, name, *args, **kwargs):
-        kwargs.setdefault("class_name", name)
+        kwargs.setdefault("class_name", f"Simulator_{name}")
         return super().__new__(cls, name, *args, **kwargs)
 
     @classmethod
@@ -158,13 +151,13 @@ def dist(  # type: ignore
         fn,
         *unnamed_params,
         params=None,
-        class_name: str,
         distance="gaussian",
         sum_stat="identity",
         epsilon=1,
         ndim_supp=0,
         ndims_params=None,
         dtype="floatX",
+        class_name: str = "Simulator",
         **kwargs,
     ):
         if not isinstance(distance, Op):
@@ -213,36 +206,36 @@ def dist(  # type: ignore
 
         return super().dist(
             params,
-            class_name=class_name,
             fn=fn,
             ndim_supp=ndim_supp,
             ndims_params=ndims_params,
             dtype=dtype,
             distance=distance,
             sum_stat=sum_stat,
             epsilon=epsilon,
+            class_name=class_name,
             **kwargs,
         )
 
     @classmethod
     def rv_op(
         cls,
         *params,
-        class_name,
         fn,
         ndim_supp,
         ndims_params,
         dtype,
         distance,
         sum_stat,
         epsilon,
+        class_name,
         **kwargs,
     ):
         sim_op = type(
-            f"Simulator_{class_name}",
+            class_name,
             (SimulatorRV,),
             dict(
-                name=f"Simulator_{class_name}",
+                name=class_name,
                 ndim_supp=ndim_supp,
                 ndims_params=ndims_params,
                 dtype=dtype,