serialization: apply new scheme for package (breaking change) (#457)

* serialization: apply new scheme for `package` (breaking)

- introduces new policy for consistent naming for serilization (see #451
  for a discussion): standard is the path of a module a class resides
  in, trucated at depth to. So for all class in bayesflow.networks, we
  set package="bayesflow.networks", even if the live in the
  bayesflow.networks.mlp submodule.
- The `serializable` decorator checks this and errors if this is not
  followed. The check can be disabled for certain cases (e.g., classes
  in the experimental module, that might eventually live somewhere
  else).
- After this commit, previously saved models will not be loadable. As we
  introduced a bug regarding this anyway (#451), we will accept this and
  should inform users about it.
- usage of direct calls to `keras.saving.register_keras_serializable`
  were replaced with our custom decorator.

* update serilization policy in dev docs

* README: add not regarding breaking changes until 2.1 release

* standardize use of serializable decorator

* [no ci] change (de)serialize to new pipeline in transform

* serialization check: exempt classes not in bayesflow module

This should ensure that users that try to use our decorator with
external classes do not encounter the error. Possible edge case: they
also name their module "bayesflow".

---------

Co-authored-by: LarsKue <lars@kuehmichel.de>

Author: vpratz

README.md

@@ -15,6 +15,12 @@ It provides users and researchers with:
 BayesFlow (version 2+) is designed to be a flexible and efficient tool that enables rapid statistical inference
 fueled by continuous progress in generative AI and Bayesian inference.
 
+> [!IMPORTANT]
+> As the 2.0 version introduced many new features, we still have to make breaking changes from time to time.
+> This especially concerns **saving and loading** of models. We aim to stabilize this from the 2.1 release onwards.
+> Until then, consider pinning your BayesFlow 2.0 installation to an exact version, or re-training after an update
+> for less costly models.
+
 ## Important Note for Existing Users
 
 You are currently looking at BayesFlow 2.0+, which is a complete rewrite of the library.

bayesflow/adapters/adapter.py

@@ -29,7 +29,7 @@
 from .transforms.filter_transform import Predicate
 
 
-@serializable
+@serializable("bayesflow.adapters")
 class Adapter(MutableSequence[Transform]):
     """
     Defines an adapter to apply various transforms to data.

bayesflow/adapters/transforms/as_set.py

@@ -5,7 +5,7 @@
 from .elementwise_transform import ElementwiseTransform
 
 
-@serializable
+@serializable("bayesflow.adapters")
 class AsSet(ElementwiseTransform):
     """The `.as_set(["x", "y"])` transform indicates that both `x` and `y` are treated as sets.
 

bayesflow/adapters/transforms/as_time_series.py

@@ -5,7 +5,7 @@
 from .elementwise_transform import ElementwiseTransform
 
 
-@serializable
+@serializable("bayesflow.adapters")
 class AsTimeSeries(ElementwiseTransform):
     """The `.as_time_series` transform can be used to indicate that variables shall be treated as time series.
 

bayesflow/adapters/transforms/broadcast.py

@@ -6,7 +6,7 @@
 from .transform import Transform
 
 
-@serializable
+@serializable("bayesflow.adapters")
 class Broadcast(Transform):
     """
     Broadcasts arrays or scalars to the shape of a given other array.

bayesflow/adapters/transforms/concatenate.py

@@ -7,7 +7,7 @@
 from .transform import Transform
 
 
-@serializable
+@serializable("bayesflow.adapters")
 class Concatenate(Transform):
     """Concatenate multiple arrays into a new key. Used to specify how data variables should be treated by the network.
 

bayesflow/adapters/transforms/constrain.py

@@ -11,7 +11,7 @@
 from .elementwise_transform import ElementwiseTransform
 
 
-@serializable
+@serializable("bayesflow.adapters")
 class Constrain(ElementwiseTransform):
     """
     Constrains neural network predictions of a data variable to specified bounds.

bayesflow/adapters/transforms/convert_dtype.py

@@ -5,7 +5,7 @@
 from .elementwise_transform import ElementwiseTransform
 
 
-@serializable
+@serializable("bayesflow.adapters")
 class ConvertDType(ElementwiseTransform):
     """
     Default transform used to convert all floats from float64 to float32 to be in line with keras framework.

bayesflow/adapters/transforms/drop.py

@@ -5,7 +5,7 @@
 from .transform import Transform
 
 
-@serializable
+@serializable("bayesflow.adapters")
 class Drop(Transform):
     """
     Transform to drop variables from further calculation.

bayesflow/adapters/transforms/elementwise_transform.py

@@ -3,7 +3,7 @@
 from bayesflow.utils.serialization import serializable, deserialize
 
 
-@serializable
+@serializable("bayesflow.adapters")
 class ElementwiseTransform:
     """Base class on which other transforms are based"""