From 6ebc5f377692afc4078be1297c9f7c046b1e03d1 Mon Sep 17 00:00:00 2001
From: LarsKue <lars@kuehmichel.de>
Date: Tue, 11 Mar 2025 16:38:54 +0100
Subject: [PATCH 01/10] add numpy transform

---
 bayesflow/adapters/transforms/__init__.py     |  1 +
 .../adapters/transforms/numpy_transform.py    | 81 +++++++++++++++++++
 2 files changed, 82 insertions(+)
 create mode 100644 bayesflow/adapters/transforms/numpy_transform.py

diff --git a/bayesflow/adapters/transforms/__init__.py b/bayesflow/adapters/transforms/__init__.py
index 1c5211d51..3f960e555 100644
--- a/bayesflow/adapters/transforms/__init__.py
+++ b/bayesflow/adapters/transforms/__init__.py
@@ -12,6 +12,7 @@
 from .lambda_transform import LambdaTransform
 from .log import Log
 from .map_transform import MapTransform
+from .numpy_transform import NumpyTransform
 from .one_hot import OneHot
 from .rename import Rename
 from .sqrt import Sqrt
diff --git a/bayesflow/adapters/transforms/numpy_transform.py b/bayesflow/adapters/transforms/numpy_transform.py
new file mode 100644
index 000000000..c110fe882
--- /dev/null
+++ b/bayesflow/adapters/transforms/numpy_transform.py
@@ -0,0 +1,81 @@
+import numpy as np
+
+from bayesflow.utils import filter_kwargs
+from .elementwise_transform import ElementwiseTransform
+
+
+class NumpyTransform(ElementwiseTransform):
+    """
+    A class to apply element-wise transformations using plain NumPy functions.
+
+    Attributes:
+    ----------
+    _forward : str
+        The name of the NumPy function to apply in the forward transformation.
+    _inverse : str
+        The name of the NumPy function to apply in the inverse transformation.
+    """
+
+    INVERSE_METHODS = {
+        "arctan": "tan",
+        "exp": "log",
+        "expm1": "log1p",
+        "square": "sqrt",
+        "reciprocal": "reciprocal",
+    }
+    # ensure the map is symmetric
+    INVERSE_METHODS |= {v: k for k, v in INVERSE_METHODS.items()}
+
+    def __init__(self, forward: np.ufunc | str, inverse: np.ufunc | str = None):
+        """
+        Initializes the NumpyTransform with specified forward and inverse functions.
+
+        Parameters:
+        ----------
+        forward : str
+            The name of the NumPy function to use for the forward transformation.
+        inverse : str
+            The name of the NumPy function to use for the inverse transformation.
+            By default, the inverse is inferred from the forward argument for supported methods.
+        """
+        super().__init__()
+
+        if isinstance(forward, np.ufunc):
+            forward = forward.__name__
+
+        if inverse is None:
+            if forward not in self.INVERSE_METHODS:
+                raise ValueError(f"Cannot infer inverse for method {forward!r}")
+
+            inverse = self.INVERSE_METHODS[forward]
+        elif isinstance(inverse, np.ufunc):
+            inverse = inverse.__name__
+
+        if forward not in dir(np):
+            raise ValueError(f"Method {forward!r} not found in numpy version {np.__version__}")
+
+        if inverse not in dir(np):
+            raise ValueError(f"Method {inverse!r} not found in numpy version {np.__version__}")
+
+        self._forward = forward
+        self._inverse = inverse
+
+    @classmethod
+    def from_config(cls, config: dict, custom_objects=None) -> "ElementwiseTransform":
+        return cls(
+            forward=config["forward"],
+            inverse=config["inverse"],
+        )
+
+    def get_config(self) -> dict:
+        return {"forward": self._forward, "inverse": self._inverse}
+
+    def forward(self, data: dict[str, any], **kwargs) -> dict[str, any]:
+        forward = getattr(np, self._forward)
+        kwargs = filter_kwargs(kwargs, forward)
+        return forward(data, **kwargs)
+
+    def inverse(self, data: np.ndarray, **kwargs) -> np.ndarray:
+        inverse = getattr(np, self._inverse)
+        kwargs = filter_kwargs(kwargs, inverse)
+        return inverse(data, **kwargs)

From d49aef8863823fcead58dc43d738b7c30afabd2e Mon Sep 17 00:00:00 2001
From: LarsKue <lars@kuehmichel.de>
Date: Tue, 11 Mar 2025 16:39:03 +0100
Subject: [PATCH 02/10] remove lambda transform

---
 bayesflow/adapters/transforms/__init__.py     |  1 -
 .../adapters/transforms/lambda_transform.py   | 65 -------------------
 2 files changed, 66 deletions(-)
 delete mode 100644 bayesflow/adapters/transforms/lambda_transform.py

diff --git a/bayesflow/adapters/transforms/__init__.py b/bayesflow/adapters/transforms/__init__.py
index 3f960e555..c0a1fce24 100644
--- a/bayesflow/adapters/transforms/__init__.py
+++ b/bayesflow/adapters/transforms/__init__.py
@@ -9,7 +9,6 @@
 from .expand_dims import ExpandDims
 from .filter_transform import FilterTransform
 from .keep import Keep
-from .lambda_transform import LambdaTransform
 from .log import Log
 from .map_transform import MapTransform
 from .numpy_transform import NumpyTransform
diff --git a/bayesflow/adapters/transforms/lambda_transform.py b/bayesflow/adapters/transforms/lambda_transform.py
deleted file mode 100644
index 91dc4c8a7..000000000
--- a/bayesflow/adapters/transforms/lambda_transform.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from collections.abc import Callable
-import numpy as np
-from keras.saving import (
-    deserialize_keras_object as deserialize,
-    register_keras_serializable as serializable,
-    serialize_keras_object as serialize,
-)
-from .elementwise_transform import ElementwiseTransform
-from ...utils import filter_kwargs
-
-
-@serializable(package="bayesflow.adapters")
-class LambdaTransform(ElementwiseTransform):
-    """
-    Transforms a parameter using a pair of forward and inverse functions.
-
-    Parameters
-    ----------
-    forward : callable, no lambda
-        Function to transform the data in the forward pass.
-        For the adapter to be serializable, this function has to be serializable
-        as well (see Notes). Therefore, only proper functions and no lambda
-        functions should be used here.
-    inverse : callable, no lambda
-        Function to transform the data in the inverse pass.
-        For the adapter to be serializable, this function has to be serializable
-        as well (see Notes). Therefore, only proper functions and no lambda
-        functions should be used here.
-
-    Notes
-    -----
-    Important: This class is only serializable if the forward and inverse functions are serializable.
-    This most likely means you will have to pass the scope that the forward and inverse functions are contained in
-    to the `custom_objects` argument of the `deserialize` function when deserializing this class.
-    """
-
-    def __init__(
-        self, *, forward: Callable[[np.ndarray, ...], np.ndarray], inverse: Callable[[np.ndarray, ...], np.ndarray]
-    ):
-        super().__init__()
-
-        self._forward = forward
-        self._inverse = inverse
-
-    @classmethod
-    def from_config(cls, config: dict, custom_objects=None) -> "LambdaTransform":
-        return cls(
-            forward=deserialize(config["forward"], custom_objects),
-            inverse=deserialize(config["inverse"], custom_objects),
-        )
-
-    def get_config(self) -> dict:
-        return {
-            "forward": serialize(self._forward),
-            "inverse": serialize(self._inverse),
-        }
-
-    def forward(self, data: np.ndarray, **kwargs) -> np.ndarray:
-        # filter kwargs so that other transform args like batch_size, strict, ... are not passed through
-        kwargs = filter_kwargs(kwargs, self._forward)
-        return self._forward(data, **kwargs)
-
-    def inverse(self, data: np.ndarray, **kwargs) -> np.ndarray:
-        kwargs = filter_kwargs(kwargs, self._inverse)
-        return self._inverse(data, **kwargs)

From 43dd432b6da3c14a752712136816548e7f85f0b7 Mon Sep 17 00:00:00 2001
From: LarsKue <lars@kuehmichel.de>
Date: Tue, 11 Mar 2025 16:39:32 +0100
Subject: [PATCH 03/10] repurpose `adapter.apply()` for `NumpyTransform`
 instead of `LambdaTransform`

---
 bayesflow/adapters/adapter.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/bayesflow/adapters/adapter.py b/bayesflow/adapters/adapter.py
index 724be81bf..1fd303959 100644
--- a/bayesflow/adapters/adapter.py
+++ b/bayesflow/adapters/adapter.py
@@ -1,4 +1,4 @@
-from collections.abc import Callable, MutableSequence, Sequence
+from collections.abc import MutableSequence, Sequence
 
 import numpy as np
 from keras.saving import (
@@ -18,9 +18,9 @@
     ExpandDims,
     FilterTransform,
     Keep,
-    LambdaTransform,
     Log,
     MapTransform,
+    NumpyTransform,
     OneHot,
     Rename,
     Sqrt,
@@ -234,8 +234,8 @@ def __len__(self):
     def apply(
         self,
         *,
-        forward: Callable[[np.ndarray, ...], np.ndarray],
-        inverse: Callable[[np.ndarray, ...], np.ndarray],
+        forward: np.ufunc | str,
+        inverse: np.ufunc | str = None,
         predicate: Predicate = None,
         include: str | Sequence[str] = None,
         exclude: str | Sequence[str] = None,
@@ -271,7 +271,7 @@ def apply(
         to the `custom_objects` argument of the `deserialize` function when deserializing this class.
         """
         transform = FilterTransform(
-            transform_constructor=LambdaTransform,
+            transform_constructor=NumpyTransform,
             predicate=predicate,
             include=include,
             exclude=exclude,

From b1eb4436cce48490a22b7450ad8c685b155d15c8 Mon Sep 17 00:00:00 2001
From: LarsKue <lars@kuehmichel.de>
Date: Tue, 11 Mar 2025 16:40:45 +0100
Subject: [PATCH 04/10] fix usages of `adapter.apply()` in example notebooks

---
 examples/Linear_Regression_Starter.ipynb | 62 ++++++++++++------------
 examples/SIR_Posterior_Estimation.ipynb  |  2 +-
 2 files changed, 32 insertions(+), 32 deletions(-)

diff --git a/examples/Linear_Regression_Starter.ipynb b/examples/Linear_Regression_Starter.ipynb
index 668633db0..95ff6597d 100644
--- a/examples/Linear_Regression_Starter.ipynb
+++ b/examples/Linear_Regression_Starter.ipynb
@@ -480,7 +480,7 @@
     "    .as_set([\"x\", \"y\"])\n",
     "    .constrain(\"sigma\", lower=0)\n",
     "    .standardize(exclude=[\"N\"])\n",
-    "    .apply(include=\"N\", forward=lambda n: np.sqrt(n), inverse=lambda n: n**2)\n",
+    "    .apply(include=\"N\", forward=np.sqrt)\n",
     "    .concatenate([\"beta\", \"sigma\"], into=\"inference_variables\")\n",
     "    .concatenate([\"x\", \"y\"], into=\"summary_variables\")\n",
     "    .rename(\"N\", \"inference_conditions\")\n",
@@ -706,65 +706,65 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 226ms/step - loss: 2.6619 - loss/inference_loss: 2.6619\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m32s\u001B[0m 226ms/step - loss: 2.6619 - loss/inference_loss: 2.6619\n",
       "Epoch 2/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 1.5383 - loss/inference_loss: 1.5383\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 1.5383 - loss/inference_loss: 1.5383\n",
       "Epoch 3/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 1.2547 - loss/inference_loss: 1.2547\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 1.2547 - loss/inference_loss: 1.2547\n",
       "Epoch 4/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.9276 - loss/inference_loss: 0.9276\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.9276 - loss/inference_loss: 0.9276\n",
       "Epoch 5/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.7913 - loss/inference_loss: 0.7913\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.7913 - loss/inference_loss: 0.7913\n",
       "Epoch 6/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.6641 - loss/inference_loss: 0.6641\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.6641 - loss/inference_loss: 0.6641\n",
       "Epoch 7/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.5110 - loss/inference_loss: 0.5110\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.5110 - loss/inference_loss: 0.5110\n",
       "Epoch 8/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.4761 - loss/inference_loss: 0.4761\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.4761 - loss/inference_loss: 0.4761\n",
       "Epoch 9/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.3466 - loss/inference_loss: 0.3466\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.3466 - loss/inference_loss: 0.3466\n",
       "Epoch 10/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.2320 - loss/inference_loss: 0.2320\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.2320 - loss/inference_loss: 0.2320\n",
       "Epoch 11/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.2532 - loss/inference_loss: 0.2532\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.2532 - loss/inference_loss: 0.2532\n",
       "Epoch 12/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.1257 - loss/inference_loss: 0.1257\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.1257 - loss/inference_loss: 0.1257\n",
       "Epoch 13/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.1499 - loss/inference_loss: 0.1499\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.1499 - loss/inference_loss: 0.1499\n",
       "Epoch 14/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.0730 - loss/inference_loss: 0.0730\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.0730 - loss/inference_loss: 0.0730\n",
       "Epoch 15/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.0074 - loss/inference_loss: 0.0074\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.0074 - loss/inference_loss: 0.0074\n",
       "Epoch 16/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.0532 - loss/inference_loss: 0.0532\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.0532 - loss/inference_loss: 0.0532\n",
       "Epoch 17/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: 0.0035 - loss/inference_loss: 0.0035\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: 0.0035 - loss/inference_loss: 0.0035\n",
       "Epoch 18/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: -0.0339 - loss/inference_loss: -0.0339\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: -0.0339 - loss/inference_loss: -0.0339\n",
       "Epoch 19/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: -0.1002 - loss/inference_loss: -0.1002\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 13ms/step - loss: -0.1002 - loss/inference_loss: -0.1002\n",
       "Epoch 20/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: -0.1655 - loss/inference_loss: -0.1655\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: -0.1655 - loss/inference_loss: -0.1655\n",
       "Epoch 21/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: -0.0874 - loss/inference_loss: -0.0874\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: -0.0874 - loss/inference_loss: -0.0874\n",
       "Epoch 22/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: -0.1598 - loss/inference_loss: -0.1598\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: -0.1598 - loss/inference_loss: -0.1598\n",
       "Epoch 23/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: -0.0770 - loss/inference_loss: -0.0770\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: -0.0770 - loss/inference_loss: -0.0770\n",
       "Epoch 24/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: -0.1823 - loss/inference_loss: -0.1823\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: -0.1823 - loss/inference_loss: -0.1823\n",
       "Epoch 25/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: -0.1833 - loss/inference_loss: -0.1833\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: -0.1833 - loss/inference_loss: -0.1833\n",
       "Epoch 26/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: -0.2606 - loss/inference_loss: -0.2606\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: -0.2606 - loss/inference_loss: -0.2606\n",
       "Epoch 27/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: -0.1786 - loss/inference_loss: -0.1786\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 13ms/step - loss: -0.1786 - loss/inference_loss: -0.1786\n",
       "Epoch 28/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: -0.2828 - loss/inference_loss: -0.2828\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: -0.2828 - loss/inference_loss: -0.2828\n",
       "Epoch 29/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: -0.1857 - loss/inference_loss: -0.1857\n",
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 13ms/step - loss: -0.1857 - loss/inference_loss: -0.1857\n",
       "Epoch 30/30\n",
-      "\u001b[1m128/128\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - loss: -0.1761 - loss/inference_loss: -0.1761\n"
+      "\u001B[1m128/128\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 12ms/step - loss: -0.1761 - loss/inference_loss: -0.1761\n"
      ]
     }
    ],
diff --git a/examples/SIR_Posterior_Estimation.ipynb b/examples/SIR_Posterior_Estimation.ipynb
index 4a4b1786d..15e478a47 100644
--- a/examples/SIR_Posterior_Estimation.ipynb
+++ b/examples/SIR_Posterior_Estimation.ipynb
@@ -375,7 +375,7 @@
     "    # since all our variables are non-negative (zero or larger)\n",
     "    # this .apply call ensures that the variables are transformed\n",
     "    # to the unconstrained real space and can be back-transformed under the hood\n",
-    "    .apply(forward=lambda x: np.log1p(x), inverse=lambda x: np.expm1(x))\n",
+    "    .apply(forward=np.log1p)\n",
     ")"
    ]
   },

From f5314352d43d72406a14a2c48275f450530252c1 Mon Sep 17 00:00:00 2001
From: LarsKue <lars@kuehmichel.de>
Date: Thu, 13 Mar 2025 16:07:31 +0100
Subject: [PATCH 05/10] fix tests

---
 tests/test_adapters/conftest.py      | 16 ++--------------
 tests/test_adapters/test_adapters.py | 14 +++++++-------
 2 files changed, 9 insertions(+), 21 deletions(-)

diff --git a/tests/test_adapters/conftest.py b/tests/test_adapters/conftest.py
index 03f214578..929f81e2a 100644
--- a/tests/test_adapters/conftest.py
+++ b/tests/test_adapters/conftest.py
@@ -2,19 +2,6 @@
 import pytest
 
 
-def forward_transform(x):
-    return x + 1
-
-
-def inverse_transform(x):
-    return x - 1
-
-
-@pytest.fixture()
-def custom_objects():
-    return dict(forward_transform=forward_transform, inverse_transform=inverse_transform)
-
-
 @pytest.fixture()
 def adapter():
     from bayesflow.adapters import Adapter
@@ -29,9 +16,10 @@ def adapter():
         .concatenate(["x1", "x2"], into="x")
         .concatenate(["y1", "y2"], into="y")
         .expand_dims(["z1"], axis=2)
-        .apply(forward=forward_transform, inverse=inverse_transform)
         .log("p1")
         .constrain("p2", lower=0)
+        .apply(include="p2", forward=np.exp, inverse=np.log)
+        .apply(include="p2", forward="logp1")
         .standardize(exclude=["t1", "t2", "o1"])
         .drop("d1")
         .one_hot("o1", 10)
diff --git a/tests/test_adapters/test_adapters.py b/tests/test_adapters/test_adapters.py
index efd58bd6e..840a71ba2 100644
--- a/tests/test_adapters/test_adapters.py
+++ b/tests/test_adapters/test_adapters.py
@@ -17,10 +17,10 @@ def test_cycle_consistency(adapter, random_data):
         assert np.allclose(value, deprocessed[key])
 
 
-def test_serialize_deserialize(adapter, custom_objects, random_data):
+def test_serialize_deserialize(adapter, random_data):
     processed = adapter(random_data)
     serialized = serialize(adapter)
-    deserialized = deserialize(serialized, custom_objects)
+    deserialized = deserialize(serialized)
     reserialized = serialize(deserialized)
 
     assert reserialized.keys() == serialized.keys()
@@ -51,7 +51,7 @@ def test_constrain():
         "x_both_disc2": np.vstack((np.zeros(shape=(16, 1)), np.ones(shape=(16, 1)))),
     }
 
-    adapter = (
+    ad = (
         Adapter()
         .constrain("x_lower_cont", lower=0)
         .constrain("x_upper_cont", upper=0)
@@ -66,7 +66,7 @@ def test_constrain():
 
     with warnings.catch_warnings():
         warnings.simplefilter("ignore", RuntimeWarning)
-        result = adapter(data)
+        result = ad(data)
 
     # continuous variables should not have boundary issues
     assert result["x_lower_cont"].min() < 0.0
@@ -93,9 +93,9 @@ def test_simple_transforms(random_data):
     # check if simple transforms are applied correctly
     from bayesflow.adapters import Adapter
 
-    adapter = Adapter().log(["p2", "t2"]).log("t1", p1=True).sqrt("p1")
+    ad = Adapter().log(["p2", "t2"]).log("t1", p1=True).sqrt("p1")
 
-    result = adapter(random_data)
+    result = ad(random_data)
 
     assert np.array_equal(result["p2"], np.log(random_data["p2"]))
     assert np.array_equal(result["t2"], np.log(random_data["t2"]))
@@ -103,7 +103,7 @@ def test_simple_transforms(random_data):
     assert np.array_equal(result["p1"], np.sqrt(random_data["p1"]))
 
     # inverse results should match the original input
-    inverse = adapter.inverse(result)
+    inverse = ad(result, inverse=True)
 
     assert np.array_equal(inverse["p2"], random_data["p2"])
     assert np.array_equal(inverse["t2"], random_data["t2"])

From 53d48d4cc68b05128edeb69ccf985b720b3b5f11 Mon Sep 17 00:00:00 2001
From: larskue <lars@kuehmichel.de>
Date: Mon, 17 Mar 2025 11:48:43 +0100
Subject: [PATCH 06/10] only allow strings as arguments (subject to be fixed by
 #323)

---
 .../adapters/transforms/numpy_transform.py    | 47 ++++++++++---------
 tests/test_adapters/conftest.py               |  4 +-
 2 files changed, 26 insertions(+), 25 deletions(-)

diff --git a/bayesflow/adapters/transforms/numpy_transform.py b/bayesflow/adapters/transforms/numpy_transform.py
index c110fe882..0e4eaa7f2 100644
--- a/bayesflow/adapters/transforms/numpy_transform.py
+++ b/bayesflow/adapters/transforms/numpy_transform.py
@@ -1,9 +1,11 @@
 import numpy as np
+from keras.saving import register_keras_serializable as serializable
 
 from bayesflow.utils import filter_kwargs
 from .elementwise_transform import ElementwiseTransform
 
 
+@serializable(package="bayesflow.adapters")
 class NumpyTransform(ElementwiseTransform):
     """
     A class to apply element-wise transformations using plain NumPy functions.
@@ -17,45 +19,46 @@ class NumpyTransform(ElementwiseTransform):
     """
 
     INVERSE_METHODS = {
-        "arctan": "tan",
-        "exp": "log",
-        "expm1": "log1p",
-        "square": "sqrt",
-        "reciprocal": "reciprocal",
+        np.arctan: np.tan,
+        np.exp: np.log,
+        np.expm1: np.log1p,
+        np.square: np.sqrt,
+        np.reciprocal: np.reciprocal,
     }
     # ensure the map is symmetric
     INVERSE_METHODS |= {v: k for k, v in INVERSE_METHODS.items()}
 
-    def __init__(self, forward: np.ufunc | str, inverse: np.ufunc | str = None):
+    def __init__(self, forward: str, inverse: str = None):
         """
         Initializes the NumpyTransform with specified forward and inverse functions.
 
         Parameters:
         ----------
-        forward : str
+        forward: str
             The name of the NumPy function to use for the forward transformation.
-        inverse : str
+        inverse: str, optional
             The name of the NumPy function to use for the inverse transformation.
             By default, the inverse is inferred from the forward argument for supported methods.
         """
         super().__init__()
 
-        if isinstance(forward, np.ufunc):
-            forward = forward.__name__
+        if isinstance(forward, str):
+            forward = getattr(np, forward)
+
+        if not isinstance(forward, np.ufunc):
+            raise ValueError("Forward transformation must be a NumPy Universal Function (ufunc).")
 
         if inverse is None:
             if forward not in self.INVERSE_METHODS:
                 raise ValueError(f"Cannot infer inverse for method {forward!r}")
 
             inverse = self.INVERSE_METHODS[forward]
-        elif isinstance(inverse, np.ufunc):
-            inverse = inverse.__name__
 
-        if forward not in dir(np):
-            raise ValueError(f"Method {forward!r} not found in numpy version {np.__version__}")
+        if isinstance(inverse, str):
+            inverse = getattr(np, inverse)
 
-        if inverse not in dir(np):
-            raise ValueError(f"Method {inverse!r} not found in numpy version {np.__version__}")
+        if not isinstance(inverse, np.ufunc):
+            raise ValueError("Inverse transformation must be a NumPy Universal Function (ufunc).")
 
         self._forward = forward
         self._inverse = inverse
@@ -68,14 +71,12 @@ def from_config(cls, config: dict, custom_objects=None) -> "ElementwiseTransform
         )
 
     def get_config(self) -> dict:
-        return {"forward": self._forward, "inverse": self._inverse}
+        return {"forward": self._forward.__name__, "inverse": self._inverse.__name__}
 
     def forward(self, data: dict[str, any], **kwargs) -> dict[str, any]:
-        forward = getattr(np, self._forward)
-        kwargs = filter_kwargs(kwargs, forward)
-        return forward(data, **kwargs)
+        kwargs = filter_kwargs(kwargs, self._forward)
+        return self._forward(data, **kwargs)
 
     def inverse(self, data: np.ndarray, **kwargs) -> np.ndarray:
-        inverse = getattr(np, self._inverse)
-        kwargs = filter_kwargs(kwargs, inverse)
-        return inverse(data, **kwargs)
+        kwargs = filter_kwargs(kwargs, self._inverse)
+        return self._inverse(data, **kwargs)
diff --git a/tests/test_adapters/conftest.py b/tests/test_adapters/conftest.py
index 929f81e2a..b020523d9 100644
--- a/tests/test_adapters/conftest.py
+++ b/tests/test_adapters/conftest.py
@@ -18,8 +18,8 @@ def adapter():
         .expand_dims(["z1"], axis=2)
         .log("p1")
         .constrain("p2", lower=0)
-        .apply(include="p2", forward=np.exp, inverse=np.log)
-        .apply(include="p2", forward="logp1")
+        .apply(include="p2", forward="exp", inverse="log")
+        .apply(include="p2", forward="log1p")
         .standardize(exclude=["t1", "t2", "o1"])
         .drop("d1")
         .one_hot("o1", 10)

From 94b8c99f6f282685f31898a60f35aa23660bd928 Mon Sep 17 00:00:00 2001
From: larskue <lars@kuehmichel.de>
Date: Mon, 17 Mar 2025 11:48:58 +0100
Subject: [PATCH 07/10] unify serialization pattern

---
 bayesflow/adapters/transforms/as_set.py         | 2 ++
 bayesflow/adapters/transforms/as_time_series.py | 2 ++
 bayesflow/adapters/transforms/expand_dims.py    | 3 ++-
 bayesflow/adapters/transforms/log.py            | 3 ++-
 bayesflow/adapters/transforms/sqrt.py           | 2 ++
 5 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/bayesflow/adapters/transforms/as_set.py b/bayesflow/adapters/transforms/as_set.py
index 2e0fe86e1..6e8e5567e 100644
--- a/bayesflow/adapters/transforms/as_set.py
+++ b/bayesflow/adapters/transforms/as_set.py
@@ -1,8 +1,10 @@
 import numpy as np
+from keras.saving import register_keras_serializable as serializable
 
 from .elementwise_transform import ElementwiseTransform
 
 
+@serializable(package="bayesflow.adapters")
 class AsSet(ElementwiseTransform):
     """The `.as_set(["x", "y"])` transform indicates that both `x` and `y` are treated as sets.
 
diff --git a/bayesflow/adapters/transforms/as_time_series.py b/bayesflow/adapters/transforms/as_time_series.py
index 201181547..9453af817 100644
--- a/bayesflow/adapters/transforms/as_time_series.py
+++ b/bayesflow/adapters/transforms/as_time_series.py
@@ -1,8 +1,10 @@
 import numpy as np
+from keras.saving import register_keras_serializable as serializable
 
 from .elementwise_transform import ElementwiseTransform
 
 
+@serializable(package="bayesflow.adapters")
 class AsTimeSeries(ElementwiseTransform):
     """The `.as_time_series` transform can be used to indicate that variables shall be treated as time series.
 
diff --git a/bayesflow/adapters/transforms/expand_dims.py b/bayesflow/adapters/transforms/expand_dims.py
index 6a9519d8e..97c106f51 100644
--- a/bayesflow/adapters/transforms/expand_dims.py
+++ b/bayesflow/adapters/transforms/expand_dims.py
@@ -1,13 +1,14 @@
 import numpy as np
-
 from keras.saving import (
     deserialize_keras_object as deserialize,
     serialize_keras_object as serialize,
 )
+from keras.saving import register_keras_serializable as serializable
 
 from .elementwise_transform import ElementwiseTransform
 
 
+@serializable(package="bayesflow.adapters")
 class ExpandDims(ElementwiseTransform):
     """
     Expand the shape of an array.
diff --git a/bayesflow/adapters/transforms/log.py b/bayesflow/adapters/transforms/log.py
index cefe468b2..9c5325da3 100644
--- a/bayesflow/adapters/transforms/log.py
+++ b/bayesflow/adapters/transforms/log.py
@@ -1,13 +1,14 @@
 import numpy as np
-
 from keras.saving import (
     deserialize_keras_object as deserialize,
     serialize_keras_object as serialize,
 )
+from keras.saving import register_keras_serializable as serializable
 
 from .elementwise_transform import ElementwiseTransform
 
 
+@serializable(package="bayesflow.adapters")
 class Log(ElementwiseTransform):
     """Log transforms a variable.
 
diff --git a/bayesflow/adapters/transforms/sqrt.py b/bayesflow/adapters/transforms/sqrt.py
index 88bb81a08..b9f693e75 100644
--- a/bayesflow/adapters/transforms/sqrt.py
+++ b/bayesflow/adapters/transforms/sqrt.py
@@ -1,8 +1,10 @@
 import numpy as np
+from keras.saving import register_keras_serializable as serializable
 
 from .elementwise_transform import ElementwiseTransform
 
 
+@serializable(package="bayesflow.adapters")
 class Sqrt(ElementwiseTransform):
     """Square-root transform a variable.
 

From 96a21c95f8f03a9d1f4298dfc44d88a3493f283a Mon Sep 17 00:00:00 2001
From: larskue <lars@kuehmichel.de>
Date: Mon, 17 Mar 2025 11:51:37 +0100
Subject: [PATCH 08/10] remove old lambda transform error message

---
 bayesflow/adapters/transforms/filter_transform.py | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/bayesflow/adapters/transforms/filter_transform.py b/bayesflow/adapters/transforms/filter_transform.py
index 1a3d40b7a..65f5750c8 100644
--- a/bayesflow/adapters/transforms/filter_transform.py
+++ b/bayesflow/adapters/transforms/filter_transform.py
@@ -86,18 +86,6 @@ def from_config(cls, config: dict, custom_objects=None) -> "Transform":
         try:
             kwargs = deserialize(config["kwargs"])
         except TypeError as e:
-            if transform_constructor.__name__ == "LambdaTransform":
-                raise TypeError(
-                    "LambdaTransform (created by Adapter.apply) could not be deserialized.\n"
-                    "This is probably because the custom transform functions `forward` and "
-                    "`backward` from `Adapter.apply` were not passed as `custom_objects`.\n"
-                    "For example, if your adapter uses\n"
-                    "`Adapter.apply(forward=forward_transform, inverse=inverse_transform)`,\n"
-                    "you have to pass\n"
-                    '`custom_objects={"forward_transform": forward_transform, '
-                    '"inverse_transform": inverse_transform}`\n'
-                    "to the function you use to load the serialized object."
-                ) from e
             raise TypeError(
                 "The transform could not be deserialized properly. "
                 "The most likely reason is that some classes or functions "

From d8bab7bffef75536f87c0a0ab58e5c2aab9c457e Mon Sep 17 00:00:00 2001
From: larskue <lars@kuehmichel.de>
Date: Mon, 17 Mar 2025 11:58:52 +0100
Subject: [PATCH 09/10] add fail fast to CI tests

---
 .github/workflows/tests.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml
index 6e8175fb8..bfd5583cd 100644
--- a/.github/workflows/tests.yaml
+++ b/.github/workflows/tests.yaml
@@ -95,7 +95,7 @@ jobs:
 
       - name: Run Tests
         run: |
-          pytest
+          pytest -x
 
       - name: Create Coverage Report
         run: |

From 54e939875a06c1e523434e50c7a962e00c3e0022 Mon Sep 17 00:00:00 2001
From: larskue <lars@kuehmichel.de>
Date: Mon, 17 Mar 2025 12:48:52 +0100
Subject: [PATCH 10/10] cannot suppport filtering kwargs for numpy ufunc in
 python 3.10

---
 bayesflow/adapters/transforms/expand_dims.py     | 2 +-
 bayesflow/adapters/transforms/log.py             | 2 +-
 bayesflow/adapters/transforms/numpy_transform.py | 7 ++-----
 bayesflow/adapters/transforms/to_array.py        | 4 +---
 4 files changed, 5 insertions(+), 10 deletions(-)

diff --git a/bayesflow/adapters/transforms/expand_dims.py b/bayesflow/adapters/transforms/expand_dims.py
index 97c106f51..eb4d712f4 100644
--- a/bayesflow/adapters/transforms/expand_dims.py
+++ b/bayesflow/adapters/transforms/expand_dims.py
@@ -1,9 +1,9 @@
 import numpy as np
 from keras.saving import (
     deserialize_keras_object as deserialize,
+    register_keras_serializable as serializable,
     serialize_keras_object as serialize,
 )
-from keras.saving import register_keras_serializable as serializable
 
 from .elementwise_transform import ElementwiseTransform
 
diff --git a/bayesflow/adapters/transforms/log.py b/bayesflow/adapters/transforms/log.py
index 9c5325da3..e264fccfa 100644
--- a/bayesflow/adapters/transforms/log.py
+++ b/bayesflow/adapters/transforms/log.py
@@ -1,9 +1,9 @@
 import numpy as np
 from keras.saving import (
     deserialize_keras_object as deserialize,
+    register_keras_serializable as serializable,
     serialize_keras_object as serialize,
 )
-from keras.saving import register_keras_serializable as serializable
 
 from .elementwise_transform import ElementwiseTransform
 
diff --git a/bayesflow/adapters/transforms/numpy_transform.py b/bayesflow/adapters/transforms/numpy_transform.py
index 0e4eaa7f2..817d98b17 100644
--- a/bayesflow/adapters/transforms/numpy_transform.py
+++ b/bayesflow/adapters/transforms/numpy_transform.py
@@ -1,7 +1,6 @@
 import numpy as np
 from keras.saving import register_keras_serializable as serializable
 
-from bayesflow.utils import filter_kwargs
 from .elementwise_transform import ElementwiseTransform
 
 
@@ -74,9 +73,7 @@ def get_config(self) -> dict:
         return {"forward": self._forward.__name__, "inverse": self._inverse.__name__}
 
     def forward(self, data: dict[str, any], **kwargs) -> dict[str, any]:
-        kwargs = filter_kwargs(kwargs, self._forward)
-        return self._forward(data, **kwargs)
+        return self._forward(data)
 
     def inverse(self, data: np.ndarray, **kwargs) -> np.ndarray:
-        kwargs = filter_kwargs(kwargs, self._inverse)
-        return self._inverse(data, **kwargs)
+        return self._inverse(data)
diff --git a/bayesflow/adapters/transforms/to_array.py b/bayesflow/adapters/transforms/to_array.py
index 393d31b6e..aefb51040 100644
--- a/bayesflow/adapters/transforms/to_array.py
+++ b/bayesflow/adapters/transforms/to_array.py
@@ -1,9 +1,7 @@
 from numbers import Number
 
 import numpy as np
-from keras.saving import (
-    register_keras_serializable as serializable,
-)
+from keras.saving import register_keras_serializable as serializable
 
 from bayesflow.utils.io import deserialize_type, serialize_type
 from .elementwise_transform import ElementwiseTransform