Fixed merge conflict

Author: stefanradev93

.github/workflows/style.yaml

@@ -0,0 +1,40 @@
+name: Check Code Style
+
+on:
+  workflow_dispatch:
+  pull_request:
+    branches:
+      - main
+      - dev
+      - update-workflows
+  push:
+    branches:
+      - main
+      - dev
+      - update-workflows
+
+jobs:
+  check-code-style:
+    name: Check Code Style
+
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+
+      - name: Install Ruff
+        run: |
+          pip install -U pip setuptools wheel
+          pip install ruff
+
+      - name: Run Linter
+        run: ruff check --config pyproject.toml --verbose
+
+      - name: Run Formatter
+        run: ruff format --config pyproject.toml --check --verbose

.github/workflows/tests.yaml

@@ -1,5 +1,5 @@
 
-name: Tests
+name: Multi-Backend Tests
 
 on:
   workflow_dispatch:
@@ -12,105 +12,82 @@ on:
       - main
       - dev
 
+defaults:
+  run:
+    shell: bash
 
-jobs:
-  lint:
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        shell: bash -el {0}
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Set up Conda
-        uses: conda-incubator/setup-miniconda@v3
-        with:
-          environment-file: environment.yaml
-          python-version: "3.10"
-
-      - name: Show Environment Info
-        run: |
-          conda info
-          conda list
-          conda config --show-sources
-          conda config --show
-          printenv | sort
-
-      - name: Run Linter
-        run: |
-          ruff check --config pyproject.toml --verbose
-
-      - name: Run Formatter
-        run: |
-          ruff format --check --config pyproject.toml --verbose
 
+jobs:
   test:
-    runs-on: ${{ matrix.os }}
+    name: Run Multi-Backend Tests
+
     strategy:
-      fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-latest]
         python-version: ["3.10", "3.11"]
         backend: ["jax", "tensorflow", "torch"]
-    defaults:
-      run:
-        shell: bash -el {0}
+
+    runs-on: ${{ matrix.os }}
+
     env:
       KERAS_BACKEND: ${{ matrix.backend }}
 
     steps:
-      - name: Checkout code
+      - name: Checkout Repository
         uses: actions/checkout@v4
 
-      - name: Set up Conda
-        uses: conda-incubator/setup-miniconda@v3
+      - name: Set up Python
+        uses: actions/setup-python@v5
         with:
-          environment-file: environment.yaml
           python-version: ${{ matrix.python-version }}
 
+      - name: Install Dependencies
+        run: |
+          pip install -U pip setuptools wheel
+          pip install .[test]
+
       - name: Install JAX
         if: ${{ matrix.backend == 'jax' }}
         run: |
           pip install -U jax
-      - name: Install NumPy
-        if: ${{ matrix.backend == 'numpy' }}
-        run: |
-          conda install numpy
-      - name: Install Tensorflow
+
+      - name: Install TensorFlow
         if: ${{ matrix.backend == 'tensorflow' }}
         run: |
           pip install -U tensorflow
+
       - name: Install PyTorch
         if: ${{ matrix.backend == 'torch' }}
         run: |
-          pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+          pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
 
       - name: Show Environment Info
         run: |
-          conda info
-          conda list
-          conda config --show-sources
-          conda config --show
+          python --version
+          pip --version
           printenv | sort
-          conda env export
           pip list
 
       - name: Run Tests
         run: |
-          pytest -x
+          pytest -x -m "not slow"
 
-      - name: Create Coverage Report
+      - name: Run Slow Tests
+        # run all slow tests only on manual trigger
+        if: github.event_name == 'workflow_dispatch'
         run: |
-          coverage xml
+          pytest -m "slow"
 
       - name: Upload test results to Codecov
         if: ${{ !cancelled() }}
-        uses: codecov/test-results-action@v1
+        uses: codecov/codecov-action@v4
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
 
+      - name: Create Coverage Report
+        run: |
+          coverage xml
+
       - name: Upload Coverage Reports to CodeCov
         uses: codecov/codecov-action@v4
         with:

README.md

@@ -54,7 +54,7 @@ For an in-depth exposition, check out our walkthrough notebooks below.
 1. [Linear regression starter example](examples/Linear_Regression_Starter.ipynb)
 2. [From ABC to BayesFlow](examples/From_ABC_to_BayesFlow.ipynb)
 3. [Two moons starter example](examples/Two_Moons_Starter.ipynb)
-4. [Rapid iteration with point estimators](examples/Lotka_Volterra_point_estimation_and_expert_stats.ipynb)
+4. [Rapid iteration with point estimators](examples/Lotka_Volterra_Point_Estimation_and_Expert_Stats.ipynb)
 5. [SIR model with custom summary network](examples/SIR_Posterior_Estimation.ipynb)
 6. [Bayesian experimental design](examples/Bayesian_Experimental_Design.ipynb)
 7. [Simple model comparison example](examples/One_Sample_TTest.ipynb)

bayesflow/__init__.py

@@ -40,8 +40,12 @@ def setup():
         torch.autograd.set_grad_enabled(False)
 
         logging.warning(
+            "\n"
             "When using torch backend, we need to disable autograd by default to avoid excessive memory usage. Use\n"
+            "\n"
             "with torch.enable_grad():\n"
+            "    ...\n"
+            "\n"
             "in contexts where you need gradients (e.g. custom training loops)."
         )
 

bayesflow/adapters/adapter.py

@@ -556,6 +556,24 @@ def rename(self, from_key: str, to_key: str):
         self.transforms.append(Rename(from_key, to_key))
         return self
 
+    def scale(self, keys: str | Sequence[str], by: float | np.ndarray):
+        from .transforms import Scale
+
+        if isinstance(keys, str):
+            keys = [keys]
+
+        self.transforms.append(MapTransform({key: Scale(scale=by) for key in keys}))
+        return self
+
+    def shift(self, keys: str | Sequence[str], by: float | np.ndarray):
+        from .transforms import Shift
+
+        if isinstance(keys, str):
+            keys = [keys]
+
+        self.transforms.append(MapTransform({key: Shift(shift=by) for key in keys}))
+        return self
+
     def sqrt(self, keys: str | Sequence[str]):
         """Append an :py:class:`~transforms.Sqrt` transform to the adapter.
 

bayesflow/adapters/transforms/__init__.py

@@ -14,6 +14,8 @@
 from .numpy_transform import NumpyTransform
 from .one_hot import OneHot
 from .rename import Rename
+from .scale import Scale
+from .shift import Shift
 from .sqrt import Sqrt
 from .standardize import Standardize
 from .to_array import ToArray

bayesflow/adapters/transforms/scale.py

@@ -0,0 +1,27 @@
+from keras.saving import (
+    deserialize_keras_object as deserialize,
+    register_keras_serializable as serializable,
+    serialize_keras_object as serialize,
+)
+import numpy as np
+
+from .elementwise_transform import ElementwiseTransform
+
+
+@serializable(package="bayesflow.adapters")
+class Scale(ElementwiseTransform):
+    def __init__(self, scale: np.typing.ArrayLike):
+        self.scale = np.array(scale)
+
+    @classmethod
+    def from_config(cls, config: dict, custom_objects=None) -> "ElementwiseTransform":
+        return cls(scale=deserialize(config["scale"]))
+
+    def get_config(self) -> dict:
+        return {"scale": serialize(self.scale)}
+
+    def forward(self, data: np.ndarray, **kwargs) -> np.ndarray:
+        return data * self.scale
+
+    def inverse(self, data: np.ndarray, **kwargs) -> np.ndarray:
+        return data / self.scale

bayesflow/adapters/transforms/shift.py

@@ -0,0 +1,27 @@
+from keras.saving import (
+    deserialize_keras_object as deserialize,
+    register_keras_serializable as serializable,
+    serialize_keras_object as serialize,
+)
+import numpy as np
+
+from .elementwise_transform import ElementwiseTransform
+
+
+@serializable(package="bayesflow.adapters")
+class Shift(ElementwiseTransform):
+    def __init__(self, shift: np.typing.ArrayLike):
+        self.shift = np.array(shift)
+
+    @classmethod
+    def from_config(cls, config: dict, custom_objects=None) -> "ElementwiseTransform":
+        return cls(shift=deserialize(config["shift"]))
+
+    def get_config(self) -> dict:
+        return {"shift": serialize(self.shift)}
+
+    def forward(self, data: np.ndarray, **kwargs) -> np.ndarray:
+        return data + self.shift
+
+    def inverse(self, data: np.ndarray, **kwargs) -> np.ndarray:
+        return data - self.shift

bayesflow/diagnostics/metrics/__init__.py

@@ -2,3 +2,4 @@
 from .posterior_contraction import posterior_contraction
 from .root_mean_squared_error import root_mean_squared_error
 from .expected_calibration_error import expected_calibration_error
+from .classifier_two_sample_test import classifier_two_sample_test