v2.0.2

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,36 @@
+---
+name: Bug report
+about: Create a bug report to help us improve BayesFlow
+title: "[BUG]"
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Minimal steps to reproduce the behavior:
+1. Import '...'
+2. Create network '....'
+3. Call '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Traceback**
+If you encounter an error, please provide a complete traceback to help explain your problem.
+
+**Environment**
+- OS: [e.g. Ubuntu]
+- Python Version: [e.g. 3.11]
+- Backend: [e.g. jax, tensorflow, pytorch]
+- BayesFlow Version: [e.g. 2.0.2]
+
+**Additional context**
+Add any other context about the problem here.
+
+**Minimality**
+- [ ] I verify that my example is minimal, does not rely on third-party packages, and is most likely an issue in BayesFlow.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest a new feature to be implemented in BayesFlow
+title: "[FEATURE]"
+labels: feature
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.github/workflows/style.yaml

@@ -6,12 +6,10 @@ on:
     branches:
       - main
       - dev
-      - update-workflows
   push:
     branches:
       - main
       - dev
-      - update-workflows
 
 jobs:
   check-code-style:

.github/workflows/tests.yaml

@@ -24,7 +24,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: ["3.10", "3.11"]
+        python-version: ["3.10"]  # we usually only need to test the oldest python version
         backend: ["jax", "tensorflow", "torch"]
 
     runs-on: ${{ matrix.os }}
@@ -73,8 +73,11 @@ jobs:
           pytest -x -m "not slow"
 
       - name: Run Slow Tests
-        # run all slow tests only on manual trigger
-        if: github.event_name == 'workflow_dispatch'
+        # Run slow tests on manual trigger and pushes/PRs to main.
+        # Limit to one OS and Python version to save compute.
+        # Multiline if statements are weird, https://github.com/orgs/community/discussions/25641,
+        # but feel free to convert it.
+        if: ${{ ((github.event_name == 'workflow_dispatch') || (github.event_name == 'push' && github.ref_name == 'main') || (github.event_name == 'pull_request' && github.base_ref == 'main')) && ((matrix.os == 'windows-latest') && (matrix.python-version == '3.10')) }}
         run: |
           pytest -m "slow"
 

README.md

@@ -3,6 +3,7 @@
 ![Codecov](https://img.shields.io/codecov/c/github/bayesflow-org/bayesflow?style=for-the-badge&link=https%3A%2F%2Fapp.codecov.io%2Fgh%2Fbayesflow-org%2Fbayesflow%2Ftree%2Fmain)
 [![DOI](https://img.shields.io/badge/DOI-10.21105%2Fjoss.05702-blue?style=for-the-badge)](https://doi.org/10.21105/joss.05702)
 ![PyPI - License](https://img.shields.io/pypi/l/bayesflow?style=for-the-badge)
+![NumFOCUS Affiliated Project](https://img.shields.io/badge/NumFOCUS-Affiliated%20Project-orange?style=for-the-badge)
 
 BayesFlow is a Python library for simulation-based **Amortized Bayesian Inference** with neural networks.
 It provides users and researchers with:
@@ -225,8 +226,10 @@ You can find and install the old Bayesflow version via the `stable-legacy` branc
 
 ## Awesome Amortized Inference
 
-If you are interested in a curated list of resources, including reviews, software, papers, and other resources related to amortized inference, feel free to explore our [community-driven list](https://github.com/bayesflow-org/awesome-amortized-inference).
+If you are interested in a curated list of resources, including reviews, software, papers, and other resources related to amortized inference, feel free to explore our [community-driven list](https://github.com/bayesflow-org/awesome-amortized-inference). If you'd like a paper (by yourself or someone else) featured, please add it to the list with a pull request, an issue, or a message to the maintainers.
 
 ## Acknowledgments
 
 This project is currently managed by researchers from Rensselaer Polytechnic Institute, TU Dortmund University, and Heidelberg University. It is partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Projects 528702768 and 508399956. The project is further supported by Germany's Excellence Strategy -- EXC-2075 - 390740016 (Stuttgart Cluster of Excellence SimTech) and EXC-2181 - 390900948 (Heidelberg Cluster of Excellence STRUCTURES), the collaborative research cluster TRR 391 – 520388526, as well as the Informatics for Life initiative funded by the Klaus Tschira Foundation.
+
+BayesFlow is a [NumFOCUS Affiliated Project](https://numfocus.org/sponsored-projects/affiliated-projects).

bayesflow/approximators/continuous_approximator.py

@@ -104,6 +104,12 @@ def compile(
 
         return super().compile(*args, **kwargs)
 
+    def compile_from_config(self, config):
+        self.compile(**deserialize(config))
+        if hasattr(self, "optimizer") and self.built:
+            # Create optimizer variables.
+            self.optimizer.build(self.trainable_variables)
+
     def compute_metrics(
         self,
         inference_variables: Tensor,
@@ -213,6 +219,16 @@ def get_config(self):
 
         return base_config | serialize(config)
 
+    def get_compile_config(self):
+        base_config = super().get_compile_config() or {}
+
+        config = {
+            "inference_metrics": self.inference_network._metrics,
+            "summary_metrics": self.summary_network._metrics if self.summary_network is not None else None,
+        }
+
+        return base_config | serialize(config)
+
     def estimate(
         self,
         conditions: Mapping[str, np.ndarray],

bayesflow/approximators/model_comparison_approximator.py

@@ -118,6 +118,12 @@ def compile(
 
         return super().compile(*args, **kwargs)
 
+    def compile_from_config(self, config):
+        self.compile(**deserialize(config))
+        if hasattr(self, "optimizer") and self.built:
+            # Create optimizer variables.
+            self.optimizer.build(self.trainable_variables)
+
     def compute_metrics(
         self,
         *,
@@ -262,6 +268,16 @@ def get_config(self):
 
         return base_config | serialize(config)
 
+    def get_compile_config(self):
+        base_config = super().get_compile_config() or {}
+
+        config = {
+            "classifier_metrics": self.classifier_network._metrics,
+            "summary_metrics": self.summary_network._metrics if self.summary_network is not None else None,
+        }
+
+        return base_config | serialize(config)
+
     def predict(
         self,
         *,

bayesflow/distributions/__init__.py

@@ -9,8 +9,6 @@
 from .diagonal_student_t import DiagonalStudentT
 from .mixture import Mixture
 
-from .find_distribution import find_distribution
-
 from ..utils._docs import _add_imports_to_all
 
 _add_imports_to_all(include_modules=[])

bayesflow/distributions/diagonal_normal.py

@@ -3,6 +3,7 @@
 import numpy as np
 
 import keras
+from keras import ops
 
 from bayesflow.types import Shape, Tensor
 from bayesflow.utils.decorators import allow_batch_size
@@ -19,7 +20,7 @@ def __init__(
         self,
         mean: int | float | np.ndarray | Tensor = 0.0,
         std: int | float | np.ndarray | Tensor = 1.0,
-        use_learnable_parameters: bool = False,
+        trainable_parameters: bool = False,
         seed_generator: keras.random.SeedGenerator = None,
         **kwargs,
     ):
@@ -39,7 +40,7 @@ def __init__(
         std : int, float, np.ndarray, or Tensor, optional
             The standard deviation of the Gaussian distribution. Can be a scalar or a tensor.
             Default is 1.0.
-        use_learnable_parameters : bool, optional
+        trainable_parameters : bool, optional
             Whether to treat the mean and standard deviation as learnable parameters. Default is False.
         seed_generator : keras.random.SeedGenerator, optional
             A Keras seed generator for reproducible random sampling. If None, a new seed
@@ -53,47 +54,41 @@ def __init__(
         self.mean = mean
         self.std = std
 
+        self.trainable_parameters = trainable_parameters
+        self.seed_generator = seed_generator or keras.random.SeedGenerator()
+
         self.dim = None
         self.log_normalization_constant = None
-
-        self.use_learnable_parameters = use_learnable_parameters
-
-        if seed_generator is None:
-            seed_generator = keras.random.SeedGenerator()
-
-        self.seed_generator = seed_generator
+        self._mean = None
+        self._std = None
 
     def build(self, input_shape: Shape) -> None:
+        if self.built:
+            return
+
         self.dim = int(input_shape[-1])
 
-        self.mean = keras.ops.broadcast_to(self.mean, (self.dim,))
-        self.mean = keras.ops.cast(self.mean, "float32")
-        self.std = keras.ops.broadcast_to(self.std, (self.dim,))
-        self.std = keras.ops.cast(self.std, "float32")
+        self.mean = ops.cast(ops.broadcast_to(self.mean, (self.dim,)), "float32")
+        self.std = ops.cast(ops.broadcast_to(self.std, (self.dim,)), "float32")
 
-        self.log_normalization_constant = -0.5 * self.dim * math.log(2.0 * math.pi) - keras.ops.sum(
-            keras.ops.log(self.std)
-        )
+        self.log_normalization_constant = -0.5 * self.dim * math.log(2.0 * math.pi) - ops.sum(ops.log(self.std))
 
-        if self.use_learnable_parameters:
+        if self.trainable_parameters:
             self._mean = self.add_weight(
-                shape=keras.ops.shape(self.mean),
-                # Initializing with const tensor https://github.com/keras-team/keras/pull/20457#discussion_r1832081248
-                initializer=keras.initializers.get(value=self.mean),
+                shape=ops.shape(self.mean),
+                initializer=keras.initializers.get(self.mean),
                 dtype="float32",
+                trainable=True,
             )
             self._std = self.add_weight(
-                shape=keras.ops.shape(self.std),
-                # Initializing with const tensor https://github.com/keras-team/keras/pull/20457#discussion_r1832081248
-                initializer=keras.initializers.get(self.std),
-                dtype="float32",
+                shape=ops.shape(self.std), initializer=keras.initializers.get(self.std), dtype="float32", trainable=True
             )
         else:
             self._mean = self.mean
             self._std = self.std
 
     def log_prob(self, samples: Tensor, *, normalize: bool = True) -> Tensor:
-        result = -0.5 * keras.ops.sum((samples - self._mean) ** 2 / self.std**2, axis=-1)
+        result = -0.5 * ops.sum((samples - self._mean) ** 2 / self._std**2, axis=-1)
 
         if normalize:
             result += self.log_normalization_constant
@@ -110,7 +105,7 @@ def get_config(self):
         config = {
             "mean": self.mean,
             "std": self.std,
-            "use_learnable_parameters": self.use_learnable_parameters,
+            "trainable_parameters": self.trainable_parameters,
             "seed_generator": self.seed_generator,
         }