v2.0.2 (#447)

* [no ci] notebook tests: increase timeout, fix platform/backend dependent code

Torch is very slow, so I had to increase the timeout accordingly.

* Enable use of summary networks with functional API again (#434)

* summary networks: add tests for using functional API

* fix build functions for use with functional API

* [no ci] docs: add GitHub and Discourse links, reorder navbar

* [no ci] docs: acknowledge scikit-learn website

* [no ci] docs: capitalize navigation headings

* More tests (#437)

* fix docs of coupling flow

* add additional tests

* Automatically run slow tests when main is involved. (#438)

In addition, this PR limits the slow test to Windows and Python 3.10.
The choices are somewhat arbitrary, my thought was to test the setup not
covered as much through use by the devs.

* Update dispatch

* Update dispatching distributions

* Improve workflow tests with multiple summary nets / approximators

* Fix zombie find_distribution import

* Add readme entry [no ci]

* Update README: NumFOCUS affiliation, awesome-abi list (#445)

* fix is_symbolic_tensor

* remove multiple batch sizes, remove multiple python version tests, remove update-workflows branch from workflow style tests, add __init__ and conftest to test_point_approximators (#443)

* implement compile_from_config and get_compile_config (#442)

* implement compile_from_config and get_compile_config

* add optimizer build to compile_from_config

* Fix Optimal Transport for Compiled Contexts (#446)

* remove the is_symbolic_tensor check because this would otherwise skip the whole function for compiled contexts

* skip pyabc test

* fix sinkhorn and log_sinkhorn message formatting for jax by making the warning message worse

* update dispatch tests for more coverage

* Update issue templates (#448)

* Hotfix Version 2.0.1 (#431)

* fix optimal transport config (#429)

* run linter

* [skip-ci] bump version to 2.0.1

* Update issue templates

* Robustify kwargs passing inference networks, add class variables

* fix convergence method to debug for non-log sinkhorn

* Bump optimal transport default to False

* use logging.info for backend selection instead of logging.debug

* fix model comparison approximator

* improve docs and type hints

* improve One-Sample T-Test Notebook:
- use torch as default backend
- reduce range of N so users of jax won't be stuck with a slow notebook
- use BayesFlow built-in MLP instead of keras.Sequential solution
- general code cleanup

* remove backend print

* [skip ci] turn all single-quoted strings into double-quoted strings

* turn all single-quoted strings into double-quoted strings

amend to trigger workflow

---------

Co-authored-by: Valentin Pratz <git@valentinpratz.de>
Co-authored-by: Valentin Pratz <112951103+vpratz@users.noreply.github.com>
Co-authored-by: stefanradev93 <stefan.radev93@gmail.com>
Co-authored-by: Marvin Schmitt <35921281+marvinschmitt@users.noreply.github.com>

Author: 4 people

.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/__init__.py

@@ -33,7 +33,7 @@ def setup():
 
     from bayesflow.utils import logging
 
-    logging.debug(f"Using backend {keras.backend.backend()!r}")
+    logging.info(f"Using backend {keras.backend.backend()!r}")
 
     if keras.backend.backend() == "torch":
         import torch

bayesflow/approximators/approximator.py

@@ -23,7 +23,7 @@ def build_adapter(cls, **kwargs) -> Adapter:
         raise NotImplementedError
 
     def build_from_data(self, data: Mapping[str, any]) -> None:
-        self.compute_metrics(**data, stage="training")
+        self.compute_metrics(**filter_kwargs(data, self.compute_metrics), stage="training")
         self.built = True
 
     @classmethod

bayesflow/approximators/continuous_approximator.py

@@ -32,6 +32,8 @@ class ContinuousApproximator(Approximator):
         Additional arguments passed to the :py:class:`bayesflow.approximators.Approximator` class.
     """
 
+    SAMPLE_KEYS = ["summary_variables", "inference_conditions"]
+
     def __init__(
         self,
         *,
@@ -51,6 +53,7 @@ def build_adapter(
         inference_variables: Sequence[str],
         inference_conditions: Sequence[str] = None,
         summary_variables: Sequence[str] = None,
+        standardize: bool = True,
         sample_weight: str = None,
     ) -> Adapter:
         """Create an :py:class:`~bayesflow.adapters.Adapter` suited for the approximator.
@@ -63,9 +66,12 @@ def build_adapter(
             Names of the inference conditions in the data
         summary_variables : Sequence of str, optional
             Names of the summary variables in the data
+        standardize : bool, optional
+            Decide whether to standardize all variables, default is True
         sample_weight : str, optional
             Name of the sample weights
         """
+
         adapter = Adapter()
         adapter.to_array()
         adapter.convert_dtype("float64", "float32")
@@ -82,7 +88,9 @@ def build_adapter(
             adapter = adapter.rename(sample_weight, "sample_weight")
 
         adapter.keep(["inference_variables", "inference_conditions", "summary_variables", "sample_weight"])
-        adapter.standardize(exclude="sample_weight")
+
+        if standardize:
+            adapter.standardize(exclude="sample_weight")
 
         return adapter
 
@@ -104,6 +112,12 @@ 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 +227,16 @@ 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],
@@ -318,12 +342,18 @@ def sample(
         dict[str, np.ndarray]
             Dictionary containing generated samples with the same keys as `conditions`.
         """
+
+        # Apply adapter transforms to raw simulated / real quantities
         conditions = self.adapter(conditions, strict=False, stage="inference", **kwargs)
-        # at inference time, inference_variables are estimated by the networks and thus ignored in conditions
-        conditions.pop("inference_variables", None)
+
+        # Ensure only keys relevant for sampling are present in the conditions dictionary
+        conditions = {k: v for k, v in conditions.items() if k in ContinuousApproximator.SAMPLE_KEYS}
+
         conditions = keras.tree.map_structure(keras.ops.convert_to_tensor, conditions)
         conditions = {"inference_variables": self._sample(num_samples=num_samples, **conditions, **kwargs)}
         conditions = keras.tree.map_structure(keras.ops.convert_to_numpy, conditions)
+
+        # Back-transform quantities and samples
         conditions = self.adapter(conditions, inverse=True, strict=False, **kwargs)
 
         if split:

bayesflow/approximators/model_comparison_approximator.py

@@ -30,11 +30,13 @@ class ModelComparisonApproximator(Approximator):
         The network backbone (e.g, an MLP) that is used for model classification.
         The input of the classifier network is created by concatenating `classifier_variables`
         and (optional) output of the summary_network.
-    summary_network: bg.networks.SummaryNetwork, optional
+    summary_network: bf.networks.SummaryNetwork, optional
         The summary network used for data summarization (default is None).
         The input of the summary network is `summary_variables`.
     """
 
+    SAMPLE_KEYS = ["summary_variables", "classifier_conditions"]
+
     def __init__(
         self,
         *,
@@ -118,6 +120,12 @@ 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 +270,16 @@ 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,
         *,
@@ -288,9 +306,13 @@ def predict(
         np.ndarray
             Predicted posterior model probabilities given `conditions`.
         """
+
+        # Apply adapter transforms to raw simulated / real quantities
         conditions = self.adapter(conditions, strict=False, stage="inference", **kwargs)
-        # at inference time, model_indices are predicted by the networks and thus ignored in conditions
-        conditions.pop("model_indices", None)
+
+        # Ensure only keys relevant for sampling are present in the conditions dictionary
+        conditions = {k: v for k, v in conditions.items() if k in ModelComparisonApproximator.SAMPLE_KEYS}
+
         conditions = keras.tree.map_structure(keras.ops.convert_to_tensor, conditions)
 
         output = self._predict(**conditions, **kwargs)

bayesflow/approximators/point_approximator.py

@@ -156,8 +156,10 @@ def log_prob(
 
     def _prepare_conditions(self, conditions: Mapping[str, np.ndarray], **kwargs) -> dict[str, Tensor]:
         """Adapts and converts the conditions to tensors."""
+
         conditions = self.adapter(conditions, strict=False, stage="inference", **kwargs)
-        conditions.pop("inference_variables", None)
+        conditions = {k: v for k, v in conditions.items() if k in ContinuousApproximator.SAMPLE_KEYS}
+
         return keras.tree.map_structure(keras.ops.convert_to_tensor, conditions)
 
     def _apply_inverse_adapter_to_estimates(

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=[])