Merge remote-tracking branch 'upstream/dev' into dev

Author: eodole

.github/workflows/multiversion-docs.yaml

@@ -3,9 +3,6 @@
 name: multiversion-docs
 
 on:
-  pull_request:
-    branches:
-      - dev
   workflow_dispatch:
   push:
     branches:
@@ -43,12 +40,12 @@ jobs:
       - name: Create local branches
         run: |
           cd ./dev
-          git branch master remotes/origin/master
+          git branch main remotes/origin/main
       - name: Make the Sphinx docs
         run: |
           cd ./dev/docsrc
           make clean
-          make github
+          make docs-sequential
       - name: Commit changes to docs
         run: |
           cd ./gh-pages-dev

.github/workflows/test-docs.yaml

@@ -0,0 +1,37 @@
+
+name: test-docs
+
+on:
+  pull_request:
+    branches:
+      - dev
+
+jobs:
+  build_docs:
+    runs-on: ubuntu-latest
+    env:
+      KERAS_BACKEND: "jax"
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.11
+          cache: "pip"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install .[docs]
+          python -m pip install jax
+      - name: Make docs from local state
+        run: |
+          cd ./docsrc
+          make clean
+          make local
+      - name: Clean up
+        run: |
+          cd ./docsrc
+          make clean-all

.github/workflows/tests.yaml

@@ -5,7 +5,7 @@ on:
   pull_request:
   push:
     branches:
-      - master
+      - main
       - dev
       - streamlined-backend
 
@@ -95,7 +95,7 @@ jobs:
 
       - name: Run Tests
         run: |
-          pytest
+          pytest -x
 
       - name: Create Coverage Report
         run: |

.gitignore

@@ -6,10 +6,11 @@ projects/
 */bayesflow.egg-info
 docsrc/_build/
 docsrc/_build_polyversion
-docsrc/.bf_doc_gen_venv
+docsrc/.docs_venvs
 docsrc/source/api
 docsrc/source/_examples
 docsrc/source/contributing.md
+examples/checkpoints/
 build
 docs/
 

.pre-commit-config.yaml

@@ -14,7 +14,7 @@
 repos:
 - repo: https://github.com/astral-sh/ruff-pre-commit
   # Ruff version.
-  rev: v0.9.6
+  rev: v0.9.10
   hooks:
     # Run the linter.
     - id: ruff

CONTRIBUTING.md

@@ -145,23 +145,37 @@ pip install .[docs]
 ```
 
 The overall *structure* of the documentation is manually designed, but the API documentation is auto-generated.
+New top-level modules (i.e., `bayesflow.mynewmodule`) have to be manually added to the list in `docsrc/source/api/bayesflow.rst` to be included.
 
-You can re-build the current documentation with
+You can re-build the for your local state with:
 
 ```bash
 cd docsrc
-make clean && make dev
+make clean && make local
 # in case of issues, try `make clean-all`
 ```
 
+Note that files ignored by git (i.e., listed in `.gitignore`) are not included in the documentation.
+
 We also provide a multi-version documentation. To generate it, run
 
 ```bash
 cd docsrc
-make clean && make github
+make clean && make docs
 ```
 
+This will create and cache virtual environments for the build at `docsrc/.docs_venvs`.
+To remove them, run `make clean-all` in the `docsrc` directory.
+
 The entry point of the rendered documentation will be at `docs/index.html`.
+To view the docs in the browser (which ensures correct redirects), run:
+
+```bash
+cd docsrc
+make view-docs
+```
+
+See `docsrc/README.md` for more details.
 
 Note that undocumented changes will likely be rejected.
 

README.md

@@ -4,12 +4,12 @@
 [![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)
 
-BayesFlow is a Python library for simulation-based **Amortized Bayesian Inference** with neural networks.
-It provides users with:
+BayesFlow 2 is a Python library for simulation-based **Amortized Bayesian Inference** with neural networks.
+It provides users and researchers with:
 
 - A user-friendly API for rapid Bayesian workflows
 - A rich collection of neural network architectures
-- Multi-Backend Support via [Keras3](https://keras.io/keras_3/): You can use [PyTorch](https://github.com/pytorch/pytorch), [TensorFlow](https://github.com/tensorflow/tensorflow), or [JAX](https://github.com/google/jax)
+- Multi-backend support via [Keras3](https://keras.io/keras_3/): You can use [PyTorch](https://github.com/pytorch/pytorch), [TensorFlow](https://github.com/tensorflow/tensorflow), or [JAX](https://github.com/google/jax)
 
 BayesFlow is designed to be a flexible and efficient tool that enables rapid statistical inference
 fueled by continuous progress in generative AI and Bayesian inference.
@@ -29,11 +29,35 @@ neural networks for parameter estimation, model comparison, and model validation
 when working with intractable simulators whose behavior as a whole is too
 complex to be described analytically.
 
-## Disclaimer
+## Getting Started
 
-This is the current dev version of BayesFlow, which constitutes a complete refactor of the library built on Keras 3. This way, you can now use any of the major deep learning libraries as backend for BayesFlow. The refactor is still work in progress with some of the advanced features not yet implemented. We are actively working on them and promise to catch up soon.
+Using the high-level interface is easy, as demonstrated by the minimal working example below:
 
-If you encounter any issues, please don't hesitate to open an issue here on [Github](https://github.com/bayesflow-org/bayesflow/issues) or ask questions on our [Discourse Forums](https://discuss.bayesflow.org/).
+```python
+import bayesflow as bf
+
+workflow = bf.BasicWorkflow(
+    inference_network=bf.networks.FlowMatching(),
+    summary_network=bf.networks.TimeSeriesTransformer(),
+    inference_variables=["parameters"],
+    summary_variables=["observables"],
+    simulator=bf.simulators.SIR()
+)
+
+history = workflow.fit_online(epochs=50, batch_size=32, num_batches_per_epoch=500)
+
+diagnostics = workflow.plot_default_diagnostics(test_data=300)
+```
+
+For an in-depth exposition, check out our walkthrough notebooks below. More tutorials are always welcome!
+
+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)
+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)
 
 ## Install
 
@@ -89,18 +113,9 @@ cd <local-path-to-bayesflow-repository>
 conda env create --file environment.yaml --name bayesflow
 ```
 
-## Getting Started
-
-Check out some of our walk-through notebooks below. We are actively working on porting all notebooks to the new interface so more will be available soon!
+## Reporting Issues
 
-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. [SIR model with custom summary network](examples/SIR_Posterior_Estimation.ipynb)
-5. [Hyperparameter optimization](examples/Hyperparameter_Optimization.ipynb)
-6. [Bayesian experimental design](examples/Bayesian_Experimental_Design.ipynb)
-7. [Simple model comparison example (One-Sample T-Test)](examples/One_Sample_TTest.ipynb)
-8. More coming soon...
+If you encounter any issues, please don't hesitate to open an issue here on [Github](https://github.com/bayesflow-org/bayesflow/issues) or ask questions on our [Discourse Forums](https://discuss.bayesflow.org/).
 
 ## Documentation \& Help
 
@@ -110,9 +125,8 @@ Documentation is available at https://bayesflow.org. Please use the [BayesFlow F
 
 You can cite BayesFlow along the lines of:
 
-- We approximated the posterior with neural posterior estimation and learned summary statistics (NPE; Radev et al., 2020), as implemented in the BayesFlow software for amortized Bayesian workflows (Radev et al., 2023a).
-- We approximated the likelihood with neural likelihood estimation (NLE; Papamakarios et al., 2019) without hand-crafted summary statistics, as implemented in the BayesFlow software for amortized Bayesian workflows (Radev et al., 2023b).
-- We performed simultaneous posterior and likelihood estimation with jointly amortized neural approximation (JANA; Radev et al., 2023a), as implemented in the BayesFlow software for amortized Bayesian workflows (Radev et al., 2023b).
+- We approximated the posterior using neural posterior estimation (NPE) with learned summary statistics (Radev et al., 2020), as implemented in the BayesFlow framework for amortized Bayesian inference (Radev et al., 2023a).
+- We approximated the likelihood using neural likelihood estimation (NLE) without hand-crafted summary statistics (Papamakarios et al., 2019), leveraging its implementation in BayesFlow for efficient and flexible inference.
 
 1. Radev, S. T., Schmitt, M., Schumacher, L., Elsemüller, L., Pratz, V., Schälte, Y., Köthe, U., & Bürkner, P.-C. (2023a). BayesFlow: Amortized Bayesian workflows with neural networks. *The Journal of Open Source Software, 8(89)*, 5702.([arXiv](https://arxiv.org/abs/2306.16015))([JOSS](https://joss.theoj.org/papers/10.21105/joss.05702))
 2. Radev, S. T., Mertens, U. K., Voss, A., Ardizzone, L., Köthe, U. (2020). BayesFlow: Learning complex stochastic models with invertible neural networks. *IEEE Transactions on Neural Networks and Learning Systems, 33(4)*, 1452-1466. ([arXiv](https://arxiv.org/abs/2003.06281))([IEEE TNNLS](https://ieeexplore.ieee.org/document/9298920))
@@ -153,6 +167,10 @@ You can cite BayesFlow along the lines of:
 }
 ```
 
+## 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).
+
 ## 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, Project 528702768). 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), as well as the Informatics for Life initiative funded by the Klaus Tschira Foundation.

assets/benchmark_network_architectures.py

@@ -1,17 +1,18 @@
 from . import (
     approximators,
-    benchmarks,
     adapters,
     datasets,
     diagnostics,
     distributions,
+    experimental,
     networks,
     simulators,
     workflows,
     utils,
 )
+
 from .adapters import Adapter
-from .approximators import ContinuousApproximator
+from .approximators import ContinuousApproximator, PointApproximator
 from .datasets import OfflineDataset, OnlineDataset, DiskDataset
 from .simulators import make_simulator
 from .workflows import BasicWorkflow
@@ -29,12 +30,6 @@ def setup():
     logger = logging.getLogger(__name__)
     logger.setLevel(logging.INFO)
 
-    if keras.backend.backend() == "torch":
-        # turn off gradients by default
-        import torch
-
-        torch.autograd.set_grad_enabled(False)
-
     from bayesflow.utils import logging
 
     logging.debug(f"Using backend {keras.backend.backend()!r}")