https://github.com/mackelab/gatsbi

GATSBI: Generative Adversarial Training for Simulation-Based Inference
https://github.com/mackelab/gatsbi

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GATSBI: Generative Adversarial Training for Simulation-Based Inference

• Host: GitHub
• URL: https://github.com/mackelab/gatsbi
• Owner: mackelab
• License: agpl-3.0
• Created: 2022-01-30T15:05:00.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-07-13T13:35:27.000Z (over 1 year ago)
• Last Synced: 2024-08-01T16:52:36.000Z (3 months ago)
• Language: Python
• Size: 15.3 MB
• Stars: 17
• Watchers: 8
• Forks: 3
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# GATSBI: Generative Adversarial Training for Simulation-Based Inference

---

Code package `gatsbi` implementing method and experiments described in the associated manuscript  ["GATSBI: Generative Adversarial Training for Simulation-Based Inference"](https://openreview.net/forum?id=kR1hC6j48Tp&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DICLR.cc%2F2022%2FConference%2FAuthors%23your-submissions))

The code depends both on the simulation-based inference package [`sbi`](https://github.com/mackelab/sbi) and the benchmark framework [`sbibm`](https://github.com/mackelab/sbibm).

___

### Installation

With a working Python environment, install `gatsbi` using `pip`:

```

pip install "git+https://github.com/mackelab/gatsbi"

```

___

### Mininmal example

For a minimal demonstration of how to use `gatsbi` see `quickstart.ipynb`.

___

### Experiments

The paper describes results for the following experiments: 2 benchmark tasks, the shallow water model, and a noisy camera model.

Code for setting up priors, simulator, GAN networks and any other pre-/post-processing code is available inside `gatsbi.task_utils`.

Hyperparameter settings for each of the experiments are available in `tasks/`

To reproduce the exact experiments described in the paper, use the following run_scripts from the repository's root directory (note that this relies on `wandb` to log experiments)

- `run_benchmarks.py` for benchmark tasks

    ```

    python run_benchmarks.py --project_name="Benchmarks" --task_name="two_moons"

    ```

    `task_name` = `slcp` or `two_moons` for amortized GATSBI, `slcp_seq` or `two_moons_seq` for sequential GATSBI.

- `run_highdim_applications.py` for high dimensional tasks

    ```

    python run_highdim_applications.py --project_name="High Dimensional Applications" --task_name="shallow_water_model"

    ```

    `task_name` = `shallow_water_model` or `camera_model`.

- `run_inference_nle/npe/nre.py` for running NPE / NLE / NRE on shallow water model.

    ```

    python run_inference_nle/npe/nre.py

    ```

Note that we **do not** provide training data for the shallow water model in this repository. Please use `sample_shallow_water.py` to generate training samples locally.

___

### Figures

Code to reproduce the figures in the paper is available in `plotting_code`, along with the required data `plotting_code/plotting_data`, and the final plots `plotting_code/plots`. Note that accessing the data requires Git LFS installation.