GATSBI: Generative Adversarial Training for Simulation-Based Inference

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Code package gatsbi implementing method and experiments described in the associated manuscript "GATSBI: Generative Adversarial Training for Simulation-Based Inference"

The code depends both on the simulation-based inference package sbi and the benchmark framework sbibm.

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Installation

With a working Python environment, install gatsbi using pip:

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

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Mininmal example

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

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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.

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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.