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https://github.com/kmzzhang/nbi
Package for Neural Posterior Estimation and Importance Sampling focused on Astronomical Applications
https://github.com/kmzzhang/nbi
Last synced: 3 months ago
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Package for Neural Posterior Estimation and Importance Sampling focused on Astronomical Applications
- Host: GitHub
- URL: https://github.com/kmzzhang/nbi
- Owner: kmzzhang
- License: other
- Created: 2022-04-18T17:15:15.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-03T17:38:21.000Z (6 months ago)
- Last Synced: 2024-05-22T14:31:29.879Z (6 months ago)
- Language: Python
- Homepage:
- Size: 20.5 MB
- Stars: 29
- Watchers: 7
- Forks: 3
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.rst
- Contributing: contributing.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-neural-sbi - [Code
README
[](https://opensource.org/licenses/BSD-3-Clause) 
[](https://github.com/kmzzhang/nbi/actions/workflows/tests.yml)
[](https://nbi.readthedocs.io/en/latest/?badge=latest)
## nbi: neural bayesian inference
[Documentation](https://nbi.readthedocs.io/en/latest/)
Do you have challenging inference problems that are difficult to solve with standard optimization and/or MCMC methods?
Are you looking to fit the same forward model to thousands or millions of observed targets?
`nbi` may be your solution.
`nbi` is an engine for Neural Posterior Estimation (NPE) focused on out-of-the-box functionality for astronomical data,
particularly light curves and spectra.
`nbi` provides effective embedding/featurizer networks for spectra and light-curve data, along
with importance-sampling integration that enables asymptotically exact inference so that the inference results are
interpretable and trustworthy.
## Installation
You may either install `nbi` with `pip install nbi` or directly from source. As `nbi` is currently under active development,
installing from source may be preferable at this stage.
```bash
git clone https://github.com/kmzzhang/nbi.git
cd nbi
pip install .
```
If you are using Mac ARM **CPU** (i.e. M1/M2/M3), you might want to install PyTorch from source and disable NNPACK, which is known to
reduce performance (see [issue](https://github.com/pytorch/pytorch/issues/107534)). Note that currently the `MPS`
Also support for `weight_norm` on Mac M1-M3 GPUs is recently
[implemented](https://github.com/pytorch/pytorch/pull/108008) but has not been included in a stable
release yet. Installing the nightly version from source also enables `weight_norm` for
the MPS device.
```bash
git clone --recursive https://github.com/pytorch/pytorch
cd pytorch
USE_NNPACK=0 python setup.py install
```
## Quick Start
The `examples/` directory contains complete examples that demonstrates the functionality of `nbi`. A bare-bone
example below illustrates the basic API, which follows the scikit-learn style. The default featurizer network for
sequential data is `resnet-gru`, which is a hybrid CNN-RNN architecture.
Here are a rule of thumb for resnet-gru hyperparameters:
- dim_in: this is your number of input data channels
- depth: number of ResNet blocks. Start near log2(L)-5, where L is length of your sequential data.
- max_hidden: Maximum hidden dimensions for ResNet. Hidden dimensions double (from hidden_conv=32 by default) every
depth. At least a few times D^2, where D is the dimension of the physical parameter space.
```python
import nbi
# hyperparameters
featurizer = {
"type": "resnet-gru",
"dim_in": 1,
"max_hidden": 64
}
flow = {
"n_dims": 1, # parameter space dimension
"flow_hidden": 32, # generally no larger than max_hidden
"num_blocks": 10 # depends on complexity of posterior shape
}
engine = nbi.NBI(
flow,
featurizer,
simulator,
noise,
priors,
device='cpu' # 'cuda', 'cuda:0', 'mps' for M1/M2 Mac GPU
)
engine.fit(
n_sims=1000,
n_rounds=1,
n_epochs=100
)
y_pred, weights = engine.predict(x_obs, x_err, n_samples=2000)
```
## References
nbi: the Astronomer's Package for Neural Posterior Estimation
([Zhang et al. 2023](https://arxiv.org/abs/2312.03824)). Accepted to NeurIPS 2023 Workshop on Deep Learning and
Inverse Problems.
Masked Autoregressive Flow for Density Estimation (Papamakarios et al. 2017)\
https://arxiv.org/abs/1705.07057
Featurizers: ResNet (He et al. 2015; https://arxiv.org/abs/1512.03385), Gated Recurrent Units
(GRU; Cho et al. 2014; https://arxiv.org/abs/1406.1078),
ResNet-GRU (Zhang et al. 2021; https://iopscience.iop.org/article/10.3847/1538-3881/abf42e)
## Acknowledgments
The `nbi` package is expanded from code originally written for _''Real-time Likelihood-free Inference of Roman Binary Microlensing Events
with Amortized Neural Posterior Estimation'''_ ([Zhang et al. 2021](https://iopscience.iop.org/article/10.3847/1538-3881/abf42e)).
The Masked Autoregressive Flow in this package is partly adapted from the implementation in
https://github.com/kamenbliznashki/normalizing_flows.
Work on this project was supported by the [National Science Foundation award #2206744](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2206744&HistoricalAwards=false) ("CDS&E: Accelerating Astrophysical Insight at Scale with Likelihood-Free Inference").
