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lampe

Likelihood-free AMortized Posterior Estimation with PyTorch

https://github.com/probabilists/lampe

Science Score: 31.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Committers with academic emails
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    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.3%) to scientific vocabulary

Keywords

bayesian density-estimation density-ratio-estimation inference likelihood-free-inference normalizing-flows probability python pytorch simulation-based-inference
Last synced: 6 months ago · JSON representation ·

Repository

Likelihood-free AMortized Posterior Estimation with PyTorch

Basic Info
  • Host: GitHub
  • Owner: probabilists
  • License: mit
  • Language: Python
  • Default Branch: master
  • Homepage: https://lampe.readthedocs.io
  • Size: 4.09 MB
Statistics
  • Stars: 130
  • Watchers: 7
  • Forks: 13
  • Open Issues: 0
  • Releases: 9
Topics
bayesian density-estimation density-ratio-estimation inference likelihood-free-inference normalizing-flows probability python pytorch simulation-based-inference
Created about 4 years ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing License Citation

README.md

LAMPE's banner

LAMPE

LAMPE is a simulation-based inference (SBI) package that focuses on amortized estimation of posterior distributions, without relying on explicit likelihood functions; hence the name Likelihood-free AMortized Posterior Estimation (LAMPE). The package provides PyTorch implementations of modern amortized simulation-based inference algorithms like neural ratio estimation (NRE), neural posterior estimation (NPE) and more. Similar to PyTorch, the philosophy of LAMPE is to avoid obfuscation and expose all components, from network architecture to optimizer, to the user such that they are free to modify or replace anything they like.

As part of the inference pipeline, lampe provides components to efficiently store and load data from disk, diagnose predictions and display results graphically.

[!IMPORTANT] In an effort to unite communities, the development of LAMPE has stopped in favor of the sbi project. The sbi package already supports many of lampe's features, and you are welcome to submit issues and PRs for the features you would like to be ported to sbi.

Installation

The lampe package is available on PyPI, which means it is installable via pip.

pip install lampe

Alternatively, if you need the latest features, you can install it from the repository.

pip install git+https://github.com/probabilists/lampe

Documentation

The documentation is made with Sphinx and Furo and is hosted at lampe.readthedocs.io.

Contributing

If you have a question, an issue or would like to contribute, please read our contributing guidelines.

Owner

  • Name: The Probabilists
  • Login: probabilists
  • Kind: organization
  • Email: theprobabilists@gmail.com

A community for open probabilistic science.

Citation (CITATION.bib) 

@software{rozet2021lampe,
  title = {{LAMPE}: Likelihood-free Amortized Posterior Estimation},
  author = {Rozet, François and Delaunoy, Arnaud and Miller, Benjamin and others},
  year = {2021},
  doi = {10.5281/zenodo.8405782},
  license = {MIT},
  url = {https://pypi.org/project/lampe},
}

GitHub Events

Total
  • Watch event: 13
  • Fork event: 2
Last Year
  • Watch event: 13
  • Fork event: 2

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 121
  • Total Committers: 4
  • Avg Commits per committer: 30.25
  • Development Distribution Score (DDS): 0.033
Past Year
  • Commits: 20
  • Committers: 2
  • Avg Commits per committer: 10.0
  • Development Distribution Score (DDS): 0.05
Top Committers
Name Email Commits
François Rozet f****t@o****m 117
Benjamin Kurt Miller 1****i 2
Maciej Falkiewicz f****j@g****m 1
Arnaud Delaunoy 4****u 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 3
  • Total pull requests: 1
  • Average time to close issues: 3 months
  • Average time to close pull requests: 7 months
  • Total issue authors: 3
  • Total pull request authors: 1
  • Average comments per issue: 6.0
  • Average comments per pull request: 15.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • zengjice1991 (1)
  • macio232 (1)
  • LGro (1)
Pull Request Authors
  • macio232 (2)
Top Labels
Issue Labels
enhancement (2) expected (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 524 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 23
  • Total maintainers: 1
pypi.org: lampe

Likelihood-free AMortized Posterior Estimation with PyTorch

  • Versions: 23
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 524 Last month
Rankings
Stargazers count: 7.7%
Dependent packages count: 10.1%
Average: 13.3%
Forks count: 13.3%
Downloads: 14.0%
Dependent repos count: 21.6%
Maintainers (1)
francois-rozet
Last synced: 6 months ago

Dependencies

docs/requirements.txt pypi
  • docutils ==0.17.1
  • furo ==2022.6.21
  • myst-nb ==0.17.1
  • sphinx ==5.0.2
requirements.txt pypi
  • h5py >=3.0.0
  • matplotlib >=3.4.0
  • numpy >=1.20.0
  • torch >=1.8.0
  • tqdm >=4.52.0
  • zuko >=0.1.3
.github/workflows/dummy.yml actions
.github/workflows/lint.yml actions
  • actions/checkout v3 composite
  • chartboost/ruff-action v1 composite
.github/workflows/test.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
pyproject.toml pypi