Skip to main content

Conditional density estimation.

Project description

PyPI version Contributions welcome GitHub license

Description

Python package for conditional density estimation. It either wraps or implements diverse conditional density estimators.

Density estimation with normalizing flows

This package provides pass-through access to all the functionalities of nflows.

Setup

Clone the repo and install all the dependencies using the environment.yml file to create a conda environment: conda env create -f environment.yml. If you already have a pyknos environment and want to refresh dependencies, just run conda env update -f environment.yml --prune.

Alternatively, you can install via setup.py using pip install -e ".[dev]" (the dev flag installs development and testing dependencies).

Examples

Examples are collected in notebooks in examples/.

Binary files and Jupyter notebooks

Using

We use git lfs to store large binary files. Those files are not downloaded by cloning the repository, but you have to pull them separately. To do so follow installation instructions here https://git-lfs.github.com/. In particular, in a freshly cloned repository on a new machine, you will need to run both git-lfs install and git-lfs pull.

Contributing

We use a filename filter to identify large binary files. Once you installed and pulled git lfs you can add a file to git lfs by appending _gitlfs to the basename, e.g., oldbase_gitlfs.npy. Then add the file to the index, commit, and it will be tracked by git lfs.

Additionally, to avoid large diffs due to Jupyter notebook outputs we are using nbstripout to remove output from notebooks before every commit. The nbstripout package is downloaded automatically during installation of pyknos. However, please make sure to set up the filter yourself, e.g., through nbstriout --install or with different options as described here.

Name

pyknós (πυκνός) is the transliterated Greek root for density (pyknótita) and also means sagacious.

Copyright notice

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see https://www.gnu.org/licenses/.

Acknowledgements

Thanks to Artur Bekasov, Conor Durkan and George Papamarkarios for their work on nflows.

The MDN implementation in this package is by Conor M. Durkan.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyknos-0.14.0.tar.gz (22.8 kB view details)

Uploaded Source

Built Distribution

pyknos-0.14.0-py2.py3-none-any.whl (20.9 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file pyknos-0.14.0.tar.gz.

File metadata

  • Download URL: pyknos-0.14.0.tar.gz
  • Upload date:
  • Size: 22.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.24.0 setuptools/50.3.0.post20201006 requests-toolbelt/0.9.1 tqdm/4.50.2 CPython/3.7.9

File hashes

Hashes for pyknos-0.14.0.tar.gz
Algorithm Hash digest
SHA256 1f532a16ca04a9cf79ae7b42bec1cf11c306d61725c6a3ac27d8685ec282d87b
MD5 db55fb4675bb9f7b736ff6e434a82b3a
BLAKE2b-256 2143d71717a400bc50c56ec7c6954c6b95f9796eed7289effc52a0d528bdddb6

See more details on using hashes here.

File details

Details for the file pyknos-0.14.0-py2.py3-none-any.whl.

File metadata

  • Download URL: pyknos-0.14.0-py2.py3-none-any.whl
  • Upload date:
  • Size: 20.9 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.24.0 setuptools/50.3.0.post20201006 requests-toolbelt/0.9.1 tqdm/4.50.2 CPython/3.7.9

File hashes

Hashes for pyknos-0.14.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 8b93298afca175d8f9905c17cac6d36de93fed68abe91748ef8583cdb624dff6
MD5 6fe6c0b99b89d6d56dcb0a276a9f4409
BLAKE2b-256 d717a7af5400b438566d162b0894ee5edfa8b0badd0f4aadd4e027c148142032

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page