{"id":15704879,"url":"https://github.com/ziatdinovmax/pyroved","last_synced_at":"2025-08-28T22:24:15.414Z","repository":{"id":48557123,"uuid":"342072049","full_name":"ziatdinovmax/pyroVED","owner":"ziatdinovmax","description":"Invariant representation learning from imaging and spectral data","archived":false,"fork":false,"pushed_at":"2024-01-29T20:33:28.000Z","size":117149,"stargazers_count":48,"open_issues_count":8,"forks_count":11,"subscribers_count":3,"default_branch":"main","last_synced_at":"2024-10-24T16:35:21.581Z","etag":null,"topics":["encoder-decoder-model","image-data","probabilistic-programming","pyro","rotation-invariant","scale-invariance","semi-supervised-learning","semi-supervised-vae","spectral-data","translation-invariant","unsupervised-machine-learning","vae","variational-autoencoder"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ziatdinovmax.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-02-25T00:13:07.000Z","updated_at":"2024-10-17T22:04:47.000Z","dependencies_parsed_at":"2024-10-24T09:00:07.160Z","dependency_job_id":null,"html_url":"https://github.com/ziatdinovmax/pyroVED","commit_stats":{"total_commits":356,"total_committers":7,"mean_commits":"50.857142857142854","dds":0.5337078651685394,"last_synced_commit":"7807ffb1cb415b3cc76c1e02d52465a8ae0eeae4"},"previous_names":[],"tags_count":10,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ziatdinovmax%2FpyroVED","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ziatdinovmax%2FpyroVED/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ziatdinovmax%2FpyroVED/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ziatdinovmax%2FpyroVED/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ziatdinovmax","download_url":"https://codeload.github.com/ziatdinovmax/pyroVED/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248837264,"owners_count":21169373,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["encoder-decoder-model","image-data","probabilistic-programming","pyro","rotation-invariant","scale-invariance","semi-supervised-learning","semi-supervised-vae","spectral-data","translation-invariant","unsupervised-machine-learning","vae","variational-autoencoder"],"created_at":"2024-10-03T20:14:10.544Z","updated_at":"2025-04-14T06:44:39.160Z","avatar_url":"https://github.com/ziatdinovmax.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# pyroVED\n\n---\n[![build](https://github.com/ziatdinovmax/pyroVED/actions/workflows/actions.yml/badge.svg)](https://github.com/ziatdinovmax/pyroVED/actions/workflows/actions.yml)\n[![codecov](https://codecov.io/gh/ziatdinovmax/pyroVED/branch/main/graph/badge.svg?token=FFA8XB0FED)](https://codecov.io/gh/ziatdinovmax/pyroVED)\n[![Documentation Status](https://readthedocs.org/projects/pyroved/badge/?version=latest)](https://pyroved.readthedocs.io/en/latest/README.html)\n[![PyPI version](https://badge.fury.io/py/pyroved.svg)](https://badge.fury.io/py/pyroved)\n\npyroVED is an open-source package built on top of the Pyro probabilistic programming framework for applications of variational encoder-decoder models in spectral and image analyses. The currently available models include variational autoencoders with translational, rotational, and scale invariances for unsupervised, class-conditioned, and semi-supervised learning, as well as *im2spec*-type models for predicting spectra from images and vice versa.\nMore models to come!\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"misc/mnist.png\" width=\"95%\" title=\"pyroved_ivae\"\u003e\n\u003cp align=\"justify\"\u003e\n\n## Documentation and Examples\n\nThe documentation of the package content can be found [here](https://pyroved.readthedocs.io/).\n  \nThe easiest way to start using pyroVED is via [Google Colab](https://colab.research.google.com/notebooks/intro.ipynb), which is a free research tool from Google for machine learning education and research built on top of Jupyter Notebook. The following notebooks can be executed in Google Colab by simply clicking on the \"Open in Colab\" icon:\n\n*   Mastering the 1D shifts in spectral data [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ziatdinovmax/pyroVED/blob/master/examples/shiftVAE.ipynb)\n\n*   Disentangling image content from rotations [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ziatdinovmax/pyroVED/blob/master/examples/rVAE.ipynb)\n\n*   Learning (jointly) discrete and continuous representations of data [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ziatdinovmax/pyroVED/blob/main/examples/jrVAE.ipynb)\n\n*   Semi-supervised learning from data with orientational disorder [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ziatdinovmax/pyroVED/blob/main/examples/ssrVAE.ipynb)\n\n*   im2spec: Predicting 1D spectra from 2D images [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ziatdinovmax/pyroVED/blob/main/examples/im2spec_VED.ipynb)  \n\n## Installation\n\n#### Requirements\n*   python \u003e= 3.6\n*   [pytorch](https://pytorch.org/) \u003e= 1.8\n*   [pyro-ppl](https://pyro.ai/) \u003e= 1.6\n\nInstall pyroVED using pip:\n\n```bash\npip install pyroved\n```\n\n#### Latest (unstable) version\n\nTo upgrade to the latest (unstable) version, run\n\n```bash\npip install --upgrade git+https://github.com/ziatdinovmax/pyroved.git\n```\n\n## Reporting bugs\nIf you found a bug in the code or would like a specific feature to be added, please create a report/request [here](https://github.com/ziatdinovmax/pyroVED/issues/new/choose).\n  \n  \n## Development\n\nTo run the unit tests, you'll need to have a pytest framework installed:\n\n```bash\npython3 -m pip install pytest\n```\n\nThen run tests as:\n\n```bash\npytest tests\n```\n\nIf this is your first time contributing to an open-source project, we highly recommend starting by familiarizing yourself with these very nice and detailed contribution [guidelines](https://github.com/firstcontributions/first-contributions).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fziatdinovmax%2Fpyroved","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fziatdinovmax%2Fpyroved","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fziatdinovmax%2Fpyroved/lists"}