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reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["data-science","invariant-features","machine-learning","matrix-factorization","python","source-separation","sparse-coding","tensor-factorization"],"created_at":"2026-01-25T08:34:59.513Z","updated_at":"2026-01-25T08:35:00.076Z","avatar_url":"https://github.com/emdgroup.png","language":"Python","readme":"[![Flake8 Linter](https://github.com/emdgroup/tnmf/actions/workflows/flake8.yml/badge.svg)](https://github.com/emdgroup/tnmf/actions/workflows/flake8.yml)\n[![Pylint Linter](https://github.com/emdgroup/tnmf/actions/workflows/pylint.yml/badge.svg)](https://github.com/emdgroup/tnmf/actions/workflows/pylint.yml)\n[![Pytest and Coverage](https://github.com/emdgroup/tnmf/actions/workflows/pytest.yml/badge.svg)](https://github.com/emdgroup/tnmf/actions/workflows/pytest.yml)\n[![Build Documentation](https://github.com/emdgroup/tnmf/actions/workflows/sphinx.yml/badge.svg)](https://github.com/emdgroup/tnmf/actions/workflows/sphinx.yml)\n[![Publish to PyPI](https://github.com/emdgroup/tnmf/actions/workflows/publish-to-pypi.yml/badge.svg)](https://github.com/emdgroup/tnmf/actions/workflows/publish-to-pypi.yml)\n[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/adriansosic/tnmf/main/demos/demo_selector.py)\n\n[![Logo](https://raw.githubusercontent.com/emdgroup/tnmf/main/logos/tnmf_header.svg)](https://github.com/emdgroup/tnmf)\n\nTransform-Invariant Non-Negative Matrix Factorization\n=====================================================\n\nA comprehensive Python package for **Non-Negative Matrix Factorization (NMF)** with a focus on learning *transform-invariant representations*.\n\nThe packages supports multiple optimization backends and can be easily extended to handle application-specific types of transforms.\n\n# General Introduction\nA general introduction to Non-Negative Matrix Factorization and the purpose of this package can be found on the corresponding [GitHub Pages](https://emdgroup.github.io/tnmf/).\n\n# Installation\nFor using this package, you will need Python version 3.7 (or higher).\nThe package is available via [PyPI](https://pypi.org/project/tnmf/).\n\nInstallation is easiest using pip:\n\n    pip install tnmf\n\n# Demos and Examples\nThe package comes with a [streamlit](https://streamlit.io) demo and a number of examples that demonstrate the capabilities of the TNMF model.\nThey provide a good starting point for your own experiments.\n\n## Online Demo\nWithout requiring any installation, the demo is accessible via [streamlit sharing](https://share.streamlit.io/adriansosic/tnmf/main/demos/demo_selector.py).\n\n## Local Execution\nOnce the package is installed, the demo and the examples can be conveniently executed locally using the `tnmf` command:\n* To execute the demo, run `tnmf demo`.\n* A specific example can be executed by calling `tnmf example \u003cexample_name\u003e`.\n\nTo show the list of available examples, type `tnmf example --help`.\n\n# License\nCopyright (c) 2021 Merck KGaA, Darmstadt, Germany\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n    http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\nThe full text of the license can be found in the file [LICENSE](LICENSE) in the repository root directory.\n\n# Usage and Citation\nIf you use this software, please cite us using the information in CITATION.cff.\n\n# Contributing\nContributions to the package are always welcome and can be submitted via a pull request.\nPlease note, that you have to agree to the [Contributor License Agreement](CONTRIBUTING.md) to contribute.\n\n## Working with the Code\nTo checkout the code and set up a working environment with all required Python packages, execute the following commands:\n\n```\ngit checkout https://github.com/emdgroup/tnmf.git ./tnmf\ncd tmnf\npython3 -m virtualenv .venv\nsource .venv/bin/activate\npip install --upgrade pip\npip install -r requirements.txt\n```\n\nNow, you should be able to execute the unit tests by calling `pytest` to verify that the code is running as expected.\n\n## Pull Requests\nBefore creating a pull request, you should always try to ensure that the automated code quality and unit tests do not fail.\nThis section explains how to run them locally to understand and fix potential issues.\n\n### Code Style and Quality\nCode style and quality are checked using [flake8](https://flake8.pycqa.org/) and [pylint](http://pylint.pycqa.org/).\nTo execute them, change into the [repository root directory](.), run the following commands and inspect their output:\n\n```\nflake8\npylint tnmf\n```\n\nIn order for a pull request to be accaptable, no errors may be reported here.\n\n### Unit Tests\nAutomated unit tests reside inside the folder [tnmf/tests](tnmf/tests). They can be executed via\n[pytest](https://docs.pytest.org/) by changing into the [repository root directory](.) and running\n\n```\npytest\n```\n\nDebugging potential failures from the command line might be cumbersome.\nMost Python IDEs, however, also support `pytest` natively in their debugger.\nAgain, for a pull request to be acceptable, no failures may be reported here.\n\n### Code Coverage\nCode coverage in the unit tests is measured using [coverage](https://coverage.readthedocs.io).\nA coverage report can be created locally from the [repository root directory](.) via\n\n```\ncoverage run\ncoverage combine\ncoverage report\n```\n\nThis will output a concise table with an overview of python files that are not fully covered with unit tests along with the line numbers of code that has not been executed.\nA more detailed, interactive report can be created using\n\n```\ncoverage html\n```\n\nThen, you can open the file `htmlcov/index.html` in a web browser of your choice to navigate through code annotated with coverage data.\nRequired overall coverage to is configured in [setup.cfg](setup.cfg), under the key `fail_under` in section `[coverage:report]`.\n\n\n## Building the Documentation\nTo build the documentation locally, change into the [doc subdirectory](doc) and run `make html`.\nThen, the documentation resides at `doc\\_build\\html\\index.html`.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Femdgroup%2Ftnmf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Femdgroup%2Ftnmf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Femdgroup%2Ftnmf/lists"}