https://github.com/emdgroup/tnmf
Transform-Invariant Non-Negative Matrix Factorization
https://github.com/emdgroup/tnmf
data-science invariant-features machine-learning matrix-factorization python source-separation sparse-coding tensor-factorization
Last synced: about 18 hours ago
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Transform-Invariant Non-Negative Matrix Factorization
- Host: GitHub
- URL: https://github.com/emdgroup/tnmf
- Owner: emdgroup
- License: apache-2.0
- Created: 2021-04-29T07:57:12.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-11-12T14:51:49.000Z (about 4 years ago)
- Last Synced: 2025-11-09T08:17:38.085Z (3 months ago)
- Topics: data-science, invariant-features, machine-learning, matrix-factorization, python, source-separation, sparse-coding, tensor-factorization
- Language: Python
- Homepage: https://emdgroup.github.io/tnmf/
- Size: 9.61 MB
- Stars: 7
- Watchers: 3
- Forks: 2
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
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README
[](https://github.com/emdgroup/tnmf/actions/workflows/flake8.yml)
[](https://github.com/emdgroup/tnmf/actions/workflows/pylint.yml)
[](https://github.com/emdgroup/tnmf/actions/workflows/pytest.yml)
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[](https://github.com/emdgroup/tnmf/actions/workflows/publish-to-pypi.yml)
[](https://share.streamlit.io/adriansosic/tnmf/main/demos/demo_selector.py)
[](https://github.com/emdgroup/tnmf)
Transform-Invariant Non-Negative Matrix Factorization
=====================================================
A comprehensive Python package for **Non-Negative Matrix Factorization (NMF)** with a focus on learning *transform-invariant representations*.
The packages supports multiple optimization backends and can be easily extended to handle application-specific types of transforms.
# General Introduction
A 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/).
# Installation
For using this package, you will need Python version 3.7 (or higher).
The package is available via [PyPI](https://pypi.org/project/tnmf/).
Installation is easiest using pip:
pip install tnmf
# Demos and Examples
The package comes with a [streamlit](https://streamlit.io) demo and a number of examples that demonstrate the capabilities of the TNMF model.
They provide a good starting point for your own experiments.
## Online Demo
Without requiring any installation, the demo is accessible via [streamlit sharing](https://share.streamlit.io/adriansosic/tnmf/main/demos/demo_selector.py).
## Local Execution
Once the package is installed, the demo and the examples can be conveniently executed locally using the `tnmf` command:
* To execute the demo, run `tnmf demo`.
* A specific example can be executed by calling `tnmf example `.
To show the list of available examples, type `tnmf example --help`.
# License
Copyright (c) 2021 Merck KGaA, Darmstadt, Germany
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
The full text of the license can be found in the file [LICENSE](LICENSE) in the repository root directory.
# Usage and Citation
If you use this software, please cite us using the information in CITATION.cff.
# Contributing
Contributions to the package are always welcome and can be submitted via a pull request.
Please note, that you have to agree to the [Contributor License Agreement](CONTRIBUTING.md) to contribute.
## Working with the Code
To checkout the code and set up a working environment with all required Python packages, execute the following commands:
```
git checkout https://github.com/emdgroup/tnmf.git ./tnmf
cd tmnf
python3 -m virtualenv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
```
Now, you should be able to execute the unit tests by calling `pytest` to verify that the code is running as expected.
## Pull Requests
Before creating a pull request, you should always try to ensure that the automated code quality and unit tests do not fail.
This section explains how to run them locally to understand and fix potential issues.
### Code Style and Quality
Code style and quality are checked using [flake8](https://flake8.pycqa.org/) and [pylint](http://pylint.pycqa.org/).
To execute them, change into the [repository root directory](.), run the following commands and inspect their output:
```
flake8
pylint tnmf
```
In order for a pull request to be accaptable, no errors may be reported here.
### Unit Tests
Automated unit tests reside inside the folder [tnmf/tests](tnmf/tests). They can be executed via
[pytest](https://docs.pytest.org/) by changing into the [repository root directory](.) and running
```
pytest
```
Debugging potential failures from the command line might be cumbersome.
Most Python IDEs, however, also support `pytest` natively in their debugger.
Again, for a pull request to be acceptable, no failures may be reported here.
### Code Coverage
Code coverage in the unit tests is measured using [coverage](https://coverage.readthedocs.io).
A coverage report can be created locally from the [repository root directory](.) via
```
coverage run
coverage combine
coverage report
```
This 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.
A more detailed, interactive report can be created using
```
coverage html
```
Then, you can open the file `htmlcov/index.html` in a web browser of your choice to navigate through code annotated with coverage data.
Required overall coverage to is configured in [setup.cfg](setup.cfg), under the key `fail_under` in section `[coverage:report]`.
## Building the Documentation
To build the documentation locally, change into the [doc subdirectory](doc) and run `make html`.
Then, the documentation resides at `doc\_build\html\index.html`.