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.


For using this package, you will need Python version 3.7 (or higher).
The package is available via PyPI.

Installation is easiest using pip:

pip install tnmf

Demos and Examples

The package comes with a streamlit 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.

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 <example_name>.

To show the list of available examples, type tnmf example --help.


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

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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 in the repository root directory.

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 ./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 and pylint.
To execute them, change into the repository root directory, run the following commands and inspect their output:

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. They can be executed via
pytest by changing into the repository root directory and running


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.
A coverage report can be created locally from the repository root directory via

coverage run
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, under the key fail_under in section [coverage:report].

Building the Documentation

To build the documentation locally, change into the doc subdirectory and run make html.
Then, the documentation resides at doc\_build\html\index.html.