mindrenee/vantage6-algorithms

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vantage6-algorithms

Algorithms developed for running on Vantage6

Installation

To install vantage6-algorithms, do:

git clone https://github.com/NLeSC/vantage6-algorithms.git
cd vantage6-algorithms
pip install .

Run tests (including coverage) with:

python setup.py test

Algorithms

The algorithms in this repo ar part of the vantage6 solution. Vantage6 allows to execute computations on federated datasets.

TODO: Table with instructions how to call the different algorithms

Analysis of Vertically Partitioned Data Using a TSE

Based on the implementation of [SOEST2020]

[SOEST2020]

van Soest PhD, Johan, Sun MSc, Chang, & Mussmann PhD, Bjoern Ole. (2020, February 4). FAIRHealth (Version v0.0.5). Zenodo. http://doi.org/10.5281/zenodo.3635839

Workarounds

As the vantage6 software is still in heavy development we sometimes have to create workarounds to get the package to work correctly.

Vantage-6 dependencies

At time of writing, the algorithms implemented in this repository are not yet compatible with the vantage6 packages from pypi. That is why requirements.txt refers to branch 1.1.0 in the github repos of the vantage6 packages. When all required changes are pushed to pypi these depencencies will have to be replaced with pypi dependencies.

Read More

See the vantage6 documentation for detailed instructions on how to install and use the server and nodes.

Contributing

If you want to contribute to the development of vantage6-algorithms, have a look at the contribution guidelines.

License

Copyright 2020

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.

Credits

This package was created with Cookiecutter and the NLeSC/python-template.