pervect 0.0.2

Description:

pervect 0.0.2

PerVect
Vectorization of persistence diagrams and approximate Wasserstein distance. This is
managed by approximating persistence diagrams with Gaussian mixture models and then
measuring the Wasserstein distance between the Gaussian mixtures. As the number of
components in mixture model increases the accuracy of the approximation increases
accordingly until, with equivalence in the limit.
The library is implemented as a Scikit-learn
transformer – taking a list of
persistence diagrams (preferably in birth-lifetime format) as input, and transforming
it into a vector representation (specifically the component weights for a Gaussian
mixture model fit to the union of all the diagrams). Distances can then be computed
as Wassterstein distance over a ground-distance matrix provided as an attribute of the
transformer. Alternatively UMAP can be used to convert toa lower dimensional
Euclidean distance representation.

How to use PerVect
The pervect library inheritis from sklearn classes and can be used as an sklearn
transformer.
import pervect
vects = pervect.PersistenceVectorizer().fit_transform(diagrams)
It can also be used in standard sklearn pipelines along with other machine learning
tools including clustering and classifiers.


Installation
Requirements:

Python >= 3.6
scikit-learn
umap-learn
numba
joblib
pot

You can install pervect from PyPI with pip:
pip install pervect
For a manual install get this package:
wget https://github.com/scikit-tda/pervect/archive/master.zip
unzip master.zip
rm master.zip
cd pervect-master
Install the requirements
sudo pip install -r requirements.txt
Install the package
pip install .


License
The pervect package is 3-clause BSD licensed.
We would like to note that the pervect package makes heavy use of
NumFOCUS sponsored projects, and would not be possible without
their support of those projects, so please consider contributing to NumFOCUS.


Contributing
Contributions are more than welcome! There are lots of opportunities
for potential projects, so please get in touch if you would like to
help out. Everything from code to notebooks to
examples and documentation are all equally valuable so please don’t feel
you can’t contribute. To contribute please
fork the project
make your changes and
submit a pull request. We will do our best to work through any issues with
you and get your code merged into the main branch.