evoc 0.3.1

Basic Usage

EVōC follows the scikit-learn API, so it should be familiar to most users. You can use EVōC wherever you might have previously been using other sklearn clustering algorithms. Here is a simple example

import evoc
from sklearn.datasets import make_blobs

data, _ = make_blobs(n_samples=100_000, n_features=1024, centers=100)

clusterer = evoc.EVoC()
cluster_labels = clusterer.fit_predict(data)

Some more unique features include the generation of multiple layers of cluster granularity, the ability to extract a hierarchy of clusters across those layers, and automatic duplicate (or very near duplicate) detection.

import evoc
from sklearn.datasets import make_blobs

data, _ = make_blobs(n_samples=100_000, n_features=1024, centers=100)

clusterer = evoc.EVoC()
cluster_labels = clusterer.fit_predict(data)
cluster_layers = clusterer.cluster_layers_
hierarchy = clusterer.cluster_tree_
potential_duplicates = clusterer.duplicates_

The cluster layers are a list of cluster label vectors with the first being the finest grained and later layers being coarser grained. This is ideal for layered topic modelling and use with DataMapPlot. See this data map for an example of using these layered clusters in topic modelling (zoom in to access finer grained topics).

Installation

EVōC has a small set of dependencies:

• numpy

• scikit-learn

• numba

• tqdm

• tbb

You can install EVōC from PyPI using pip:

pip install evoc

To install the latest version of EVōC from source:

pip install git+https://github.com/TutteInstitute/evoc.git

License

EVōC is BSD (2-clause) licensed. See the LICENSE file for details.

Contributing

Contributions are more than welcome! If you have ideas for features of projects please get in touch. 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 in.