SpectralNet

SpectralNet is a Python package that performs spectral clustering with deep neural networks.

This package is based on the following paper - SpectralNet

Contributions to the original project

naive implementation of the standard spectral clustering algorithms (Shi and Malik, Ng-Jordan-Weiss).
added adjusted rand index as clustering metrics.
introduction of a clustering report, which summarizes the overall performance of each method.
added support to other datasets (such as Iris datasets and OpenML datasets).
introduction of scripts for visualizing t-SNE representation of the datasets.
added examples for comparing SpectralNet and classical spectral clustering algorithms on several datasets.

Installation

You can install the latest package version via

python setup.py install

in order to benefit from contributions of this repository.

Usage

Clustering

The basic functionality is quite intuitive and easy to use, e.g.,

from spectralnet import SpectralNet

spectralnet = SpectralNet(n_clusters=10)
spectralnet.fit(X) # X is the dataset and it should be a torch.Tensor
cluster_assignments = spectralnet.predict(X) # Get the final assignments to clusters

If you have labels to your dataset and you want to measure ACC and NMI you can do the following:

from spectralnet import SpectralNet
from spectralnet import Metrics


spectralnet = SpectralNet(n_clusters=2)
spectralnet.fit(X, y) # X is the dataset and it should be a torch.Tensor
cluster_assignments = spectralnet.predict(X) # Get the final assignments to clusters

y = y_train.detach().cpu().numpy() # In case your labels are of torch.Tensor type.

# in one line, Metrics.clustering_report({"SpectralNet": cluster_assignments}, y, n_clusters=2)
# or
acc_score = Metrics.acc_score(cluster_assignments, y, n_clusters=2)
nmi_score = Metrics.nmi_score(cluster_assignments, y)
ari_score = Metrics.ari_score(cluster_assignments, y)
print(f"ACC: {np.round(acc_score, 3)}")
print(f"NMI: {np.round(nmi_score, 3)}")
print(f"ARI: {np.round(ari_score, 3)}")

You can read the code docs for more information and functionalities

Running examples

In order to run the model on twomoons or MNIST datasets, you should first cd to the examples folder and then run:
python3 cluster_twomoons.py
or
python3 cluster_mnist.py

Citation


@inproceedings{shaham2018,
author = {Uri Shaham and Kelly Stanton and Henri Li and Boaz Nadler and Ronen Basri and Yuval Kluger},
title = {SpectralNet: Spectral Clustering Using Deep Neural Networks},
booktitle = {Proc. ICLR 2018},
year = {2018}
}