openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE)¹, a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings², massive speed improvements³⁴, enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations⁵.

• Documentation
• User Guide and Tutorial
• Examples: basic, advanced, preserving global alignment, embedding large data sets
• Speed benchmarks

openTSNE requires Python 3.6 or higher in order to run.

openTSNE can be easily installed from conda-forge with

conda install --channel conda-forge opentsne

Conda package

openTSNE is also available through pip and can be installed with

pip install opentsne

PyPi package

If you wish to install openTSNE from source, please run

python setup.py install

in the root directory to install the appropriate dependencies and compile the necessary binary files.

Please note that openTSNE requires a C/C++ compiler to be available on the system. Additionally, numpy must be pre-installed in the active environment.

In order for openTSNE to utilize multiple threads, the C/C++ compiler must support OpenMP. In practice, almost all compilers implement this with the exception of older version of clang on OSX systems.

To squeeze the most out of openTSNE, you may also consider installing FFTW3 prior to installation. FFTW3 implements the Fast Fourier Transform, which is heavily used in openTSNE. If FFTW3 is not available, openTSNE will use numpy’s implementation of the FFT, which is slightly slower than FFTW. The difference is only noticeable with large data sets containing millions of data points.

Getting started with openTSNE is very simple. First, we'll load up some data using scikit-learn

from sklearn import datasets

iris = datasets.load_iris()
x, y = iris["data"], iris["target"]

then, we'll import and run

from openTSNE import TSNE

embedding = TSNE().fit(x)

If you make use of openTSNE for your work we would appreciate it if you would cite the paper

@article {Poli{\v c}ar731877,
    author = {Poli{\v c}ar, Pavlin G. and Stra{\v z}ar, Martin and Zupan, Bla{\v z}},
    title = {openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding},
    year = {2019},
    doi = {10.1101/731877},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2019/08/13/731877},
    eprint = {https://www.biorxiv.org/content/early/2019/08/13/731877.full.pdf},
    journal = {bioRxiv}
}

openTSNE implements two efficient algorithms for t-SNE. Please consider citing the original authors of the algorithm that you use. If you use FIt-SNE (default), then the citation is⁷ below, but if you use Barnes-Hut the citation is⁸.