This is a fork of a fork of a fork. I have not looked at most of the code in this repository, I just use it.
This project, also known as "Python-TSNE", may be the only working Python 3 implementation of Barnes-Hut t-SNE. It's a sadly unloved project, perhaps because everyone expects it to be superseded by scikit-learn any day now.
But, as of May 2017, scikit-learn's t-SNE has a bug that causes it to get no benefit from Barnes-Hut; it uses as much space as plain t-SNE, and runs out of memory on moderately large data. So, given that:
- osdf made a Cython wrapper of the original C++ code by L. J. P. van der Maaten.
- danielfrg made it into a Python package
that could be installed with
pip, using a GitHub URL. - alexisbcook fixed namespace problems that were causing danielfrg's package to fail to import in some situations.
- I made alexisbcook's version into a package on PyPI with a coherent version number, so that other code can depend on it.
I have no intention of continuing to maintain this project. If something about it doesn't quite work for you, I suggest you continue the tradition and make your own fork.
Anaconda is one way to get these requirements. (I don't use it myself.)
The point of this particular fork is to be installable through PyPI:
pip install barnes-hut-tsne
Basic usage:
from tsne import bh_sne
X_2d = bh_sne(X)
I found this docstring:
bh_sne(data)
Run Barnes-Hut T-SNE on _data_.
@param data The data.
@param pca_d The dimensionality of data is reduced via PCA
to this dimensionality.
@param d The embedding dimensionality. Must be fixed to
2.
@param perplexity The perplexity controls the effective number of
neighbors.
@param theta If set to 0, exact t-SNE is run, which takes
very long for dataset > 5000 samples.
@param random_state A numpy RandomState object; if None, use
the numpy.random singleton. Init the RandomState
with a fixed seed to obtain consistent results
from run to run.
@param copy_data Copy the data to prevent it from being modified
by the C code
See Barnes-Hut-SNE (2013), L.J.P. van der Maaten. It is available on arxiv.