megaman: Manifold Learning for Millions of Points
megaman is a scalable manifold learning package implemented in
python. It has a front-end API designed to be familiar
to scikit-learn but harnesses
the C++ Fast Library for Approximate Nearest Neighbors (FLANN)
and the Sparse Symmetric Positive Definite (SSPD) solver
Locally Optimal Block Precodition Gradient (LOBPCG) method
to scale manifold learning algorithms to large data sets.
On a personal computer megaman can embed 1 million data points
with hundreds of dimensions in 10 minutes.
megaman is designed for researchers and as such caches intermediary
steps and indices to allow for fast re-computation with new parameters.
Package documentation can be found at http://mmp2.github.io/megaman/
If you use our software please cite the following JMLR paper:
McQueen, Meila, VanderPlas, & Zhang, "Megaman: Scalable Manifold Learning in Python", Journal of Machine Learning Research, Vol 17 no. 14, 2016. http://jmlr.org/papers/v17/16-109.html
You can also find our arXiv paper at http://arxiv.org/abs/1603.02763
Installation with Conda
The easiest way to install
megaman and its dependencies is with
conda, the cross-platform package
manager for the scientific Python ecosystem.
To install megaman and its dependencies, run
$ conda install megaman --channel=conda-forge
Currently builds are available for OSX and Linux, on Python 2.7, 3.4, and 3.5. For other operating systems, see the full install instructions below.
Installation from source
To install megaman from source requires the following:
- python tested with versions 2.7, 3.4, and 3.5
- numpy version 1.8 or higher
- scipy version 0.16.0 or higher
- a C++ compiler such as
- plotly an graphing library for interactive plot
Optional requirements include
- pyamg, which allows for faster decompositions of large matrices
- pyflann which offers another method of computing distance matrices (this is bundled with the FLANN source code)
- nose for running the unit tests
- h5py for reading testing .mat files
These requirements can be installed on Linux and MacOSX using the following conda command:
$ conda install --channel=conda-forge pip nose coverage gcc cython numpy scipy scikit-learn pyflann pyamg h5py plotly
Finally, within the source repository, run this command to install the
megaman package itself:
$ python setup.py install
nose for unit tests. With
nose installed, type
$ make test
to run the unit tests.
megaman is tested on Python versions 2.7, 3.4, and 3.5.
- Xiao Wang: lazy rmetric, Nystrom Extension
See this issues list for what we have planned for upcoming releases: