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Subsampled Online Matrix Factorization in Python

Project description

# MODL: Massive Online Dictionary Learning


This python package ([webpage]( implements the two following papers:

>Arthur Mensch, Julien Mairal, Bertrand Thirion, Gaël Varoquaux.
[Stochastic Subsampling for Factorizing Huge Matrices]( <hal-01431618> 2017.

>Arthur Mensch, Julien Mairal, Bertrand Thirion, Gaël Varoquaux.
[Dictionary Learning for Massive Matrix Factorization]( International Conference
on Machine Learning, Jun 2016, New York, United States. 2016

It allows to perform sparse / dense matrix factorization on fully-observed/missing data very efficiently, by leveraging random subsampling with online learning.
It is able to factorize matrices of terabyte scale with hundreds of components in the latent space in a few hours.

This package allows to reproduce the
experiments and figures from the papers.

More importantly, it provides [](scikit-learn) compatible
estimators that fully implements the proposed algorithms.

## Installing from source with pip

Installation from source is simple. In a command prompt:

git clone
cd modl
pip install -r requirements.txt
pip install .
cd $HOME
py.test --pyargs modl

*This package is only tested with Python 3.5+ !*

## Core code

The package essentially provides three estimators:

- `DictFact`, that computes a matrix factorization from Numpy arrays
- `fMRIDictFact`, that computes sparse spatial maps from fMRI images
- `ImageDictFact`, that computes a patch dictionary from an image
- `RecsysDictFact`, that allows to predict score from a collaborative filtering approach

## Examples

### fMRI decomposition

A fast running example that decomposes a small dataset of resting-fmri data into a 70 components map is provided

python examples/

It can be adapted for running on the 2TB HCP dataset, by changing the source parameter into 'hcp' (you will need to download the data first)

### Hyperspectral images

A fast running example that extracts the patches of a HD image can be run from

python examples/

It can be adapted to run on AVIRIS data, changing the image source into 'aviris' in the file.

### Recommender systems

Our core algorithm can be run to perform collaborative filtering very efficiently:

python examples/

You will need to download datasets beforehand:

make download-movielens1m
make download-movielens10m

## Future work

- `sacred` dependency will be removed
- Release a fetcher for HCP from S3 bucker
- Release examples with larger datasets and benchmarks

## Contributions

Please feel free to report any issue and propose improvements on github.

## References

Related projects :
- [spira]( is a python library to perform collaborative filtering based on coordinate descent. It serves as the baseline for recsys experiments - we hard included it for simplicity.
- [scikit-learn]( is a python library for machine learning. It serves as the basis of this project.
- [nilearn]( is a neuro-imaging library that we wrap in our fMRI related estimators.

## Author

Licensed under simplified BSD.

Arthur Mensch, 2015 - present

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