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scikit-tensor

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scikit-tensor is a Python module for multilinear algebra and tensor factorizations. Currently, scikit-tensor supports basic tensor operations such as folding/unfolding, tensor-matrix and tensor-vector products as well as the following tensor factorizations:

Moreover, all operations support dense and tensors.

Dependencies

The required dependencies to build the software are Numpy >= 1.3, SciPy >= 0.7.

Usage

Example script to decompose sensory bread data (available from http://www.models.life.ku.dk/datasets) using CP-ALS

import logging
from scipy.io.matlab import loadmat
from sktensor import dtensor, cp_als

# Set logging to DEBUG to see CP-ALS information
logging.basicConfig(level=logging.DEBUG)

# Load Matlab data and convert it to dense tensor format
mat = loadmat('../data/sensory-bread/brod.mat')
T = dtensor(mat['X'])

# Decompose tensor using CP-ALS
P, fit, itr, exectimes = cp_als(T, 3, init='random')

Install

This package uses distutils, which is the default way of installing python modules. The use of virtual environments is recommended.

pip install scikit-tensor

To install in development mode

git clone git@github.com:mnick/scikit-tensor.git
pip install -e scikit-tensor/

Contributing & Development

scikit-tensor is still an extremely young project, and I'm happy for any contributions (patches, code, bugfixes, documentation, whatever) to get it to a stable and useful point. Feel free to get in touch with me via email (mnick at AT mit DOT edu) or directly via github.

Development is synchronized via git. To clone this repository, run

git clone git://github.com/mnick/scikit-tensor.git

Authors

Maximilian Nickel: Web, [Email](mailto://mnick AT mit DOT edu), Twitter

License

scikit-tensor is licensed under the GPLv3

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