Python library for multilinear algebra and tensor factorizations
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Latest commit fe517e9 Oct 4, 2016


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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:

  • Canonical / Parafac Decomposition
  • Tucker Decomposition

Moreover, all operations support dense and tensors.


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


Example script to decompose sensory bread data (available from using CP-ALS

import logging
from import loadmat
from sktensor import dtensor, cp_als

# Set logging to DEBUG to see CP-ALS information

# 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')


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
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://


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


scikit-tensor is licensed under the GPLv3

Related Projects

  • Matlab Tensor Toolbox: A Matlab toolbox for tensor factorizations and tensor operations freely available for research and evaluation.
  • Matlab Tensorlab A Matlab toolbox for tensor factorizations, complex optimization, and tensor optimization freely available for non-commercial academic research.