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 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')
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 email@example.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
scikit-tensor is licensed under the GPLv3