pyttb: Python Tensor Toolbox

Tensors (also known as multidimensional arrays or N-way arrays) are used in a variety of applications ranging from chemometrics to network analysis.

  • This Python package is an adaptation of the open source Tensor Toolbox for MATLAB.

  • This is open source software. Please see LICENSE for the terms of the license (2-clause BSD).

  • The pyttb code repository is hosted at github.

  • Contributions are welcome; see CONTRIBUTING.

  • Reports of bugs and feature requests can be submitted on github.

  • For more information or for feedback on this project, please contact us.

Installing

  • Via pypi

    • Install the latest release from pypi (pip install pyttb or python -m pip install pyttb)

  • From source

    • Clone the repository from github.

    • Install the package with pip install . from the pyttb root directory.

    • Note: Use pip install -e ".[dev,doc]" if you are planning development.

Functionality

pyttb provides the following classes and functions for manipulating dense, sparse, and structured tensors, along with algorithms for computing low-rank tensor models.

  • Tensor Classes

    pyttb supports multiple tensor types, including dense and sparse, as well as specially structured tensors, such as the Kruskal format (stored as factor matrices).

  • Algorithms

    CP methods such as alternating least squares, direct optimization, and weighted optimization (for missing data). Also alternative decompositions such as Poisson Tensor Factorization via alternating Poisson regression.

  • IO

    Storing and retrieving tensors from disk.

Getting Started

For historical reasons we use Fortran memory layouts, where numpy by default uses C. This is relevant for indexing. In the future we hope to extend support for both.

>>> import numpy as np
>>> c_order = np.arange(8).reshape((2,2,2))
>>> f_order = np.arange(8).reshape((2,2,2), order="F")
>>> print(c_order[0,1,1])
3
>>> print(f_order[0,1,1])
6

Python API

How to Cite

Please see references for how to cite a variety of algorithms implemented in this project.

Contact

Please email dmdunla@sandia.gov with any questions about pyttb that cannot be resolved via issue reporting. Stories of its usefulness are especially welcome. We will try to respond to every email may not always be successful due to the volume of emails.

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