What is TensorTools?

TensorTools is a bare bones Python package for fitting and visualizing canonical polyadic (CP) tensor decompositions of higher-order data arrays. Check out the examples/ folder for usage. It handles several important practical steps:

Fitting an ensemble of models

Since CP decompositions can be caught in local minima during optimization [2]. If our data is held in data_tensor (a multi-dimensional numpy array), we can fit an ensemble of models with:

from tensortools import fit_ensemble

# fit models with 1-10 components, and fit each from 3 random initializations 
results = fit_ensemble(data_tensor, range(1,11), replicates=3)

Diagnostic Plots

TensorTools provides code to visualize fitted ensembles of CP decomposition models. These plots help assess (a) whether models are becoming stuck in highly suboptimal local minima, (b) whether the final model parameters are sensitive to the initialization. Together these can help a practitioner determine the appropriate number of latent components in the model.

from tensortools import plot_error, plot_similarity

plot_error(results)
plot_similarity(results)

Visualizing Factors

After fitting your models and assessing their goodness of fit (see above), TensorTools provides methods to visualize the low-rank factors.

from tensortools import plot_factors

# plot the best model with R = 5 components
R = 5
plot_factors(results[R]['factors'][0])

What isn't TensorTools?

TensorTools does not support many other interesting tensor models, such as Tucker decompositions and tensor regression problems. See [1] for a broad review of many tensor models. Additionally, TensorTools does not support specialized data structures for sparse tensors (see [3]). Some of these models are supported by other tensor packages:

• MATLAB
  • TensorToolbox
  • TensorLab
• Python
  • TensorLy

Installation

For now, install manually from the command line:

$ git clone https://github.com/ahwillia/tensortools
$ cd tensortools
$ python setup.py install

Or, if you plan to modify the source code yourself you can use:

$ python setup.py develop

which will conveniently incorporate any edits you make to the code after you restart the Python kernel. Open an issue if you have trouble installing tensortools.

References

[ 1 ] Kolda TG, Bader BW (2009). "Tensor Decompositions and Applications." SIAM Review 51:3, 455–500.

[ 2 ] Comon P, Luciani X, de Almeida ALF (2009). "Tensor decompositions, alternating least squares and other tales." Journal of Chemometrics 23:7-8, 393–405.

[ 3 ] Bader BW, Kolda TG (2006). "Efficient MATLAB computations with sparse and factored tensors." Technical Report, Sandia National Laboratories.