tred 0.0.6

Dimensionality reduction techniques for order-3 tensors

tred
----
tred implements a range of order-3 tensor decompositions. Mathematically, 
they rely on a novel tensor algebra introduced in [1]. Within this framework, 
natural tensor analogues of SVD and PCA were recently formulated; these 
decomposition techniques also have analytically proven optimality properties 
that mirror those of their matrix counterparts. 

The only top-level dependency, for an end user, is scikit-learn. We inherit from 
their base classes, so tred's class API's should be natural to any past 
scikit-learn users. tred's function API's also mirror scipy counterparts as much
as possible. 

For the underlying tensor-product framework and tensor t-SVDM, see [1]. 
For the explicit rank truncation, and the TCAM algorithm, see [2]. 

Literature
----------
[1] Kilmer, M.E., Horesh, L., Avron, H. and Newman, E., 2021. Tensor-tensor 
algebra for optimal representation and compression of multiway data. Proceedings 
of the National Academy of Sciences, 118(28), p.e2015851118.

[2] Mor, U., Cohen, Y., Valdés-Mas, R., Kviatcovsky, D., Elinav, E. and Avron, 
H., 2022. Dimensionality reduction of longitudinal’omics data using modern 
tensor factorizations. PLoS Computational Biology, 18(7), p.e1010212.

NOTE: In literature, the authors use m, p, n as the dimensions of the tensors, 
whereas throughout this package one will see we prefer n, p, t instead. We will 
also use k = min(n, p), where from an `omics analysis perspective, typically 
means k = n, as p >> n typically. 

Development
-----------
For anyone who is interested in adding to the package.

For development dependencies
    pip install -r requirements.txt

To test any changes, invoke
    pytest . 
In the root to run the tests in the test folder.

Please kindly run 
    black . 
In the root to autoformat code when opening pull requests to this repo.  

Much of the implementation and code practice mirrors that of scikit-learn. We
adopt their utilities and general coding guidelines whenever we can. Be very
liberal in adding tests, both for new and existing features!

Credit
------
Our implementation was inspired by analogues at 
    https://github.com/scikit-learn/scikit-learn
And also by 
    https://github.com/UriaMorP/mprod_package

Future
------
New experimental methods may be added to tred. 

Users are highly encouraged to visit the GitHub site, and open issues. We are 
very interested in optimizing the efficiency of the existing implementations, or 
providing support for more general Python array frameworks - if needs arise.