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New 0.8.2 features:

We're excited to release version 0.8.2 of TensorLy. As always, a huge thank you to the core team and all the contributors!

This version adds many improvements to TensorLy 0.8, including:

Tensor-Ring via ALS

We now provide an ALS-based method for tensor ring decomposition, as well as a randomized sampling-based version, thanks to @OsmanMalik in https://github.com/tensorly/tensorly/pull/501 and https://github.com/tensorly/tensorly/pull/511

Big improvements to PARAFAC2

MXNet deprecated

We are now deprecating MXNet, and both MXNet and TensorFlow backend will be remove in the near future.

Better interfaces for SVD

We provide a neat, clean and simple to use interface to use all the major variants of SVD, and it keeps improving!

Improvements to CP

Other changes and other quality of life improvements

New Contributors

Full Changelog: https://github.com/tensorly/tensorly/compare/0.8.1...0.8.2

New features in 0.8.0

Transparent support for einsum

There are two main ways to implement tensor algebraic methods: 1. Perhaps the most common, using existing matrix based algebraic methods, which typically involves unfolding the tensor (reshaping the tensor into a matrix and permuting its dimensions) 2. Directly leverage tensor contraction, e.g. through an ensue interface. This implies that the einsum actually performs tensor contraction.

We improved the tenalg backend, you can transparently dispatch all tensor algebraic operations to the backend's einsum:

:::python
import tensorly as tl
# Tensor algebra
from tensorly import tenalg

# Dispatch all operations to einsum
tenalg.set_backend('einsum')

Now all tenalg functions will call einsum under the hood!

Opt-Einsum support

In addition, for each einsum call, you can now use opt-einsum to compute a (near) optimal contraction path and cache it with just one call!

:::python
# New opt-einsum plugin
from tensorly.plugins import use_opt_einsum

# Transparently compute and cache contraction path using opt-einsum
use_opt_einsum('optimal')

Switch back to the original backend's einsum:

:::python
# New opt-einsum plugin
from tensorly.plugins import use_default_einsum

use_default_einsum()

Efficient contraction on GPU with cuQuantum

If you want to accelerate your computation, you probably want to use the GPU. TensorLy has been supporting GPU transparently for a while, through its MXNet, CuPy, TensorFlow, PyTorch and more recently, JAX backends.

Now you can also get efficient tensor contractions on GPU using NVIDIA's cuQuantum library!

:::python
Now any function to the `tenalg` module
# New opt-einsum plugin
from tensorly.plugins import use_cuquantum

# Transparently compute and cache contraction path using opt-einsum
use_cuquantum('optimal')

# Create a new tensor on GPU
tensor = tl.randn((32, 256, 256, 3), device='cuda')

# Decompose it with CP, keep 5% of the parameters
parafac(tensor, rank=0.05, init='random', n_iter_max=10)

Similarity measure

We now provide CorrIndex, a correlation invariant index * Paper

CP-partial least square regression

This release brings a new multi-linear partial least squares regression, as first introduce by Rasmus Bro, exposed in a convenient scikit-learn-like class, CP_PLSR

Tensor-Train via orthogonal iteration

We have a new tensor decomposition tensor_train_OI class for tensor-train decomposition via orthogonal iteration.

Unified SVD interface

We now have a unified interface for Singular Value Decomposition svd_interface.

It has support for resolving sign indeterminacy, returning a non-negative output, missing values (masked input), and various computation methods, all in one, neat interface!

New datasets

TensorLy now includes real-world datasets well-suited for tensor analysis, that you can now directly load/download in a ready to use form!

COVID-19 Serology Dataset

Systems serology is a new technology that examines the antibodies from a patient’s serum, aiming to comprehensively profile the interactions between the antibodies and Fc receptors alongside other types of immunological and demographic data. Here, we will apply CP decomposition to a COVID-19 system serology dataset. In this dataset, serum antibodies of 438 samples collected from COVID-19 patients were systematically profiled by their binding behavior to SARS-CoV-2 (the virus that causes COVID-19) antigens and Fc receptors activities. The data is formatted in a three-mode tensor of samples, antigens, and receptors Samples are labeled by the status of the patients.

IL2

IL-2 signals through the Jak/STAT pathway and transmits a signal into immune cells by phosphorylating STAT5 (pSTAT5). When phosphorylated, STAT5 will cause various immune cell types to proliferate, and depending on whether regulatory (regulatory T cells, or Tregs) or effector cells (helper T cells, natural killer cells, and cytotoxic T cells, or Thelpers, NKs, and CD8+ cells) respond, IL-2 signaling can result in immunosuppression or immunostimulation respectively. Thus, when designing a drug meant to repress the immune system, potentially for the treatment of autoimmune diseases, IL-2 which primarily enacts a response in Tregs is desirable. Conversely, when designing a drug that is meant to stimulate the immune system, potentially for the treatment of cancer, IL-2 which primarily enacts a response in effector cells is desirable. In order to achieve either signaling bias, IL-2 variants with altered affinity for its various receptors (IL2Rα or IL2Rβ) have been designed. Furthermore IL-2 variants with multiple binding domains have been designed as multivalent IL-2 may act as a more effective therapeutic.

The data contains the responses of 8 different cell types to 13 different IL-2 mutants, at 4 different timepoints, at 12 standardized IL-2 concentrations. It is formatted as a 4th order tensor of shape (13 x 4 x 12 x 8), with dimensions representing IL-2 mutant, stimulation time, dose, and cell type respectively.

Kinetic

A Kinetic fluorescence dataset, well suited for Parafac and multi-way partial least squares regression (N-PLS). The data is represented as a four-way data set with the modes: Concentration, excitation wavelength, emission wavelength and time.

Indian Pines

Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor data. It consists of 145 times 145 pixels and 220 spectral reflectance bands in the wavelength range 0.4–2.5 10^(-6) meters.

Black linting

We now automatically check for code formatting and the CI tests the code style against the Black styleguides.

List of merged pull requests in this release

In addition to these big features, this release also comes with a whole lot of improvements, better documentation and bug fixes!

Non-exhaustive list of changes:

Credit

This release is only possible thanks to a lot of voluntary work by the whole TensorLy team that work hard to maintain and improve the library! Thanks in particular to the core devs * @aarmey * @caglayantuna * @cohenjer * @JeanKossaifi * @MarieRoald * @yngvem

New Contributors

Big thanks to all the new contributors and welcome to the TensorLy community!

Full Changelog: https://github.com/tensorly/tensorly/compare/0.7.0...0.8.0

Source: README.md, updated 2024-04-11