Package: tensorly-musco
TensorLyTensorLy is a Python library that aims at making tensor learning simple and accessible. It allows to easily perform tensor decomposition, tensor learning and tensor algebra. Its backend system allows to seamlessly perform computation with NumPy, MXNet, PyTorch, TensorFlow or CuPy, and run methods at scale on CPU or GPU.Website:http://tensorly.orgSource-code:https://github.com/tensorly/tensorlyJupyter Notebooks:https://github.com/JeanKossaifi/tensorly-notebooksInstalling TensorLyThe only pre-requisite is to havePython 3installed. The easiest way is via theAnaconda distribution.With pip(recommended)With condapip install -U tensorlyconda install -c tensorly tensorlyDevelopment (from git)# clone the repository
git clone https://github.com/tensorly/tensorly
cd tensorly
# Install in editable mode with `-e` or, equivalently, `--editable`
pip install -e .Note:TensorLy depends on NumPy by default. If you want to use the MXNet or PyTorch backends, you will need to install these packages separately.For detailed instruction, please see thedocumentation.Running the testsTesting and documentation are an essential part of this package and all functions come with uni-tests and documentation.The tests are ran using thepytestpackage (though you can also usenose).
First installpytest:pip install pytestThen to run the test, simply run, in the terminal:pytest -v tensorlyAlternatively, you can specify for which backend you wish to run the tests:TENSORLY_BACKEND='numpy' pytest -v tensorlyQuickstartCreate a small third order tensor of size 3 x 4 x 2 and perform simple operations on it:importtensorlyastlimportnumpyasnptensor=tl.tensor(np.arange(24).reshape((3,4,2)),dtype=tl.float64)unfolded=tl.unfold(tensor,mode=0)tl.fold(unfolded,mode=0,shape=tensor.shape)Applying tensor decomposition is easy:fromtensorly.decompositionimporttucker# Apply Tucker decompositiontucker_tensor=tucker(tensor,rank=[2,2,2])# Reconstruct the full tensor from the decomposed formtl.tucker_to_tensor(tucker_tensor)You can change the backend to perform computation with a different framework. Note that using MXNet, PyTorch, TensorFlow or CuPy requires to have installed them first. For instance, after setting the backend to PyTorch, all the computation is done by PyTorch, and tensors can be created on GPU:tl.set_backend('pytorch')# Or 'mxnet', 'numpy', 'tensorflow' or 'cupy'tensor=tl.tensor(np.arange(24).reshape((3,4,2)),device='cuda:0')type(tensor)# torch.TensorFor more information on getting started, checkout theuser-guideand for a detailed reference of the functions and their documentation, refer to
theAPIIf you see a bug, open anissue, or better yet, apull-request!CitingIf you use TensorLy in an academic paper, please cite[1]:@article{tensorly,
  author  = {Jean Kossaifi and Yannis Panagakis and Anima Anandkumar and Maja Pantic},
  title   = {TensorLy: Tensor Learning in Python},
  journal = {Journal of Machine Learning Research},
  year    = {2019},
  volume  = {20},
  number  = {26},
  pages   = {1-6},
  url     = {http://jmlr.org/papers/v20/18-277.html}
}[1]Jean Kossaifi, Yannis Panagakis, Anima Anandkumar and Maja Pantic,TensorLy: Tensor Learning in Python,Journal of Machine Learning Research (JMLR), 2019, volume 20, number 26.