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https://github.com/patrick-kidger/signatory

Differentiable computations of the signature and logsignature transforms, on both CPU and GPU. (ICLR 2021)
https://github.com/patrick-kidger/signatory

deep-learning deep-neural-networks logsignature logsignatures machine-learning pytorch rough-paths signature signatures

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Differentiable computations of the signature and logsignature transforms, on both CPU and GPU. (ICLR 2021)

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README 

        

|Signatory|

###########



.. |Signatory| image:: https://raw.githubusercontent.com/patrick-kidger/signatory/master/docs/_static/signatory.png



Differentiable computations of the signature and logsignature transforms, on both CPU and GPU.



What is the signature transform?

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The *signature transform* is roughly analogous to the Fourier transform, in that it operates on a stream of data (often a time series). Whilst the Fourier transform extracts information about frequency, the signature transform extracts information about *order* and *area*. Furthermore (and unlike the Fourier transform), order and area represent all possible nonlinear effects: the signature transform is a *universal nonlinearity*, meaning that every continuous function of the input stream may be approximated arbitrary well by a *linear* function of its signature. If you're doing machine learning then you probably understand why this is such a desirable property!



Besides this, the signature transform has many other nice properties -- robustness to missing or irregularly sampled data; optional translation invariance; optional sampling invariance. Furthermore it can be used to encode certain physical quantities, and may be used for data compression.



Check out `this `__ for a primer on the use of the signature transform in machine learning, just as a feature transformation, and `this `__ for a more in-depth look at integrating the signature transform into neural networks.



Installation

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.. code-block:: bash



    pip install signatory==. --no-cache-dir --force-reinstall



where ```` is the version of Signatory you would like to download (the most recent version is 1.2.7) and ```` is the version of PyTorch you are using.



Available for Python 3.7--3.9 on Linux and Windows. Requires `PyTorch `__ 1.8.0--1.11.0.



(If you need it, then previous versions of Signatory included support for older versions of Python, PyTorch, and MacOS, see `here `__.)



After installation, just ``import signatory`` inside Python.



Take care **not** to run ``pip install signatory``, as this will likely download the wrong version.



Example:

--------



For example, if you are using PyTorch 1.11.0 and want Signatory 1.2.7, then you should run:



.. code-block:: bash



    pip install signatory==1.2.7.1.11.0 --no-cache-dir --force-reinstall

        

Why you need to specify all of this:

------------------------------------



Yes, this looks a bit odd. This is needed to work around `limitations of PyTorch `__ and `pip `__.



The ``--no-cache-dir --force-reinstall`` flags are because ``pip`` doesn't expect to need to care about versions quite as much as this, so it will sometimes erroneously use inappropriate caches if not told otherwise.



Installation from source is also possible; please consult the `documentation `__. This also includes information on how to run the tests and benchmarks.



If you have any problems with installation then check the `FAQ `__. If that doesn't help then feel free to `open an issue `__.



Documentation

#############

The documentation is available `here `__.



Example

#######

Usage is straightforward. As a simple example,



.. code-block:: python



    import signatory

    import torch

    batch, stream, channels = 1, 10, 2

    depth = 4

    path = torch.rand(batch, stream, channels)

    signature = signatory.signature(path, depth)

    # signature is a PyTorch tensor



For further examples, see the `documentation `__.



Citation

########

If you found this library useful in your research, please consider citing `the paper `__.



.. code-block:: bibtex



    @inproceedings{kidger2021signatory,

      title={{S}ignatory: differentiable computations of the signature and logsignature transforms, on both {CPU} and {GPU}},

      author={Kidger, Patrick and Lyons, Terry},

      booktitle={International Conference on Learning Representations},

      year={2021},

      note={\url{https://github.com/patrick-kidger/signatory}}

    }