signatory 1.1.3

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

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

What are signatures?

If you’re reading this then it’s probably because you already know what the signature transform is, and are looking to use it in your project. But in case you’ve stumbled across this and are curious what this ‘signature’ thing is…

The signature transform is a transformation that takes in a stream of data (often a time series), and returns a collection of statistics about that stream of data, called the signature. This collection of statistics determines the path essentially uniquely. Importantly, the signature is rich enough that every continuous function of the input stream may be approximated arbitrarily well by a linear function of its signature; the signature transform is what we call a universal nonlinearity. If you’re doing machine learning then you probably understand why this is such a desirable property!

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

Available for Python 2.7, Python 3.5, Python 3.6, Python 3.7 and Linux, Mac, Windows.

Requires PyTorch. Tested with PyTorch version 1.2.0, but should work with all recent versions.

Install via pip install signatory. Then just import signatory inside Python.

Installation from source is also possible; please consult the documentation.

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.

Citation

If you found this library useful in your research, please consider citing

@misc{signatory,
    title={{Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU}},
    author={Kidger, Patrick},
    note={https://github.com/patrick-kidger/signatory},
    year={2019}
}

Acknowledgements

The Python bindings for the C++ code were written with the aid of pybind11.

For NumPy-based CPU-only signature calculations, you may also be interested in the iisignature package. The notes accompanying the iisignature project greatly helped with the implementation of Signatory.