Computing Log Signatures

Computing Log Signatures#

For \(x \in T(\mathbb{R}^d)\), the logarithm in tensor space is defined by

\[\log(1 + x) = \sum_{n \geq 1} \frac{(-1)^{n-1} x^n}{n}.\]

Applying this logarithm map to the signature yields the log signature. A useful property of the log signature is its ability to be compressed into a smaller tensor without losing information, by considering its values at Lyndon words. pysiglib implements three methods for log signature computation, controlled by the method flag, described in detail below.

Preparing for Log Signature Computations#

Before computing the log signature, pysiglib requires a call to pysiglib.prepare_log_sig, which will pre-compute and cache certain objects which are required for the computation. This function should be run only once before the computation, for each required (dimension, degree, method) combination. This call is not thread safe.

import torch
import pysiglib

# We know in advance that we will need log signatures
# for dimensions 5 and 10 with degree 3 and method 2.
pysiglib.prepare_log_sig(5, 3, method=2)
pysiglib.prepare_log_sig(10, 3, method=2)

for i in range(10):
    X = torch.rand((200, 5))
    Y = torch.rand((100, 10))

    X_ls = pysiglib.log_sig(X, 3, method=2)
    Y_ls = pysiglib.log_sig(Y, 3, method=2)

The ordering of the methods is chosen such that higher methods require strictly more preparation than lower methods. As such, preparing for method=2 is also sufficient to run method=1. We note also that method=0 does not require a call to pysiglib.prepare_log_sig.

import torch
import pysiglib

X = torch.rand((100, 5))

pysiglib.log_sig(X, 3, method=0) # No error: method=0 does not require preparation

pysiglib.prepare_log_sig(5, 3, method=2)
pysiglib.log_sig(X, 3, method=1) # No error: prepare already called with a higher method

Methods#

We give a brief overview of the methods below. For details concerning the Lyndon basis, we refer the user to the documentation for the iisignature and signatory packages here and here, as well as their corresponding papers here and here.

method = 0#

This option corresponds to the full uncompressed log signature, obtained by first computing the signature and then applying the tensor logarithm. The output is the same length as a signature.

This method corresponds to methods="x" in the iisignature package and mode="expand" in the signatory package.

method = 1#

This option computes the log signature as in the method=0 case, and then extracts those coefficients which are indexed by Lyndon words. Whilst the result is not strictly the log signature, it is equivalent to the log signature up to a linear transformation. This makes it a faster alternative to method=2, suitable for machine learning applications where the missing linear transformation can be learnt implicitly.

This method corresponds to mode="words" in the signatory package.

method = 2#

This option computes the log signature as in the method=2 case, but projecting the result to the Lyndon basis. The result is the compressed log signature. This method is required for use cases outside of machine learning, where the Lyndon basis projection cannot be omitted as in the method=1 case.

This method corresponds to methods="s" in the iisignature package and mode="brackets" in the signatory package.


Citation#

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

@article{shmelev2025pysiglib,
  title={pySigLib-Fast Signature-Based Computations on CPU and GPU},
  author={Shmelev, Daniil and Salvi, Cristopher},
  journal={arXiv preprint arXiv:2509.10613},
  year={2025}
}
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