sig-light 0.2.4

Pure-Python path signature and log signature computation

sig-light

Pure-Python path signature and log signature computation, mirroring the iisignature API.

sig-light computes signatures and log signatures of multidimensional piecewise-linear paths using Chen's identity and truncated tensor algebra operations. No C extensions or compilation required — just numpy.

Installation

pip install sig-light

Or with uv:

uv add sig-light

Quick Start

import numpy as np
import sig_light

# Define a 2D path
path = np.array([[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0]])

# Compute signature at depth 3
signature = sig_light.sig(path, 3)

# Compute log signature
s = sig_light.prepare(2, 3)
log_signature = sig_light.logsig(path, s)

# Backpropagation
deriv = np.ones_like(signature)
grad = sig_light.sigbackprop(deriv, path, 3)  # gradient w.r.t. path

# Batching: process multiple paths at once
paths = np.random.randn(10, 50, 2)  # 10 paths, 50 points, 2D
sigs = sig_light.sig(paths, 3)  # shape (10, siglength(2, 3))

API Reference

Signature

sig(path, m, format=0)

Compute the signature of a path truncated at depth m.

• path: numpy array of shape (..., n, d). Extra leading dims are batched.
• m: truncation depth (positive integer).
• format: output format.
  • 0: flat array of shape (..., siglength(d, m)).
  • 1: list of m arrays, one per level.
  • 2: cumulative prefix signatures, shape (..., n-1, siglength(d, m)).
• Returns: the path signature, excluding the level-0 term (always 1).

siglength(d, m)

Length of the signature output: d + d^2 + ... + d^m.

sigcombine(sig1, sig2, d, m)

Combine two signatures via Chen's identity. Supports batching.

Backpropagation

sigbackprop(deriv, path, m)

Compute gradient of a loss w.r.t. the path, given gradient w.r.t. the signature.

• deriv: shape (..., siglength(d, m)).
• path: shape (..., n, d).
• Returns: shape (..., n, d).

sigjacobian(path, m)

Full Jacobian matrix of sig() w.r.t. the path.

• Returns: shape (n, d, siglength(d, m)).

logsigbackprop(deriv, path, s)

Compute gradient of a loss w.r.t. the path, given gradient w.r.t. the log signature.

• deriv: shape (..., logsiglength(d, m)).
• path: shape (..., n, d).
• s: prepared data from prepare(d, m).
• Returns: shape (..., n, d).

Log Signature

prepare(d, m)

Precompute data for log signature computation.

logsig(path, s)

Compute the log signature in the Lyndon basis. Supports batching.

logsig_expanded(path, s)

Compute the log signature in the full tensor expansion. Supports batching.

logsiglength(d, m)

Length of the log signature output (Witt's formula).

basis(s)

Get the Lyndon bracket labels for the log signature basis elements.

Transforms

sigjoin(sig, segment, d, m, fixedLast=nan)

Extend a signature by appending a linear segment. Equivalent to sigcombine(sig, sig_of_segment(segment), d, m).

sigjoinbackprop(deriv, sig, segment, d, m, fixedLast=nan)

Gradient through sigjoin. Returns (dsig, dsegment) or (dsig, dsegment, dfixedLast).

sigscale(sig, scales, d, m)

Rescale a signature as if each path dimension were multiplied by a factor. At level k, the multi-index (i1,...,ik) component is multiplied by scales[i1] * ... * scales[ik].

sigscalebackprop(deriv, sig, scales, d, m)

Gradient through sigscale. Returns (dsig, dscales).

Rotation Invariants (2D paths)

rotinv2dprepare(m, type="a")

Precompute rotation-invariant features for 2D paths.

• m: depth (should be even).
• type: "a" for all invariants.

rotinv2d(path, s)

Compute rotation-invariant features of a 2D path signature.

rotinv2dlength(s) / rotinv2dcoeffs(s)

Get the number of invariants and their coefficient matrices.

Utility

version()

Return the sig-light version string.

Algorithm

sig-light uses the standard approach for computing signatures of piecewise-linear paths:

1. Segment signature: for each linear segment with displacement h, the signature is the truncated exponential exp(h) = 1 + h + h^2/2! + h^3/3! + ... in the tensor algebra.

2. Chen's identity: the signature of a concatenated path equals the tensor product of the individual segment signatures: S(path) = S(seg_1) * S(seg_2) * ... * S(seg_n).

3. Log signature: computed by taking the tensor logarithm log(S) = (S-1) - (S-1)^2/2 + (S-1)^3/3 - ... and projecting onto the Lyndon word basis.

4. Backpropagation: reverse-mode differentiation through the Chen's identity chain using adjoint operations on the tensor algebra.

This approach is exact for piecewise-linear paths (no numerical approximation from ODE solvers).

Comparison with iisignature

Feature	sig-light	iisignature
Language	Pure Python + numpy	C++ with Python bindings
Installation	pip install (no compilation)	Requires C++ compiler
Performance	Slower (~7-25x for sig)	Faster
API	Full parity	Full
Backpropagation	Yes	Yes
Batching	Yes	Yes
Rotation invariants	Yes (type "a")	Yes (types a/s/q/k)

sig-light implements the full iisignature API with an identical interface. It trades performance for portability and simplicity.

Development

Requires just for task running.

# Clone and install
git clone https://github.com/yousif-toama/sig-light.git
cd sig-light
just sync

# Run tests
just test

# Code quality (lint + format check + type check)
just check

# Run tests with coverage
just test-cov

# Run benchmark
just bench

# Install iisignature for cross-validation testing (requires C++ compiler)
uv pip install setuptools
uv pip install iisignature --no-build-isolation
uv run pytest tests/test_api_compat.py

License

MIT