# Copyright 2025 Daniil Shmelev
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =========================================================================
from typing import Union
import numpy as np
import torch
from .load_siglib import CPSIG
from .param_checks import check_type, check_non_neg, check_cpu
from .error_codes import err_msg
from .dtypes import CPSIG_SIGNATURE, CPSIG_BATCH_SIGNATURE
from .sig_length import sig_length
from .data_handlers import PathInputHandler, DoubleSigInputHandler, SigOutputHandler, DeviceToHost
######################################################
# Python wrappers
######################################################
[docs]
def sig_combine(
sig1 : Union[np.ndarray, torch.tensor],
sig2 : Union[np.ndarray, torch.tensor],
dimension : int,
degree : int,
n_jobs : int = 1
) -> Union[np.ndarray, torch.tensor]:
"""
Combines two truncated signatures of the same degree and dimension into one signature. In particular, let :math:`x_1, x_2`
be two paths such that the first point of :math:`x_2` is the last point of :math:`x_1`. Let :math:`S(x_1), S(x_2)`
be the truncated signatures of :math:`x_1, x_2` respectively. Then calling this function on :math:`S(x_1), S(x_2)` returns
the truncated signature of the concatenated path,
.. math::
S(x_1 * x_2) = S(x_1) \\otimes S(x_2),
where :math:`x_1 * x_2` is the concatenation of the two paths :math:`x_1, x_2`.
:param sig1: The first truncated signature
:type sig1: numpy.ndarray | torch.tensor
:param sig2: The second truncated signature. Must have the same degree and dimension as the first.
:type sig2: numpy.ndarray | torch.tensor
:param dimension: Dimension of the underlying space, :math:`d`.
:type dimension: int
:param degree: Truncation level of the signatures, :math:`N`
:type degree: int
:param n_jobs: Number of threads to run in parallel. If n_jobs = 1, the computation is run serially.
If set to -1, all available threads are used. For n_jobs below -1, (max_threads + 1 + n_jobs)
threads are used. For example if n_jobs = -2, all threads but one are used.
:type n_jobs: int
:return: Combined signature, :math:`S(x_1 * x_2)`
:rtype: numpy.ndarray | torch.tensor
.. note::
Parallelising the computation by setting ``n_jobs != 1`` can be beneficial when the
workload is large. However, if the workload is too small, it may be faster to set this
to ``1`` and run the computation serially, due to parallelisation overhead.
.. note::
Ideally, any array passed to ``pysiglib.sig_combine`` should be both contiguous and own its data.
If this is not the case, ``pysiglib.sig_combine`` will internally create a contiguous copy, which may be
inefficient.
Example usage::
import pysiglib
batch_size = 32
length = 100
dimension = 5
degree = 3
X1 = np.random.uniform(size=(batch_size, length, dimension))
X2 = np.random.uniform(size=(batch_size, length, dimension))
X_concat = np.concatenate((X1, X2), axis=1)
X2 = np.concatenate((X1[:, [-1], :], X2), axis=1) # Make sure first pt of X2 is last pt of X1
sig1 = pysiglib.signature(X1, degree)
sig2 = pysiglib.signature(X2, degree)
# The tensor product...
sig_mult = pysiglib.sig_combine(sig1, sig2, dimension, degree)
# ... is the same as the signature of the concatenated path:
sig = pysiglib.signature(X_concat, degree)
"""
check_type(dimension, "dimension", int)
check_type(degree, "degree", int)
check_non_neg(dimension, "dimension")
check_non_neg(degree, "degree")
check_type(n_jobs, "n_jobs", int)
if n_jobs == 0:
raise ValueError("n_jobs cannot be 0")
# If sig1 and sig2 on GPU, move to CPU
device_handler = DeviceToHost([sig1, sig2], ["sig1", "sig2"])
sig1, sig2 = device_handler.data
sig_len = sig_length(dimension, degree)
data = DoubleSigInputHandler(sig1, sig2, sig_len, "sig1", "sig2")
result = SigOutputHandler(data, sig_len)
if data.is_batch:
err_code = CPSIG.batch_sig_combine(
data.sig1_ptr,
data.sig2_ptr,
result.data_ptr,
data.batch_size,
dimension,
degree,
n_jobs
)
else:
err_code = CPSIG.sig_combine(
data.sig1_ptr,
data.sig2_ptr,
result.data_ptr,
dimension,
degree
)
if err_code:
raise Exception("Error in pysiglib.signature: " + err_msg(err_code))
res = result.data
if device_handler.device is not None:
res = res.to(device_handler.device)
return res
def signature_(data, result, degree, horner = True):
err_code = CPSIG_SIGNATURE[data.dtype](
data.data_ptr,
result.data_ptr,
data.data_dimension,
data.data_length,
degree,
data.time_aug,
data.lead_lag,
data.end_time,
horner
)
if err_code:
raise Exception("Error in pysiglib.signature: " + err_msg(err_code))
return result.data
def batch_signature_(data, result, degree, horner = True, n_jobs = 1):
err_code = CPSIG_BATCH_SIGNATURE[data.dtype](
data.data_ptr,
result.data_ptr,
data.batch_size,
data.data_dimension,
data.data_length,
degree,
data.time_aug,
data.lead_lag,
data.end_time,
horner,
n_jobs
)
if err_code:
raise Exception("Error in pysiglib.signature: " + err_msg(err_code))
return result.data
[docs]
def signature(
path : Union[np.ndarray, torch.tensor],
degree : int,
time_aug : bool = False,
lead_lag : bool = False,
end_time : float = 1.,
horner : bool = True,
n_jobs : int = 1
) -> Union[np.ndarray, torch.tensor]:
"""
Computes the truncated signature of single path or a batch of paths. For
a single path :math:`x`, the signature is given by
.. math::
S(x)_{[s,t]} := \\left( 1, S(x)^{(1)}_{[s,t]}, \\ldots, S(x)^{(N)}_{[s,t]}\\right) \\in T((\\mathbb{R}^d)),
.. math::
S(x)^{(k)}_{[s,t]} := \\int_{s < t_1 < \\cdots < t_k < t} dx_{t_1} \\otimes dx_{t_2} \\otimes \\cdots \\otimes dx_{t_k} \\in \\left(\\mathbb{R}^d\\right)^{\\otimes k}.
:param path: The underlying path or batch of paths, given as a `numpy.ndarray` or `torch.tensor`.
For a single path, this must be of shape ``(length, dimension)``. For a batch of paths, this must
be of shape ``(batch_size, length, dimension)``.
:type path: numpy.ndarray | torch.tensor
:param degree: The truncation level of the signature, :math:`N`.
:type degree: int
:param time_aug: If set to True, will compute the signature of the time-augmented path, :math:`\\hat{x}_t := (t, x_t)`,
defined as the original path with an extra channel set to time, :math:`t`. This channel spans :math:`[0, t_L]`,
where :math:`t_L` is given by the parameter ``end_time``.
:type time_aug: bool
:param lead_lag: If set to True, will compute the signature of the path after applying the lead-lag transformation.
:type lead_lag: bool
:param end_time: End time for time-augmentation, :math:`t_L`.
:type end_time: float
:param horner: If True, will use Horner's algorithm for polynomial multiplication.
:type horner: bool
:param n_jobs: Number of threads to run in parallel. If n_jobs = 1, the computation is run serially.
If set to -1, all available threads are used. For n_jobs below -1, (max_threads + 1 + n_jobs)
threads are used. For example if n_jobs = -2, all threads but one are used.
:type n_jobs: int
:return: Truncated signature, or a batch of truncated signatures.
:rtype: numpy.ndarray | torch.tensor
.. note::
Ideally, any array passed to ``pysiglib.signature`` should be both contiguous and own its data.
If this is not the case, ``pysiglib.signature`` will internally create a contiguous copy, which may be
inefficient.
"""
check_type(degree, "degree", int)
check_type(horner, "horner", bool)
# If path is on GPU, move to CPU
device_handler = DeviceToHost([path], ["path"])
path = device_handler.data[0]
data = PathInputHandler(path, time_aug, lead_lag, end_time, "path")
sig_len = sig_length(data.dimension, degree)
result = SigOutputHandler(data, sig_len)
if data.is_batch:
check_type(n_jobs, "n_jobs", int)
if n_jobs == 0:
raise ValueError("n_jobs cannot be 0")
res = batch_signature_(data, result, degree, horner, n_jobs)
else:
res = signature_(data, result, degree, horner)
if device_handler.device is not None:
res = res.to(device_handler.device)
return res
