Iterated-sums signature in Rust
Project description
Iterated-sums signature in Rust
This is an implementation of the one-dimensional iterated-sums signature in Rust, with Python 3 bindings.
Installation
From PyPI
Normally, this package can be installed by running
pip3 install o3iss
From source
Curretly we provide wheels for Linux, macOS and 64-bit Windows. If your system is not included in any of these targets, you will have to compile the package yourself. This will require you to have a Rust compiler, which can be obtained from [https://www.rust-lang.org/tools/install].
Start by cloning this repository by running
git clone https://github.com/ntapiam/o3iss.git
Then, enter the directory and run
python3 setup.py install
This should compile and install the package on your system.
Usage
This implementation offers only a single function: iss.compute
with signature (np.ndarray, int) -> np.ndarray
where both the input and ouput arrays are one-dimensional.
In v0.1.2
, we introduced partial support for the sklearn
framework.
Example (assuming that sktime
is present):
from iss import IssClassifier
from sktime.datasets import load_gunpoint
from sktime.utils.data_processing import from_nested_to_2d_array
Xtrain, ytrain = load_gunpoint(split="train", return_X_y=True)
Xtest, ytest = load_gunpoint(split="test", return_X_y=True)
Xtrain, Xtest = (
from_nested_to_2d_array(Xtrain).to_numpy(),
from_nested_to_2d_array(Xtest).to_numpy(),
)
ytrain, ytest = ytrain.astype(int), ytest.astype(int)
clf = IssClassifier(level=3, n_jobs=7)
clf.fit(Xtrain, ytrain)
print(clf.score(Xtest, ytest))
>> 0.9133333333333333
TO DO
- Multidimensional signature
- Proper
sklearn
integration - Produce a proper Python 3 package
Project details
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