Metadata-Version: 2.4
Name: petsc_ts
Version: 1.1
Summary: Pattern-based Embedding for Time Series Classification
Author-email: Len Feremans <len.feremans@gmail.com>
Maintainer-email: "Stijn J. Rotman" <s.j.rotman@uvt.nl>
License: MIT
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: aeon
Requires-Dist: sax-ts
Provides-Extra: test
Requires-Dist: pytest; extra == "test"
Provides-Extra: examples
Requires-Dist: matplotlib; extra == "examples"
Dynamic: license-file

# PETSC #

Pattern-based Embedding for Time Series Classification (PETSC).

This repository contains a Python/C++ implementation of PETSC, that is compatible with [`aeon`](https://aeon-toolkit.org). Two variants of PETSC are supported as well: MR-PETSC and PETSC-SOFT.

# Installation
The easiest way to install `petsc-ts` is via pip:
```
pip install petsc-ts
```

If you want to run the tests locally (currently only an `aeon` test to check if the estimator complies with the required format), install the optional dependency:
```
pip install "petsc-ts[test]"
```

You can then import the `PetsClassifier` class as follows and use it as any other `scikit-learn`-compatible estimator:
```Python
from petsc_ts.classifier import PetsClassifier

clsf = PetsClassifier()
clsf.fit(X_train, y_train)
clsf.predict(X_test)
clsf.predict_proba(X_test)
```
