PETSC
Pattern-based Embedding for Time Series Classification (PETSC).
This repository contains a Python/C++ implementation of PETSC, that is compatible with aeon. 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:
from petsc_ts.classifier import PetsClassifier
clsf = PetsClassifier()
clsf.fit(X_train, y_train)
clsf.predict(X_test)
clsf.predict_proba(X_test)
Metadata
Release files for petsc-ts 1.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| petsc_ts-1.5.tar.gz | 10.1 kB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| petsc_ts-1.5-cp314-cp314-win_amd64.whl | CPython 3.14 | CPython 3.14 | Windows x86-64 | Details |
| petsc_ts-1.5-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.14 | CPython 3.14 | Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 | Details |
| petsc_ts-1.5-cp314-cp314-macosx_11_0_arm64.whl | CPython 3.14 | CPython 3.14 | macOS 11.0+ ARM64 | Details |
| petsc_ts-1.5-cp313-cp313-win_amd64.whl | CPython 3.13 | CPython 3.13 | Windows x86-64 | Details |
| petsc_ts-1.5-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.13 | CPython 3.13 | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| petsc_ts-1.5-cp313-cp313-macosx_11_0_arm64.whl | CPython 3.13 | CPython 3.13 | macOS 11.0+ ARM64 | Details |
| petsc_ts-1.5-cp312-cp312-win_amd64.whl | CPython 3.12 | CPython 3.12 | Windows x86-64 | Details |
| petsc_ts-1.5-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| petsc_ts-1.5-cp312-cp312-macosx_11_0_arm64.whl | CPython 3.12 | CPython 3.12 | macOS 11.0+ ARM64 | Details |
| petsc_ts-1.5-cp311-cp311-win_amd64.whl | CPython 3.11 | CPython 3.11 | Windows x86-64 | Details |
| petsc_ts-1.5-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.11 | CPython 3.11 | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| petsc_ts-1.5-cp311-cp311-macosx_11_0_arm64.whl | CPython 3.11 | CPython 3.11 | macOS 11.0+ ARM64 | Details |
| petsc_ts-1.5-cp310-cp310-win_amd64.whl | CPython 3.10 | CPython 3.10 | Windows x86-64 | Details |
| petsc_ts-1.5-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.10 | CPython 3.10 | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| petsc_ts-1.5-cp310-cp310-macosx_11_0_arm64.whl | CPython 3.10 | CPython 3.10 | macOS 11.0+ ARM64 | Details |
| petsc_ts-1.5-cp39-cp39-win_amd64.whl | CPython 3.9 | CPython 3.9 | Windows x86-64 | Details |
| petsc_ts-1.5-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.9 | CPython 3.9 | Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64 | Details |
| petsc_ts-1.5-cp39-cp39-macosx_11_0_arm64.whl | CPython 3.9 | CPython 3.9 | macOS 11.0+ ARM64 | Details |
Total release size: 2.5 MB
Release files / petsc_ts-1.5.tar.gz
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Transparency logRelease files / petsc_ts-1.5-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
| Download URL | petsc_ts-1.5-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl |
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| Size | 159.5 kB |
| Tags | CPython 3.9 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64 |
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SHA-256 checksum How to use checksums |
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Yes |
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twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / petsc_ts-1.5-cp39-cp39-macosx_11_0_arm64.whl
| Download URL | petsc_ts-1.5-cp39-cp39-macosx_11_0_arm64.whl |
|---|---|
| Size | 128.6 kB |
| Tags | CPython 3.9 macOS 11.0+ ARM64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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Provenance
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