Skip to main content

A template for scikit-learn compatible packages.

Project description

Travis Pypi PythonVersion Coveralls Downloads

seglearn

Seglearn is a python package for machine learning time series or sequences. It provides an integrated pipeline for segmentation, feature extraction, feature processing, and final estimator. Seglearn provides a flexible approach to multivariate time series and related contextual (meta) data for classification, regression, and forecasting problems. Support and examples are provided for learning time series with classical machine learning and deep learning models. It is compatible with scikit-learn.

Documentation

Installation documentation, API documentation, and examples can be found on the documentation.

Dependencies

seglearn is tested to work under Python 3.5, 3.6, and 3.8. The dependency requirements are:

  • scipy(>=0.17.0)

  • numpy(>=1.11.0)

  • scikit-learn(>=0.21.3)

seglearn is now also compatible with sklearn 1.0+

To run the examples, you need:

  • matplotlib(>=2.0.0)

  • keras (>=2.1.4) for the neural network examples

  • pandas

In order to run the test cases, you need:

  • pytest

The neural network examples were tested on keras using the tensorflow-gpu backend, which is recommended.

Installation

seglearn-learn is currently available on the PyPi’s repository and you can install it via pip:

pip install -U seglearn

or if you use python3:

pip3 install -U seglearn

If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from GitHub and install all dependencies:

git clone https://github.com/dmbee/seglearn.git
cd seglearn
pip install .

Or install using pip and GitHub:

pip install -U git+https://github.com/dmbee/seglearn.git

Testing

After installation, you can use pytest to run the test suite from seglearn’s root directory:

python -m pytest

Change Log

Version history can be viewed in the Change Log.

Development

The development of this scikit-learn-contrib is in line with the one of the scikit-learn community. Therefore, you can refer to their Development Guide.

Please submit new pull requests on the dev branch with unit tests and an example to demonstrate any new functionality / api changes.

Citing seglearn

If you use seglearn in a scientific publication, we would appreciate citations to the following paper:

@article{arXiv:1803.08118,
author  = {David Burns, Cari Whyne},
title   = {Seglearn: A Python Package for Learning Sequences and Time Series},
journal = {arXiv},
year    = {2018},
url     = {https://arxiv.org/abs/1803.08118}
}

If you use the seglearn test data in a scientific publication, we would appreciate citations to the following paper:

@article{arXiv:1802.01489,
author  = {David Burns, Nathan Leung, Michael Hardisty, Cari Whyne, Patrick Henry, Stewart McLachlin},
title   = {Shoulder Physiotherapy Exercise Recognition: Machine Learning the Inertial Signals from a Smartwatch},
journal = {arXiv},
year    = {2018},
url     = {https://arxiv.org/abs/1802.01489}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

seglearn-1.2.5.tar.gz (11.5 MB view details)

Uploaded Source

Built Distribution

seglearn-1.2.5-py3-none-any.whl (11.3 MB view details)

Uploaded Python 3

File details

Details for the file seglearn-1.2.5.tar.gz.

File metadata

  • Download URL: seglearn-1.2.5.tar.gz
  • Upload date:
  • Size: 11.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.1 requests-toolbelt/0.9.1 tqdm/4.42.1 CPython/3.6.9

File hashes

Hashes for seglearn-1.2.5.tar.gz
Algorithm Hash digest
SHA256 90c45b5ced6bac355016c768cb405835815c51ef794c24b4f727d0c7855266c8
MD5 fcd1db196cf4e73db799cd3a46c0ea27
BLAKE2b-256 084bf287b7bcbdca0e8b1bce37fed9277896207c69fed97c7495c6a557a1ddb9

See more details on using hashes here.

File details

Details for the file seglearn-1.2.5-py3-none-any.whl.

File metadata

  • Download URL: seglearn-1.2.5-py3-none-any.whl
  • Upload date:
  • Size: 11.3 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.1 requests-toolbelt/0.9.1 tqdm/4.42.1 CPython/3.6.9

File hashes

Hashes for seglearn-1.2.5-py3-none-any.whl
Algorithm Hash digest
SHA256 ec6b952c85c9627050102854b372d85951563d3b990df677d7c55547e87413dd
MD5 c24d5b5f895a732963ae93fb9241f659
BLAKE2b-256 8e1d786c3ff9c0452d0a6461a97ae248478319473777bf074ad1738d4749f2ac

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page