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seglearn

Python module for machine learning time series:

https://github.com/dmbee/seglearn

Science Score: 20.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ✓
    Committers with academic emails
    2 of 13 committers (15.4%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (16.1%) to scientific vocabulary

Keywords

data-science machine-learning python time-series
Last synced: 6 months ago · JSON representation

Repository

Python module for machine learning time series:

Basic Info
Statistics
  • Stars: 567
  • Watchers: 27
  • Forks: 64
  • Open Issues: 5
  • Releases: 0
Topics
data-science machine-learning python time-series
Created almost 8 years ago · Last pushed over 3 years ago
Metadata Files
Readme License

README.rst 

.. -*- mode: rst -*-

.. _scikit-learn: http://scikit-learn.org/stable/

.. _scikit-learn-contrib: https://github.com/scikit-learn-contrib

|Travis|_ |Pypi|_ |PythonVersion|_ |Coveralls|_ |Downloads|_

.. |Travis| image:: https://travis-ci.com/dmbee/seglearn.svg?branch=master
.. _Travis: https://app.travis-ci.com/github/dmbee/seglearn

.. |Pypi| image:: https://badge.fury.io/py/seglearn.svg
.. _Pypi: https://badge.fury.io/py/seglearn

.. |PythonVersion| image:: https://img.shields.io/pypi/pyversions/seglearn.svg
.. _PythonVersion: https://img.shields.io/pypi/pyversions/seglearn.svg

.. |Coveralls| image:: https://coveralls.io/repos/github/dmbee/seglearn/badge.svg?branch=master&&service=github
.. _Coveralls: https://coveralls.io/github/dmbee/seglearn?branch=master&service=github

.. |Downloads| image:: https://pepy.tech/badge/seglearn
.. _Downloads: https://pepy.tech/project/seglearn

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_.

.. _documentation: https://dmbee.github.io/seglearn/

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}
  }

Owner

  • Name: David Burns
  • Login: dmbee
  • Kind: user
  • Company: University of Toronto

Orthopaedic Surgery Resident PhD Candidate, Biomedical Engineering Sunnybrook Research Institute University of Toronto, Canada

GitHub Events

Total
  • Watch event: 10
  • Fork event: 1
Last Year
  • Watch event: 10
  • Fork event: 1

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 243
  • Total Committers: 13
  • Avg Commits per committer: 18.692
  • Development Distribution Score (DDS): 0.198
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
david d****s@g****m 195
Matthias Gazzari m****l@q****u 28
Vighnesh Birodkar v****r@n****u 9
Tom Dupré la Tour T****T 2
ichkoar i****r@g****m 1
InferiorBrain p****r@m****a 1
Tyler Marrs t****s@l****m 1
carrowsm a****n@g****m 1
kjacks21 k****4@g****m 1
Boyuan Deng b****g@g****m 1
Vighnesh Birodkar v****r@g****m 1
arokem a****m@g****m 1
Andreas Mueller t****t@g****m 1
Committer Domains (Top 20 + Academic)
leepfrog.com: 1 mail.utoronto.ca: 1 nyu.edu: 1 qtux.eu: 1

Issues and Pull Requests

Last synced: over 2 years ago

All Time
  • Total issues: 29
  • Total pull requests: 31
  • Average time to close issues: about 1 month
  • Average time to close pull requests: 15 days
  • Total issue authors: 19
  • Total pull request authors: 7
  • Average comments per issue: 2.31
  • Average comments per pull request: 3.0
  • Merged pull requests: 22
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • dmbee (9)
  • emial637 (2)
  • orko19 (2)
  • ChangWeiTan (1)
  • Betaglutamate (1)
  • quancore (1)
  • MyRespect (1)
  • rosgori (1)
  • Sandy4321 (1)
  • fairread (1)
  • adalseno (1)
  • ninfueng (1)
  • jmrichardson (1)
  • goodfoxs (1)
  • SergioEanX (1)
Pull Request Authors
  • qtux (21)
  • InferiorBrain (4)
  • Drishtant-Shri (2)
  • carrowsm (1)
  • MyRespect (1)
  • tylerwmarrs (1)
  • chkoar (1)
Top Labels
Issue Labels
enhancement (4) help wanted (2)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 1,247 last-month
  • Total dependent packages: 1
  • Total dependent repositories: 7
  • Total versions: 24
  • Total maintainers: 1
pypi.org: seglearn

A template for scikit-learn compatible packages.

  • Versions: 24
  • Dependent Packages: 1
  • Dependent Repositories: 7
  • Downloads: 1,247 Last month
Rankings
Stargazers count: 2.7%
Dependent packages count: 4.7%
Average: 5.1%
Forks count: 5.4%
Dependent repos count: 5.6%
Downloads: 7.2%
Maintainers (1)
dmbee
Last synced: 6 months ago

Dependencies

environment.yml conda
  • numpy
  • scikit-learn
  • scipy
requirements.txt pypi
  • numpy *
  • scikit-learn >=0.21.3
  • scipy *