{"id":13415356,"url":"https://github.com/dmbee/seglearn","last_synced_at":"2025-03-14T22:33:23.536Z","repository":{"id":30489188,"uuid":"123977530","full_name":"dmbee/seglearn","owner":"dmbee","description":"Python module for machine learning time series:","archived":false,"fork":false,"pushed_at":"2022-08-27T09:01:18.000Z","size":19553,"stargazers_count":567,"open_issues_count":5,"forks_count":64,"subscribers_count":27,"default_branch":"master","last_synced_at":"2024-08-01T22:29:58.200Z","etag":null,"topics":["data-science","machine-learning","python","time-series"],"latest_commit_sha":null,"homepage":"https://dmbee.github.io/seglearn/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dmbee.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-03-05T20:53:59.000Z","updated_at":"2024-07-05T17:53:22.000Z","dependencies_parsed_at":"2022-07-16T05:30:37.063Z","dependency_job_id":null,"html_url":"https://github.com/dmbee/seglearn","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmbee%2Fseglearn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmbee%2Fseglearn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmbee%2Fseglearn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmbee%2Fseglearn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dmbee","download_url":"https://codeload.github.com/dmbee/seglearn/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243658058,"owners_count":20326459,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","machine-learning","python","time-series"],"created_at":"2024-07-30T21:00:47.539Z","updated_at":"2025-03-14T22:33:23.527Z","avatar_url":"https://github.com/dmbee.png","language":"Python","readme":".. -*- mode: rst -*-\n\n.. _scikit-learn: http://scikit-learn.org/stable/\n\n.. _scikit-learn-contrib: https://github.com/scikit-learn-contrib\n\n|Travis|_ |Pypi|_ |PythonVersion|_ |Coveralls|_ |Downloads|_\n\n.. |Travis| image:: https://travis-ci.com/dmbee/seglearn.svg?branch=master\n.. _Travis: https://app.travis-ci.com/github/dmbee/seglearn\n\n.. |Pypi| image:: https://badge.fury.io/py/seglearn.svg\n.. _Pypi: https://badge.fury.io/py/seglearn\n\n.. |PythonVersion| image:: https://img.shields.io/pypi/pyversions/seglearn.svg\n.. _PythonVersion: https://img.shields.io/pypi/pyversions/seglearn.svg\n\n.. |Coveralls| image:: https://coveralls.io/repos/github/dmbee/seglearn/badge.svg?branch=master\u0026\u0026service=github\n.. _Coveralls: https://coveralls.io/github/dmbee/seglearn?branch=master\u0026service=github\n\n.. |Downloads| image:: https://pepy.tech/badge/seglearn\n.. _Downloads: https://pepy.tech/project/seglearn\n\nseglearn\n========\n\nSeglearn 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_.\n\nDocumentation\n-------------\n\nInstallation documentation, API documentation, and examples can be found on the\ndocumentation_.\n\n.. _documentation: https://dmbee.github.io/seglearn/\n\nDependencies\n~~~~~~~~~~~~\n\nseglearn is tested to work under Python 3.5, 3.6, and 3.8.\nThe dependency requirements are:\n\n* scipy(\u003e=0.17.0)\n* numpy(\u003e=1.11.0)\n* scikit-learn(\u003e=0.21.3)\n\nseglearn is now also compatible with sklearn 1.0+\n\nTo run the examples, you need:\n\n* matplotlib(\u003e=2.0.0)\n* keras (\u003e=2.1.4) for the neural network examples\n* pandas\n\nIn order to run the test cases, you need:\n\n* pytest\n\nThe neural network examples were tested on keras using the tensorflow-gpu backend, which is recommended.\n\nInstallation\n~~~~~~~~~~~~\n\nseglearn-learn is currently available on the PyPi's repository and you can\ninstall it via `pip`::\n\n  pip install -U seglearn\n\nor if you use python3::\n\n  pip3 install -U seglearn\n\nIf you prefer, you can clone it and run the setup.py file. Use the following\ncommands to get a copy from GitHub and install all dependencies::\n\n  git clone https://github.com/dmbee/seglearn.git\n  cd seglearn\n  pip install .\n\nOr install using pip and GitHub::\n\n  pip install -U git+https://github.com/dmbee/seglearn.git\n\nTesting\n~~~~~~~\n\nAfter installation, you can use `pytest` to run the test suite from seglearn's root directory::\n\n  python -m pytest\n\nChange Log\n----------\n\nVersion history can be viewed in the `Change Log\n\u003chttps://dmbee.github.io/seglearn/change_log.html\u003e`_.\n\nDevelopment\n-----------\n\nThe development of this scikit-learn-contrib is in line with the one\nof the scikit-learn community. Therefore, you can refer to their\n`Development Guide\n\u003chttp://scikit-learn.org/stable/developers\u003e`_.\n\nPlease submit new pull requests on the dev branch with unit tests and an example to\ndemonstrate any new functionality / api changes.\n\nCiting seglearn\n~~~~~~~~~~~~~~~\n\nIf you use seglearn in a scientific publication, we would appreciate\ncitations to the following paper::\n\n  @article{arXiv:1803.08118,\n  author  = {David Burns, Cari Whyne},\n  title   = {Seglearn: A Python Package for Learning Sequences and Time Series},\n  journal = {arXiv},\n  year    = {2018},\n  url     = {https://arxiv.org/abs/1803.08118}\n  }\n\n\nIf you use the seglearn test data in a scientific publication, we would appreciate\ncitations to the following paper::\n\n  @article{arXiv:1802.01489,\n  author  = {David Burns, Nathan Leung, Michael Hardisty, Cari Whyne, Patrick Henry, Stewart McLachlin},\n  title   = {Shoulder Physiotherapy Exercise Recognition: Machine Learning the Inertial Signals from a Smartwatch},\n  journal = {arXiv},\n  year    = {2018},\n  url     = {https://arxiv.org/abs/1802.01489}\n  }\n","funding_links":[],"categories":["Libraries","时间序列","📦 Packages","相关包","Uncategorized","Related Software"],"sub_categories":["Python","预测","Uncategorized","Time-Series Analysis"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmbee%2Fseglearn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdmbee%2Fseglearn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmbee%2Fseglearn/lists"}