{"id":28429613,"url":"https://github.com/sktime/sktime-tutorial-pydata-seattle-2023","last_synced_at":"2025-10-27T18:05:08.170Z","repository":{"id":156790827,"uuid":"631279307","full_name":"sktime/sktime-tutorial-pydata-seattle-2023","owner":"sktime","description":"skbase - a workbench for creating scikit-learn like parametric objects and libraries","archived":false,"fork":false,"pushed_at":"2023-04-26T21:31:29.000Z","size":8136,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-06-12T03:23:13.200Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sktime.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2023-04-22T14:14:55.000Z","updated_at":"2023-06-02T08:02:02.000Z","dependencies_parsed_at":"2024-02-02T02:10:32.180Z","dependency_job_id":"bdd41b3c-4f15-4e4d-b754-2c7320db94fe","html_url":"https://github.com/sktime/sktime-tutorial-pydata-seattle-2023","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sktime/sktime-tutorial-pydata-seattle-2023","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-pydata-seattle-2023","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-pydata-seattle-2023/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-pydata-seattle-2023/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-pydata-seattle-2023/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sktime","download_url":"https://codeload.github.com/sktime/sktime-tutorial-pydata-seattle-2023/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-pydata-seattle-2023/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261040084,"owners_count":23101155,"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":[],"created_at":"2025-06-05T13:38:34.177Z","updated_at":"2025-10-27T18:05:08.110Z","avatar_url":"https://github.com/sktime.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ca href=\"https://sktime.net\"\u003e\u003cimg src=\"https://github.com/sktime/sktime/blob/main/docs/source/images/sktime-logo-no-text.jpg?raw=true)\" width=\"175\" align=\"right\" /\u003e\u003c/a\u003e\n\n`skbase` - a workbench for creating scikit-learn like parametric objects and libraries\n======================================================================================\n\n**Welcome to the skbase tutorial at PyData Seattle 2023**\n\n`skbase` is a meta-toolkit that makes it easy to build your own python package that follows `scikit-learn` design patterns, e.g., parametric composable objects, and fittable objects. It contains standalone `BaseObject` and `BaseEstimator` base classes, that is, base class templates to write your own base classes, templateable test classes and object checks, object retrieval and inspection, and more.\n\n`skbase` is the base component of `sktime` turned workbench, and is an evolution on the base class framework of `sklearn`.\n\n[sktime]: https://sktime.net\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sktime/sktime-tutorial-pydata-seattle-2023/main?filepath=notebooks)\n\nIf you are unfamiliar with `sklearn` or `sktime`, consider working through **general sklearn and sktime introduction tutorials** first:\n\n:movie_camera: **[general sktime intro tutorial](https://github.com/sktime/sktime-tutorial-pydata-glboal-2021) from PyData Global 2021**\\\n:tv: [youtube video of sktime intro at PyData Global 2021](https://www.youtube.com/watch?v=ODspi8-uWgo)\n\n## :bulb: Description\n\nThe workshop will walk the audience through an example of creating their own package with parametric objects, custom base classes and objects inheriting from these, and a full testing framework.\n\nThis will also showcase skbase's (https://github.com/sktime/skbase) core functionality which is contained in submodules:\n\n* `skbase.base` provides: `BaseObject` - parameteric object with get/set_params, tag system, etc; `BaseEstimator`, for objects with fit, with `is_fitted`, `get_fitted_params`; mixin classes such as BaseMetaObject for homogenous and heterogeneous composites (e.g., ensembles, pipelines, graph objects).\n* `skbase.lookup` provides search tools such as all_objects that retrieves all `BaseObject`-s with certain tags from a project.\n* `skbase.validate` provides tools for validating and comparing `BaseObject`-s and collections of `BaseObject`-s\n* `skbase.testing` provides tools for testing `BaseObject`-s, and for setting up testing frameworks and object checkers, for dependent base classes.\n\n## sktime tutorials\n\n`skbase` has been consolidated from the base class frameworks of `sklearn` and `sktime`.\n\nYou may find the following tutorials for `sktime` useful:\n\n- [PyData 2021 - Intro to sktime](https://www.youtube.com/watch?v=ODspi8-uWgo)\n\n- [Pydata 2022 - How to implement your own estimator in sktime](https://www.youtube.com/watch?v=S_3ewcvs_pg)\n\n- [Pydata 2022 - Advanced Forecasting Tutorial](https://www.youtube.com/watch?v=4Rf9euAhjNc)\n\n## :rocket: How to get started\n\nYou have different options how to run the tutorial notebooks:\n\n* Run the notebooks in the cloud on [Binder] - for this you don't have to install anything!\n* Run the notebooks on your machine. [Clone] this repository, get [conda], install the required packages (`skbase`, `sktime`, `pytest`, `seaborn`, `jupyter`) in an environment, and open the notebooks with that environment. For detail instructions, see below. For troubleshooting, see sktime's more detailed [installation instructions].\n* or, use python venv, and/or an editable install of this repo as a package. Instructions below.\n\n[Binder]: https://mybinder.org/v2/gh/sktime/sktime-tutorial-pydata-seattle-2023/main?filepath=notebooks\n[clone]: https://help.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository\n[conda]: https://docs.conda.io/en/latest/\n[installation instructions]: https://www.sktime.net/en/latest/installation.html\n\n## :wave: How to contribute\n\nIf you're interested in contributing to sktime, you can find out more how to get involved [here](https://www.sktime.net/en/stable/get_involved.html).\n\nAny contributions are welcome, not just code!\n\n## Installation instructions in detail\n\n### Cloning the repository\n\nTo clone the repository locally:\n\n`git clone https://github.com/sktime/sktime-tutorial-pydata-seattle-2023.git`\n\n\n### Using conda env\n\n#### option 1: installing requirements manually\n\n1. Create a python virtual environment:  \n`conda create -y -n pydata_skbase python=3.9`\n2. Install required packages:  \n`conda install -y -n pydata_skbase pip scikit-base sktime pytest seaborn jupyter pmdarima`\n3. Activate your environment:  \n`conda activate pydata_skbase`\n4. If using jupyter: make the environment available in jupyter:  \n`python -m ipykernel install --user --name=pydata_skbase`\n\n#### option 2: installing repo as package\n\n1. Create a python virtual environment:  \n`conda create -y -n pydata_skbase python=3.9`\n2. Make sure the environment has pip:  \n`conda install -y -n pydata_skbase pip`\n3. Activate your environment:  \n`conda activate pydata_skbase`\n4. Install the package in development mode:  \n`pip install -e .`\n5. If using jupyter: make the environment available in jupyter:  \n`python -m ipykernel install --user --name=pydata_skbase`\n\n### Using python venv\n\n#### option 1: installing requirements manually\n\n1. Create a python virtual environment:  \n`python -m venv .venv`\n2. Activate your environment:  \n`source .venv/bin/activate`\n3. Install the requirements:  \n`pip install scikit-base sktime pytest seaborn jupyter pmdarima`\n4. If using jupyter: make the environment available in jupyter:  \n`python -m ipykernel install --user --name=pydata_skbase`\n\n#### option 2: installing repo as package\n\n1. Create a python virtual environment:  \n`python -m venv .venv`\n2. Activate your environment:  \n`source .venv/bin/activate`\n3. Install the package in development mode:  \n`pip install -e .`\n4. If using jupyter: make the environment available in jupyter:  \n`python -m ipykernel install --user --name=pydata_skbase`\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsktime%2Fsktime-tutorial-pydata-seattle-2023","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsktime%2Fsktime-tutorial-pydata-seattle-2023","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsktime%2Fsktime-tutorial-pydata-seattle-2023/lists"}