skbase - a workbench for creating scikit-learn like parametric objects and libraries

Welcome to the skbase tutorial at PyData Seattle 2023

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.

skbase is the base component of sktime turned workbench, and is an evolution on the base class framework of sklearn.

If you are unfamiliar with sklearn or sktime, consider working through general sklearn and sktime introduction tutorials first:

🎥 general sktime intro tutorial from PyData Global 2021
📺 youtube video of sktime intro at PyData Global 2021

💡 Description

The 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.

This will also showcase skbase's (https://github.com/sktime/skbase) core functionality which is contained in submodules:

• 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).
• skbase.lookup provides search tools such as all_objects that retrieves all BaseObject-s with certain tags from a project.
• skbase.validate provides tools for validating and comparing BaseObject-s and collections of BaseObject-s
• skbase.testing provides tools for testing BaseObject-s, and for setting up testing frameworks and object checkers, for dependent base classes.

sktime tutorials

skbase has been consolidated from the base class frameworks of sklearn and sktime.

You may find the following tutorials for sktime useful:

• PyData 2021 - Intro to sktime

• Pydata 2022 - How to implement your own estimator in sktime

• Pydata 2022 - Advanced Forecasting Tutorial

🚀 How to get started

You have different options how to run the tutorial notebooks:

• Run the notebooks in the cloud on Binder - for this you don't have to install anything!
• 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.
• or, use python venv, and/or an editable install of this repo as a package. Instructions below.

👋 How to contribute

If you're interested in contributing to sktime, you can find out more how to get involved here.

Any contributions are welcome, not just code!

Installation instructions in detail

Cloning the repository

To clone the repository locally:

git clone https://github.com/sktime/sktime-tutorial-pydata-seattle-2023.git

Using conda env

option 1: installing requirements manually

1. Create a python virtual environment:
   conda create -y -n pydata_skbase python=3.9
2. Install required packages:
   conda install -y -n pydata_skbase pip scikit-base sktime pytest seaborn jupyter pmdarima
3. Activate your environment:
   conda activate pydata_skbase
4. If using jupyter: make the environment available in jupyter:
   python -m ipykernel install --user --name=pydata_skbase

option 2: installing repo as package

1. Create a python virtual environment:
   conda create -y -n pydata_skbase python=3.9
2. Make sure the environment has pip:
   conda install -y -n pydata_skbase pip
3. Activate your environment:
   conda activate pydata_skbase
4. Install the package in development mode:
   pip install -e .
5. If using jupyter: make the environment available in jupyter:
   python -m ipykernel install --user --name=pydata_skbase

Using python venv

option 1: installing requirements manually

1. Create a python virtual environment:
   python -m venv .venv
2. Activate your environment:
   source .venv/bin/activate
3. Install the requirements:
   pip install scikit-base sktime pytest seaborn jupyter pmdarima
4. If using jupyter: make the environment available in jupyter:
   python -m ipykernel install --user --name=pydata_skbase

option 2: installing repo as package

1. Create a python virtual environment:
   python -m venv .venv
2. Activate your environment:
   source .venv/bin/activate
3. Install the package in development mode:
   pip install -e .
4. If using jupyter: make the environment available in jupyter:
   python -m ipykernel install --user --name=pydata_skbase