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scikit-lego

<a href="https://koaning.github.io/scikit-lego/"><img src="images/logo.png" width="35%" height="35%" align="right" /></a>

We love scikit learn but very often we find ourselves writing custom transformers, metrics and models. The goal of this project is to attempt to consolidate these into a package that offers code quality/testing. This project started as a collaboration between multiple companies in the Netherlands but has since received contributions from around the globe. It was initiated by Matthijs Brouns and Vincent D. Warmerdam as a tool to teach people how to contribute to open source.

Note that we're not formally affiliated with the scikit-learn project at all, but we aim to strictly adhere to their standards.

The same holds with lego. LEGO® is a trademark of the LEGO Group of companies which does not sponsor, authorize or endorse this project.

Installation

Install scikit-lego via pip with

python -m pip install scikit-lego

Via conda with

conda install -c conda-forge scikit-lego

Alternatively, to edit and contribute you can fork/clone and run:

python -m pip install -e ".[dev]"
python setup.py develop

Documentation

The documentation can be found here.

Usage

We offer custom metrics, models and transformers. You can import them just like you would in scikit-learn.

# the scikit learn stuff we love
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# from scikit lego stuff we add
from sklego.preprocessing import RandomAdder
from sklego.mixture import GMMClassifier

...

mod = Pipeline([
    ("scale", StandardScaler()),
    ("random_noise", RandomAdder()),
    ("model", GMMClassifier())
])

...

Features

Here's a list of features that this library currently offers:

New Features

We want to be rather open here in what we accept but we do demand three things before they become added to the project:

  1. any new feature contributes towards a demonstrable real-world usecase
  2. any new feature passes standard unit tests (we use the ones from scikit-learn)
  3. the feature has been discussed in the issue list beforehand

We automate all of our testing and use pre-commit hooks to keep the code working.