index.rst

scikit-lego

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 is a collaboration between multiple companies in the Netherlands. Note that we're not formally affiliated with the scikit-learn project at all.

Disclaimer

LEGO® is a trademark of the LEGO Group of companies which does not sponsor, authorize or endorse this project. Also note this package, albeit designing to be used on top of scikit-learn, is not associated with that project in any formal manner.

The goal of the package is to allow you to joyfully build with new building blocks that are scikit-learn compatible.

Installation

Install scikit-lego via pip with

pip install scikit-lego

Alternatively you can fork/clone and run:

pip install --editable .

Usage

from sklego.transformers import RandomAdder

from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline

...

mod = Pipeline([
    ("scale", StandardScaler()),
    ("random_noise", RandomAdder()),
    ("model", LogisticRegression(solver='lbfgs'))
])

...

.. toctree::
   :maxdepth: 2
   :caption: Contents:

   install
   contribution
   datasets.ipynb
   linear-models.ipynb
   mixture-methods
   naive-bayes
   meta.ipynb
   fairness.ipynb
   outliers.ipynb
   timegapsplit.ipynb
   preprocessing.ipynb
   debug_pipeline.ipynb
   pandas_pipeline.ipynb
   contributors
   rstudio.md
   this.md

   api/modules