.. scikit-lego documentation master file, created by
   sphinx-quickstart on Tue Mar 19 20:15:46 2019.
   You can adapt this file completely to your liking, but it should at least
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scikit-lego
===========

.. image:: _static/logo.png

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

.. code-block:: bash

   pip install scikit-lego


Alternatively you can fork/clone and run:

.. code-block:: bash

   pip install --editable .


Usage
*****

.. code-block:: python

   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