Metadata-Version: 2.1
Name: scikit-lego
Version: 0.1.2
Summary: UNKNOWN
Home-page: UNKNOWN
License: UNKNOWN
Platform: UNKNOWN
Description-Content-Type: text/markdown
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![](https://travis-ci.com/koaning/scikit-lego.svg?branch=master) [![Build status](https://ci.appveyor.com/api/projects/status/66r9jjs844v8c5qh?svg=true)](https://ci.appveyor.com/project/koaning/scikit-lego) [![Documentation Status](https://readthedocs.org/projects/scikit-lego/badge/?version=latest)](https://scikit-lego.readthedocs.io/en/latest/?badge=latest)



# scikit-lego

![](images/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. 

## Installation 

Install `scikit-lego` via pip with 

```bash
pip install scikit-lego
```

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

```bash
$ pip install -e ".[dev]"
```

## Usage 

```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'))
])

...
```

## 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 demonstratable real-world usecase
2. any new feature passes standard unit tests (we have a few for transformers and predictors)
3. the feature has been discussed in the issue list beforehand 


