Metadata-Version: 2.1
Name: scikit-lego
Version: 0.1.4
Summary: UNKNOWN
Home-page: UNKNOWN
License: UNKNOWN
Description: ![](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]"
        python setup.py develop
        ```
        
        ## Documentation 
        
        The documentation can be found [here](scikit-lego.readthedocs.io). 
        
        ## 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 
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: docs
Provides-Extra: dev
