No project description provided
Project description
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.
Installation
Install scikit-lego
via pip with
pip install scikit-lego
Alternatively, to edit and contribute you can fork/clone and run:
pip install -e ".[dev]"
python setup.py develop
Documentation
The documentation can be found here.
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'))
])
...
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:
- any new feature contributes towards a demonstratable real-world usecase
- any new feature passes standard unit tests (we have a few for transformers and predictors)
- the feature has been discussed in the issue list beforehand
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for scikit_lego-0.1.3-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d9a8414f27024f02dc97324429a28901f8d390c5e5b144f006b1e2b972132a98 |
|
MD5 | d3ebdc66317cad507996a38ebb0bdc3a |
|
BLAKE2b-256 | abccad7b55d7f2773958dd6ceb5b0e416aecc63b7ec3dfb526ccceba857e47fb |