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https://github.com/DataCanvasIO/Cooka
A lightweight and visual AutoML system
https://github.com/DataCanvasIO/Cooka
automated-feature-engineering automated-machine-learning automl data-science deep-learning hyperparameter-optimization machine-learning neural-network
Last synced: about 2 months ago
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A lightweight and visual AutoML system
- Host: GitHub
- URL: https://github.com/DataCanvasIO/Cooka
- Owner: DataCanvasIO
- License: apache-2.0
- Created: 2020-10-22T02:42:49.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-11-22T09:14:13.000Z (10 months ago)
- Last Synced: 2024-07-27T23:34:34.437Z (about 2 months ago)
- Topics: automated-feature-engineering, automated-machine-learning, automl, data-science, deep-learning, hyperparameter-optimization, machine-learning, neural-network
- Language: Python
- Homepage: https://cooka.readthedocs.io
- Size: 11.2 MB
- Stars: 39
- Watchers: 7
- Forks: 107
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-AutoML - Cooka
README
[](https://pypi.org/project/hypergbm)
[](https://pepy.tech/project/hypergbm)
[](https://pypi.org/project/hypergbm)
[Doc](https://cooka.readthedocs.io) | [简体中文](README_zh_CN.md)
Cooka is a lightweight and visualization toolkit to manage datasets and design model learning experiments through web UI.
It's using [DeepTables](https://github.com/DataCanvasIO/DeepTables) and [HyperGBM](https://github.com/DataCanvasIO/HyperGBM) as experiment engine to complete feature engineering, neural architecture search and hyperparameter tuning automatically.
## Features overview
Through the web UI provided by cooka you can:
- Add and analyze datasets
- Design experiment
- View experiment process and result
- Using models
- Export experiment to jupyter notebook
Screen shots：
The machine learning algorithms supported are ：
- XGBoost
- LightGBM
- Catboost
The neural networks supported are：
- WideDeep
- DeepFM
- xDeepFM
- AutoInt
- DCN
- FGCNN
- FiBiNet
- PNN
- AFM
- [...](https://deeptables.readthedocs.io/en/latest/models.html)
The search algorithms supported are：
- Evolution
- MCTS(Monte Carlo Tree Search)
- [...](https://github.com/DataCanvasIO/HyperGBM)
The supported feature engineering provided by [scikit-learn](https://scikit-learn.org) and [featuretools](https://github.com/alteryx/featuretools) are：
- Scaler
- StandardScaler
- MinMaxScaler
- RobustScaler
- MaxAbsScaler
- Normalizer
- Encoder
- LabelEncoder
- OneHotEncoder
- OrdinalEncoder
- Discretizer
- KBinsDiscretizer
- Binarizer
- Dimension Reduction
- PCA
- Feature derivation
- featuretools
- Missing value filling
- SimpleImputer
It can also extend the search space to support more feature engineering methods and modeling algorithms.
## Installation
### Using pip
The python version should be >= 3.6, for CentOS , install the system package:
```shell script
pip install --upgrade pip
pip install cooka
```
Start the web server：
```shell script
cooka server
```
Then open `http://` with your browser to use cooka.
By default, the cooka configuration file is at `~/.config/cooka/cooka.py`, to generate a template:
```shell script
mkdir -p ~/.config/cooka/
cooka generate-config > ~/.config/cooka/cooka.py
```
### Using Docker
Launch a Cooka docker container:
```shell script
docker run -ti -p 8888:8888 -p 8000:8000 -p 9001:9001 -e COOKA_NOTEBOOK_PORTAL=http://:8888 datacanvas/cooka:latest
```
Open `http://` with your browser to visit cooka.
## Citation
If you use Cooka in your research, please cite us as follows:
Haifeng Wu, Jian Yang. Cooka: A lightweight and visual AutoML system. https://github.com/DataCanvasIO/Cooka, 2021. Version 0.1.x
```
@misc{cooka,
author={Haifeng Wu, Jian Yang},
title={{Cooka}: {A lightweight and visual AutoML system}},
howpublished={https://github.com/DataCanvasIO/Cooka},
note={Version 0.1.x},
year={2021}
}
```
## DataCanvas
Cooka is an open source project created by [DataCanvas](https://www.datacanvas.com/).