Metadata-Version: 2.1
Name: cooka
Version: 0.1.1
Summary: A lightweight AutoML system.
Home-page: UNKNOWN
Author: DataCanvas Community
Author-email: yangjian@zetyun.com
License: Apache License 2.0
Description: # Cooka
        [![Python Versions](https://img.shields.io/pypi/pyversions/hypergbm.svg)](https://pypi.org/project/hypergbm)
        [![Downloads](https://pepy.tech/badge/hypergbm)](https://pepy.tech/project/hypergbm)
        [![PyPI Version](https://img.shields.io/pypi/v/hypergbm.svg)](https://pypi.org/project/hypergbm)
        
        [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 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.
        
        <img src="docs/img/datacanvas_automl_toolkit.png" alt="drawing" width="700" height="450"/>
        
        ## 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：
        <table style="border: none">
            <th><img src="docs/img/cooka_home_page.png" width="500"/></th>
            <th><img src="docs/img/cooka_train.gif" width="500"/></th>
        </table>
        
        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://<your_ip:8000>` 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 -it -p 8000:8000 -p 8888:8888 -e NOTEBOOK_PORTAL="http://<your_ip>:8888"  datacanvas/cooka
        ```
        
        Open `http://<your_ip:8000>` with your browser to visit cooka.
        
        ## DataCanvas
        
        ![](docs/static/dc_logo_1.png)
        
        Cooka is an open source project created by [DataCanvas](https://www.datacanvas.com/). 
        
        
        
Platform: Linux
Platform: Mac OS X
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.6.*
Description-Content-Type: text/markdown
