Welcome to Cooka

Cooka is a lightweight and visualization system to manage datasets and design model learning experiments through web UI.

It using DeepTables and 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:

  1. Add and analyze datasets

  2. Design experiment

  3. View experiment process and result

  4. Using models

  5. Export experiment to jupyter notebook

Screen shots

_images/cooka_home_page.png _images/cooka_train.gif

The machine learning algorithms supported are :

  • XGBoost

  • LightGBM

  • Catboost

The neural networks supported are:

  • WideDeep

  • DeepFM

  • xDeepFM

  • AutoInt

  • DCN


  • FiBiNet

  • PNN

  • AFM

The search algorithms supported are:

  • Evolution

  • MCTS(Monte Carlo Tree Search)

The supported feature engineering provided by scikit-learn and 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.

Read more:



Cooka is an open source project created by DataCanvas .