Skip to main content

A lightweight AutoML system.

Project description

Cooka

Python Versions Downloads PyPI Version

Doc | 简体中文

Cooka is a lightweight and visualization toolkit 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.

drawing

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
  • ...

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.

Installation

Using pip

The python version should be >= 3.6, for CentOS , install the system package:

pip install --upgrade pip
pip install cooka

Start the web server:

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:

mkdir -p ~/.config/cooka/
cooka generate-config > ~/.config/cooka/cooka.py

Using Docker

Launch a Cooka docker container:

docker run -ti -p 8888:8888 -p 8000:8000 -p 9001:9001 -e COOKA_NOTEBOOK_PORTAL=http://<your_ip>:8888 datacanvas/cooka:latest

Open http://<your_ip:8000> with your browser to visit cooka.

DataCanvas

Cooka is an open source project created by DataCanvas.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

cooka-0.1.2-py3-none-any.whl (1.9 MB view details)

Uploaded Python 3

File details

Details for the file cooka-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: cooka-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 1.9 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.4.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.7.9

File hashes

Hashes for cooka-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 2d40a9b70abd2960a940e37a2d99f30d46b2efd2d56f63ec75c501f688f31f09
MD5 472c325cd01a6bc1d67643c4808f409e
BLAKE2b-256 ca7d6e15ec0224ddd847a3e2e50c3b2973a6c9d8ae229406c78b23f6baab3a63

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page