A lightweight AutoML system.
Project description
Cooka
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.
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.
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