automlk

Automated and distributed machine learning toolkit

This toolkit is designed to be integrated within a python project, but also independently through the interface of the app.

The framework is designed with similar principles than auto-sklearn, with the following improvements:

web interface (flask) to review the datasets, the search results and graphs
distributed architecture with multiple search workers on multiple machines
can also be run on a single machine
automated data analysis, feature per feature with graphs
include sklearn models, but also Xgboost, LightGBM, CatBoost and keras Neural Networks*
2nd level ensembling with model selection and stacking
can be used in competition mode (to generate a submit file from a test set), on public mode (separate train set and public set) and standard mode.
pre-processing hyper-optimization in parallel with model hyper-parameter optimization
text pre-processing on specific text columns with bow, word2vec, doc2vec, ...
automatic documentation of the models in html and pdf format

We have provided some public datasets to initialize the framework and compare results with best scores.

Find the documentation here

This framework is currently an alpha version

Installation

download and then install:

python setup.py install

then run the web server to define the set-up and launch the workers (see documentation)

Usage

Launch the web app in /web folder:

python run.py

This will launch the web app, which can be accessed via a web browser, at address:

http://localhost:5001

From the web app, you can now import the example of datasets (import in the menu, then select the dataset.csv in the /data folder)

You can launch the search in a dataset simply by clicking on the |> button in the home screen, and view the results through with the web interface. The search will continue automatically until the search is completed.

Requirements

sklearn (>0.19)
seaborn
category_encoders
imbalanced-learn

optional:

lightGBM
Xgboost
Catboost
Keras with Theano or Tensorflow (Neural Networks)
Gensim (word2vec, doc2vec)
wordcloud (text word clouds)
Redis (for in memory key/value storage and queues)

Status of the project

AutoMLk is currently in alpha mode. During this period, incompatible changes may happen until the finalization of the version.

We have run this version during several months on multiple machines and multiple datasets. We expect to be in beta soon.

References

Feurer, Matthias, et al. "Efficient and robust automated machine learning." Advances in Neural Information Processing Systems. 2015.