Skip to content
AutoGBT is used for AutoML in a lifelong machine learning setting to classify large volume high cardinality data streams under concept-drift. AutoGBT was developed by a joint team ('autodidact.ai') from Flytxt, Indian Institute of Technology Delhi and CSIR-CEERI as a part of NIPS 2018 AutoML for Lifelong Machine Learning Challenge.
Branch: master
Clone or download
Latest commit edab179 Dec 17, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
COPYING.txt
NIPS 2018 AutoML_camera_ready_0.1.pdf
StreamProcessor.py
libscores.py
metadata
model.py
readme.md

readme.md

AutoGBT

AutoGBT stands for Automatically Optimized Gradient Boosting Trees, and is used for AutoML in a lifelong machine learning setting to classify large volume high cardinality data streams under concept-drift. AutoGBT was developed by a joint team ('autodidact.ai') from Flytxt, Indian Institute of Technology Delhi and CSIR-CEERI as a part of NIPS 2018 AutoML Challenge (The 3rd AutoML Challenge: AutoML for Lifelong Machine Learning). Our team won the first prize in the challenge. More details of the challenge is available at https://www.4paradigm.com/competition/nips2018. The work will be presented at NIPS 2018 during the Competition Track session (https://nips.cc/Conferences/2018/Schedule?showEvent=10945)

Team:
1.Jobin Wilson (jobin.wilson@flytxt.com)
2.Amit Kumar Meher (amit.meher@flytxt.com)
3.Bivin Vinodkumar Bindu (bivin.vinod@flytxt.com)
4.Manoj Sharma (mksnith@gmail.com)
5.Vishakha Pareek (vishakhapareek@ceeri.res.in)
6.Prof.Santanu Chaudhury
7.Prof.Brejesh Lall

How to Run

Download the starter kit from the NIPS AutoML from competion webpage (https://competitions.codalab.org/competitions/20203#participate-get_starting_kit) and setup locally as instructed in the readme file within the starter kit. Copy the folder "AutoGBT" into the starting_k folder inside the starter kit. Install docker from https://docs.docker.com/get-started/ and issue the following command to invoke the docker image corresponding to python3 bundle for the challenge.
docker run -it -u root -v $(pwd):/app/codalab codalab/codalab-legacy:py3 bash

For ingestion, use the following command from the docker shell prompt

python3 AutoML3_ingestion_program/ingestion.py AutoML3_sample_data AutoML3_sample_predictions AutoML3_sample_ref AutoML3_ingestion_program AutoGBT\

For scoring, use the following command from the docker shell prompt
python3 AutoML3_scoring_program/score.py 'AutoML3_sample_data/*/' AutoML3_sample_predictions AutoML3_scoring_output

If you used AutoGBT in one of your projects, please consider citing us:

@misc{AutoGBT-2018, title={AutoGBT:Automatically Optimized Gradient Boosting Trees for Classifying Large Volume High Cardinality Data Streams under Concept-Drift}, author={Jobin Wilson and Amit Kumar Meher and Bivin Vinodkumar Bindu and Manoj Sharma and Vishakha Pareek and Santanu Chaudhury and Brejesh Lall}, year={2018}, publisher={GitHub}, howpublished={\url{ https://github.com/flytxtds/AutoGBT }} }

You can’t perform that action at this time.
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session.