{"id":13531911,"url":"https://github.com/flytxtds/AutoGBT","last_synced_at":"2025-04-01T20:30:45.370Z","repository":{"id":88115487,"uuid":"159947316","full_name":"flytxtds/AutoGBT","owner":"flytxtds","description":"AutoGBT is used for AutoML in a lifelong machine learning setting to classify large volume high cardinality data streams under concept-drift. 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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).\n\nMore details are available in our paper: https://link.springer.com/chapter/10.1007/978-3-030-29135-8_13\n\nTeam:\\\n1.Jobin Wilson (jobin.wilson@flytxt.com)\\\n2.Amit Kumar Meher (amit.meher@flytxt.com)\\\n3.Bivin Vinodkumar Bindu (bivin.vinod@flytxt.com)\\\n4.Manoj Sharma (mksnith@gmail.com)\\\n5.Vishakha Pareek (vishakhapareek@ceeri.res.in)\\\n6.Prof.Santanu Chaudhury\\\n7.Prof.Brejesh Lall\n# How to Run\nDownload 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.\\\ndocker run -it -u root -v $(pwd):/app/codalab codalab/codalab-legacy:py3 bash\n\nFor ingestion, use the following command from the docker shell prompt\n\npython3 AutoML3_ingestion_program/ingestion.py AutoML3_sample_data AutoML3_sample_predictions AutoML3_sample_ref AutoML3_ingestion_program AutoGBT\\\n\nFor scoring, use the following command from the docker shell prompt\\\npython3 AutoML3_scoring_program/score.py 'AutoML3_sample_data/*/' AutoML3_sample_predictions AutoML3_scoring_output\n\nIf you used AutoGBT in one of your projects, please consider citing us:\n\n@incollection{wilson2020automatically,\n  title={Automatically Optimized Gradient Boosting Trees for Classifying Large Volume High Cardinality Data Streams Under Concept Drift},\n  author={Wilson, Jobin and Meher, Amit Kumar and Bindu, Bivin Vinodkumar and Chaudhury, Santanu and Lall, Brejesh and Sharma, Manoj and Pareek, Vishakha},\n  booktitle={The NeurIPS'18 Competition},\n  pages={317--335},\n  year={2020},\n  publisher={Springer}\n}\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fflytxtds%2FAutoGBT","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fflytxtds%2FAutoGBT","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fflytxtds%2FAutoGBT/lists"}