Scikit-Multiflow: A Multi-output Streaming Framework
This framework addresses the problem of accessible and standardized tools for researchers and practitioners working with streaming and multi-label data, though it is incremental as it builds upon existing open-source frameworks.
They tackled the need for a comprehensive platform for multi-output streaming data mining in Python, resulting in Scikit-Multiflow, which provides state-of-the-art methods, generators, and evaluators to democratize stream learning research.
Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language. Conceived to serve as a platform to encourage democratization of stream learning research, it provides multiple state of the art methods for stream learning, stream generators and evaluators. scikit-multiflow builds upon popular open source frameworks including scikit-learn, MOA and MEKA. Development follows the FOSS principles and quality is enforced by complying with PEP8 guidelines and using continuous integration and automatic testing. The source code is publicly available at https://github.com/scikit-multiflow/scikit-multiflow.