River: machine learning for streaming data in Python
University of Waikato（怀卡托大学）；Alan；University of São Paulo（圣保罗大学）；Volvo Car Corporation（沃尔沃汽车公司）；IRIT, Université Paul Sabatier（保罗·萨巴捷大学图卢兹信息学研究所）；Renault（雷诺）；Octogone Lordat, Université Jean-Jaures（让·饶勒斯大学 Octogone Lordat）；IRT Saint Exupéry（圣埃克苏佩里技术研究院）；École Polytechnique, Institut Polytechnique de Paris（巴黎理工学院巴黎综合理工学院）；Télécom Paris, Institut Polytechnique de Paris（巴黎理工学院巴黎电信学院）
Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abd… 展开作者
Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet
River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performance metrics and evaluators for different stream learning problems. It is the result from the merger of the two most popular packages for stream learning in Python: Creme and scikit-multiflow. River introduces a revamped architecture based on the lessons learnt from the seminal packages. River's ambition is to be the go-to library for doing machine learning on streaming data. Additionally, this open source package brings under the same umbrella a large community of practitioners and researchers. The source code is available at https://github.com/online-ml/river.
RiverText: A Python Library for Training and Evaluating Incremental Word Embeddings from Text Data Streams
Department of Computer Science, University of Chile（智利大学计算机科学系）；National Center for Artificial Intelligence (CENIA)（人工智能国家中心）；Millennium Institute for Foundational Research on Data (IMFD)（数据基础研究千年研究所）
Wax is what you put on a surfboard to avoid slipping. It is an essential tool to go surfing... We introduce WAX-ML a research-oriented Python library providing tools to design powerful machine learning algorithms and feedback loops working on streaming data. It strives to complement JAX with tools dedicated to time series. WAX-ML makes JAX-based programs easy to use for end-users working with pandas and xarray for data manipulation. It provides a simple mechanism for implementing feedback loops, allows the implementation of online learning and reinforcement learning algorithms with functions, …
Amazon（亚马逊）；University of Tartu（塔尔图大学）；University of Bristol（布里斯托大学）
This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabilities. HyperStream overcomes the limitations of other computational engines and provides high-level interfaces to execute complex nesting, fusion, and prediction both in online and offline forms in streaming environments. HyperStream is a general purpose tool that is well-suited for the design, development, and deployment of Machine Learning algorithms and predictive models in a wide space of sequential predictive pro…
Télécom ParisTech（巴黎高等电信学院）；Université Paris-Saclay（巴黎-萨克雷大学）；École Polytechnique（巴黎综合理工学院）；CNRS（法国国家科学研究中心）；National University of Singapore（新加坡国立大学）
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 a…
A Retrospective of the Tutorial on Opportunities and Challenges of Online Deep Learning
FZI Research Center for Information Technology（FZI信息技术研究中心）；AI Institute, University of Waikato（怀卡托大学人工智能学院）；LCTI, Télécom Paris, Institut Polytechnique de Paris（巴黎综合理工学院巴黎电信学院LCTI）
FiLark: a streaming-first software framework for end-to-end exploration, annotation, and algorithm integration in distributed acoustic sensing
organization= State Key Laboratory of Ocean Sensing \& Ocean College, Zhejiang University , city= Zhoushan , postcode= 316021 , country= China；organization= College of Information Science；Electronic Engineering, Zhejiang University , city= Hangzhou , country= China；organization= College of Computer Science；Technology, Zhejiang University , city= Hangzhou , country= China
This project was the first attempt to use big data analytics approaches and machine learning along with Natural Language Processing (NLP) of tweets for flood risk assessment and decision making. Multiple Python packages were developed and integrated within the Flood Analytics Information System (FAIS). FAIS workflow includes the use of IoTs-APIs and various machine learning approaches for transmitting, processing, and loading big data through which the application gathers information from various data servers and replicates it to a data warehouse (IBM database service). Users are allowed to di…
Due to the unspecified and dynamic nature of data streams, online machine learning requires powerful and flexible solutions. However, evaluating online machine learning methods under realistic conditions is difficult. Existing work therefore often draws on different heuristics and simulations that do not necessarily produce meaningful and reliable results. Indeed, in the absence of common evaluation standards, it often remains unclear how online learning methods will perform in practice or in comparison to similar work. In this paper, we propose a comprehensive set of properties for high-quali…
Pontifícia Universidade Católica do Paraná (PUCPR)（巴拉那天主教大学）；Université de Rouen Normandie（鲁昂诺曼底大学）
Mining data streams is a challenge per se. It must be ready to deal with an enormous amount of data and with problems not present in batch machine learning, such as concept drift. Therefore, applying a batch-designed technique, such as dynamic selection of classifiers (DCS) also presents a challenge. The dynamic characteristic of ensembles that deal with streams presents barriers to the application of traditional DCS techniques in such classifiers. scikit-dyn2sel is an open-source python library tailored for dynamic selection techniques in streaming data. scikit-dyn2sel's development follows c…
pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data
College of Computer Engineering, Jimei University（嘉应大学计算机工程学院）；Chengyi College, Jimei University（嘉应大学 Chengyi 学院）；Department of Technology, Management and Economics, Technical University of Denmark（丹麦技术大学技术、管理与经济系）
