Scikit-multiflow

scikit-mutliflow
Original authors	Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem
Developers	The scikit-mutliflow development team and the open research community
Initial release	January 2018 (2018-01)
Stable release	0.5.3 / 17 June 2020 (2020-06-17)12
Written in	Python, Cython
Operating system	Linux, macOS, Windows
Type	Library for machine learning
License	BSD 3-clause license
Website	scikit-multiflow.github.io
Repository	https://github.com/scikit-multiflow/scikit-multiflow

scikit-mutliflow (also known as skmultiflow) is a free and open source software machine learning library for multi-output/multi-label and stream data written in Python.³

scikit-multiflow allows to easily design and run experiments and to extend existing stream learning algorithms.³ It features a collection of classification, regression, concept drift detection and anomaly detection algorithms. It also includes a set of data stream generators and evaluators. scikit-multiflow is designed to interoperate with Python's numerical and scientific libraries NumPy and SciPy and is compatible with Jupyter Notebooks.

The scikit-multiflow library is implemented under the open research principles and is currently distributed under the BSD 3-clause license. scikit-multiflow is mainly written in Python, and some core elements are written in Cython for performance. scikit-multiflow integrates with other Python libraries such as Matplotlib for plotting, scikit-learn for incremental learning methods⁴ compatible with the stream learning setting, Pandas for data manipulation, Numpy and SciPy.

The scikit-multiflow is composed of the following sub-packages:

• anomaly_detection: anomaly detection methods.
• data: data stream methods including methods for batch-to-stream conversion and generators.
• drift_detection: methods for concept drift detection.
• evaluation: evaluation methods for stream learning.
• lazy: methods in which generalisation of the training data is delayed until a query is received, i.e., neighbours-based methods such as kNN.
• meta: meta learning (also known as ensemble) methods.
• neural_networks: methods based on neural networks.
• prototype: prototype-based learning methods.
• rules: rule-based learning methods.
• transform: perform data transformations.
• trees: tree-based methods, e.g. Hoeffding trees which are a type of decision tree for data streams.

scikit-multiflow started as a collaboration between researchers at Télécom Paris (Institut Polytechnique de Paris⁵) and École Polytechnique. Development is currently carried by the University of Waikato, Télécom Paris, École Polytechnique and the open research community.