Scikit-multiflow explained

scikit-mutliflow
Author:	Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem
Developer:	The scikit-mutliflow development team and the open research community
Latest Release Version:	0.5.3
Latest Release Date:	^[1] ^[2]
Repo:	https://github.com/scikit-multiflow/scikit-multiflow
Programming Language:	Python, Cython
Operating System:	Linux, macOS, Windows
Genre:	Library for machine learning
License:	BSD 3-clause license

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]

Overview

scikit-multiflow allows to easily design and run experiments and to extend existing stream learning algorithms. 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.

Implementation

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 methods^[4] compatible with the stream learning setting, Pandas for data manipulation, Numpy and SciPy.

Components

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.

History

scikit-multiflow started as a collaboration between researchers at Télécom Paris (Institut Polytechnique de Paris^[5]) and École Polytechnique. Development is currently carried by the University of Waikato, Télécom Paris, École Polytechnique and the open research community.

See also

• Massive Online Analysis (MOA)^[6]
• MEKA^[7]

Notes and References

1. Web site: scikit-mutliflow Version 0.5.3 .
2. Web site: scikit-learn 0.5.3 . Python Package Index.
3. Montiel. Jacob. Read. Jesse. Bifet. Albert. Abdessalem. Talel. 2018. Scikit-Multiflow: A Multi-output Streaming Framework. Journal of Machine Learning Research. 19. 72. 1–5. 1533-7928.
4. Web site: scikit-learn — Incremental learning. scikit-learn.org. 2020-04-08.
5. Web site: Institut Polytechnique de Paris. en-GB. 2020-04-08.
6. Bifet. Albert. Holmes. Geoff. Kirkby. Richard. Pfahringer. Bernhard. 2010. MOA: Massive Online Analysis. Journal of Machine Learning Research. 11. 52. 1601–1604. 1533-7928.
7. Read. Jesse. Reutemann. Peter. Pfahringer. Bernhard. Holmes. Geoff. 2016. MEKA: A Multi-label/Multi-target Extension to WEKA. Journal of Machine Learning Research. 17. 21. 1–5. 1533-7928.