scikit-query 0.4

scikit-query is a Python library for active query strategies in constrained clustering on top of SciPy and scikit-learn.

scikit-query

Clustering aims to group data into clusters without the help of labels, unlike classification algorithms. A well-known shortcoming of clustering algorithms is that they rely on an objective function geared toward specific types of clusters (convex, dense, well-separated), and hyperparameters that are hard to tune. Semi-supervised clustering mitigates these problems by injecting background knowledge in order to guide the clustering. Active clustering algorithms analyze the data to select interesting points to ask the user about, generating constraints that allow fast convergence towards a user-specified partition.

scikit-query is a library of active query strategies for constrained clustering inspired by scikit-learn and the now inactive active-semi-supervised-clustering library by Jakub Švehla.

It is focused on algorithm-agnostic query strategies, i.e. methods that do not rely on a particular clustering algorithm. From an input dataset, they produce a set of constraints by making insightful queries to an oracle. A variant for incremental constrained clustering is provided for applicable algorithms, taking a data partition into account.

In typical scikit way, the library is used by instanciating a class and using its fit method.

from skquery.pairwise import AIPC
from skquery.oracle import MLCLOracle

qs = AIPC()
oracle = MLCLOracle(truth=labels, budget=10)
constraints = qs.fit(dataset, oracle)

Algorithms

Algorithm	Description	Constraint type	Works in incremental setting ?	Source	Date
Random sampling		ML/CL, triplet	:heavy_check_mark:
FFQS	Neighborhood-based	ML/CL	:heavy_check_mark:	Basu et al.	2004
MMFFQS (MinMax)	Neighborhood-based, similarity	ML/CL	:heavy_check_mark:	Mallapragada et al.	2008
NPU	Neighborhood-based, information theory	ML/CL	:heavy_check_mark:	Xiong et al.	2013
SASC	SVDD, greedy approach	ML/CL		Abin & Beigy	2014
AIPC	Fuzzy clustering, information theory	ML/CL		Zhang et al.	2019

Dependencies

scikit-query is developed on Python >= 3.10, and requires the following libraries :

• pandas>=2.0.1
• matplotlib>=3.7.1
• numpy>=1.24.3
• scikit-learn>=1.2.2
• cvxopt>=1.3.1
• scikit-fuzzy>=0.4.2
• scipy>=1.10.1
• plotly>=5.14.1

Contributors

FFQS, MinMax and NPU are based upon Jakub Švehla's implementation. Other algorithms have been implemented by Aymeric Beauchamp or his students from the University of Orléans :

• Salma Badri, Elis Ishimwe, Brice Jacquesson, Matthéo Pailler (2023)