clusterlets 0.0.3

A library for clusterlet induction, that is, sets of fair clusters.

Clusterlets

clusterlets is a Python library collecting algorithms for fair clustering, mainly aimed at clusterlet-based approaches. A clusterlet defines (like fair coresets and fairlets) a clustering of data which respects some notion of fairness or balance. Under some assumptions, (centroids of) clusterlets can be clustered themselves achieve a fair clustering where each cluster approximately follows the original label distribution.

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Quickstart

Installation

Developed on Python 3.11.

pip install clusterlets

Getting started

import numpy

from clusterlets.extractors import RandomExtractor


# generate some data
data = numpy.random.rand(1000, 5)
labels = numpy.random.choice([0, 1], 1000, replace=True)

# creates a clusterlet extractor
extractor = RandomExtractor(random_state=42)
# extracts clusterlets, assigning a clusterlet to each data point
extracted_clusterlets = extractor.extract(data, labels, size_per_label="auto")

# you can access the data of each clusterlet!
for clusterlet in extracted_clusterlets:
    print(data[clusterlet.index])

Clusterlets. The Clusterlet class implements a clusterlet, which is defined by

• _id: int an id to identify it
• label_frequecies: Optional[numpy.ndarray] label frequencies associated to id
• centroid: Optional[numpy.ndarray] a centroid
• index: Optional[numpy.ndarray] indicating which instances of the starting data compose this clusterlet. Used in place of the data itself for a lighter object

Since clusterlets are extracted from a dataset, data[clusterlet.index] yields the data of the clusterlet. Clusterlets support == and hash, thus can be aggregated into set[Clusterlet], and used as dictionary keys.

Extractors. Clusterlets are extracted with a set of extractors implementing the ClusterletExtractor interface (extractors.*), which extracts clusterlets through the extract(data, labels) method.

• RandomExtractor selects random subsets of each label, then pairs them to satisfy dataset balance. One can also specify how many samples per label each clusterlet must have with a parameter dictionary size_per_label
• KMeansExtractor clusters each label separately (through K-Means), creating label-specific clusterlets. Then, matches clusterlets to achieve both clustering and balance.

The KMeansExtractor is an implementation of the ClusteringExtractor interface, which can be adapted to any clustering algorithm by overriding the cluster(data) method.

Matchers. Matchers (extractors.matches.*) are objects which "match" existing clusterlets, creating larger ones, i.e., they cluster clusterlets. A Matcher implements a match(clusterlets, **kwargs) method, which is given a list of clusterings (one per label), and a desired label balance to achieve. Currently, we implement:

• PinballMatcher, which provides matches by hopping hops times through two sets of clusterlets of different labels, each hop following the clusterlet of opposite label at minimum distance.
• GreedyPinballMatcher, which greedily matches clusterlets maximizing some given objective:
  • GreedyBalanceMatcher maximizes label balance
  • GreedyDPbMatcher maximizes clusterlet distance
• CentroidMatcher, which creates a set of candidate partitions of the set of clusterlets, then scores them for balance and compactness. Note: only a subsample of size sample_size is tested due to the superexponential number of possible partitions.