datamole-ai / active-semi-supervised-clustering

Active semi-supervised clustering algorithms for scikit-learn

active-semi-supervised-clustering

Active semi-supervised clustering algorithms for scikit-learn.

Algorithms

Semi-supervised clustering

• Seeded-KMeans
• Constrainted-KMeans
• COP-KMeans
• Pairwise constrained K-Means (PCK-Means)
• Metric K-Means (MK-Means)
• Metric pairwise constrained K-Means (MPCK-Means)

Active learning of pairwise clustering

• Explore & Consolidate
• Min-max
• Normalized point-based uncertainty (NPU) method

Installation

pip install active-semi-supervised-clustering

Usage

from sklearn import datasets, metrics
from active_semi_clustering.semi_supervised.pairwise_constraints import PCKMeans
from active_semi_clustering.active.pairwise_constraints import ExampleOracle, ExploreConsolidate, MinMax

X, y = datasets.load_iris(return_X_y=True)

First, obtain some pairwise constraints from an oracle.

# TODO implement your own oracle that will, for example, query a domain expert via GUI or CLI
oracle = ExampleOracle(y, max_queries_cnt=10)

active_learner = MinMax(n_clusters=3)
active_learner.fit(X, oracle=oracle)
pairwise_constraints = active_learner.pairwise_constraints_

Then, use the constraints to do the clustering.

clusterer = PCKMeans(n_clusters=3)
clusterer.fit(X, ml=pairwise_constraints[0], cl=pairwise_constraints[1])

Evaluate the clustering using Adjusted Rand Score.

metrics.adjusted_rand_score(y, clusterer.labels_)