Skip to main content

Active semi-supervised clustering algorithms for scikit-learn

Project description


Active semi-supervised clustering algorithms for scikit-learn.


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


pip install active-semi-supervised-clustering


from sklearn import datasets, metrics
from active_semi_clustering.semi_supervised.pairwise_constraints import PCKMeans
from 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), oracle=oracle)
pairwise_constraints = active_learner.pairwise_constraints_

Then, use the constraints to do the clustering.

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

Evaluate the clustering using Adjusted Rand Score.

metrics.adjusted_rand_score(y, clusterer.labels_)

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

active-semi-supervised-clustering-0.0.1.tar.gz (10.5 kB view hashes)

Uploaded source

Built Distribution

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page