clumppy
Clustering algorithms (k-means, DBSCAN, HDBSCAN, COP-Kmeans, correlation clustering) with evaluation metrics, backed by a Rust implementation.
Python bindings for the clump Rust crate.
Install
pip install clumppy
Quick start
import clumppy
# Correlation clustering -- determines k automatically from signed edges
edges = [
(0, 1, 1.0), # positive = should be together
(0, 2, -1.0), # negative = should be apart
(1, 2, -1.0),
]
labels = clumppy.correlation_clustering(edges, n_nodes=3)
# Constrained k-means with must-link / cannot-link
data = [[0.0, 0.0], [0.1, 0.1], [10.0, 10.0], [10.1, 10.1]]
labels = clumppy.cop_kmeans(
data, k=2,
must_link=[(0, 1)],
cannot_link=[(0, 2)],
seed=42,
)
# Standard algorithms
labels, centroids = clumppy.kmeans(data, k=2, seed=42)
labels = clumppy.dbscan(data, eps=0.5, min_points=5) # noise = -1
labels = clumppy.hdbscan(data, min_cluster_size=5) # noise = -1
# Evaluation
score = clumppy.silhouette_score(data, labels)
API
| Name | Description |
|---|---|
correlation_clustering |
Signed-edge graph partitioning, auto-determines k |
cop_kmeans |
K-means with must-link and cannot-link constraints |
kmeans |
Standard k-means, returns (labels, centroids) |
dbscan |
Density-based clustering, noise labeled -1 |
hdbscan |
Hierarchical DBSCAN, noise labeled -1 |
silhouette_score |
Mean silhouette coefficient, range [-1, 1] |
calinski_harabasz_score |
Variance ratio criterion (higher = better) |
davies_bouldin_score |
Cluster similarity index (lower = better) |
numpy support
All functions accept numpy arrays or Python lists. Labels are returned as numpy int64 arrays; centroids as numpy float64 arrays.
License
MIT OR Apache-2.0
1 maintainer
Release files for clumppy 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 15.9 MB
Release files / clumppy-0.1.1-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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