skclust

Scikit-learn compatible clustering toolkit for hierarchical clustering, k-nearest neighbors graphs, consensus network analysis, and cluster validation.

Installation

pip install skclust

Optional dependencies for specific features:

pip install dynamicTreeCut     # Dynamic tree cutting
pip install leidenalg igraph   # Leiden clustering
pip install faiss-cpu           # Fast approximate kNN (or faiss-gpu)
pip install deslib              # FAISS-based kNN classifier/transformer
pip install matplotlib seaborn  # Visualization

Quick Start

Hierarchical Clustering

from sklearn.datasets import make_blobs
from skclust.hierarchical import HierarchicalClustering

X, y = make_blobs(n_samples=100, centers=4, random_state=42)

hc = HierarchicalClustering(method='ward', cut_method='dynamic', min_cluster_size=5)
labels = hc.fit_transform(X)

fig, axes = hc.plot(show_clusters=True)

Consensus Leiden Clustering

import igraph as ig
from skclust.graph import ConsensusLeidenClustering

graph = ig.Graph.Famous('Zachary')
graph.vs['name'] = [f'node_{i}' for i in range(graph.vcount())]

leiden = ConsensusLeidenClustering(n_iter=100, resolution_parameter=1.0, n_jobs=-1, random_state=42)
labels = leiden.fit_transform(graph)

K-Nearest Neighbors Graph

import numpy as np
from skclust.neighbors import KNeighborsCosineGraph

embeddings = np.random.randn(1000, 128).astype(np.float32)
embeddings /= np.linalg.norm(embeddings, axis=1, keepdims=True)

knn = KNeighborsCosineGraph(n_neighbors=10)
knn.fit(embeddings)
graph = knn.to_igraph()

Modules

skclust.hierarchical

skclust.graph

skclust.neighbors

Utility functions: kneighbors_graph_from_transformer, kneighbors_to_snn_graph, brute_force_kneighbors_graph_from_rectangular_distance, pairwise_distances_kneighbors, convert_distance_matrix_to_kneighbors_matrix, kneighbors_to_igraph, kneighbors_classification_assignment_score

skclust.distance

skclust.metrics

skclust.utils

Author

Josh L. Espinoza

License

Apache License 2.0 — see LICENSE for details.

Citation

The hierarchical clustering implementation is based on the Soothsayer framework:

Espinoza JL, Dupont CL, O'Rourke A, Beyhan S, Morales P, et al. (2021) Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach. PLOS Computational Biology 17(3): e1008857. doi:10.1371/journal.pcbi.1008857

@article{espinoza2021predicting,
  title={Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach},
  author={Espinoza, Josh L and Dupont, Chris L and O'Rourke, Aubrie and Beyhan, Seherzada and Morales, Paula and others},
  journal={PLOS Computational Biology},
  volume={17},
  number={3},
  pages={e1008857},
  year={2021},
  doi={10.1371/journal.pcbi.1008857}
}