rustcluster 0.3.3

A Rust-backed Python clustering library

rustcluster

Fast, Rust-backed clustering for Python. Six algorithms, sklearn-compatible API, purpose-built embedding clustering with 11x PCA speedup.

Highlights

• 6 algorithms: KMeans, MiniBatchKMeans, DBSCAN, HDBSCAN, AgglomerativeClustering, EmbeddingCluster
• EmbeddingCluster — purpose-built pipeline for OpenAI/Cohere/Voyage embeddings (L2-normalize → PCA → spherical K-means)
• EmbeddingReducer — standalone PCA transformer with save/load (fit once, cluster for free)
• faer-accelerated PCA — 11x faster than hand-rolled matmul via SIMD-optimized GEMM
• 3 distance metrics: euclidean, cosine, manhattan
• 3 evaluation metrics: silhouette score, Calinski-Harabasz, Davies-Bouldin
• KD-tree acceleration for DBSCAN/HDBSCAN neighbor queries (10-200x on low-d data)
• Native f32/f64 — no silent upcast, doubles cache efficiency with f32
• Pickle serialization for all fitted models
• GIL released during all compute — plays well with threads and async
• 416 tests across Rust and Python

Installation

pip install rustcluster

Or from source (requires Rust toolchain + Python 3.10+):

pip install maturin
git clone https://github.com/mfbaig35r/rustcluster.git
cd rustcluster
maturin develop --release

Quickstart

K-Means

from rustcluster import KMeans

model = KMeans(n_clusters=3, random_state=42)
model.fit(X)
model.labels_           # cluster assignments
model.cluster_centers_  # centroids (k x d)
model.inertia_          # sum of squared distances
model.predict(X_new)    # assign new data

Embedding Clustering

Purpose-built pipeline for dense embedding vectors (OpenAI, Cohere, Voyage, etc.):

from rustcluster.experimental import EmbeddingCluster

model = EmbeddingCluster(n_clusters=50, reduction_dim=128)
model.fit(embeddings)          # L2-normalize → PCA → spherical K-means
model.labels_                  # cluster assignments
model.cluster_centers_         # unit-norm centroids in reduced space
model.intra_similarity_        # per-cluster cosine similarity
model.reduced_data_            # access PCA-reduced data

EmbeddingReducer (Fit Once, Cluster Many)

PCA is 99% of the embedding pipeline runtime. Separate reduction from clustering to iterate for free:

from rustcluster.experimental import EmbeddingReducer

# Pay the PCA cost once
reducer = EmbeddingReducer(target_dim=128)
X_reduced = reducer.fit_transform(embeddings)  # 323K × 1536 → 128 in ~56s
reducer.save("pca_128.bin")

# Iterate on clustering for free
reducer = EmbeddingReducer.load("pca_128.bin")
X_reduced = reducer.transform(new_embeddings)

EmbeddingCluster(n_clusters=50, reduction_dim=None).fit(X_reduced)   # ~4s
EmbeddingCluster(n_clusters=100, reduction_dim=None).fit(X_reduced)  # ~8s
EmbeddingCluster(n_clusters=200, reduction_dim=None).fit(X_reduced)  # ~15s

Matryoshka models (e.g., text-embedding-3-small) can skip PCA entirely:

reducer = EmbeddingReducer(target_dim=128, method="matryoshka")
X_reduced = reducer.fit_transform(embeddings)  # instant — just truncates + L2-normalizes

See the embedding clustering guide for full documentation.

Mini-Batch K-Means

from rustcluster import MiniBatchKMeans

model = MiniBatchKMeans(n_clusters=3, batch_size=256, random_state=42)
model.fit(X_large)      # scales to large datasets

DBSCAN

from rustcluster import DBSCAN

model = DBSCAN(eps=0.5, min_samples=5)
model.fit(X)
model.labels_                # -1 for noise
model.core_sample_indices_   # core point indices

HDBSCAN

from rustcluster import HDBSCAN

model = HDBSCAN(min_cluster_size=5)
model.fit(X)
model.labels_              # -1 for noise
model.probabilities_       # soft membership [0, 1]
model.cluster_persistence_ # per-cluster stability

Agglomerative Clustering

from rustcluster import AgglomerativeClustering

model = AgglomerativeClustering(n_clusters=3, linkage="ward")
model.fit(X)
model.labels_     # cluster assignments
model.children_   # merge history
model.distances_  # distance at each merge

Evaluation Metrics

from rustcluster import silhouette_score, calinski_harabasz_score, davies_bouldin_score

silhouette_score(X, labels)         # [-1, 1], higher is better
calinski_harabasz_score(X, labels)  # higher is better
davies_bouldin_score(X, labels)     # lower is better

Distance Metrics

All algorithms accept a metric parameter:

KMeans(n_clusters=5, metric="cosine")
DBSCAN(eps=0.3, metric="manhattan")
HDBSCAN(min_cluster_size=5, metric="euclidean")

Metric	Aliases	KD-tree acceleration	Notes
"euclidean"	"l2"	Yes	Default for all algorithms
"cosine"		No (brute force)	K-means forces Lloyd (Hamerly assumes Euclidean)
"manhattan"	"cityblock", "l1"	Yes

Ward linkage requires euclidean metric.

Performance

K-Means vs scikit-learn

Single-threaded, n_init=1, median of 5 runs:

n	d	k	Speedup vs sklearn
1,000	8	8	2.9x
10,000	8	8	2.4x
100,000	8	32	3.2x
100,000	32	32	1.4x

DBSCAN and HDBSCAN use KD-tree acceleration for d <= 16 with euclidean or manhattan metrics, reducing neighbor queries from O(n^2) to O(n log n).

Embedding Clustering

Measured on 323K embeddings (text-embedding-3-small, 1536d → 128d, K=98, Apple Silicon):

Workflow	Time
Full pipeline (PCA + cluster)	58s
Subsequent run (cached reduced data)	7.5s
5 clustering configs on cached data	74s
Matryoshka (no PCA needed)	~5s

Full benchmarks: python benches/benchmark.py

Serialization

All models support pickle:

import pickle

model = KMeans(n_clusters=3).fit(X)
data = pickle.dumps(model)
model_restored = pickle.loads(data)  # fitted state preserved

EmbeddingReducer uses a compact binary format:

reducer.save("pca_128.bin")                   # 1.5 KB
reducer = EmbeddingReducer.load("pca_128.bin") # instant

Development

maturin develop --release              # build
cargo test --no-default-features --lib # Rust tests (182)
pytest tests/ -v                       # Python tests (234)
python benches/benchmark.py            # benchmark vs sklearn
cargo fmt -- --check                   # formatting
cargo clippy --no-default-features --lib -- -D warnings  # linting

Architecture

Three-layer kernel design separating concerns:

1. PyO3 boundary (src/lib.rs) — input validation, GIL release, dtype dispatch
2. Algorithm logic (src/kmeans.rs, etc.) — iteration, convergence, ndarray types
3. Hot kernel (src/utils.rs, src/distance.rs) — raw &[F] slices for auto-vectorization

The embedding pipeline adds:

4. Embedding module (src/embedding/) — spherical K-means, PCA (faer-backed), vMF refinement, EmbeddingReducer

See docs/architecture-decisions.md for details and docs/lessons-building-rustcluster.md for the full build story.

Contributing

See CONTRIBUTING.md for how to add algorithms, distance metrics, and tests.

License

MIT