rustcluster 0.6.2

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
• Cluster slotting — snapshot fitted clusters, assign new points 100x faster than refitting
• Pickle serialization for all fitted models
• GIL released during all compute — plays well with threads and async
• 492 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.

Cluster Slotting (Incremental Assignment)

Fit once, assign new data forever. Snapshot freezes cluster centroids — new points are assigned without re-clustering:

from rustcluster import KMeans, ClusterSnapshot

# Fit and snapshot
model = KMeans(n_clusters=50).fit(X_train)
snapshot = model.snapshot()
snapshot.save("clusters/")

# Later: load and assign new data (no refit needed)
snapshot = ClusterSnapshot.load("clusters/")
labels = snapshot.assign(X_new)                   # 100x faster than refitting

Works with KMeans, MiniBatchKMeans, and EmbeddingCluster. EmbeddingCluster snapshots bake in the full preprocessing pipeline (L2-normalize, PCA, spherical assignment).

Confidence scoring and rejection:

result = snapshot.assign_with_scores(X_new, confidence_threshold=0.3)
result.labels_       # -1 for rejected points
result.confidences_  # [0, 1) — higher means more decisive assignment
result.distances_    # distance to nearest centroid
result.rejected_     # boolean mask

Adaptive thresholds (v2): Per-cluster rejection thresholds calibrated from training data. Fixes the problem where a global threshold rejects too many points from diffuse clusters:

snapshot.calibrate(X_train)  # compute per-cluster confidence distributions
result = snapshot.assign_with_scores(X_new, adaptive_threshold=True, adaptive_percentile="p10")

Mahalanobis boundaries (v2): Diagonal Mahalanobis distance accounts for per-cluster, per-dimension variance:

snapshot.calibrate(X_train)
labels = snapshot.assign(X_new, boundary_mode="mahalanobis")

Drift detection:

report = snapshot.drift_report(X_recent)
report.global_mean_distance_  # compare to training baseline
report.relative_drift_        # per-cluster drift
report.kappa_drift_           # vMF concentration shift (spherical only, v2)
report.direction_drift_       # centroid direction shift (spherical only, v2)
report.rejection_rate_        # fraction of points beyond per-cluster bounds (requires calibrate())

rejection_rate_ is NaN until snapshot.calibrate(X_train) is called. The per-cluster bounds come from the calibration distribution, not the fit-time data.

Hierarchical slotting (v2): Cascading snapshots for multi-level classification (e.g., commodity → sub-commodity):

from rustcluster.experimental import HierarchicalSnapshot

hier = HierarchicalSnapshot.build(X_train, root_model, n_sub_clusters=10)
root_labels, child_labels = hier.assign(X_new)
hier.save("clusters/hierarchy/")

Persistence: safetensors (centroids) + JSON (metadata). A 50-cluster, 128d snapshot is ~50 KB vs GBs of training data.

Validated on 323K CROSS ruling embeddings (113x speedup, 99.86% fidelity) and 312K supplier embeddings (453x speedup, 99.94% fidelity). Hierarchical slotting improved heading purity from 37% to 54% on CROSS rulings.

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

ClusterSnapshot uses safetensors + JSON for portable, safe persistence:

snapshot = model.snapshot()
snapshot.save("clusters/")                     # safetensors + metadata.json
snapshot = ClusterSnapshot.load("clusters/")   # zero-copy load

Development

maturin develop --release              # build
cargo test --no-default-features --lib # Rust tests (208)
pytest tests/ -v                       # Python tests (284)
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