OneBatchPAM
Implementation of OneBatchPAM: A Fast and Frugal K-Medoids Algorithm (AAAI 2025)
Install the package:
pip install git+https://github.com/antoinedemathelin/onebatch.git
Minimal examples
From data:
import numpy as np
from onebatch import OneBatchPAM
X = np.random.RandomState(0).randn(10000, 2).astype(np.float32)
km = OneBatchPAM(
n_medoids=9,
distance="euclidean",
batch_size="auto",
weighting=True,
max_iter=100,
tol=1e-6,
n_jobs=None,
random_state=0,
)
# Option 1: fit + attributes
km.fit(X)
medoids = km.medoid_indices_
labels = km.predict(X) # or use km.labels_
# Option 2: fit_predict returns medoid indices
medoids2 = km.fit_predict(X)
From pre-computed distance matrix:
import numpy as np
from sklearn.metrics import pairwise_distances
from onebatch import OneBatchPAM
X = np.random.RandomState(0).randn(10000, 2).astype(np.float32)
# Build a (n_samples, m) distance matrix to m candidate points
m = 1000
cand_idx = np.random.choice(X.shape[0], size=m, replace=False)
K = pairwise_distances(X, X[cand_idx], metric="euclidean")
km = OneBatchPAM(
n_medoids=9,
distance="precomputed",
weighting=True,
max_iter=100,
tol=1e-6,
random_state=0,
)
km.fit(K)
medoids = km.medoid_indices_
# Labels are available after fit via km.labels_
# Note: predict() is intended for feature-space distances; for precomputed, prefer km.labels_
For visualization:
import matplotlib.pyplot as plt
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap="tab10", s=3, alpha=0.4)
plt.plot(X[medoids, 0], X[medoids, 1], "ko", markersize=12, markeredgewidth=2, markerfacecolor="none")
plt.show()
API notes
- n_jobs: number of parallel jobs for
sklearn.metrics.pairwise_distances. - random_state:
intornumpy.random.Generatorcontrolling sampling and initialization. - fit_predict: returns medoid indices (not labels). After
fit, labels are inlabels_. - distance != 'precomputed':
batch_sizecontrols candidate columns;'auto'uses a heuristic. - distance == 'precomputed': pass a distance matrix of shape
(n_samples, m)where each column contains distances to a candidate point.batch_sizeis ignored in this mode.
1 maintainer
Metadata
1 maintainer
Release files for onebatch 0.1.0
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| Tags | CPython 3.9 Linux glibc 2.17+ x86-32 Linux glibc 2.5+ x86-32 |
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twine/6.1.0 CPython/3.13.7
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Transparency logRelease files / onebatch-0.1.0-cp39-cp39-macosx_11_0_arm64.whl
| Download URL | onebatch-0.1.0-cp39-cp39-macosx_11_0_arm64.whl |
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| Size | 253.3 kB |
| Tags | CPython 3.9 macOS 11.0+ ARM64 |
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SHA-256 checksum How to use checksums |
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twine/6.1.0 CPython/3.13.7
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