kabena — K-ABENA (v2.1.0)

PyPI Python License: MIT Preprint

Même modèle, ~28 % de calcul en moins. K-ABENA exclut à chaque époque une partie des exemples déjà appris et fait voter chaque exemple conservé au nom des dispensés (repondération Horvitz-Thompson) : le gradient reste design-unbiased — preuves et mesures dans le preprint.

La promesse : 2 lignes

from kabena import Kabena
kb = Kabena()                                   # 1. défauts du preprint (v3, N=0.3)

for epoch in range(E):
    losses = per_sample_loss(model, X, y)
    active, w = kb.select(losses, y=y)          # 2. masque + poids HT
    model.fit(X[active], y[active], sample_weight=w[active])

print(kb.last_gain_)                            # fraction de calcul économisée

PyTorch : KabenaTorch().reduce(losses) · Keras : callback KabenaKeras · Hugging Face : KabenaTrainer.

Trois paramètres, pas un de plus

Paramètre Défaut Rôle
N 0.3 proportion de mineures conservées (budget)
strategy "auto" auto(=v3) / v3 / v2 / v1 — bascule transparente
seed None reproductibilité du tirage

v2 = mode régularisé optionnel (garde-fou automatique hors zone de validité) · v1 = compatibilité 1.x.

Tutoriels — 4 familles × 3 niveaux

Famille Niveau 1 (notebook) Niveau 2 (script) Niveau 3 (MLOps)
ML classique (logistique, SVM, softmax) notebook script train + serve + Docker
MLP / ANN (PyTorch et TF/Keras) notebook PyTorch · TensorFlow pipeline TorchScript
CNN (PyTorch et TF/Keras) notebook PyTorch · TensorFlow SavedModel / TF Serving
Transformer / NLP (Hugging Face) notebook DistilBERT fine-tune + serve

Reproduire le preprint (Reproducibility)

Chaque table du papier a son script seedé dans validation/ :

Table du preprint Script
Table 1 — jeux réels, parité à coût réduit table1_real_datasets.py
Table 2 — déséquilibre extrême 0,17 % table2_extreme_imbalance.py
Table 3 — moments du gradient, bruit table3_moments_noise.py
Table 4 — rivaux à budget égal (tests de permutation) table4_rivals.py
§Sensitivity — grille K × N table5_sensitivity.py 
python validation/run_all.py          # les 5 tables (QUICK=1 pour un smoke test <2 min)
python experiments/playground.py --regime fraude --strategy v3   # bac à sable

Installation

pip install kabena                     # coeur (numpy seul)
pip install "kabena[sklearn]"          # + helpers scikit-learn
pip install "kabena[torch]"            # + intégration PyTorch
pip install "kabena[tensorflow]"       # + intégration Keras
pip install "kabena[huggingface]"      # + KabenaTrainer

Licence MIT — © Jean-François Bonbhel · Changelog · Issues

Project links

Data verified by PyPI on Jul 6, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (2.1.1)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for kabena from gravatar.com kabena

Credits

Author: Jean-Francois Bonbhel

GitHub Statistics

Data verified by PyPI on Jul 6, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT

Requires

Python >=3.10

Provides Extra

sklearn torch tensorflow huggingface dev

Tags

machine-learning selective-backpropagation sample-selection horvitz-thompson sgd

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

kabena-2.1.0.tar.gz (32.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

kabena-2.1.0-py3-none-any.whl (15.0 kB view details)

Uploaded Python 3

File details

Details for the file kabena-2.1.0.tar.gz.

File metadata

  • Download URL: kabena-2.1.0.tar.gz
  • Upload date:
  • Size: 32.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for kabena-2.1.0.tar.gz
Algorithm Hash digest
SHA256 510fb55cd0d07482f2f0a4f21ef4d17a191c8bfabd4a4bbc41d94040b210b777
MD5 8919f32afd9e9be5898ddb6ac4ccb391
BLAKE2b-256 4ccf0fccbe95d56ee50cf62802c9d0e18d52474b97a0f8d9e53450123eaae44f

See more details on using hashes here.

Provenance

The following attestation bundles were made for kabena-2.1.0.tar.gz:

Publisher: release.yml on Bonbhel/kabena-ml

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file kabena-2.1.0-py3-none-any.whl.

File metadata

  • Download URL: kabena-2.1.0-py3-none-any.whl
  • Upload date:
  • Size: 15.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for kabena-2.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 cfc351f980290e5282e512633ed3e89690f0915c145f10c876e78faca56455fe
MD5 fba70cc9b910a7f0ebab5ebca7ded481
BLAKE2b-256 d516942d433b076d4bfe7cd89b61c7b974a26f8342236d18c0f6d79ea86c3de3

See more details on using hashes here.

Provenance

The following attestation bundles were made for kabena-2.1.0-py3-none-any.whl:

Publisher: release.yml on Bonbhel/kabena-ml

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

2.1.1

2 files

This release

2.1.0 This release

2 files

1.2.0

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page