pyloras 0.1.0b1

LoRAS: An oversampling approach for imbalanced datasets

LoRAS

Localized Random Affine Shadowsampling

This repo provides a python implementation of an imbalanced dataset oversampling technique known as Localized Random Affine Shadowsampling (LoRAS). This implementation piggybacks off the package imbalanced-learn and thus aims to be as compatible as possible with it.

Dependencies

• imbalanced-learn

Installation

Installation requires an installation of poetry and the following shell commands:

$ git clone https://github.com/zoj613/pyloras.git
$ cd pyloras/
$ poetry install
# add package to python's path
$ export PYTHONPATH=$PWD:$PYTHONPATH 

Usage

from collections import Counter
from pyloras import LORAS
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=5000, n_features=2, n_informative=2,
                           n_redundant=0, n_repeated=0, n_classes=3,
                           n_clusters_per_class=1,
                           weights=[0.01, 0.05, 0.94],
                           class_sep=0.8, random_state=0)

lrs = LORAS(random_state=0, embedding_params={'init':'pca', 'n_iter':250})
X_resampled, y_resampled = lrs.fit_resample(X, y)
print(sorted(Counter(y_resampled).items()))
[(0, 4672), (1, 4454), (2, 4674)]

References

Bej, S., Davtyan, N., Wolfien, M. et al. LoRAS: an oversampling approach for imbalanced datasets. Mach Learn 110, 279–301 (2021). https://doi.org/10.1007/s10994-020-05913-4