Class Imbalance

quprep.clean.imbalance

Class imbalance handling — oversample, undersample, SMOTE, ADASYN.

Classes

ImbalanceHandler(strategy='oversample', sampling_strategy='auto', k_neighbors=5, random_state=42)

Balance class distributions before quantum encoding.

Supports four strategies:

• "oversample" — random duplication of minority samples (no extra deps).
• "undersample" — random removal of majority samples (no extra deps).
• "smote" — Synthetic Minority Over-sampling Technique; interpolates in feature space using k-nearest neighbours (requires scikit-learn, already a core dependency).
• "adasyn" — Adaptive Density-based Synthetic sampling; focuses synthetic samples on harder-to-learn regions (requires imbalanced-learn: pip install quprep[imbalance]).

Parameters:

Name	Type	Description	Default
strategy	('oversample', 'undersample', 'smote', 'adasyn')	Resampling strategy.	"oversample"
sampling_strategy	float or 'auto'	"auto" balances all classes to the majority class count (oversampling) or the minority class count (undersampling). A float r targets majority_count × r samples per class for oversampling, or minority_count / r for undersampling.	'auto'
k_neighbors	int	Number of nearest neighbours for SMOTE and ADASYN.	5
random_state	int	Seed for reproducibility.	42

Examples:

>>> import numpy as np
>>> import quprep as qd
>>> from quprep.core.dataset import Dataset
>>> rng = np.random.default_rng(0)
>>> X = rng.uniform(0, 1, (110, 4))
>>> y = np.array([0] * 100 + [1] * 10)
>>> ds = Dataset(data=X, labels=y)
>>> handler = qd.ImbalanceHandler(strategy="smote")
>>> ds_bal = handler.fit_transform(ds)
>>> from collections import Counter
>>> print(Counter(ds_bal.labels))
Counter({0: 100, 1: 100})

Functions

fit(dataset)

Compute class distribution and target count from dataset.

Parameters:

Name	Type	Description	Default
dataset	Dataset	Must have labels set (1-D array, single-target only).	required

fit_transform(dataset)

Fit and transform in one step.

transform(dataset)

Apply the fitted resampling strategy to dataset.

Parameters:

Name	Type	Description	Default
dataset	Dataset		required

Returns:

Type	Description
Dataset	New Dataset with resampled data and labels (shuffled).