fairsample 1.0.0

Techniques for handling class overlapping with complexity measures

FairSample

Fair sampling for imbalanced datasets with 14+ resampling techniques and 40+ complexity measures.

Why FairSample?

Most imbalanced learning packages only provide resampling techniques. FairSample adds complexity measures to help you understand why your dataset is difficult and which technique works best.

Installation

pip install fairsample

Quick Start

from fairsample import RFCL
from fairsample.complexity import ComplexityMeasures
import pandas as pd

# Load data
df = pd.read_csv('data.csv')
X = df.drop('target', axis=1)
y = df['target']

# Check complexity
cm = ComplexityMeasures(X, y)
complexity = cm.analyze_overlap()
print(f"Overlap (N3): {complexity['N3']:.4f}")

# Apply resampling
sampler = RFCL(random_state=42)
X_resampled, y_resampled = sampler.fit_resample(X, y)

# Use resampled data
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(X_resampled, y_resampled)

Features

14+ Resampling Techniques:

• RFCL, NUS, URNS - Overlap-based undersampling
• SVDDWSMOTE, ODBOT, EHSO - Hybrid methods
• NBUS, KMeansUndersampling - Clustering-based (multiple variants)
• OSM - Comprehensive overlap handling
• RandomOverSampler, RandomUnderSampler - Baselines

40+ Complexity Measures:

• Feature Overlap: F1, F1v, F2, F3, F4, Input Noise
• Instance Overlap: N3, N4, kDN, CM, R-value, D3, SI, Borderline, Degree of Overlap
• Structural: N1, N2, T1, DBC, LSC, Clust, NSG, ICSV, ONB
• Multiresolution: Purity, Neighbourhood Separability, MRCA, C1, C2

Usage

Compare Multiple Techniques

from fairsample.utils import compare_techniques

results = compare_techniques(
    X, y,
    techniques=['RFCL', 'NUS', 'URNS'],
    complexity_measures='basic'
)
print(results.sort_values('N3'))  # Lower N3 = less overlap

Get All Complexity Measures

# All measures
all_measures = cm.get_all_complexity_measures(measures='all')

# By category
feature_measures = cm.get_all_complexity_measures(measures='feature')

# Specific measures
selected = cm.get_all_complexity_measures(measures=['N3', 'F1', 'N1'])

Compare Before/After

from fairsample.complexity import compare_pre_post_overlap

X_resampled, y_resampled = sampler.fit_resample(X, y)
comparison = compare_pre_post_overlap(X, y, X_resampled, y_resampled)
print(comparison['improvements'])

API

All techniques follow scikit-learn's API:

sampler = RFCL(random_state=42)
X_resampled, y_resampled = sampler.fit_resample(X, y)

Requirements

Python 3.8+ with numpy, scikit-learn, scipy, pandas, matplotlib, seaborn

Contributing

Contributions welcome! Submit a PR or open an issue.

License

MIT License

Citation

@software{fairsample,
  author = {Mohd Uwaish},
  title = {FairSample: Techniques for handling class overlapping problems},
  year = {2026},
  url = {https://github.com/mohdUwaish59/fairsample}
}