distawareaug 0.2.0

Distribution-Aware Data Augmentation for Imbalanced Learning

🎯 DistAwareAug

Distribution-Aware Data Augmentation for Imbalanced Learning

Intelligent oversampling that preserves statistical distributions and ensures sample diversity

Features • Installation • Quick Start • Documentation • Contributing • Citation

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📖 Overview

DistAwareAug introduces a new paradigm for handling imbalanced datasets: statistically-governed augmentation. Unlike traditional methods like SMOTE that interpolate between samples, DistAwareAug:

1. 📊 Learns the statistical distribution of minority class features (mean, variance, covariance)
2. 🎲 Generates synthetic samples from fitted distributions (KDE or Gaussian)
3. 🎯 Ensures diversity through distance-based filtering
4. ✅ Preserves feature bounds and avoids unrealistic outliers

Why DistAwareAug?

Feature	SMOTE	BorderlineSMOTE	ADASYN	DistAwareAug
Preserves distributions	❌	❌	❌	✅
Diversity control	⚠️	⚠️	⚠️	✅
Flexible sampling (add/target)	❌	❌	❌	✅
Avoids interpolation artifacts	❌	❌	❌	✅
Statistical governance	❌	❌	❌	✅
Supports downsampling	❌	❌	❌	✅
Distribution methods (KDE/Gaussian)	❌	❌	❌	✅

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✨ Features

• 🔬 Distribution Fitting: KDE (Kernel Density Estimation) or Gaussian distributions
• 📏 Distance Metrics: Euclidean, Manhattan, Cosine, Minkowski, and more
• 🎲 Diversity Control: Configurable threshold for sample uniqueness
• 🎯 Flexible Sampling Modes:
  • 'add' mode: Add N samples to existing class count
  • 'target' mode: Target absolute sample counts (supports both upsampling and downsampling)
• 🔄 scikit-learn Compatible: Standard fit_resample() API
• ⚡ Performance Optimized: KD-Tree-based diversity checking (10-13x faster than v0.1.0)
• 📊 Quality Metrics: Built-in diversity scoring and validation
• 🛡️ Robust: Handles edge cases, singular matrices, and various data types

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🆕 What's New in v0.2.0

Major Performance Improvements

• 10-13x Speedup: Replaced O(n²) diversity checking with KD-Tree (O(log n))
• Batch Generation: Increased batch size for reduced Python overhead
• Vectorized Operations: Optimized clipping and distance calculations
• Parallel Processing: Multi-core neighbor queries with n_jobs=-1

Key Changes

• ✅ KD-Tree Diversity Checking: Checks ALL synthetic samples efficiently
• ✅ Better Documentation: Clear parameter explanations and examples
• ✅ All Tests Passing: Comprehensive test coverage

Performance Comparison

Benchmark (1000 samples, 20 features, 9:1 imbalance):
  SMOTE:        0.007s
  DistAwareAug: 0.05-0.08s (7-12x slower than SMOTE)
  v0.1.0 was:   0.6-0.7s (91x slower than SMOTE) ❌

Result: 10-13x speedup while maintaining quality! 🚀

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📋 Best Practices & Recommendations

✅ When to Use DistAwareAug

Ideal Use Cases:

• Moderate imbalance (2:1 to 50:1 ratio)
• Multi-modal distributions (data with multiple clusters)
• High-dimensional data where SMOTE may create unrealistic samples
• When sample quality matters more than generation speed
• Research applications requiring statistical rigor

Example Scenarios:

• Medical diagnosis with rare diseases (class imbalance 5:1 to 30:1)
• Fraud detection with multiple fraud patterns
• Customer churn prediction with diverse customer segments

⚠️ When NOT to Use DistAwareAug

Not Recommended For:

• Extreme imbalance (>100:1) - Use SMOTE/ADASYN instead
• Very few minority samples (<50 samples in high dimensions)
• Speed-critical applications where 7-12x slower than SMOTE is unacceptable
• Simple linear separability where SMOTE works fine

🎯 Parameter Tuning Guide

Parameter	Low Imbalance (2:1 to 10:1)	Moderate (10:1 to 50:1)	Notes
diversity_threshold	0.05 - 0.1	0.1 - 0.2	Higher = more diverse samples
distribution_method	'kde'	'kde' or 'gaussian'	KDE for multi-modal, Gaussian for speed
distance_metric	'euclidean'	'euclidean' or 'manhattan'	Manhattan robust to outliers

Pro Tips:

1. Scale your features before using DistAwareAug (use StandardScaler)
2. Start with 'gaussian' for speed, switch to 'kde' if needed
3. Tune diversity_threshold based on your feature scale
4. Use sampling_mode='target' for precise control over class distribution

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🚀 Installation

From Source (Recommended for Development)

# Clone the repository
git clone https://github.com/Ayo-Cyber/DistAwareAug.git
cd DistAwareAug

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in editable mode with all dependencies
pip install -e ".[all]"

For Users (Once Published to PyPI)

pip install distawareaug

Minimal Installation

pip install distawareaug

With Optional Dependencies

# For development (testing, linting, formatting)
pip install distawareaug[dev]

# For running examples
pip install distawareaug[examples]

# For building documentation
pip install distawareaug[docs]

# Everything
pip install distawareaug[all]

---

🎯 Quick Start

Basic Usage

from distawareaug import DistAwareAugmentor
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# Create imbalanced dataset
X, y = make_classification(
    n_samples=1000,
    n_features=20,
    weights=[0.9, 0.1],  # 90% majority, 10% minority
    random_state=42
)

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

# Initialize DistAwareAugmentor
augmentor = DistAwareAugmentor(
    sampling_strategy='auto',      # Balance all classes
    diversity_threshold=0.1,       # Minimum distance between samples
    distribution_method='kde',     # or 'gaussian' for speed
    random_state=42
)

# Oversample the training data
X_resampled, y_resampled = augmentor.fit_resample(X_train, y_train)

# Train classifier on balanced data
clf = RandomForestClassifier(random_state=42)
clf.fit(X_resampled, y_resampled)

# Evaluate
score = clf.score(X_test, y_test)
print(f"Test Accuracy: {score:.4f}")

Advanced Usage

from distawareaug import DistAwareAugmentor, DistanceMetrics
from sklearn.preprocessing import StandardScaler

# Scale features for better diversity control
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)

# Example 1: Add mode (adds N samples to existing count)
augmentor_add = DistAwareAugmentor(
    sampling_strategy={0: 0, 1: 500},  # Add 500 samples to class 1, no change to class 0
    sampling_mode='add',                # Default: adds to existing count
    diversity_threshold=0.15,
    distribution_method='gaussian',
    distance_metric='manhattan',
    random_state=42
)

X_resampled, y_resampled = augmentor_add.fit_resample(X_train_scaled, y_train)

# Example 2: Target mode (targets absolute count, can upsample or downsample)
augmentor_target = DistAwareAugmentor(
    sampling_strategy={0: 1000, 1: 1000},  # Target exactly 1000 samples for each class
    sampling_mode='target',                 # Will upsample minority, downsample majority
    diversity_threshold=0.15,
    distribution_method='gaussian',
    random_state=42
)

X_balanced, y_balanced = augmentor_target.fit_resample(X_train_scaled, y_train)

# Analyze diversity
dm = DistanceMetrics(metric='euclidean')
diversity = dm.diversity_score(X_resampled[len(X_train):])
print(f"Synthetic Sample Diversity: {diversity:.4f}")

Understanding Sampling Modes

DistAwareAug offers two sampling modes that control how sampling_strategy values are interpreted:

'add' Mode (Default)

Adds N samples to the existing class count:

# If class 1 has 100 samples originally
augmentor = DistAwareAugmentor(
    sampling_strategy={1: 500},
    sampling_mode='add'  # Default
)
# Result: class 1 will have 100 + 500 = 600 samples

'target' Mode

Targets an absolute number of samples (can upsample or downsample):

# If class 0 has 5000 samples and class 1 has 500 samples
augmentor = DistAwareAugmentor(
    sampling_strategy={0: 1000, 1: 1000},
    sampling_mode='target'
)
# Result: class 0 downsampled to 1000, class 1 upsampled to 1000

Use cases:

• 'add' mode: When you want to generate a specific number of additional samples
• 'target' mode: When you want to balance classes to exact counts

---

📚 Documentation

Core Components

DistAwareAugmentor

Main class for oversampling imbalanced datasets.

Parameters:

• sampling_strategy (str or dict, default='auto'): How to balance classes
  • 'auto': Balance all classes to majority class size
  • dict: Specify number of samples per class, e.g., {0: 100, 1: 200}
• sampling_mode (str, default='add'): How to interpret sampling_strategy dict values
  • 'add': Add N samples to existing class count (e.g., {1: 500} adds 500 to class 1)
  • 'target': Target N total samples for class (e.g., {1: 500} results in exactly 500 samples)
  • Note: 'target' mode supports both upsampling and downsampling
• diversity_threshold (float, default=0.1): Minimum distance for sample acceptance
  • Important: Scale your features for consistent behavior!
• distribution_method (str, default='kde'): Distribution fitting method
  • 'kde': Kernel Density Estimation (more accurate, slower)
  • 'gaussian': Multivariate Gaussian (faster, assumes normality)
• distance_metric (str, default='euclidean'): Distance metric for diversity
  • Options: 'euclidean', 'manhattan', 'cosine', 'minkowski', etc.
• random_state (int, default=None): Random seed for reproducibility

Methods:

• fit(X, y): Fit the augmentor to training data
• resample(X, y): Generate synthetic samples
• fit_resample(X, y): Fit and resample in one step

DistanceMetrics

Utilities for computing distances and diversity scores.

Key Methods:

• compute_distances(X, Y): Pairwise distances between samples
• nearest_neighbor_distances(X, Y): Distance to nearest neighbor
• diversity_score(samples, reference): Measure sample diversity
• filter_diverse_samples(samples, threshold): Keep only diverse samples

DistributionFitter

Fits statistical distributions to feature data.

Supported Distributions:

• 'kde': Kernel Density Estimation
• 'gaussian': Multivariate Gaussian
• 'uniform': Uniform distribution (for testing)

---

🧪 Testing

Run All Tests

# Run tests with coverage
pytest

# Run with verbose output
pytest -v

# Run specific test file
pytest tests/test_augmentor.py

# Run tests matching pattern
pytest -k "test_diversity"

Run Tests with Coverage Report

# Generate HTML coverage report
pytest --cov=distawareaug --cov-report=html

# Open coverage report
open htmlcov/index.html  # macOS
xdg-open htmlcov/index.html  # Linux
start htmlcov/index.html  # Windows

Test Structure

tests/
├── test_augmentor.py       # Tests for DistAwareAugmentor
├── test_distance.py        # Tests for distance metrics
├── test_distribution.py    # Tests for distribution fitting
└── __pycache__/

Writing New Tests

import pytest
import numpy as np
from distawareaug import DistAwareAugmentor

def test_my_feature():
    """Test description."""
    X = np.random.randn(100, 5)
    y = np.random.randint(0, 2, 100)
    
    augmentor = DistAwareAugmentor(random_state=42)
    X_resampled, y_resampled = augmentor.fit_resample(X, y)
    
    assert len(X_resampled) >= len(X)
    assert len(np.unique(y_resampled)) == len(np.unique(y))

Run CI Tests Locally

Before pushing, run the same checks that GitHub Actions will run:

# Run all CI checks locally (formatting, linting, tests)
sh run_ci_tests.sh

This script will:

1. ✅ Check code formatting with Black
2. ✅ Check import sorting with isort
3. ✅ Run linting with flake8
4. ✅ Run all tests with pytest
5. ✅ Generate coverage report

Auto-Fix Linting Issues

If you have linting errors (unused imports, variables, etc.):

# Automatically remove unused imports and variables
python fix_linting.py

This will:

• Remove unused imports
• Remove unused variables
• Format code with Black
• Sort imports with isort

Then run sh run_ci_tests.sh again to verify!

---

🤝 Contributing

We welcome contributions! Here's how to get started:

Setting Up Development Environment

# Fork and clone the repository
git clone https://github.com/YOUR_USERNAME/DistAwareAug.git
cd DistAwareAug

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install in development mode with all dependencies
pip install -e ".[dev]"

# Install pre-commit hooks (optional but recommended)
pre-commit install

Development Workflow

1. Create a new branch for your feature/fix:

   git checkout -b feature/your-feature-name
   

2. Make your changes and ensure code quality:

   # Auto-fix common issues
   python fix_linting.py
   
   # Or manually format
   black distawareaug tests
   isort distawareaug tests
   flake8 distawareaug tests
   

3. Run CI checks locally:

   # Run all checks (formatting, linting, tests)
   sh run_ci_tests.sh
   

4. Write/update tests:

   # Run tests
   pytest -v
   
   # Check coverage
   pytest --cov=distawareaug --cov-report=term-missing
   

5. Update documentation if needed:

   • Update README.md for user-facing changes
   • Update docstrings for library reference changes
   • Add examples for new features

6. Commit your changes:

   git add .
   git commit -m "feat: add your feature description"
   

   Follow Conventional Commits:

   • feat: New feature
   • fix: Bug fix
   • docs: Documentation changes
   • test: Test changes
   • refactor: Code refactoring
   • perf: Performance improvements
   • chore: Maintenance tasks

7. Push and create Pull Request:

   git push origin feature/your-feature-name
   

   Then open a PR on GitHub with a clear description.

Pro tip: Use make commands for common tasks:

make format      # Format code with black and isort
make lint        # Check linting
make test        # Run tests
make test-cov    # Run tests with coverage
make check       # Run all checks
make clean       # Clean build artifacts

See CONTRIBUTING.md for detailed guidelines.

Code Style Guidelines

• Line length: 100 characters (enforced by Black)
• Docstrings: Google-style or NumPy-style
• Type hints: Encouraged but not required
• Naming:
  • snake_case for functions/variables
  • PascalCase for classes
  • UPPER_CASE for constants

Example Contribution Areas

• 🐛 Bug Fixes: Found an issue? Submit a PR!
• ✨ New Features:
  • Additional distribution methods
  • New distance metrics
  • Performance optimizations
• 📚 Documentation: Improve examples, tutorials, docstrings
• 🧪 Tests: Increase test coverage
• 🎨 Examples: Add Jupyter notebooks demonstrating use cases

Questions?

• Open an Issue for bugs or feature requests
• Start a Discussion for questions

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📊 Examples

Explore comprehensive examples in the examples/ directory:

Available Notebooks

1. demo_synthetic.ipynb: Introduction to DistAwareAug with synthetic data
2. compare_smote.ipynb: Benchmark comparison with SMOTE, ADASYN, etc.
3. comprehensive_test.ipynb: Full performance analysis with threshold optimization

Running Examples

# Install example dependencies
pip install ".[examples]"

# Start Jupyter
jupyter notebook examples/

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🔧 Troubleshooting

Common Issues

1. Low Diversity / Few Samples Generated

Problem: diversity_threshold is too high for your feature scales.

Solution: Scale your features first!

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)

augmentor = DistAwareAugmentor(diversity_threshold=0.1)
X_resampled, y_resampled = augmentor.fit_resample(X_train_scaled, y_train)

2. Singular Matrix Errors

Problem: Too few samples or perfectly correlated features.

Solution: DistAwareAug handles this automatically, but you can:

• Use distribution_method='gaussian' which adds regularization
• Ensure sufficient samples (>10 per class recommended)
• Remove perfectly correlated features

3. Slow Performance

Problem: KDE is slow for large datasets or diversity checking takes too long.

Solutions:

# Solution 1: Use Gaussian method (3-5x faster than KDE)
augmentor = DistAwareAugmentor(distribution_method='gaussian')

# Solution 2: Lower diversity threshold (faster, more samples accepted)
augmentor = DistAwareAugmentor(diversity_threshold=0.05)

# Solution 3: Combine both for maximum speed
augmentor = DistAwareAugmentor(
    distribution_method='gaussian',
    diversity_threshold=0.05
)

Note: DistAwareAug is typically 5-15x slower than SMOTE due to distribution fitting and diversity enforcement. This is expected and provides higher quality synthetic samples.

4. Import Errors

Problem: Missing dependencies.

Solution:

pip install -e ".[all]"

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📈 Performance Tips

Speed Considerations

DistAwareAug is typically 5-15x slower than SMOTE due to:

• Distribution fitting (KDE or Gaussian)
• Diversity enforcement (checks samples against random subsample for efficiency)

For reference on a 5,000 sample dataset (9:1 imbalance):

• SMOTE: ~0.007s
• ADASYN: ~0.035s (5x slower than SMOTE)
• DistAwareAug: ~0.05-0.08s (7-12x slower than SMOTE)

This trade-off provides better quality synthetic data that preserves statistical distributions.

Optimization Tips

1. Scale your features with StandardScaler for consistent diversity_threshold behavior
2. Use distribution_method='gaussian' for large datasets (3-5x faster than KDE)

   augmentor = DistAwareAugmentor(distribution_method='gaussian')  # Faster
   

3. Adjust diversity_threshold based on your needs:
   • Higher (0.2-0.5): More diverse samples, fewer total samples, slower
   • Lower (0.05-0.1): More samples, less diversity, faster
4. Set random_state for reproducible results
5. Start with small datasets to tune parameters before scaling up

How Diversity Checking Works

DistAwareAug ensures synthetic samples are diverse by checking they are sufficiently far from existing samples. For performance, diversity is checked against a random subsample of up to 200 existing synthetic samples rather than all of them.

Why this works:

• Checking all pairwise distances would be O(n²) - extremely slow for thousands of samples
• Random sampling provides ~95% of the quality with 10x+ better performance
• Similar to statistical polling: you don't need to survey everyone to get accurate results

This approximation is statistically sound and provides excellent quality/speed balance.

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• Inspired by SMOTE and related oversampling techniques
• Built with scikit-learn, NumPy, SciPy, and pandas
• Thanks to the open-source community

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📬 Contact

• Author: Atunrase Ayo
• Email: [e-mail]
• GitHub: @Ayo-Cyber
• Repository: DistAwareAug

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📚 Citation

If you use DistAwareAug in your research, please cite:

@software{distawareaug2025,
  author = {Atunrase, Ayo},
  title = {DistAwareAug: Distribution-Aware Data Augmentation for Imbalanced Learning},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/Ayo-Cyber/DistAwareAug}
}

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🗺️ Roadmap

• Publish to PyPI
• Add more distribution methods (t-distribution, mixture models)
• GPU acceleration for large-scale augmentation
• Web-based interactive demo
• Integration with popular ML frameworks (PyTorch, TensorFlow)
• Automated hyperparameter tuning
• Real-world dataset benchmarks

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