VaganBoost: Hybrid VAE-GAN + LightGBM for Advanced Classification 0.7.6

Introduction

VAGANBoost is a hybrid generative model combining Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) with boosting techniques to enhance high-energy gamma-ray analysis.

Outlines

• Implements cVAE+cGAN and cGAN+cVAE+RandomForest models
• Designed for high-energy physics applications
• Utilizes deep learning and gradient boosting techniques

Key Features

• Hybrid Architecture: Combines deep generative models with gradient boosting
• VAE-GAN Integration: Joint latent space learning for improved feature representation
• LightGBM Classifier: State-of-the-art gradient boosting for final classification
• Automatic Feature Fusion: Combines VAE latent features with GAN-generated features
• Visualization Tools: Built-in metrics visualization and feature analysis
• PyTorch Backend: GPU-accelerated training with seamless CUDA support

Key Features Table

Troubleshooting

Common Issues:

1. CUDA Out of Memory: Reduce batch size or input dimensions
2. Poor Classification Performance:
   • Increase VAE latent dimensions
   • Adjust GAN-LightGBM feature ratio
3. Training Instability:

   model = VaganBoost(
       ...,
       vae_kl_weight=0.5,  # Adjust KL loss weight
       gan_gp_weight=10.0  # Add gradient penalty
   )
   

Installation

Prerequisites

• Python 3.6+
• NVIDIA GPU (recommended) with CUDA 11.0+

Install via pip

pip install vaganboost

From source

git clone https://github.com/AliBavarchee/vaganboost.git
cd vaganboost
pip install -e .

Quick Start

Basic Usage

from vaganboost import VaganBoost, load_data, split_data, normalize_data

# Prepare data
X, y = load_data("data.csv", target_column="label")
X_train, X_test, y_train, y_test = split_data(X, y, test_size=0.2)
X_train_norm, X_test_norm = normalize_data(X_train, X_test)

# Initialize model
model = VaganBoost(
    vae_input_dim=X_train_norm.shape[1],
    vae_latent_dim=64,
    gan_input_dim=100,
    num_class=4,
    device="cuda"
)

# Train components
model.train_vae(X_train_norm, epochs=100)
model.train_gan(X_train_norm, epochs=50)
model.train_lgbm(X_train_norm, y_train)

# Evaluate
accuracy = model.evaluate(X_test_norm, y_test)
print(f"Test Accuracy: {accuracy:.2%}")

Advanced Configuration

# Custom LightGBM parameters
lgbm_params = {
    'objective': 'multiclass',
    'num_class': 4,
    'metric': 'multi_logloss',
    'num_leaves': 63,
    'learning_rate': 0.1,
    'feature_fraction': 0.7
}

model = VaganBoost(
    vae_input_dim=128,
    vae_latent_dim=64,
    gan_input_dim=100,
    num_class=4,
    lgbm_params=lgbm_params,
    device="cuda"
)

Documentation

Core Components

Dependencies

See requirements.txt for required packages.

License

This project is licensed under the MIT License.

Contact Ali Bavarchee - ali.bavarchee@gmail.com

Project Link: https://github.com/AliBavarchee/vaganboost

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