sdg-core-lib 0.1.9.dev6

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GENESIS Core Lib

🧬 Advanced Synthetic Data Generation Library for Python 3.12+

GENESIS Core Lib is a powerful, extensible library for generating high-quality synthetic data using state-of-the-art machine learning models. Perfect for data augmentation, privacy preservation, and ML model testing.

✨ Key Features

• 🎯 Multiple Model Types: VAEs (TabularVAE, TimeSeriesVAE) and CTGAN
• 📊 Data Type Support: Tabular data, time series with group_index, and custom datasets
• 🔧 Function-Based Generation: Mathematical functions for controlled data generation
• 📈 Quality Evaluation: Built-in metrics for data quality assessment
• 🚀 High Performance: Optimized for both CPU and GPU processing
• 🔒 Privacy Focused: Designed with privacy preservation in mind

🛠️ Installation

Quick Install

pip install sdg-core-lib

Development Install

git clone https://github.com/emiliocimino/generator_core_lib.git
cd generator_core_lib
pip install -e ".[dev]"

🚀 Quick Start

from sdg_core_lib import Job

# Text-based JSON configuration (no file needed)
config = {
    "n_rows": 1000,
    "model": {
        "algorithm_name": "sdg_core_lib.data_generator.models.VAEs.implementation.TabularVAE.TabularVAE",
        "model_name": "customer_synthetic_model"
    },
    "dataset": {
        "dataset_type": "table",
        "data": [
            {
                "column_data": [13.71, 13.4, 13.27, 13.17, 14.13, 13.88, 13.24, 13.73],
                "column_name": "alcohol",
                "column_type": "continuous",
                "column_datatype": "float64"
            },
            {
                "column_data": [5.65, 3.91, 4.28, 2.59, 4.1, 3.9, 3.8, 4.2],
                "column_name": "malic_acid",
                "column_type": "continuous",
                "column_datatype": "float64"
            },
            {
                "column_data": [1.28, 1.05, 1.02, 1.03, 1.71, 1.23, 1.07, 1.5],
                "column_name": "ash",
                "column_type": "continuous",
                "column_datatype": "float64"
            }
        ]
    },
    "save_filepath": "./models"
}

# Create and run a synthetic data generation job
job = Job(
    n_rows=config["n_rows"],
    model_info=config["model"],
    dataset=config["dataset"],
    save_filepath=config.get("save_filepath", "./models")
)

# Generate synthetic data
results, metrics, model, schema = job.train()
print(f"Generated {len(results)} synthetic rows")
print(f"Quality metrics: {metrics}")

📖 See Quick Start Guide for detailed examples

🔧 Function-Based Generation

# Generate data using mathematical functions
from sdg_core_lib import Job

functions = [
    {
        "feature": "linear_data",
        "function_name": "LinearFunction",
        "parameters": {
            "m": 2.0,
            "q": 1.0,
            "min_value": 0.0,
            "max_value": 100.0
        }
    }
]

job = Job(n_rows=100, functions=functions)
synthetic_data = job.generate_from_functions()

📚 Documentation

📖 User Documentation

Complete guide for users including:

• Core concepts and architecture
• Data types (tabular, time series, custom)
• Model configurations (VAEs, CTGAN)
• API reference and examples
• Best practices and troubleshooting

🔧 Developer Documentation

Technical documentation for developers:

• Architecture overview and design patterns
• Extension points and customization
• Development setup and testing
• Code organization and standards

⚡ Quick Start Guide

Get started immediately with:

• Installation instructions
• Basic examples and tutorials
• Common use cases
• Troubleshooting tips

📋 Step-by-Step Tutorial

Hands-on tutorial covering:

• Complete project workflow
• Real-world examples
• Advanced techniques
• Performance optimization

🏗️ Architecture

GENESIS Core Lib follows a modular architecture:

• Data Generator: ML models (TabularVAE, TimeSeriesVAE, CTGAN)
• Dataset: Data abstraction (Table, TimeSeries) with proper column structure
• Preprocess: Data transformation and normalization strategies
• Postprocess: Function application and data modification
• Evaluate: Quality assessment and statistical metrics

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Setup

# Clone repository
git clone https://github.com/emiliocimino/generator_core_lib.git
cd generator_core_lib

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

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

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=sdg_core_lib

# Run specific test file
pytest tests/test_job.py

📄 License

This project is licensed under the GNU Affero General Public License v3.0 - see the LICENSE file for details.

🙏 Acknowledgments

• Built with TensorFlow and Keras for deep learning models
• Statistical evaluation using scipy and numpy
• Inspired by state-of-the-art synthetic data generation research

📞 Support

• 📖 Documentation
• 🐛 Issues
• 💬 Discussions

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GENESIS Core Lib - Generating Tomorrow's Data, Today 🚀