GENESIS Core Lib
What is GENESIS Core Lib?
GENESIS Core Lib is an open-source, advanced synthetic data generation library for Python 3.12+ that provides state-of-the-art machine learning models including VAEs (TabularVAE, TimeSeriesVAE) and CTGAN for generating high-quality tabular and time series data. The library features adaptive on-the-fly training with automatic model adaptation to data characteristics, model persistence for reusing trained models, behavior control through custom mathematical functions, and integrated quality evaluation metrics. Designed with privacy preservation in mind and optimized for both CPU and GPU processing, it offers a comprehensive Job API for data augmentation, privacy preservation, and ML model testing across various data types and use cases.
GENESIS Core Lib is the algorithmic core of the GENESIS project. All developments are focused on building a compact, easy to use system for everyone.
Why use GENESIS core Lib?
GENESIS Core Lib is the ideal solution for synthetic data generation because it specializes in industrial sensor data and time series applications, offering state-of-the-art models like TimeSeriesVAE that preserve temporal patterns and statistical properties essential for real-world scenarios. It provides immediate access to high-quality synthetic data without the costs and delays of physical sensor deployments, enabling rapid prototyping, algorithm development, and comprehensive testing while maintaining data privacy through synthetic data sharing. The library ensures data fidelity with advanced evaluation metrics including Dynamic Time Warping and correlation preservation, making it perfect for manufacturing IoT, environmental monitoring, energy utilities, and medical device applications where realistic temporal data is critical.
Nyquist plots created merging two time-series of real and imaginary impedances. Can you tell the difference?
✨ Key Features
- Generative AI Architectures: Advanced VAEs (TabularVAE, TimeSeriesVAE) and CTGAN for Tabular and Time series data
- Adaptive Training: On-the-fly model training with automatic adaptation to your data characteristics
- Model Persistence: Save and reuse trained generative models for consistent data generation
- Behavior Control: Manipulate generation patterns with custom mathematical functions
- Integrated Evaluation: Built-in quality assessment metrics for comprehensive data evaluation
- High Performance: Optimized for both CPU and GPU processing
🛠️ Quick Start
Quick Install
pip install sdg-core-lib
🚀 Try it
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
📚 Documentation
📖 User Documentation
Complete guide for users including:
- Core concepts and the Job API
- Data Types and Datasets
- Fantastic Models and how to use them
- How to handle raw data with Processors
- How to control generation of synthetic data with Functions
- Evaluate your work with Evaluators
🔧 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
📋 Standalone Installation
Info about installation:
- Requirements
- Installation instructions
- Environment Variables
- GPU Support
Roadmap
A detailed Roadmap can be found following the Roadmap Link
TL;DR
Pre-1.0: Enhanced model quality, flexible architectures, advanced evaluation metrics, and expansion to images/text generation with auto hyperparameter tuning.
Post-1.0: Production-ready stability, mixed data types, intelligent automation, cloud integration, and advanced AI capabilities including federated learning and quantum computing exploration.
🤝 Contributing
We welcome contributions! Please see our Contributing Guidelines for details.
📄 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
GENESIS Core Lib - Generating Tomorrow's Data, Today 🚀
1 maintainer
Metadata
1 maintainer
Release files for sdg-core-lib 0.1.9
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sdg_core_lib-0.1.9.tar.gz | 36.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sdg_core_lib-0.1.9-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 103.0 kB
Release files / sdg_core_lib-0.1.9.tar.gz
| Download URL | sdg_core_lib-0.1.9.tar.gz |
|---|---|
| Size | 36.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
cee04d9b3f39fa956c187632e4ef2e6aba29939afb7c2ffc5de0893f6032fe21
|
|
BLAKE2b-256 checksum How to use checksums |
1be5dced8e1e5df0de00e003008ceb1328e5b627f79ebf32ba3835713a77b487
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.11.13 {"installer":{"name":"uv","version":"0.11.13","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}
|
Release files / sdg_core_lib-0.1.9-py3-none-any.whl
| Download URL | sdg_core_lib-0.1.9-py3-none-any.whl |
|---|---|
| Size | 66.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
e4264c0ad3a9b531f9d5fe5c627ed9f0bf58980aa7f8e41f84896e6fd2dcf947
|
|
BLAKE2b-256 checksum How to use checksums |
3c21ef1e8d2f89dd78126fe3ec84e57c5b9cf98f7ec029e35c3882b72a3d8e2e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.11.13 {"installer":{"name":"uv","version":"0.11.13","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}
|
