Genuity: A Unified Synthetic Data Library with Offline License Activation
Project description
Genuity v2.0.0
Enterprise-grade synthetic data generation library. 22+ generators across 5 domains: tabular, time series, financial, textual, and multi-table. Privacy-preserving with differential privacy support. Offline license activation.
Installation
pip install genuity
Optional Extras
pip install genuity[time_series] # Time series generators
pip install genuity[copula] # Advanced copula generators
pip install genuity[privacy] # Differential privacy (Opacus)
pip install genuity[llm] # LLM-based generators (GReaT)
pip install genuity[forest] # Forest-based generators
Quick Start
import genuity
# Activate your license (one-time)
genuity.activate_license("YOUR_LICENSE_KEY")
# Generate synthetic tabular data
from genuity.tabular import create_generator
gen = create_generator("ctgan", random_seed=42)
gen.fit(your_dataframe)
result = gen.generate(n_samples=1000)
print(result.data.head())
print(result.summary())
Supported Domains
| Domain | Generators | Entry Point |
|---|---|---|
| Tabular | 22 (CTGAN, TVAE, TabuDiff, Copula, ARF, ...) | genuity.tabular.create_generator() |
| Time Series | 6 (TimeGAN, DGAN, DiffusionTS, ...) | genuity.time_series |
| Financial | 8 (CopulaGARCH, RegimeConditioned, ...) | genuity.financial |
| Textual | 4 (CorpusGenerator, MarkovText, ...) | genuity.textual |
| Multi-Table | 3 (GraphSchema, Federated, ...) | genuity.multi_table |
Evaluation
from genuity.tabular.evaluator import TabularEvaluator
evaluator = TabularEvaluator()
report = evaluator.evaluate(real_df, result.data)
print(report)
evaluator.to_html(report, output_path="report.html")
Documentation
See DOCUMENTATION.md for the complete API reference.
License
Proprietary. See LICENSE for details. Contact: cto@genuitydata.com
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