Metadata-Version: 2.4
Name: Synthaxe
Version: 0.0.1
Summary: A high-level data generation framework based on grammar rules
Author: Anthony Tedde
License-Expression: BSD-3-Clause
Project-URL: Homepage, https://synthaxe.com
Project-URL: Documentation, https://syntaxe.org/doc
Project-URL: Repository, https://github.com/understorytale/synthaxe.git
Project-URL: Issues, https://github.com/understorytale/synthaxe/issues
Classifier: Development Status :: 1 - Planning
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: Python :: 3.15
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries
Classifier: Typing :: Typed
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# Synthaxe: Python Framework for the Grammar of Synthetic Data

Synthaxe helps you generate synthetic datasets using a grammar-based approach built around expressive, domain-specific verbs.

Define a model, specify its fields, describe the relationships between them, set the underlying probabilities, and run the generator to produce realistic synthetic data tailored to your project's needs.

## Why Synthetic Data?

Synthetic data is useful whenever real data is unavailable, insufficient, sensitive, biased, or difficult to collect. It allows you to design controlled datasets, simulate realistic scenarios, and test systems under conditions that may be rare or expensive to observe in production.

Synthetic data can help with:

- **Pretraining machine learning models**
  Generate large volumes of structured data to expose models to useful patterns before fine-tuning them on real-world datasets.

- **Upsampling underrepresented cases**
  Create additional examples for rare classes, edge cases, or minority populations to reduce imbalance and improve model coverage.

- **Assessing model robustness**
  Stress-test models against noisy inputs, unusual combinations, missing values, outliers, and controlled variations in the data distribution.

- **Exploring model limitations**
  Build targeted datasets to identify where a model fails, how it behaves outside its comfort zone, and which assumptions it relies on.

- **Creating demo and test data**
  Populate applications with realistic, privacy-safe data for demos, development, testing, onboarding, and documentation.

- **Training and education in data-related fields**
  Create realistic datasets for teaching data science, data engineering, analytics, machine learning, statistics, or database concepts without relying on sensitive or hard-to-access real-world data.

- **Experimenting safely**
  Prototype data pipelines, validation rules, analytics workflows, and ML systems without exposing sensitive or production data.

# How does it work ?

Under construction.
