synthro 0.1.0

A Python library for generating synthetic datasets

SynthGen

SynthGen is a Python library designed to generate synthetic datasets for testing, prototyping, and research purposes. It supports generating tabular datasets with customizable features and is designed for extensibility.

Features

• Generate synthetic tabular datasets with numeric and categorical columns
• Add Gaussian noise to numeric data for variability
• Fully customizable columns and data types
• Set random seeds for reproducibility
• Easy-to-use API

Installation

You can install the library using pip:

pip install synthgen

Alternatively, clone the repository and install locally:

git clone https://github.com/davitacols/synthgen.git
cd synthgen
pip install .

Quick Start

Here's how you can use SynthGen to generate synthetic tabular data:

Example Usage

from synthgen.core import SynthGen

# Initialize the generator
generator = SynthGen(seed=42)

# Generate a dataset with 100 rows, 3 columns
dataset = generator.generate_tabular(
    rows=100,
    cols=3,
    col_types=['numeric', 'categorical', 'numeric'],
    noise=0.1
)

# Display the first few rows
print(dataset.head())

# Save the dataset to a CSV file
dataset.to_csv('synthetic_dataset.csv', index=False)

API Reference

Class: SynthGen

The SynthGen class provides methods for generating synthetic datasets.

Constructor

SynthGen(seed: int = None)

Parameters:

• seed (int, optional): Random seed for reproducibility. Default is None.

Method: generate_tabular

generate_tabular(rows=100, cols=5, col_types=None, noise=0.0)

Parameters:

• rows (int): Number of rows in the dataset
• cols (int): Number of columns in the dataset
• col_types (list): List of column types (numeric, categorical). Defaults to numeric for all columns
• noise (float): Standard deviation of Gaussian noise for numeric data. Defaults to 0.0

Returns:

• pandas.DataFrame: A DataFrame containing the generated dataset

Directory Structure

synthgen/
├── synthgen/
│   ├── __init__.py    # Package initializer
│   ├── core.py        # Core functionality
│   ├── tabular.py     # Tabular data generation
│   └── utils.py       # Helper functions
├── tests/             # Unit tests
│   ├── test_core.py   # Tests for core functionality
│   └── test_tabular.py# Tests for tabular data
├── examples/          # Example usage scripts
│   └── generate_tabular.py
├── README.md          # Project documentation
├── setup.py          # Package configuration for PyPI
├── requirements.txt   # List of dependencies
└── .gitignore        # Ignored files for Git

Contributing

Contributions are welcome! Feel free to submit issues or pull requests.

1. Fork the repository
2. Create a new branch for your feature or bug fix
3. Commit your changes with a clear message
4. Push the branch and open a pull request

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions or support, reach out to:

• Name: David Ansa
• Email: [e-mail]
• GitHub: davitacols