<div align="center">
# synloc: An Algorithm to Create Synthetic Tabular Data
<img src="https://raw.githubusercontent.com/alfurka/synloc/main/assets/logo_white_bc.png" alt = 'synloc'>
[Overview](#overview) | [Installation](#installation) | [A Quick Example](#a-quick-example) | [Documentation](https://alfurka.github.io/synloc/) | [How to cite?](#how-to-cite)
[](https://pypi.org/project/synloc) [](https://pepy.tech/project/synloc)
</div>
## Overview
`synloc` is an algorithm to sequentially and locally estimate distributions to create synthetic versions of a tabular data. The proposed methodology can be combined with parametric and nonparametric distributions.
## Installation
`synloc` can be installed through [PyPI](https://pypi.org/):
```
pip install synloc
```
## A Quick Example
Assume that we have a sample with three variables with the following distributions:
$$x \sim Beta(0.1,\,0.1)$$
$$y \sim Beta(0.1,\, 0.5)$$
$$z \sim 10 y + Normal(0,\,1)$$
The distribution can be generated by `tools` module in `synloc`:
```python
from synloc.tools import sample_trivariate_xyz
data = sample_trivariate_xyz() # Generates a sample with size 1000 by default.
```
Initializing the resampler:
```python
from synloc import LocalCov
resampler = LocalCov(data = data, K = 30)
```
**Subsample** size is defined as `K=30`. Now, we locally estimate the multivariate normal distribution and from each estimated distributions we draw "synthetic values."
```python
syn_data = resampler.fit()
```
100%|██████████| 1000/1000 [00:01<00:00, 687.53it/s]
`syn_data` is a [pandas.DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) where all variables are synthesized. Comparing the original sample using a 3-D Scatter:
```python
resampler.comparePlots(['x','y','z'])
```
## How to cite?
If you use `synloc` in your research, please cite the following paper:
```bibtex
@misc{kalay2025generatingsyntheticdatalocally,
title={Generating Synthetic Data with Locally Estimated Distributions for Disclosure Control},
author={Ali Furkan Kalay},
year={2025},
eprint={2210.00884},
archivePrefix={arXiv},
primaryClass={stat.CO},
url={https://arxiv.org/abs/2210.00884},
}
```
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"description": "<div align=\"center\">\r\n\r\n# synloc: An Algorithm to Create Synthetic Tabular Data\r\n\r\n<img src=\"https://raw.githubusercontent.com/alfurka/synloc/main/assets/logo_white_bc.png\" alt = 'synloc'>\r\n\r\n[Overview](#overview) | [Installation](#installation) | [A Quick Example](#a-quick-example) | [Documentation](https://alfurka.github.io/synloc/) | [How to cite?](#how-to-cite)\r\n\r\n[](https://pypi.org/project/synloc) [](https://pepy.tech/project/synloc)\r\n\r\n</div>\r\n\r\n## Overview\r\n\r\n`synloc` is an algorithm to sequentially and locally estimate distributions to create synthetic versions of a tabular data. The proposed methodology can be combined with parametric and nonparametric distributions. \r\n\r\n## Installation\r\n\r\n`synloc` can be installed through [PyPI](https://pypi.org/):\r\n\r\n```\r\npip install synloc\r\n```\r\n\r\n## A Quick Example\r\n\r\nAssume that we have a sample with three variables with the following distributions:\r\n\r\n$$x \\sim Beta(0.1,\\,0.1)$$\r\n\r\n$$y \\sim Beta(0.1,\\, 0.5)$$\r\n\r\n$$z \\sim 10 y + Normal(0,\\,1)$$\r\n\r\nThe distribution can be generated by `tools` module in `synloc`:\r\n\r\n\r\n```python\r\nfrom synloc.tools import sample_trivariate_xyz\r\ndata = sample_trivariate_xyz() # Generates a sample with size 1000 by default. \r\n```\r\n\r\nInitializing the resampler:\r\n\r\n\r\n```python\r\nfrom synloc import LocalCov\r\nresampler = LocalCov(data = data, K = 30)\r\n```\r\n\r\n**Subsample** size is defined as `K=30`. Now, we locally estimate the multivariate normal distribution and from each estimated distributions we draw \"synthetic values.\"\r\n\r\n\r\n```python\r\nsyn_data = resampler.fit() \r\n```\r\n\r\n 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:01<00:00, 687.53it/s]\r\n \r\n\r\n`syn_data` is a [pandas.DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) where all variables are synthesized. Comparing the original sample using a 3-D Scatter:\r\n\r\n\r\n```python\r\nresampler.comparePlots(['x','y','z'])\r\n``` \r\n\r\n\r\n## How to cite?\r\n\r\nIf you use `synloc` in your research, please cite the following paper:\r\n\r\n```bibtex\r\n@misc{kalay2025generatingsyntheticdatalocally,\r\n title={Generating Synthetic Data with Locally Estimated Distributions for Disclosure Control}, \r\n author={Ali Furkan Kalay},\r\n year={2025},\r\n eprint={2210.00884},\r\n archivePrefix={arXiv},\r\n primaryClass={stat.CO},\r\n url={https://arxiv.org/abs/2210.00884}, \r\n}\r\n```\r\n",
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