Non-parametric-multivariate-data-generator

This repository contains the code for the Python3 implementation of the method explained in the following article: Restrepo, J.P.; Rivera, J.C.; Laniado, H.; Osorio, P.; Becerra, O.A. Nonparametric Generation of Synthetic Data Using Copulas. Electronics 2023, 12, 1601. https://doi.org/10.3390/electronics12071601

License

This package is part of this stack will be "source available", not “open source”, although that will provide nearly all of the openness that users would typically get from an Apache 2.0 license:

• It provides access to the source code, just like Apache 2.0.
• It provides the ability for users to modify the source code, just like Apache 2.0.
• It provides the ability for users to run the source code on their own behalf, just like Apache 2.0.
• It has a very cool provision whereby all code released actually graduates into true Apache 2.0 open source after a certain period (we plan to use 3 years).

The main difference between the BSL and Apache 2.0 for the community will be that recent versions of the the package will not be able to be incorporated into other commercial products. A user will be able to deploy and use all versions of the server for themselves / their company just as they would with Apache 2.0 code.

Installation

pip install non-parametric-multivariate-data-generator

Examples of synthetic data generation

The file example.ipynb contains five examples of synthetic data generation

Example usage

import numpy as np
import pandas as pd
data = pd.DataFrame(np.random.normal(size=(100, 3)), columns=['a', 'b', 'c'])

from  non_parametric import generate_multivariate_data
generated_data = generate_multivariate_data(data, N=1000)
generated_data.head()
"""
          a         b         c
0 -0.811318 -2.199115  0.911802
1  1.006849  0.695056  0.027386
2  0.839067 -0.194327 -0.641318
3 -0.955104 -0.384175  0.462385
4 -0.197083  2.520539 -0.835229
"""

Dependecies

To install the dependencies use pip

pip install -r requirements.txt

The main packages are:

• numpy
• pandas
• matplotlib

To install, test locally and develop

1. Create and activate a python virtual env
2. pip install -e ".[dev]"
3. pytest
4. pre-commit install

To install anywhere else

1. Create and activate a python virtual env
2. python -m pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple non-parametric-multivariate-data-generator

To deploy to pypi

1. Create and activate a python virtual env
2. python3 -m pip install --upgrade pip
3. python3 -m pip install --upgrade build
4. python3 -m pip install --upgrade twine
5. Change project version property in pyproject.toml
6. python3 -m build
7. python3 -m twine upload --repository testpypi dist/* El usuario es __token__ y la contraseña se genera en https://test.pypi.org/manage/account/token/