semvec/pypi-packages huggingface dataset:
ds4ml 0.3.6
A Python library for data synthesis and evaluation


The recent enforcement of data privacy protection regulations, such as GDPR,
has made data sharing more difficult. This tool intends to facilitate data
sharing from a customer by synthesizing a dataset based on the original dataset
for later machine learning.

There are two parts to this tool:

- Data synthesizer
  Synthesize a dataset based on the original dataset. It accepts CSV data as
  input, and output a synthesized dataset based on Differential Privacy. The
  algorithm in the data synthesizer reference to the paper (
  http://dimacs.rutgers.edu/~graham/pubs/papers/privbayes-tods.pdf).
- Data utility evaluation
  Evaluate the data utility for the synthesized dataset. The original dataset
  and the synthesized dataset as the input, one utility evaluation report will
  be generated with several indicators.
simpleppt 1.1.4
Python implementation of SimplePPT algorithm, with GPU acceleration

[![PyPI](https://img.shields.io/pypi/v/simpleppt.svg)](https://pypi.python.org/pypi/simpleppt/)
[![Documentation Status](https://readthedocs.org/projects/simpleppt/badge/?version=latest)](https://simpleppt.readthedocs.io/en/latest/?badge=latest)
[![Build & Test](https://github.com/LouisFaure/simpleppt/actions/workflows/test.yml/badge.svg)](https://github.com/LouisFaure/simpleppt/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/LouisFaure/simpleppt/branch/main/graph/badge.svg)](https://codecov.io/gh/LouisFaure/simpleppt)
[![GitHub license](https://img.shields.io/github/license/LouisFaure/simpleppt)](https://github.com/LouisFaure/simpleppt/blob/master/LICENSE)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

# SimplePPT
Python implementation of [SimplePPT algorithm](https://doi.org/10.1137/1.9781611974010.89), with GPU acceleration.

Please cite the following paper if you use it:
```
Mao et al. (2015), SimplePPT: A simple principal tree algorithm, SIAM International Conference on Data Mining.
```

Installation
------------

```bash
pip install -U simpleppt
```

Usage
-----

```python
from sklearn.datasets import make_classification
import simpleppt

X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,
                             n_clusters_per_class=1, n_classes=3)

ppt=simpleppt.ppt(X1,Nodes=30,seed=1,progress=False,lam=10)
simpleppt.project_ppt(ppt,X1, c=Y1)
```

![result](./ppt.png)


GPU dependencies (optional)
---------------------------

If you have a nvidia GPU, simpleppt can leverage CUDA computations for speedup in tree inference. The latest version of rapids framework is required (at least 0.17) it is recommanded to create a new conda environment:

    conda create -n SimplePPT-gpu -c rapidsai -c nvidia -c conda-forge -c defaults \
        rapids=0.19 python=3.8 cudatoolkit=11.0 -y
    conda activate SimplePPT-gpu
    pip install simpleppt