semvec/pypi-packages huggingface dataset:
diamentowa-bukmacherska 0.1.1
Zaawansowana biblioteka do analiz bukmacherskich
License: MIT
Requires Python: >=3.7

# Diamentowa Bukmacherska

**Diamentowa Bukmacherska** to zaawansowana biblioteka do analiz bukmacherskich, ktĂłra umoĹĽliwia prognozowanie wynikĂłw sportowych i zdarzeĹ„ meczowych.

## Funkcje
- Obliczanie przewidywanej liczby bramek.
- RozkĹ‚ad Poissona do analizy zdarzeĹ„.
- Zaawansowane modele do prognozowania.

## Instalacja
Zainstaluj bibliotekÄ™ za pomocÄ… pip:
```bash
pip install diamentowa_bukmacherska
synthius 0.2.0
A toolkit for generating and evaluating synthetic data in terms of utility, privacy, and similarity
License: MIT
Requires Python: <3.12,>=3.10
Keywords: synthetic data, privacy, utility, similarity, fidelity, machine learning

# Synthetic Data Generation Toolkit

This repository provides a comprehensive toolkit for generating synthetic data using seven different models. The toolkit evaluates the generated data for utility, similarity/fidelity, and privacy, specifically tailored for tabular datasets with binary classification problems (e.g., True/False, Yes/No).

## Models Included
The project implements the following models for synthetic data generation:
1. **CopulaGAN**
2. **CTGAN**
3. **Gaussian Copula**
4. **TVAE**
5. **Gaussian Multivariate**
6. **WGAN**
7. **ARF**

## Quick Start

### Step 1: Install the Package
Install the package using pip:
```bash
pip install synthius
```

### Step 2: Usage Example
To understand how to use this package, explore the three example Jupyter notebooks included in the repository:

1. **[Generator](examples/1_generator.ipynb)**
   - Demonstrates how to generate synthetic data using seven different models.
   - Update paths and configurations (e.g., file paths, target column) to fit your dataset.
   - Run the cells to generate synthetic datasets.

2. **[AutoGloun](examples/2_autogloun.ipynb)**
   - Evaluates the utility.
   - Update the paths as needed to analyze your data.

3. **[Evaluation](examples/3_evaluation.ipynb)**
   - Provides examples of computing metrics for evaluating synthetic data, including:
     - Utility
     - Fidelity/Similarity
     - Privacy
   - Update paths and dataset-specific configurations and run the cells to compute the results.

These notebooks serve as practical examples to demonstrate how to effectively utilize the toolkit.


### Additional Setup for Mac Users
Mac users may encounter errors during installation. To resolve these issues, install the required dependencies and set up the environment:

1. Install dependencies using Homebrew:
   ```bash
   brew install libomp llvm
   ```

2. Set up the environment:
   ```bash
   export PATH="/opt/homebrew/opt/llvm/bin:$PATH"
   export CC=$(brew --prefix llvm)/bin/clang
   export CXX=$(brew --prefix llvm)/bin/clang++
   export CXXFLAGS="-I$(brew --prefix llvm)/include -I$(brew --prefix libomp)/include"
   export LDFLAGS="-L$(brew --prefix llvm)/lib -L$(brew --prefix libomp)/lib -lomp"
   ```


## Acknowledgments
Special thanks to all contributors and the libraries used in this project.