semvec/pypi-packages huggingface dataset:
yachecker 1.0.2
YAC: Yes Another Checker
Requires Python: >=3.6

# YAC: Yes Another Checker - main.py

## Overview

The `yac` module of YAC (Yes Another Checker) is a command-line tool for checking the validity of login credentials across multiple websites. It supports customizable website configurations and logging capabilities, making it an efficient solution for automated credential validation.

## Features

- Perform single or multiple login checks on specified websites.
- Support for custom payloads and headers for each website.
- Logging of results with timestamps, including successful and failed login attempts.
- Save results to a CSV file.
- Command-line interface with argparse for ease of use.

## Prerequisites

- Python 3.6 or higher
- Required Python packages:
  - `requests`
  - `argparse`
  - Any other dependencies as specified in the `setup.py` file

## Installation

Clone the repository and install the package:

```bash
git clone -b package https://github.com/hithmast/YAC.git
cd YAC
sudo python setup.py install # or pip install . 
```
Or
```bash
pip install yachecker
```
Ensure that you have the necessary configuration file located at `~/.yac/config/websites_config.ini`. The script will utilize this file to perform login checks.

## Usage

### Command-Line Interface

The script can be run from the command line with various options:

```bash
yac [options]
```

### Options

- `-c`, `--config`: Print a list of websites in the config file.
- `-s`, `--single`: Perform a single check on the specified website number.
- `-m`, `--multiple`: Perform multiple checks on specified website numbers (comma-separated).
- `-C`, `--combo`: Path to a credentials file for a single check.
- `-o`, `--output`: Specify the output file for the results of a single check.

### Examples

#### Print Configured Websites

```bash
yac -c
```

#### Perform a Single Check

```bash
yac -s 1 -C credentials.csv -o output.csv
```

#### Perform Multiple Checks

```bash
python main.py -m 1,2,3
```

## Logging

The script generates a log file `output.log` in a `logs` directory, creating a subdirectory based on the current date. This log file contains detailed information about the execution, including successful and failed login attempts.

## Saving Results

Upon termination (either by completing the checks or by pressing `Ctrl+C`), the user will be prompted to save the results to a file named `results.csv`.

## Configuration

The website configurations should be defined in the `~/.yac/config/websites_config.ini` file. The configuration file should specify the login URLs, success and failure indicators, payloads, and headers for each website.

## Contributing

Contributions are welcome! Please fork the repository, make your changes, and submit a pull request. Ensure that your code follows the project’s coding standards and includes relevant tests.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Author

**Ali Emara**  
[ali.emara175@gmail.com](mailto:ali.emara175@gmail.com)  
[GitHub](https://github.com/hithmast)
psruq 0.0.1
Code for uncertainty quantification with proper scoring rules.
Requires Python: >=3.10

# Predictive Uncertainty Quantification via Risk Decompositions for Strictly Proper Scoring Rules

This repository contains code for the paper "Predictive Uncertainty Quantification via Risk Decompositions for Strictly Proper Scoring Rules," submitted to NeurIPS 2024.

### UV
We use uv to manage dependecies. You can find an installation guide and documentation [here](https://docs.astral.sh/uv/getting-started/installation/#standalone-installer).

**Install uv**

For Linux/MacOS the following line will install uv on your system:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

**Install dependencies**

**All the commands should be executed from the folder with pyproject.toml file**

To install dependencies from pyproject.toml file using
```bash
uv sync
```
It will create a .venv folder, that will contain a virtual environment and all the needed dependencies.

**Run python script**

uv installs dependencies in special virutal env, that is located in .venv/. To run any python code snippet with it you should run:
```bash
uv run python ...
```
e.g.
```bash
cd experiments/laplace
uv run python main.py -f checkpoints/resnet18_ce.pth -d cifar10_one_batch -v
```

### Repository Structure

The repository is organized as follows:

### Experiments
This folder contains different experiments related to the paper.
- cifar10: Contains training scripts for cifar10 dataset.
- laplace: Contains inference scripts for Laplace Redux – Effortless Bayesian Deep Learning model.

### Embedding Extraction
- source/source/evaluation_utils.py: Script to extract embeddings after the ensembles are trained.

### Notebooks
Folder notebooks/ have notebooks with data analysis of experiments.
- joint_tables.ipynb: Prepares comprehensive pandas tables for both problems.
- ood_analysis.ipynb: Generates plots for the OOD problem.
- ood_visualizations.ipynb: Creates visualizations for the OOD problem.
- mis_analysis.ipynb: Generates plots for the misclassification problem.
- mis_visualizations.ipynb: Creates visualizations for the misclassification problem.

### Notes on Trained Models
Due to a supplementary material size limit of 100MB, we cannot include the trained models in this repository. However, we are ready to provide them through any resource that maintains anonymity upon request.