semvec/pypi-packages huggingface dataset:
practice-tests 0.1.0
A simple SDK to manage names
Requires Python: >=3.6

# Practice Tests

A simple SDK to manage names.

## Overview

This package provides a basic implementation of a Name class that allows users to create and manage full names by combining first and last names.

## Features

- Create a `Name` object with first and last names.
- Retrieve the full name using the `fullname` method.

## Installation

You can install this package using pip:

```bash
pip install practice_tests
psrqu 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.
nonebot-plugin-BFVsearch 0.1.4
基于 Nonebot2 的战地 5 QQ 群管理插件。
Requires Python: >=3.8

# nonebot_plugin_bfvsearch

基于Nonebot2的《战地5》QQ群管理插件。

## 安装

```bash
pip install nonebot-plugin-bfvsearch
rus-guard-integration 0.1.0
Модуль интеграции со СКУД РусГард
Requires Python: <4.0,>=3.10

# Example Package

This is a simple example package. You can use
[Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
to write your content.

Данный модуль предназначен для интеграции со СКУД РусГард