semvec/pypi-packages huggingface dataset:
nonebot-plugin-zzzpanel 0.1.6
绝区零角色数据获取插件
License: MIT
Requires Python: >=3.11

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# nonebot_plugin_zzzpanel

_✨ NoneBot 插件描述 ✨_

自己写的ZZZ角色数据获取插件
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## 📖 介绍

能获取所有角色的练度（因为是用的官方api）


## 💿 安装

<details open>
<summary>使用 nb-cli 安装</summary>
在 nonebot2 项目的根目录下打开命令行, 输入以下指令即可安装

    nb plugin install nonebot_plugin_zzzpanel

</details>

<details>
<summary>使用包管理器安装</summary>
在 nonebot2 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令

<details>
<summary>pip</summary>

    pip install nonebot_plugin_zzzpanel
</details>

打开 nonebot2 项目根目录下的 `pyproject.toml` 文件, 在 `[tool.nonebot]` 部分追加写入

    plugins = ["nonebot_plugin_zzzpanel"]

</details>

## 待完善的功能（更新较慢，所以期待pr哦）

- [ ] 所有米游的数据获取
- [ ] 单用户多个账号的登录与切换
- [ ] 圣遗物/遗器/驱动盘 评分系统相关（为了防止散套冲榜，我计划会加入针对每个角色定制一套套装评分权重，散套和毕业套的；评分差距约在40分左右）
- [ ] 优化面板样式
- [ ] playwright库自动下载依赖
- [ ] 加入管理员和群权限
- [ ] 加入功能菜单
- [ ] 完善查询角色面板时的名字（目前未设计角色别名库，只做了一定的模糊判断，例如api返回的是零号·安比，中间的符号不好打，输入零号安比即可）
- [ ] 想不到更多的了

## 指令大全

- ZZZ绑定
- ZZZ图鉴
- ZZZ练度+角色名字
- ZZZ更新数据\[无/图鉴/练度\]

## 配置

```bash
x_rpc_device_fp = "" #输入你常用设备的设备指纹
localstore_use_cwd = False #是否使用当前目录存储数据，c盘空间不够的话建议为true
```

## 效果图展示

![demo](/example/效果图1.png)
![demo](/example/效果图2.png)
![demo](/example/效果图3.png)
benchpred 1.0.0
Tools for benchmark prediction with fewer data.
License: MIT
Requires Python: >=3.7

# Benchmark Prediction with Fewer Data

**BenchPred** is a Python package that provides a suite of tools to predict benchmark performances with fewer data. It provides all methods that we examined in our [paper](https://arxiv.org/).

Our results shows that, these methods are most proficient at interpolating scores among similar models.  However, except Random-Sampling and AIPW, all methods face significant difficulties when predicting target models that differ substantially from those they have encountered before.

We caution against the indiscriminate use of benchmark prediction techniques and recommend to use **AIPW** or, alternatively, **Random-Sampling** with a larger sampling budget.

## Installation

Install the package with the following command,

```bash
pip install -e .
```

or only install the requirements,

```bash
pip install -r requirement.txt
```

## Example Usage

```python
from benchpred import all_methods


method = all_methods["aipw"]()
method.fit(
    source_full_scores,  # (n_source_model * n_data) binary matrix
    coreset_size=50,  
    seed=42
)
coreset = method.get_coreset()
target_coreset_scores = ...  # (n_target_model * coreset_size) binary matrix based on coreset
pred_acc = method.predict(target_coreset_scores)
```

## Quick Start

```python
python main.py --dataset_name imagenet --coreset_size 50 --no-multi_process --methods aipw --num_run 1 --seed_start 0 --no-use_git
```

The supported methods are listed as follows,

- Random-Sampling: --methods random_sampling
- AIPW: --methods aipw
- Random-Sampling-Learn: --methods random_sampling_and_learn
- Random-Search-Learn: --methods random_search_and_learn
- PCA: --methods pca
- Lasso: --methods lasso
- Double-Optimize: --methods double_optimize
- Anchor-Points-Weighted: --methods anchor_points_weighted
- Anchor-Points-Predictor: --methods anchor_points_predictor
- PIRT: --methods pirt
- GPIRT: --methods gpirt