semvec/pypi-packages huggingface dataset:
entari-plugin-tsugu 0.1.7
Tsugu BanGDream Bot
License: MIT
Requires Python: >=3.8
Keywords: entari, tsugu, bangdream, bot

# entari-plugin-tsugu

一个用于 Entari 框架的 Tsugu BanGDream Bot 插件.

## 安装

```bash
pip install entari-plugin-tsugu
```

## 配置项

`qq_passive`   
此项设置用于 QQ 官方机器人平台驱动器例如[GlycCat](https://github.com/WindowsSov8forUs/GlycCat) 的被动消息支持, 默认为布尔值 `False` 

`prefix`   
此项设置用于命令前缀配置, 默认为 `['/', '']`   
请记住, 如果需要无前缀触发一定要在最后加上空字符串

`platform`   
此项设置用于指定平台，默认为 `None` 时自动检测驱动器返回的平台信息  
请注意: 如果你获取到的 `user.id` 为 QQ号, 将此值设置为 `red` 以共享数据.


> 例如: 你是一个 普通的使用 `napcat` / `Lagrange` 等驱动器的用户, 请保持 `prefix` 与 `qq_passive` 默认配置, 将 `platform` 设置为 `red` 是一个合理的选择.


## Tsugu 配置

Tsugu 内部配置建议使用 `.env` 文件配置, 在命令行使用:
```bash
tsugu -e 
```
以查看支持修改的配置项.
OSAnlizer 1.0.9
Educational PoC for dependency confusion and typo squatting testing
License: MIT

# mulaptested-pakname

This is an educational proof-of-concept (PoC) package designed to demonstrate typo squatting or dependency confusion testing in PyPI. It is intended for learning purposes only and does not collect sensitive user data.

## Purpose
- Simulates a notification mechanism to highlight potential installation errors.
- Does not send data to external servers by default (disabled via `NOTIFY_SERVER = False`).
- Can be enabled for testing with explicit user consent.

## Usage
- Install: `pip install mulaptested-pakname`
- Run: `python -m mulaptested_pakname.notification`

## Disclaimer
This package is experimental. Use it responsibly and only for educational purposes. The authors are not responsible for misuse.

## License
MIT License
LogicExplainML 0.1.1
XAI explainers to Machine Learning Models
License: MIT
Requires Python: >=3.7

# Logic Explain Machine Learning - LEML

`LogicExplainML` is a Python library for explaining predictions of Machine Learning models.
It provides an easy-to-use interface to inspect feature importance and generate human-readable explanations for individual predictions.
By this date it only explains XGBoost models.

## Installation

You can install the package via pip:

```bash
pip install LogicExplainML
````

## Features

* Fit an explainer for XGBoost models.
* Compute feature importance.
* Generate interpretable explanations for individual predictions.

## Usage

Here's a simple example using the Iris dataset and `XGBClassifier`:

```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
import pandas as pd

from logic_explain_ml.xgboost import XGBoostExplainer

# Load the dataset
iris = load_iris()
X = pd.DataFrame(iris.data, columns=iris.feature_names)
y = iris.target

# Convert to binary classification
y[y == 2] = 0

# Split the data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=101
)

# Train an XGBoost classifier
xgbc = XGBClassifier(n_estimators=100, max_depth=3, learning_rate=0.1)
xgbc.fit(X_train, y_train)

# Make predictions
preds = xgbc.predict(X_test)
print(preds)

# Initialize and fit the explainer
explainer = XGBoostExplainer(xgbc, X)
explainer.fit()

# Inspect feature importance
print(xgbc.feature_importances_, xgbc.feature_names_in_)

# Explain a single sample
sample = [5.5, 4.2, 1.4, 0.2]
exp = explainer.explain(sample, reorder="asc")
print(exp)
```

### Example Output

```text
init: -0.73
prob: -2.96
tree prob: 4.65
[petal length (cm) == 1.4]
```

## License

This project is licensed under the MIT License.