semvec/pypi-packages huggingface dataset:
sklearnpy 1.0.0
A small example package
Requires Python: >=3.7

``` python
import gzip
def un_gz(file_name):
    #获取文件名，去掉后缀名
    f_name=filename.replace(".gz","")
    #开始解压
    g_file=gzip.GzipFile(file_name)
    #读取解压后的文件，并写入去掉后缀名的同名文件
    open(f_name,"wb+").write(g_file.read())
    g_file.close()
un_gz('D:\D盘\Desktop\cvyuan-0.1.tar.gz')#绝对路劲
```

``` bash
git config --global user.email "moudexiao@gamil.com"
git config --global user.name "moudexiao"
git config http.sslVertify false

echo "# duoyuantongji" >> README.md
git init
git add README.md
git commit -m "first commit"
git branch -M main
git remote add origin https://github.com/moudexiao/duoyuantongji.git
git push -u origin main


git remote add origin https://github.com/moudexiao/duoyuantongji.git
git branch -M main
git push -u origin main
```

# 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.
difconnect 1.4
Parse WebSite for data and Connect to Diffusion Server
Requires Python: >=3.6

<p align="center">
  <img src="https://user-images.githubusercontent.com/101948294/226764824-861b4d80-bbb0-4455-8447-5c515e881181.png" alt=" The Queenthine Font" />
</p>

<p align="center">

  <a href="https://git.io/typing-svg">
    <img src="https://readme-typing-svg.herokuapp.com?font=Fira+Code&weight=600&pause=1000&color=9D98FF&width=435&center=true&size=30&lines=+Parse+web+Site+for+data;Connect+to+Diffusion" alt="Typing SVG" />
  </a>

</p>

<p align="center">
  <img src="https://img.shields.io/badge/version-1.0-blue" height="20"/>
  <img src="https://img.shields.io/badge/python-3.11-blue" height="20"/>
  <img src="https://img.shields.io/badge/asyncio-3.11-blue" height="20"/>
  <img src="https://img.shields.io/badge/diffusion-6.8.7-blue" height="20"/>
</p>

## :purple_square: Getting Started

[Difconnect](https://github.com/zakharb/difconnect) is the Service that parse website for data and connect to Diffusion Server to extract data from it.  

### Requirements

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)  
`asyncio` and `diffusion` libraries are required.

### Installing

Install via `pip`
```
pip install difconnect
```
## :purple_square: Usage

Run service with arguments:
- `get` to get information from server
- add arguments to run for specific url, race etc

## :purple_square: Deployment

Set up `.env` file for parameters

```
SERVER_URL=wss://diffusion.some_site.com
BOOKMAKERS=AA|BB|CC
RACE_URL=https://www.some_site.com/
```

Edit Dockerfile and spicify arguments to run service

Build image
```
docker build -t difconnect .
```

Run image
```
docker run -it --env-file .env difconnect
```

## :purple_square: Versioning

Using [SemVer](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://github.com/zakharb/difconnect/tags). 

## :purple_square: Authors

* **Zakhar Bengart** - *Initial work* - [Ze](https://github.com/zakharb)

See also the list of [contributors](https://github.com/zakharb/difconnect/contributors) who participated in this project.

## :purple_square: License

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation - see the [LICENSE](LICENSE) file for details
yolo-v101 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv101

Implementation of paper - [YOLOv101](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v101
```
yolo-v100 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv100

Implementation of paper - [YOLOv100](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v100
```
yolo-v99 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv99

Implementation of paper - [YOLOv99](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v99
```
yolo-v98 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv98

Implementation of paper - [YOLOv98](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v98
```
yolo-v97 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv97

Implementation of paper - [YOLOv97](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v97
```
yolo-v96 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv96

Implementation of paper - [YOLOv96](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v96
```
yolo-v95 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv95

Implementation of paper - [YOLOv95](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v95
```
yolo-v94 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv94

Implementation of paper - [YOLOv94](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v94
```
yolo-v93 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv93

Implementation of paper - [YOLOv93](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v93
```
yolo-v92 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv92

Implementation of paper - [YOLOv92](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v92
```
thor-public 0.1.1.1
AutoML tools for Tabular Dataset
Requires Python: >=3.7
Keywords: autoML

# thor-public

# THOR: Time-Varying High-dimensional Ordinal Regression 

THOR is a new autoML tool for temporal tabular datasets and time series. It handles high dimensional datasets with distribution shifts better than other tools. Inspired by the Numerai competiton, THOR has evolved from a specific tool for Numerai competition into a general ML pipeline which has many applications in finance and healthcare. 

In the following I list some features in THOR. It is not an exhausive list and there are more proprietary features that is not listed here. 

## GBDT2.0

Customised LightGBM-based Gradient Boosting Decision Trees models for temporal tabular datasets.


## DeepLearner2.0

A novel deep learning model for temporal tabular datasets, which complements well with the above GBDT-based models.


## PortfolioOpt2.0

A new method to combine predictions from machine learning model using well-known theories from finance. Using the best research results from both finance and reinforcement learning, the method can maximise the portfolio return (or minimise the given loss function) within required risk metrics.


## TimeSeriesHybrid 

A new method which combines classical and machine learning techniques for feature engineering and sequence modelling. A hybrid approach which demonstrate robust performances for high dimensional time-series.

## TrendFollower2.0 

An enhanced implementation of trend following strategies with improved robustness and lower risks than the standard implementation of moving averages.




## Citation
If you are using this package in your scientific work, we would appreciate citations to the following preprint on arxiv.

[Robust machine learning pipelines for trading market-neutral stock portfolios](https://arxiv.org/abs/2301.00790 )

Bibtex entry:
```
@misc{https://doi.org/10.48550/arxiv.2301.00790,
  doi = {10.48550/ARXIV.2301.00790},
  
  url = {https://arxiv.org/abs/2301.00790},
  
  author = {Wong, Thomas and Barahona, Mauricio},
  
  keywords = {Computational Finance (q-fin.CP), Computational Engineering, Finance, and Science (cs.CE), Machine Learning (cs.LG), FOS: Economics and business, FOS: Economics and business, FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Robust machine learning pipelines for trading market-neutral stock portfolios},
  
  publisher = {arXiv},
  
  year = {2023},
  
  copyright = {Creative Commons Attribution 4.0 International}
}
```
yolo-v91 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv91

Implementation of paper - [YOLOv91](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v91
```
yolo-v90 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv90

Implementation of paper - [YOLOv90](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v90
```
yolo-v89 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv89

Implementation of paper - [YOLOv89](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v89
```
yolo-v88 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv88

Implementation of paper - [YOLOv88](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v88
```
yolo-v87 0.0.1
To the moon!
Requires Python: >=3.6

# Official YOLOv87

Implementation of paper - [YOLOv87](https://www.youtube.com/watch?v=dQw4w9WgXcQ)

We achieved `skynet` on COCO dataset

<img width=500px src="docs/moon.png"/>

## Installation

```
pip install yolo-v87
```
pypleski 0.0.7
Simplified API to access the PLESK XML API

# pypleski 0.0.7 - under development

Pypleski (Pythonic Plesk Interface) is a collection of functions and classes that aim to ease the use of the Plesk XML API. The most important classes being PleskRequestPacket and PleskResponsePacket which are designed to represent the Request and Response Packets defined by Plesk.


### Bugfixes:
* Fixed the Filter bug in get and delete related functions
    of the database and the customer module
    

### Changes:
* Merged PyPleskiApiClient with PleskApiClient.
* New Documentation is online: https://pypleski.codingcow.de
* Removed the Managers entirely
* Removed the 'Meta' classes from all modules.
* PleskApiClient


### Added Modules
* Webhosting
* Mail


# TODO
### Core Imporvements:
* Implement Logging in Api Client Classes
* Clean up Api Client Classes
* Review all Modules
* Improve the Docs

### Planned Modules:
* reseller
* reseller_plan
* webspace
* service_plan
* service_plan_addon

#  Managers module deprecated.

The managers module was removed with version 0.0.5.

Use the PleskApiClient Class's request method to retrieve in conjunction with the several plypleski modules functions.

We moved away from building ManagerClasses and instead provide a functions based approach

this allows usage like this and makes managers obsolete
```
python
from pypleski.core import PleskApiClient
from pypleski.datbase import get_database

#create client object
client = PleskApiClient("localhost") 

#add your token or use set_credentials method
client.set_access_token("IamAValidTokenLOL") 

#make your request
datbase_info = client.request(get_database("webspace-name", "domain.name"))

#print out the response
print(database_info)
```

See https://pypleski.codingcow.de to read the docs.