semvec/pypi-packages huggingface dataset:
libscol 0.9.1
LIBrary to convert integers into Spreadsheet COLumn names, and vice versa
Requires Python: >=3.7

```
INSTALLATION

    $ pip3 install libscol

HELP

    $ pydoc libscol

NAME

    libscol - LIBrary to convert integers into Spreadsheet COLumn names, and vice versa

PACKAGE CONTENTS



FUNCTIONS

    int2scol(num)
        convert integer num into spreadsheet column name
        >>> for n in [-2, -1, 0, 1, 2, 25, 26, 27, 28, 701, 702, 703, 704]: print((n, int2scol(n)))
        (-2, '-C')
        (-1, '-B')
        (0, 'A')
        (1, 'B')
        (2, 'C')
        (25, 'Z')
        (26, 'AA')
        (27, 'AB')
        (28, 'AC')
        (701, 'ZZ')
        (702, 'AAA')
        (703, 'AAB')
        (704, 'AAC')

    scol2int(scol)
        convert spreadsheet column name scol into integer
        >>> all(n == scol2int(int2scol(n)) for n in range(-100000, 100000))
        True

VERSION

    0.9.1

FILE

    /home/xxxx/Documents/pypi/libscol/libscol/__init__.py


```
alacrity 1.4.1
Quickstart your Python development with CLI package templating

# <div align="center">Alacrity</div>

<div align="center">

[![Release](https://img.shields.io/github/v/release/vishnuvardhan-kumar/alacrity.svg)](https://img.shields.io/github/v/release/vishnuvardhan-kumar/alacrity.svg)
[![Build Status](https://travis-ci.org/vishnuvardhan-kumar/alacrity.svg?branch=master)](https://travis-ci.org/vishnuvardhan-kumar/alacrity)
[![Issues](https://img.shields.io/github/issues/vishnuvardhan-kumar/alacrity.svg)](https://img.shields.io/github/issues/vishnuvardhan-kumar/alacrity.svg)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
[![Stars](https://img.shields.io/github/stars/vishnuvardhan-kumar/alacrity.svg?style=social&label=Star)](https://img.shields.io/github/stars/vishnuvardhan-kumar/alacrity?style=social&label=Star)


Quickstart your Python project with a single handy command.

</div>

![Screenshot](https://raw.githubusercontent.com/vishnuvardhan-kumar/alacrity/master/alacrity/tests/scr.png)

## Installation

`pip install alacrity`

## Running Alacrity

To run alacrity, use:

`alacrity <package_name>`

To display all the options, use:

`alacrity -h`

Answer some questions interactively, and poof, your package structure is ready.
Based on the [sample Python package](https://github.com/kennethreitz/samplemod) structure by Kenneth Reitz.

## Features
 - Customized setup.py file
 - Automatic git repository initialization
 - Automatic virtual environment setup
 - Automatic Sphinx docs initialization
 - Easily extensible workflow for custom install steps 

## Platforms
 - Windows (and Cygwin)
 - Linux
 - Android (Termux)

## Testing
 - To run the built-in tests, run `tox` in the project root
 - To add custom testing, edit `tox.ini`
this-person-does-not-exist-but-has-age-and-gender 1.0.0
Get the person with right gender and age
Requires Python: >=3.6

# This Person Does Not Exist But Has Age And Gender

Python package for getting the person with requested gender and age from 
[This Person Does Not Exist](https://thispersondoesnotexist.com/) website.

It's getting right person, by downloading image of a randomly generated person from 
[This Person Does Not Exist](https://thispersondoesnotexist.com/) and then checking gender and age of that person,
with use of OpenCV library.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install.

```bash
pip install this_person_does_not_exist_but_has_age_and_gender
```

## Usage

```python
from this_person_does_not_exist_but_has_age_and_gender import get_person

get_person("male", 30) # returns filename of downloaded picture of a person
```

It can be also used from command line
```bash 
python this_person_does_not_exist_but_has_age_and_gender.py male 30
```


## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)
fast-automl 0.0.1
ML for reasonable results in a reasonable amount of time
Requires Python: >=3.6

# Fast AutoML

Most autoML packages aim for exceptional performance but need to train for an exceptional amount of time. Fast-autoML aims for reasonable performance in a reasonable amount of time.

Fast-autoML includes additional utilities, such as tools for comparing model performance by repeated cross-validation.

## Installation

```
$ pip install fast-automl
```

## Quickstart

```python
from fast_automl.automl import AutoClassifier

from sklearn.datasets import load_digits
from sklearn.model_selection import cross_val_score, train_test_split

X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=True, stratify=y)

clf = AutoClassifier(ensemble_method='stepwise', n_jobs=-1, verbose=True).fit(X_train, y_train)
print('CV score: {:.4f}'.format(cross_val_score(clf.best_estimator_, X_train, y_train).mean()))
print('Test score: {:.4f}'.format(clf.score(X_test, y_test)))
```

This runs for about 6-7 minutes and typically achieves a test accuracy of 
96-99% and ROC AUC above .999.

```python
from fast_automl.automl import AutoRegressor

from sklearn.datasets import load_diabetes
from sklearn.model_selection import cross_val_score, train_test_split

X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=True)

reg = AutoRegressor(n_jobs=-1, verbose=True).fit(X_train, y_train)
print('CV score: {:.4f}'.format(cross_val_score(reg.best_estimator_, X_train, y_train).mean()))
print('Test score: {:.4f}'.format(reg.score(X_test, y_test)))
```

This runs for about 30 seconds and typically achieves a test R-squared of 
.47-.53.

## Citation

```
@software{bowen2021fast-automl,
  author = {Dillon Bowen},
  title = {Fast-AutoML},
  url = {https://dsbowen.github.io/fast-automl/},
  date = {2021-02-05},
}
```

## License

Users must cite this package in any publications which use it.

It is licensed with the MIT [License](https://github.com/dsbowen/fast-automl/blob/master/LICENSE).

## Acknowledgments

This package and its documentation draw heavily on [scikit-learn](https://scikit-learn.org/stable/).
pornhubSpider 1.1.2
A simple spider of Pornhub
Requires Python: >=3.8

# pornhun爬虫
可以获取pornhub的视频信息
## getVideoList 任意页面的视频列表
传入一个url如 www.pornhub.com/video?c=111&page=1 <br>
该函数会返回一个PornhubVideo类列表，格式如下：<br>

## PornhubVideo类(Class)
用于获取并保存视频信息<br>

### 函数(methdos)

| 函数 | 解释 |
| ---- | ---- |
| PornhubVideo(url_or_viewkey, ifGetInfo=False) | 构造函数<br>-*url_or_viewkey: str* - 视频页面链接或viewkey<br>-*ifGetInfo: bool* - 是否获取信息,默认为 *False*<br>&emsp;&emsp;-*True*: 生成对象时获取视频信息。<br>&emsp;&emsp;-*False*: 生成对象时不获取视频信息，之后若需获取，可用 GetInfo() 函数。<br> |
| GetInfo() | 获取视频信息 |
| GetRelated(page=1, ifGetInfo=False) | 利用视频ID,获取该视频ID的全部相似推荐（共10个），返回格式与getVideoList相同<br>*page*: 选择要获取第几页，默认为1。<br>*ifGetInfo: bool* - 是否获取信息,默认为 *False*<br>&emsp;&emsp;-*True*: 生成对象时获取视频信息。<br>&emsp;&emsp;-*False*: 生成对象时不获取视频信息，之后若需获取，可用 GetInfo() 函数。<br> |
| RandomPorn() | 获取随机视频 | 

### 属性(properties)
| 属性 | 解释 |
| ---- | ----|
| pageUrl: str | 原始网页链接地址 |
| viewkey: str | 视频viewkey |
| videoID: str | 视频ID,用于获取相关视频 |
| title: str | 视频标题 |
| imgUrl: str | 视频封面图片链接地址 |
| duration: str | 视频时长，格式为 hh:mm:ss |
| definitions: dict | 视频所有格式、分辨率及链接 |
| isPremium: bool | 是否为会员视频 |
fair-loss 0.5
A fair loss function
Requires Python: >=3.5, <4
Keywords: pytorch loss fair

<!--
SPDX-License-Identifier: GPL-3.0-only
SPDX-FileCopyrightText: 2020 Vincent Lequertier <vi.le@autistici.org>
-->

# A fair PyTorch loss function

[![REUSE status](https://api.reuse.software/badge/gitlab.com/vi.le/fair-loss)](https://api.reuse.software/info/gitlab.com/vi.le/fair-loss)
[![PyPI version](https://img.shields.io/pypi/v/fair-loss.svg)](https://pypi.python.org/pypi/fair-loss)

The goal of this loss function is to take fairness into account during the training of a
PyTorch model. It works by adding a fairness measure to a regular loss value,
following this equation:

<img src="https://latex.codecogs.com/svg.latex?\Large&space;loss%20=%20loss%20+%20\lambda{{\sum_{i=0}^{k}w_i%20f_i(y_{pred},%20y_{true})}%20\over%20\min\limits_{%20\forall%20i\in%20[0,k[}%20f_i(y_{pred},%20y_{true})}" />

## Installation

```bash
pip install fair-loss
```

## Example

```python
import torch
from fair_loss import FairLoss

model = torch.nn.Sequential(torch.nn.Linear(5, 1), torch.nn.ReLU())
data = torch.randint(0, 5, (100, 5), dtype=torch.float, requires_grad=True)
y_true = torch.randint(0, 5, (100, 1), dtype=torch.float)
y_pred = model(data)
# Let's say the sensitive attribute is in the second dimension
dim = 1
criterion = FairLoss(torch.nn.MSELoss(), data[:, dim].detach().unique(), accuracy)
loss = criterion(data[:, dim], y_pred, y_true)
loss.backward()
```

## Documentation

See [the documentation](http://vi.le.gitlab.io/fair-loss/).
gb-menu 1.0.0
A lightweight Python library useful for creating powerful yet simple console-based user interfaces
Requires Python: >=3.6

<h1 align="center">Welcome to gb-menu 👋</h1>
<p>
  <a href=https://pypi.org/project/gb-menu" target="_blank">
    <img alt="Python Versions" src="https://img.shields.io/pypi/pyversions/gb-menu">
  </a>
  <a href=https://github.com/gbeard2/gb-menu target="_blank">
    <img alt="Release" src="https://img.shields.io/github/v/release/gbeard2/gb-menu">
  </a>
  <a href=https://pypi.org/project/gb-menu" target="_blank">
    <img alt="Downloads" src="https://img.shields.io/pypi/dm/gb-menu">
  </a>
  <a href="https://github.com/gbeard2/gb-menu/commits" target="_blank">
    <img alt="Weekly Commits" src="https://img.shields.io/github/commit-activity/w/gbeard2/gb-menu" />
  </a>
</p>

> A lightweight Python library useful for creating powerful yet simple console-based user interfaces

## Install

```sh
pip install gb-menu
```

## Usage

```python
from gb_menu import menu, choice, action

main_menu = menu.Menu()
cont_action = action.Action(function=type(None))
cont_choice = choice.Choice(key='c', text='Continue', action=cont_action)
main_menu.add_choice(cont_choice)
quit_action = action.Action(function=exit)
quit_choice = choice.Choice(key='q', text='Quit', action=quit_action)
main_menu.add_choice(quit_choice)

while True:
    main_menu.show()
```

## Output

```sh
c: Continue
q: Quit
Choice: c
c: Continue
q: Quit
Choice: c
c: Continue
q: Quit
Choice: q

Process finished with exit code 0
```

## Author

👤 **Garrett Beard**

* Github: [@gbeard2](https://github.com/gbeard2)
* LinkedIn: [@garrett-beard](https://linkedin.com/in/garrett-beard)

## 🤝 Contributing

Contributions, issues and feature requests are welcome!<br />Feel free to check [issues page](https://github.com/gbeard2/gb-menu/issues). 

## Show your support

Give a ⭐️ if this project helped you!

## 📝 License

Copyright © 2021 [Garrett Beard](https://github.com/gbeard2). <br/>
This project is [MIT](https://github.com/gbeard2/gb-menu/blob/master/LICENSE) licensed.

***
_This README was generated with ❤️ by [readme-md-generator](https://github.com/kefranabg/readme-md-generator)_
pywer 0.1.1
A simple Python package to calculate word error rate (WER).
Requires Python: >=3.6

# pywer
![pytest](https://github.com/jumon/pywer/workflows/pytest/badge.svg)

Pywer is a simple Python package to calculate word error rate (WER). Pywer can also
calculate character error rate (CER).

## Install
```
pip install pywer
```

## Usage
```python
import pywer

references = [
    "this is a simple python package",
    "it calculates word error rate",
    "it can also calculate cer",
]
hypotheses = [
    "this is the simple python package",
    "it calculates word error",
    "it can also calculate see er",
]

wer = pywer.wer(references, hypotheses)
cer = pywer.cer(references, hypotheses)
print(f"WER: {wer:.2f}, CER: {cer:.2f}")
```