semvec/pypi-packages huggingface dataset:
surgeo 1.1.2
Bayesian Improved Surname Geocoder model
License: MIT
Keywords: bisg,disparate,race

**Surgeo** is an impelmentation of the Bayesian Improved Surname
        Geocode (BISG) model created by Mark N. Elliot et al. and
        incluenced by the Consumer Financial Protection Bureau's (CFPB)
        implementation of the same. It also includes an implementation
        of the Bayesian Improved First Name Surname Geocode (BIFSG) model
        created by Ioan Voicu.
PyMails 0.0.5
Simple tools for sending mail
License: MIT
Keywords: email

### PyMails
---
## Required
* [Python >= 3.8][link1]
## Send mail 
```python
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from PyMails import *

smtp_server = '' 
port = ''
sender_email = ''
password = ''
receiver_email = ''

## Html form
msg = MIMEMultipart('alternative')
msg['Subject'] = 'Test mail'
msg['From'] = sender_email
msg['To'] = receiver_email

html = open('./html/mail.html','r+').read()
msg.attach(MIMEText(html, 'html'))

## Without html form
msg = MIMEText('Test')
msg['Subject'] = 'Test mail'
msg['From'] = sender_email
msg['To'] = receiver_email

send_mail(smtp_server,port,sender_email,password,receiver_email,msg);
```
## Connection test
```python
    connection_test(smtp_server,port,sender_email,password)
```

[link1]: https://www.python.org/



Change Log
==========

0.0.5 (05.04.2021)
-------------------
- Add connection test

0.0.1 (04.04.2021)
-------------------
- First Release
pytorch-polkadots 0.1.1
Provides the ability to construct a simulated image dataset on the fly with different characteristics.
License: MIT
Requires Python: >=3.7,<4.0
Keywords: machine learning,simulated datasets,images

## Overview
This project provides the ability to construct a simulated image dataset
on the fly with different characteristics. While it is possible to simulate
datasets that models can be trained against successfully, it is also useful to
adjust dataset parameters to yields datasets that much more challenging to train
against.

## Installation

### With poetry

```bash
poetry install 
```
### With pip
```bash
pip install pytorch-polkadots 
```
### From cloned repo
```bash
pip install -r requirements.txt
```
# Tests
```
bash
export PYTHONTEST=.
pytest
```

# Example image
You can see an image generated by the code with:
```bash
python main.py
```
This will display the following image:
![](example.png)

# PyTorch dataset
The code provides a pytorch dataset that you can use in benchmarks. 

```python
class PolkaDotDataset(Dataset)
```