semvec/pypi-packages huggingface dataset:
pywebcrawler 0.0.1
A fast web crawler to satisfy all your needs
Requires Python: >=3.6

# Python Web Crawler [![Build Status](https://travis-ci.org/eckarthik/PyWebCrawler.svg?branch=master)](https://travis-ci.org/eckarthik/PyWebCrawler)

A web crawler written in Python to crawl a given website.

# Features!

  - Faster
  - Ablility to specify the number of threads to use to crawl the given website
  - Ability to use proxies to bypass IP restrictions
  - Clear summary of all the urls that were crawled. View the crawled.txt file to see the complete list of all the links crawled
  - Ability to specify delay between each HTTP Request
  - Stop and resume crawler whenever you need
  - Gather all the urls with their titles to a csv, incase if you are planning to create a search engine
  - Search for specific text throughout the website
  - Clear statistics about how many links ended up as Files,Timeout Errors,Connecrion Errors
  - Crawl until you need. You can specify upto what level the crawler should crawl.
  - Random browser user agents will be used while crawling.

# Upcoming Features!

  - Gather AWS Buckets,Emails,Phone Numbers etc
  - Download all images

### Dependencies

This tool uses a number of open source projects to work properly:

* [BeautifulSoup](https://pypi.org/project/beautifulsoup4/) - Parser to parse the HTML response of each request made.
* [Requests](https://pypi.org/project/requests/) - To make GET requests to the URLs.

### Usage
If you like to see the list of supported features, simply run
![Usage Demo](https://i.ibb.co/8zVss64/Running-Main-Py.png)

###### Specifying only to crawl for 3 levels
![Depth Crawl](https://i.ibb.co/TTF8g2X/Running-Main-Py.png>)
###### Search for specific text throughout the website
![Text Search](https://i.ibb.co/q9trhVp/Running-Main-Py.png)
###### Gather all the links along with their titles to a CSV file. A CSV file with the links and their titles will be created after the crawl completes
![Gather Titles](https://i.ibb.co/6sDD2cC/Running-Main-Py.png)
###### Use proxies to crawl the site. 
![Use Proxies](https://i.ibb.co/51SwP7m/Running-Main-Py.png)
MaLeMBa 0.4.14
Machine Learning Models Base

# MaLeMBa - Machine Learning Models Base

Python package for base manipulations with datasets and machine learning models wrapping

## Installation

To install the package from PyPI type:

```
pip install malemba
```

Or from the repo:

```
pip install git+https://github.com/loven-doo/MaLeMBa.git
```

For 'dev' branch version:

```
pip install git+https://github.com/loven-doo/MaLeMBa.git@dev
```

## Package reference

### ModelBase

### ds_tools
torchfm 0.7.0
PyTorch implementation of Factorization Machine Models

# Factorization Machine models in PyTorch
  
This package provides a PyTorch implementation of factorization machine models and common datasets in CTR prediction.


## Available Datasets

* [MovieLens Dataset](https://grouplens.org/datasets/movielens)
* [Criteo Display Advertising Challenge](https://www.kaggle.com/c/criteo-display-ad-challenge)
* [Avazu Click-Through Rate Prediction](https://www.kaggle.com/c/avazu-ctr-prediction)


## Available Models

| Model | Reference |
|-------|-----------|
| Logistic Regression | |
| Factorization Machine | [S Rendle, Factorization Machines, 2010.](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf) |
| Field-aware Factorization Machine | [Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 2015.](https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf) |
| Factorization-Supported Neural Network | [W Zhang, et al. Deep Learning over Multi-field Categorical Data - A Case Study on User Response Prediction, 2016.](https://arxiv.org/abs/1601.02376) |
| Wide&Deep | [HT Cheng, et al. Wide & Deep Learning for Recommender Systems, 2016.](https://arxiv.org/abs/1606.07792) |
| Attentional Factorization Machine | [J Xiao, et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, 2017.](https://arxiv.org/abs/1708.04617) |
| Neural Factorization Machine | [X He and TS Chua, Neural Factorization Machines for Sparse Predictive Analytics, 2017.](https://arxiv.org/abs/1708.05027) |
| Neural Collaborative Filtering | [X He, et al. Neural Collaborative Filtering, 2017.](https://arxiv.org/abs/1708.05031) |
| Field-aware Neural Factorization Machine | [L Zhang, et al. Field-aware Neural Factorization Machine for Click-Through Rate Prediction, 2019.](https://arxiv.org/abs/1902.09096) |
| Product Neural Network | [Y Qu, et al. Product-based Neural Networks for User Response Prediction, 2016.](https://arxiv.org/abs/1611.00144) |
| Deep Cross Network | [R Wang, et al. Deep & Cross Network for Ad Click Predictions, 2017.](https://arxiv.org/abs/1708.05123) |
| DeepFM | [H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017.](https://arxiv.org/abs/1703.04247) |
| xDeepFM | [J Lian, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, 2018.](https://arxiv.org/abs/1803.05170) |
| AutoInt (Automatic Feature Interaction Model) | [W Song, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks, 2018.](https://arxiv.org/abs/1810.11921) |
| AFN(AdaptiveFactorizationNetwork Model) | [Cheng W, et al. Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions, AAAI'20.](https://arxiv.org/pdf/1909.03276.pdf) |

Each model's AUC values are about 0.80 for criteo dataset, and about 0.78 for avazu dataset. (please see [example code](examples/main.py))


## Installation

    pip install torchfm


## API Documentation

https://rixwew.github.io/pytorch-fm


## Licence

MIT