semvec/pypi-packages huggingface dataset:
domain-network 0.1.2
Makes a network out of a URLs in a dataset of tweets
Requires Python: >=3.6

# Domain Network
A package to create a domain network of the URLs mentioned in a dataset of texts. 
In the current version it works for tweets. It may process any kind of text in the future versions.

## Installation

The easiest way to install the domain_network package is to use the following command in a terminal:

``` bash
pip install domain-network

```
## Usage

To run the module using Command Line Interface (CLI) run the following:

- For the whole process starting with raw tweets:

``` bash
python -m domainNetwork  --input_dir ["data/twitterAPI_lang_en/*/*.json"] --conf_dir  [‘config/sample_config.ini’] --min_edge_weight [20] --min_node_size [20] \
--min_stand_alone_size [50]   --urls_file_name  ["output/urls.csv"] \
--network_output_file_name  ["output/network.csv"] --netloc_output_file_name ["output/netloc.csv"] \
--netloc_origin_output_file_name  ["output/netloc_origin.csv"] 
```

- For making domain network of a pre-processed file which includes extracted netlocs: 
``` bash
python -m domainNetwork  --conf_dir  [‘config/sample_config.ini’] --min_edge_weight [20] --min_node_size [20] \
--min_stand_alone_size [50]  --network_only true  --urls_file_name  ["data/urls.csv"] \
--network_output_file_name  ["output/network.csv"] --netloc_output_file_name ["output/netloc.csv"] \
--netloc_origin_output_file_name  ["output/netloc_origin.csv"] 
```
### Parameters:

--input_dir : Directory of tweet files

--conf_dir : File path of the config file. Read Config file section for more details.

--min_edge_weight : Min number of users that mentioned both source and target of the edge in their tweets.

--min_node_size : Min number of times that a web page is mentioned in total, for connected nodes.

--min_stand_alone_size: Min number of times that a web page is mentioned in total, for stand-alone nodes.

--network_only : If you want to use a preprocessed file which includes the netlocs

--urls_file_name : File path of preprocessed tweets with netlocs. Can be output/input file in the above mentioned situations.

--network_output_file_name: File path of the generated network, in .csv format.

--netloc_output_file_name : File path of the list of web sites, after filtering, in .csv format.

--netloc_origin_output_file_name : File path of the original list of web sites, in .csv format.

--selected_users_fp : Specifies the target group of users, i.e. active users that we are interested in their domain network

### Output
The main output of this package is network.csv which includes source, target and the weight.
Output file can be given to a visualization tool, e.g. networkx in python for the visualization
autoencoder 0.0.8
A toolkit for flexibly building convolutional autoencoders in pytorch

# Introduction
This repository contains the tools necessary to flexibly build an autoencoder in pytorch. In the future some more investigative tools may be added. The main goal of this toolkit is to enable quick and flexible experimentation with convolutional autoencoders of a variety of architectures.

The implementation is such that the architecture of the autoencoder can be altered by passing different arguments. Tunable aspects are:
- number of layers
- number of residual blocks at each layer of the autoencoder
- functions used for downsampling and upsampling convolutions and convolutions in the residual blocks
- number of channels at each layer of the autoencoder
- activation function performed after each convolution
- symmetry (or lack thereof) of the encoder-decoder architecture
- etc

Some usefull wrappers and custom classes, such as ResidualBlock or GeneralConvolution, can be found in model_parts.py.
The file models.py is where the actual autoencoder classes are. It contains one base class as well as two extension for 2d and 3d data.

# Installation
The latest stable version can be obtained using `pip install autoencoder`.

Otherwise, you can download and use the files directly in your projects.

# Usage
The ConvAE base class expects parameters that specify the overall architecture (see documentation) and one function for the downsampling layer, upsampling layer and residual block.
Conv2dAE and Conv3dAE on the other hand provide an interface to easily create the aforementioned functions from parameters and create the autoencoder from there.