Metadata-Version: 2.1
Name: dortmund2array
Version: 0.2
Summary: Add a short description here!
Home-page: https://github.com/LeviBorodenko/dortmund2array
Author: Levi Borodenko
Author-email: Levi.borodenko@gmail.com
License: mit
Project-URL: Documentation, https://github.com/LeviBorodenko/dortmund2array
Description: ## dortmund2array
        
        Tool to convert datasets from [_Benchmark Data Sets for Graph Kernels (K. Kersting et al., 2016)_](http://graphkernels.cs.tu-dortmund.de) into a format suitable for deep learning research in graph classification.
        
        <hr>
        
        #### Installation
        
        Simply run `pip install dortmund2array` to install the command-line interface. The only dependencies are `numpy networkx pandas`.
        
        #### Output
        
        Given any benchmark dataset, this tool will create a file `DATASET.pickle` that contains a pickled list. At index `i` the list has a dictionary with the adjacency matrix, the graph signal (also known as graph feature matrix) and the corresponding label for the `i`th graph.
        
        ```python
        {
            "adjacency":    ...  # as numpy array. Shape: (nodes, nodes)
            "graph_signal": ...  # as numpy array. Shape: (nodes, features)
            "label":        ...  # usually a scalar.
        }
        ```
        
        The graph signal is an array of shape `(nodes, features)` where the features are either attributes given by the dataset or if no attributes are available, we simply take the node labels as attributes.
        
        #### How to use
        
        Simply get the `dortmund2array` command line tool via `pip install dortmund2array`.
        
        ```
        usage: dortmund2array [-h] [--version] [--output OUTPUT_FOLDER]
                              [--input INPUT_FOLDER]
        
        Tool to convert datasets from 'Benchmark Data Sets for Graph Kernels' (K.
        Kersting et al., 2016)
        
        optional arguments:
          -h, --help            show this help message and exit
          --version             show program's version number and exit
          --output OUTPUT_FOLDER, -o OUTPUT_FOLDER
                                Output folder.
          --input INPUT_FOLDER, -i INPUT_FOLDER
                                Input folder containing the dataset of the same name.
          -e                    Output edge list instead of adjacency for each
                                graph.
        ```
        
        Thus, download and unzip a dataset. Make sure the folder-name agrees with the dataset-name on the files inside of it. If you for instance download `MUTAG` and the corresponding folder is `.\MUTAG\` and you want the array data saved in `.\MUTAG_array\` then you need to simply run:
        
        ```
        dortmund2array -i ./MUTAG/ -o ./MUTAG_array/
        ```
        
        #### Requirements
        Make sure you meet all the dependencies inside the `requirements.txt`.
        
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown; charset=UTF-8
Provides-Extra: testing
