Metadata-Version: 2.1
Name: knox-mi-graph
Version: 1.1.2
Summary: Knowledge graph builder
Home-page: https://git.its.aau.dk/Knox/mi-graph
Author: Foersteholdet
Author-email: sw514e20@cs.aau.dk
License: UNKNOWN
Description: # MI-Graph
        
        MI-graph (Machine learning graph) is a pip module supporting various configurations for knowledge extraction.
        
        ## Usage
        
        To download the module run the following command:
        
        ```bash
        pip install --extra-index-url https://repos.knox.cs.aau.dk knox-mi-graph
        ```
        
        ### List of commandline arguments
        
        MI-Graph supports the following arguments.
        
        ```
        usage: mi-graph [-h] [-v] [--file] [--visualisation] {file,flask}
        
        positional arguments:
          {file,flask}     Choose if you want to process a file, or run the program as a rest api
        
        optional arguments:
          -h, --help       show this help message and exit
          -v, --version    show program's version number and exit
          --file , -f      Please indicate the json file you want to process.
          --visualisation  This option visualizes the graph with plotly, after the script has run
        ```
        
        ### Run under development
        
        There is two alternatives to run the program when developing:
        
        #### Option 1
        
        ```
        python -c "from mi_graph import cli; cli()"
        ```
        
        #### Option 2
        
        Wrap option 1 into a Python file which then can be executed:
        
        ```
        from mi_graph import cli
        
        cli()
        
        ```
        
        Now run the file:
        
        ```
        python <file path>
        ```
        
        ## Setup: virtualenv environment
        
        Follow [this guide](https://wiki.knox.cs.aau.dk/en/SettingUpPython) to setup a virtual environment for development
        
        ## Setup: conda environment
        
        Install conda from [their website](https://docs.anaconda.com/anaconda/install/). (We recommend the mini version /
        miniconda)
        
        Initialize the environment with:
        
        - `conda create --name knox-env python=3.8`
        - `activate knox-env`
        - `pip install -r requirements.txt`
        
        ## Test dependencies
        
        For developers, you also need to install the test requirements:
        
        - `pip install -r tests/requirements.txt`
        
        ## Pylint
        
        Before you make a pull request to master, you should run branch though pylint.
        
        you can use `pylint_runner` to run all folders in the solution, or with pylint like so Run:
        
        ```bash
        pip install pylint
        ```
        
        And then
        
        ```bash
        pylint folder/
        ```
        
        ## Build module
        
        You cannot be in a virtual environment, when building
        
        - `python3 setup.py sdist bdist_wheel`
        
        Output artifacts:
        
        - `knox-mi-graph-1.0.2.tar.gz`
        - `knox_mi_graph-1.0.2-py3-none-any.whl`
        
        ---
        When the command is finished, it outputs a set of artifacts in a `dist` folder. Copy the artifacts to the knox
        server `knox-master02.srv.aau.dk` in the following path: `/srv/web/repos.knox.cs.aau.dk/https/`
        
        The final result after completing the steps above:
        
        - `knox-master02.srv.aau.dk/srv/web/repos.knox.cs.aau.dk/https/knox-mi-graph-1.0.2.tar.gz`
        - `knox-master02.srv.aau.dk/srv/web/repos.knox.cs.aau.dk/https/knox_mi_graph-1.0.2-py3-none-any.whl`
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
