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:
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 to setup a virtual environment for development
Setup: conda environment
Install conda from their website. (We recommend the mini version / miniconda)
Initialize the environment with:
conda create --name knox-env python=3.8activate knox-envpip 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:
pip install pylint
And then
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.gzknox_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.gzknox-master02.srv.aau.dk/srv/web/repos.knox.cs.aau.dk/https/knox_mi_graph-1.0.2-py3-none-any.whl
Metadata
Release files for knox-mi-graph 1.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| knox-mi-graph-1.1.2.tar.gz | 19.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| knox_mi_graph-1.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 47.8 kB
Release files / knox-mi-graph-1.1.2.tar.gz
| Download URL | knox-mi-graph-1.1.2.tar.gz |
|---|---|
| Size | 19.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
dc967f5f29434fc8b0e47f30efb83d61ab73d68b464c82b4e8651ddb693cd571
|
|
BLAKE2b-256 checksum How to use checksums |
65a9a1a5f375ab432c2682a38860c8e0179cb82255a9c6545e76c6a879a3de73
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.6.1 requests/2.25.0 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.6
|
Release files / knox_mi_graph-1.1.2-py3-none-any.whl
| Download URL | knox_mi_graph-1.1.2-py3-none-any.whl |
|---|---|
| Size | 28.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
d3ee59cf73dd08070ff4a9c8f4ae9cdbd4950c944f21a2258e148ded6725ba73
|
|
BLAKE2b-256 checksum How to use checksums |
2a24031a51756619e3be46b6c7de22a1fe21137bc3a3472a0a1bb53364f3e334
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.2.0 pkginfo/1.6.1 requests/2.25.0 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.8.6
|