An educational dashboard built as a proof of concept for investigating Random Forest models. You can see the hosted version here.
This project was built for the master's thesis found under: PLACEHOLDER
This project relies mainly on the following packages:
The dashboard builds 2 example use cases using the Iris and the Digits dataset from the Scikit-learn package.
| Folder | Description |
|---|---|
| src/dashboardv1/ | Contains all of the necessary python code |
| src/dashboardv1/images/ | Contains the images used in the dashboard |
| src/dashboardv1/pickle/ | Contains the precomputed pickle files for the pairwise distance matrix |
| src/dashboardv1/text/ | Contains the markdown files used in the dashboard |
| tests/ | Is currently empty |
| .streamlit/ | Contains the *.toml file for the custom Streamlit theme |
| .vscode/ | Contains the launch.json and settings.json for VSCode development |
To run the code, clone the repository and install the dependencies by installing any environment manager that you prefer. You can then use the requirements.txt file to install all necessary dependencies. The code was developed using pipenv, but you can use any environment manager you prefer. For more information on how to use pipenv: https://pipenv.pypa.io/en/latest/basics/#example-pipenv-workflow
If problems arise with the pygraphviz installation when installing the requirements, install Graphviz and try again. This can be done in one of two ways:
- (For conda environments:) Install Graphviz inside your conda environment
$ conda install -c anaconda Graphviz- Install Graphviz outside of your environment
$ apt-get install -y graphviz-devFor more information refer to this github issue.
After installing all necessary dependencies, you can run the dashboard by running the following command in the terminal:
$ streamlit run src/dashboardv1/st_dashboard.pyStreamlit should display a link to the dashboard in the terminal. If it does not, you can also access the dashboard by navigating to http://localhost:8501 in your browser.
If you are using VS Code for development, the following launch.json configuration is recommended:
{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Streamlit",
"type": "python",
"request": "launch",
"program": "path/to/streamlit", // use your environments' specific streamlit path
"args": [
"run",
"${cwd}/src/dashboardv1/st_dashboard.py"
]
}
]
}For more information please refer to this entry on the streamlit forum.
Licensed under the MIT Licence,(LICENSE)
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