Python implementation to the paper "Evaluation of computationally intelligent techniques for breast cancer diagnosis" published on Neural Computing and Aplications journal (2021).
- Install pipenv
- Install dependencies using pipenv:
pipenv install --skip-lock(it is important to always use the flag--skip-lockbecause pipenv has a problem which can take a lot of time to install if not used) - To access the virtual environment created, run
pipenv shell, now all commands which you run will be performed into virtual enviroment created - Activate pre-commit hooks to use black formatter, flake8 lint and Isort references. Run
pre-commit install. Now every time you make a commit, black formatter, flake8 and isort will make tests to verify if your code is following the patterns (you can adapt your IDE or text editor to follow this patterns, e.g. vs code)
- Instructions to download the dataset
- Instructions to execute the training of ML models
- Inside the virtual environment, execute the test server using
python SCRIPT_NAME_HERE
-
Run:
pipenv shell; -
Than run:
jupyter notebook; -
Open the slide/slide.ipynb;
-
Click on
Enter/Exit Live Reveal Slideshowor typeAlt-r -
You can execute the conde on the slides by typing:
shift-enter
To read more about the slides read the documentation at: https://rise.readthedocs.io/en/stable/
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