Impact of using synthetic data on MIA and AIA

API documentation

The documentation is generated using sphinx. To view it, clone the repo and use your web browser to open docs/build/html/index.html. To regenerate the API documentation:

sphinx-apidoc -f -F -o docs src;
sphinx-build -M html docs docs/build/

Tests

Unit tests are setup in the test directory. They use the python unittest packages. To run all tests:

python -m unittest discover -v -p "*.py" -s test

Installation

Instal with pip in a venv. In the directory containing pyproject.toml :

 pip install --editable .

Usage

#Load data
from synthetic.fetch_data import adult
from synthetic.fetch_data import utk
data_adult = adult.load()
data_utk = utk.load()

#Train a neural network an adult 
form synthetic.predictor.adult import AdultNN
adultNN = AdultNN()
adultNN.fit(data_adult["train"])

#Evalute trained neural network
adultNN.predict(data_adult["test"])

Datasets

Adult

We are using folktables adult.

UTKFaces

From Kaggle: jangedoo/utkfaces-new. For loading and parsing files we use aia_fairness.dataset_processing