We use poetry as a package manager, which should take care of all dependencies. You can install poetry here
The only requirement is python>=3.9 and python<3.11.
Simply run poetry install
The data will be generated automatically when running the models.
cd condgen/counterfactuals
poetry run python train_cf_cluster.py --EM=true --data_type=MNIST --max_epochs1=50 --max_epochs2=50 --noise_std=0.05 -non_additive_noise=True -num_classes_model=-1 --update_period=10
poetry run python train_cf_cluster.py --EM=true --data_type=SimpleTraj --max_epochs1=50 --max_epochs2=50 --noise_std=0.05 -non_additive_noise=True -num_classes_model=-1 --update_period=10
poetry run python train_cf_cluster.py --EM=true --data_type=CV --max_epochs1=50 --max_epochs2=50 --noise_std=0.05 -non_additive_noise=True -num_classes_model=-1 --update_period=10
The CF_eval.ipynb notebook is used to process the results of the counterfactual reconstructions experiments.
MNIST_comparison.ipynb produces the image comparison figure.