def main()

in scripts/make_figures.py [0:0]

• 20 lines of code
• 8 McCabe index (conditional complexity)

def main(args):
    labels = {"mnist" : ["0", "1"], "cifar10": ["Plane", "Car"]}
    for dataset in ["mnist", "cifar10"]:
        train = dataloading.load_dataset(
            name=dataset, split="train", normalize=False,
            num_classes=2, reshape=False, root=args.data_folder)

        for model in ["linear", "logistic"]:
            prefix = f"{dataset}_{model}"

            # Histogram of etas:
            eta_histogram(
                args.results_path, args.save_path,
                prefix, train, labels[dataset])

            # Most and least leaked images:
            view_images(train, args.results_path, args.save_path, prefix)

        eta_overlap(args.results_path, f"{dataset}")

        # Plot of eta stds vs iterations of reweighting
        iterated_reweighted_etas(
            args.results_path, args.save_path, f"{dataset}")

    # Plot correlations of eta with other metrics
    correlations(args.results_path, args.save_path, "mnist_linear")

    # IWPC MSE and FIL with output pertubration
    private_mse_and_fil(args.results_path, args.save_path)

    # IWPC Fredrikson and whitebox attribute inversion results.
    private_inversion_accuracy(args.results_path, args.save_path)

    # IWPC and UCI Adult attribute inversion results as a function of
    # iterations of IRFIL
    for dataset in ["iwpc", "uciadult"]:
        irfil_inversion(args.results_path, dataset, args.save_path)