df_skew.py

skews = {0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99}
scores = {}
for skew in skews:
    print("eval", skew)
    hyperparams = {
        'n_runners': 10,
        'sample_size': 10,
        'rounds': 1,
        'combine': 'weighted', # or 'mean'
        'partition_params': {
            'scheme': 'label-skew', # uniform, label-skew
            'args': {
                'randomise': True,
                'skew': skew
            }
        },
        'runner_hyperparams': {
            'epochs': 10,
            'lr': 0.15,
            'batch_size': 0
        }
    }
    local_mean_f1_scores, local_max_f1_scores, fed_avg_f1_scores = compare_f1(hyperparams, 1000)
    scores[str(skew)] = (local_mean_f1_scores, local_max_f1_scores, fed_avg_f1_scores)