def main_pcg_sample_entry()

in train_procgen/graph.py [0:0]

• 40 lines of code
• 6 McCabe index (conditional complexity)

def main_pcg_sample_entry(distribution_mode, normalize_and_reduce, restrict_training_set):
    params = {
        'xtick.labelsize': 12,
        'ytick.labelsize': 12,
        'axes.titlesize': 16,
        'axes.labelsize': 24,
        'legend.fontsize': 18,
        'figure.figsize': [9, 9]
    }
    matplotlib.rcParams.update(params)

    kwargs = {'smoothing': .9}

    if distribution_mode == 'easy':
        kwargs['x_scale'] = 1 * 256 * 64 / 1e6 # num_workers * num_steps_per_rollout * num_envs_per_worker / graph_scaling
        num_train_levels = 200
        normalization_ranges = EASY_GAME_RANGES
    elif distribution_mode == 'hard':
        kwargs['x_scale'] = 4 * 256 * 64 / 1e6 # num_workers * num_steps_per_rollout * num_envs_per_worker / graph_scaling
        num_train_levels = 500
        normalization_ranges = HARD_GAME_RANGES
    else:
        assert False, "specify distribution_mode as 'easy' or 'hard'"

    y_label = 'Score'
    x_label = 'Timesteps (M)'

    run_directory_prefix = f"{distribution_mode}-{num_train_levels if restrict_training_set else 'all'}"
    kwargs['run_directory_prefix'] = f"{run_directory_prefix}-run"

    # We throw out the first few datapoints to give the episodic reward buffers time to fill up
    # Otherwise, there could be a short-episode bias
    kwargs['first_valid'] = 10

    if restrict_training_set:
        kwargs['suffixes'] = ['train', 'test']

    if normalize_and_reduce:
        kwargs['normalization_ranges'] = normalization_ranges
        y_label = 'Mean Normalized Score'

    fig, axarr = plot_experiment(**kwargs)

    if normalize_and_reduce:
        axarr.set_xlabel(x_label, labelpad=20)
        axarr.set_ylabel(y_label, labelpad=20)
    else:
        ax0 = switch_to_outer_plot(fig)
        ax0.set_xlabel(x_label, labelpad=40)
        ax0.set_ylabel(y_label, labelpad=35)

    return run_directory_prefix