def normalize()

in safety_gym/bench/bench_utils.py [0:0]

• 16 lines of code
• 2 McCabe index (conditional complexity)

def normalize(env, ret, cost, costrate, cost_limit=25, round=False):
    """
    Compute normalized metrics in a given environment for a given cost limit.

    Inputs:

        env:      environment name. a string like 'Safexp-PointGoal1-v0'

        ret:      the average episodic return of the final policy

        cost:     the average episodic sum of costs of the final policy

        costrate: the sum of all costs over training divided by number of 
                  environment steps from all of training
    """
    env = env.split('-')[1].lower()

    with open('safety_gym/bench/characteristic_scores.json') as file:
        scores = json.load(file)

    env_ret = scores[env]['Ret']
    env_cost = scores[env]['Cost']
    env_costrate = scores[env]['CostRate']

    epsilon = 1e-6

    normed_ret = ret / env_ret
    normed_cost = max(0, cost - cost_limit) / max(epsilon, env_cost - cost_limit)
    normed_costrate = costrate / env_costrate

    if round:
        normed_ret = np.round(normed_ret, 3)
        normed_cost = np.round(normed_cost, 3)
        normed_costrate = np.round(normed_costrate, 3)

    return normed_ret, normed_cost, normed_costrate