def run_experiment():
    from notears import utils
    # utils.set_random_seed(1) this line cannot be used to ensure different outcomes in each round of the loop

    n, d, s0, graph_type, sem_type = 1000, 20, 20, 'ER', 'gauss'
    B_true = utils.simulate_dag(d, s0, graph_type)
    W_true = utils.simulate_parameter(B_true)
    np.savetxt('W_true.csv', W_true, delimiter=',')

    X = utils.simulate_linear_sem(W_true, n, sem_type)
    np.savetxt('X.csv', X, delimiter=',')

    W_est = notears_linear(X, lambda1=0.1, loss_type='l2')
    assert utils.is_dag(W_est)
    np.savetxt('W_est.csv', W_est, delimiter=',')
    acc = utils.count_accuracy(B_true, W_est != 0)
    print(acc)

if __name__ == '__main__':
    num_experiments = 3

    for _ in range(num_experiments):
        run_experiment()

Now I need to compute the running time of the whole process. How to do this? I don’t know where I should put the start time and end time.

>Solution :

You can use the timeit module:

from timeit import default_timer as timer

if __name__ == '__main__':
    num_experiments = 3
    start = timer()
    for _ in range(num_experiments):
        run_experiment()
    end = timer()
    print(end - start)
   

The previous code should be able to measure the total time it took to run the 3 experiments.