How to do 100 runs simulation based on the current codes?

[dududada]

What do you want to average, acc?

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=',')
    return utils.count_accuracy(B_true, W_est != 0)
 
if __name__ == '__main__':
    acc = [run_experiment() for _ in range(num_experiments)]
    print("Average acc =", sum(acc) / len(acc))

You should not use assert.

Yes, I want to get the average value of acc. The current acc gives me a list like this: {'fdr': 0.0, 'tpr': 0.95, 'fpr': 0.0, 'shd': 1, 'nnz': 19}