668a64ffe429a4caf58cabf82f3679c129365d6f,rankeval/analysis/statistical.py,,_randomization,#Any#Any#Any#,67

Before Change

		sel = np.random.choice([False, True], len(metric_scores_A))
		// compute avg performance of randomized models
		A_mean = (np.sum(best_metrics [np.logical_not(sel)]) + np.sum(worst_metrics[sel]) )/N
		B_mean = (np.sum(worst_metrics[np.logical_not(sel)]) + np.sum(best_metrics [sel]) )/N
		// performance difference
		delta = A_mean - B_mean

After Change

	N = float(len(metric_scores_A))

	A_sum = np.sum(best_metrics)
	B_sum = np.sum(worst_metrics)

	// repeat n_prem times
	for i in range(n_perm):
		// select a random subset

Instances