def get_statistics(alignment_matrix,
groundtruth,
groundtruth_matrix=None,
use_greedy_match=False,
get_all_metric=False):
if use_greedy_match:
print("This is greedy match accuracy")
pred = greedy_match(alignment_matrix)
else:
pred = get_nn_alignment_matrix(alignment_matrix)
acc = compute_accuracy(pred, groundtruth)
if get_all_metric:
MAP, Hit, AUC = compute_MAP_Hit_AUC(alignment_matrix, groundtruth)
pred_top_5 = top_k(alignment_matrix, 5)
precision_5 = compute_precision_k(pred_top_5, groundtruth)
pred_top_10 = top_k(alignment_matrix, 10)
precision_10 = compute_precision_k(pred_top_10, groundtruth)
pred_top_20 = top_k(alignment_matrix, 20)
precision_20 = compute_precision_k(pred_top_20, groundtruth)
pred_top_30 = top_k(alignment_matrix, 30)
precision_30 = compute_precision_k(pred_top_30, groundtruth)
pred_top_50 = top_k(alignment_matrix, 50)
precision_50 = compute_precision_k(pred_top_50, groundtruth)
pred_top_100 = top_k(alignment_matrix, 100)
precision_100 = compute_precision_k(pred_top_100, groundtruth)
return acc, MAP, Hit, AUC, precision_5, precision_10, precision_20, precision_30, precision_50, precision_100
return acc
# compute the approximate closed-form solution
s = (1 - alpha) * h + D * N * np.dot(
U,
np.linalg.lstsq(Lambda, np.dot(U.T, x), rcond=-1)[0])
S = s.reshape((n1, n2))
return S
if __name__ == "__main__":
args = parse_args()
print(args)
data1 = Dataset(args.prefix1)
data2 = Dataset(args.prefix2)
A1 = data1.adjacency # (3096x3096)
A2 = data2.adjacency # (1118x1118)
N1 = data1.features # (3096x538)
N2 = data2.features # (1118x538)
groundtruth = load_groundtruth(args.groundtruth)
H = get_H(args.H, data1, data2) # (1118, 3096)
S = FINAL_NFLOW(A1, A2, N1, N2, H, args.alpha, args.r)
matched = get_top_k(S, data1.id2idx, data2.id2idx, k=args.k)
acc = compute_accuracy(matched, groundtruth)
print("Top-k accuracy (%d pairs): %.2f%%" % (len(matched.items()), acc))
matched = greedy_match(S, data1.id2idx, data2.id2idx)
acc = compute_accuracy(matched, groundtruth)
print("Greedy match accuracy (%d pairs): %.2f%%" %
(len(matched.items()), acc))
def get_statistics(alignment_matrix, groundtruth_matrix):
pred = greedy_match(alignment_matrix)
greedy_match_acc = compute_accuracy(pred, groundtruth_matrix)
print("Accuracy: %.4f" % greedy_match_acc)