def calc_metric(score_label_u):
score_label_u = sorted(score_label_u, key=lambda d: d[0], reverse=True)
precision = np.array(
[eval.precision_k(score_label_u, k) for k in range(1, 21)])
ndcg = np.array([eval.ndcg_k(score_label_u, k) for k in range(1, 21)])
auc = eval.auc(score_label_u)
mae = eval.mae(score_label_u)
mrse = eval.mrse(score_label_u)
return precision, ndcg, auc, mae, mrse
def calc_metric(score_label_u):
score_label_u = sorted(score_label_u, key=lambda d: d[0], reverse=True)
precision = eval.precision_k(score_label_u, 3)
recall = eval.recall_k(score_label_u, 3)
try:
f1 = 2 * precision * recall / (precision + recall)
except:
f1 = 0
auc = eval.auc(score_label_u)
ndcg = eval.ndcg_k(score_label_u, 3)
return precision, recall, f1, auc, ndcg