def get_performance(r, Ks):
Ks = [1]
auc = []
for K in Ks:
auc.append(metrics.hit_at_k(r, K))
return {'auc': np.array(auc)}
def get_performance(user_pos_test, r, auc, Ks):
precision, recall, ndcg, hit_ratio = [], [], [], []
for K in Ks:
precision.append(metrics.precision_at_k(r, K))
recall.append(metrics.recall_at_k(r, K, len(user_pos_test)))
ndcg.append(metrics.ndcg_at_k(r, K))
hit_ratio.append(metrics.hit_at_k(r, K))
return {'recall': np.array(recall), 'precision': np.array(precision),
'ndcg': np.array(ndcg), 'hit_ratio': np.array(hit_ratio), 'auc': auc}
def evaluate_users(ratings, rel, num_pos):
# exclude trainin
nrows = len(ratings)
inds = np.argsort(ratings, axis=-1)[:, -K:]
r = rel[np.arange(nrows).reshape(-1, 1), inds]
precision = metrics.precision_at_k(r, K)
recall = metrics.recall_at_k(r, K, num_pos)
ndcg = metrics.ndcg_at_k(r, K)
hit_ratio = metrics.hit_at_k(r, K)
return {
'precision': precision.mean(),
'recall': recall.mean(),
'ndcg': ndcg.mean(),
'hit_ratio': hit_ratio.mean()
}
