def get_performance(user_pos_test, r, auc, Ks): ''' :param user_pos_test: user 测试集中真实交互的item :param r: r = [1,0,1] 表示预测TOP-K是否命中 :param auc: auc =0 标量 :param Ks: TOP-K :return: ''' precision, recall, ndcg, hit_ratio, MAP = [], [], [], [], [] 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)) MAP.append(metrics.AP_at_k(r, K, len(user_pos_test))) return {'recall': np.array(recall), 'precision': np.array(precision), 'ndcg': np.array(ndcg), 'hit_ratio': np.array(hit_ratio), 'MAP': np.array(MAP), 'auc': auc}
preds[u] = list(top_n) # get actual interaction info. of test users test_ur = defaultdict(list) for u in test_set.user.unique(): test_ur[u] = test_set.loc[test_set.user == u, 'item'].values.tolist() for u in preds.keys(): preds[u] = [1 if e in test_ur[u] else 0 for e in preds[u]] # calculate metrics precision_k = np.mean( [precision_at_k(r, args.topk) for r in preds.values()]) fnl_precision.append(precision_k) recall_k = np.mean([ recall_at_k(r, len(test_ur[u]), args.topk) for u, r in preds.items() ]) fnl_recall.append(recall_k) map_k = map_at_k(list(preds.values())) fnl_map.append(map_k) ndcg_k = np.mean([ndcg_at_k(r, args.topk) for r in preds.values()]) fnl_ndcg.append(ndcg_k) hr_k = hr_at_k(list(preds.values()), list(preds.keys()), test_ur) fnl_hr.append(hr_k) mrr_k = mrr_at_k(list(preds.values())) fnl_mrr.append(mrr_k)
actual_cands = set(candidates[u]) neg_item_pool = set(item_pool) - set(test_ur[u]) - set(ur[u]) neg_cands = random.sample(neg_item_pool, max_i_num - len(candidates[u])) cands = actual_cands | set(neg_cands) else: cands = random.sample(candidates[u], max_i_num) pred_rates = algo.user_vec[u, :].dot(algo.item_vec).toarray()[0, list(cands)] rec_idx = np.argsort(pred_rates)[::-1][:args.topk] preds[u] = list(np.array(list(cands))[rec_idx]) for u in preds.keys(): preds[u] = [1 if i in test_ur[u] else 0 for i in preds[u]] precision_k = np.mean([precision_at_k(r, args.topk) for r in preds.values()]) fnl_precision.append(precision_k) recall_k = np.mean([recall_at_k(r, len(test_ur[u]), args.topk) for u, r in preds.items()]) fnl_recall.append(recall_k) map_k = map_at_k(list(preds.values())) fnl_map.append(map_k) ndcg_k = np.mean([ndcg_at_k(r, args.topk) for r in preds.values()]) fnl_ndcg.append(ndcg_k) hr_k = hr_at_k(list(preds.values()), list(preds.keys()), test_ur) fnl_hr.append(hr_k) mrr_k = mrr_at_k(list(preds.values())) fnl_mrr.append(mrr_k) for i in range(len(val_kpi)):
# get top-N list for test users preds = reco.predict(test_set) # get actual interaction info. of test users test_ur = defaultdict(list) for u in test_set.user.unique(): test_ur[u] = test_set.loc[test_set.user == u, 'item'].values.tolist() for u in preds.keys(): preds[u] = [1 if e in test_ur[u] else 0 for e in preds[u]] # calculate metrics precision_k = np.mean([precision_at_k(r, k) for r in preds.values()]) fnl_precision.append(precision_k) recall_k = np.mean( [recall_at_k(r, len(test_ur[u]), k) for u, r in preds.items()]) fnl_recall.append(recall_k) map_k = map_at_k(list(preds.values())) fnl_map.append(map_k) ndcg_k = np.mean([ndcg_at_k(r, k) for r in preds.values()]) fnl_ndcg.append(ndcg_k) hr_k = hr_at_k(list(preds.values()), list(preds.keys()), test_ur) fnl_hr.append(hr_k) mrr_k = mrr_at_k(list(preds.values())) fnl_mrr.append(mrr_k) print('---------------------------------')
top_n = np.array(test_u_is[u])[rec_idx] preds[u] = list(top_n) # get actual interaction info. of test users ur = defaultdict(list) for u in test_set.user.unique(): ur[u] = test_set.loc[test_set.user == u, 'item'].values.tolist() for u in preds.keys(): preds[u] = [1 if e in ur[u] else 0 for e in preds[u]] # calculate metrics precision_k = np.mean( [precision_at_k(r, args.topk) for r in preds.values()]) fnl_precision.append(precision_k) recall_k = np.mean( [recall_at_k(r, len(ur[u]), args.topk) for u, r in preds.items()]) fnl_recall.append(recall_k) map_k = map_at_k(list(preds.values())) fnl_map.append(map_k) ndcg_k = np.mean([ndcg_at_k(r, args.topk) for r in preds.values()]) fnl_ndcg.append(ndcg_k) hr_k = hr_at_k(list(preds.values()), list(preds.keys()), ur) fnl_hr.append(hr_k) mrr_k = mrr_at_k(list(preds.values())) fnl_mrr.append(mrr_k) gc.collect()