コード例 #1
0
        def _eval_one_user(idx):
            scores = score_matrix[idx]  # all scores of the test user
            test_item = test_items[idx]

            ranking = argmax_top_k(scores, top_k)  # Top-K items
            result = [metric_dict[m](ranking, test_item) for m in metric]

            result = np.array(result, dtype=np.float32).flatten()
            return result
コード例 #2
0
ファイル: evaluate_loo.py プロジェクト: GuoTong96/LocalGCN
    def _eval_one_user(idx):
        scores = score_matrix[idx]  # all scores of the test user
        test_item = test_items[idx]  # all test items of the test user

        ranking = argmax_top_k(scores, top_k)  # Top-K items
        result = []
        result.extend(hit(ranking, test_item))
        result.extend(ndcg(ranking, test_item))
        result.extend(mrr(ranking, test_item))

        result = np.array(result, dtype=np.float32).flatten()
        return result