计算精度

Question

拜托，我实现了一个推荐系统，我想计算它的全局精度@k:

我有这两部分代码，返回命中率和NDCG@k，但是有没有可能对这段代码进行修改，以便也能计算出精度@k？你知道吗

评估单个评级的第一部分：

def eval_one_rating(idx, full_dataset: InteractionsDataset):
    rating = _testRatings[idx]
    items = _testNegatives[idx]
    u = rating[0]

    gtItem = rating[1]
    items.append(gtItem)
    # Get prediction scores
    map_item_score = {}
    users = np.full(len(items), u, dtype='int32')

    feed_dict = {
        'personId': users,
        'contentId': np.array(items),
    }
    predictions = _model.predict(feed_dict)
    for i in range(len(items)):
        item = items[i]
        map_item_score[item] = predictions[i]

    # Evaluate top rank list
    ranklist = heapq.nlargest(_topk, map_item_score, key=map_item_score.get)
    hr = getHitRatio(ranklist, gtItem)
    ndcg = getNDCG(ranklist, gtItem)
    return (hr, ndcg)


def getHitRatio(ranklist, gtItem):
    for item in ranklist:
        if item == gtItem:
            return 1
    return 0


def getNDCG(ranklist, gtItem):
    for i in range(len(ranklist)):
        item = ranklist[i]
        if item == gtItem:
            return math.log(2) / math.log(i+2)
    return 0

第二个评估模型：

def evaluate_model(model, full_dataset: InteractionsDataset, topK: int):
    """
    Evaluate the performance (Hit_Ratio, NDCG) of top-K recommendation
    Return: score of each test rating.
    """
    global _model
    global _testRatings
    global _testNegatives
    global _topk
    _model = model
    _testRatings = full_dataset.testRatings
    _testNegatives = full_dataset.testNegatives
    _topk = topK

    hits, ndcgs = [], []
    for idx in range(len(_testRatings)):
        (hr, ndcg) = eval_one_rating(idx, full_dataset)
        hits.append(hr)
        ndcgs.append(ndcg)

    return (hits, ndcgs)

谢谢你