榜单评估:AvgP@K 和 R@K 一样吗?

Evaluation of lists: AvgP@K and R@K are they same?

我的目标是理解平均值 Precision at KRecall at K。我有两个列表,一个是预测的,另一个是实际的(基本事实)

让我们将这两个列表称为预测的和实际的。现在我想做 precision@krecall@k.

我使用 python 在 K 处实现了 Avg 精度,如下所示:

def apk(actual, predicted, k=10):
    """
    Computes the average precision at k.

    This function computes the average precision at k between two lists of items.

    Parameters
    ----------
    actual: list
            A list of elements that are to be predicted (order doesn't matter)
    predicted : list
            A list of predicted elements (order does matter)
    k: int, optional

    Returns
    -------
    score : double
            The average precision at k over the input lists

    """
    if len(predicted) > k:
        predicted = predicted[:k]

    score = 0.0
    num_hits = 0.0

    for i,p in enumerate(predicted):
        if p in actual and p not in predicted[:i]:
            num_hits += 1.0
            score += num_hits / (i + 1.0)

    if not actual:
        return 1.0
    if min(len(actual), k) == 0:
        return 0.0
    else:
        return score / min(len(actual), k)

假设我们的预测有 5 个字符串,顺序如下: predicted = ['b','c','a','e','d'] and实际 = ['a','b','e']since we are doing @k would the precision@k is same asrecall@k? If not how would I dorecall@k`

如果我想做 f-measure (f-score) 上面提到的列表的最佳路线是什么?

我想,您已经检查过了 wiki。根据它的公式,第三个也是最大的一个(在单词 'This finite sum is equivalent to:' 之后),让我们看看每次迭代的示例:

  1. i=1 p=1
  2. i=2 相对 = 0
  3. i=3 p = 2/3
  4. i=4 p = 3/4
  5. i=5 相对 = 0

因此,avp@4 = avp@5 = (1 + 0.66 + 0.75) / 3 = 0.805; avp@3 = (1 + 0.66) / 3 依此类推。

召回@5 = 召回@4 = 3/3 = 1;召回@3 = 2/3;召回@2 =召回@1 = 1/3

下面是precision@k和recall@k的代码。我保留了你的符号,虽然使用 actual 表示 observed/returned 值和使用 expected 表示基本事实似乎更常见(例如,参见 JUnit 默认值)。

def precision(actual, predicted, k):
    act_set = set(actual)
    pred_set = set(predicted[:k])
    result = len(act_set & pred_set) / float(k)
    return result

def recall(actual, predicted, k):
    act_set = set(actual)
    pred_set = set(predicted[:k])
    result = len(act_set & pred_set) / float(len(act_set))
    return result