evaluations.py 文件源码

def recall(k=10):
    def top_k(y_true, y_pred, rel_threshold=0.):
        if k <= 0:
            return 0.
        s = 0.
        y_true = _to_list(np.squeeze(y_true).tolist()) # y_true: the ground truth scores for documents under a query
        y_pred = _to_list(np.squeeze(y_pred).tolist()) # y_pred: the predicted scores for documents under a query
        pos_count = sum(i > rel_threshold for i in y_true) # total number of positive documents under this query
        c = list(zip(y_true, y_pred))
        random.shuffle(c)
        c = sorted(c, key=lambda x: x[1], reverse=True)
        ipos = 0
        recall = 0.
        for i, (g, p) in enumerate(c):
            if i >= k:
                break
            if g > rel_threshold:
                recall += 1
        recall /= pos_count
        return recall
    return top_k