rank_evaluations.py 文件源码

def eval_precision(y_true, y_pred, k = 10, rel_threshold=0.):
    if k <= 0:
        return 0.
    s = 0.
    y_true = np.squeeze(y_true)
    y_pred = np.squeeze(y_pred)
    c = zip(y_true, y_pred)
    random.shuffle(c)
    c = sorted(c, key=lambda x:x[1], reverse=True)
    ipos = 0
    precision = 0.
    for i, (g,p) in enumerate(c):
        if i >= k:
            break
        if g > rel_threshold:
            precision += 1
    precision /=  k
    return precision