Calculating Average Precision (AP) without Sklearn

import numpy as np

def calculate_ap(recalls, precisions):
    # Make precision monotonically non-increasing (all-point interpolation)
    m_rec = np.concatenate(([0.0], recalls, [1.0]))
    m_pre = np.concatenate(([0.0], precisions, [0.0]))

    for i in range(len(m_pre) - 1, 0, -1):
        m_pre[i - 1] = np.maximum(m_pre[i - 1], m_pre[i])

    # Area under the interpolated step curve: sum of recall steps × precision
    indices = np.where(m_rec[1:] != m_rec[:-1])[0]
    ap = np.sum((m_rec[indices + 1] - m_rec[indices]) * m_pre[indices + 1])
    return ap

Why it matters: Object detection and retrieval metrics break when you only eyeball curves. Manual AP keeps leaderboard numbers reproducible.