Calculate recall and precision of a hit/miss query

recall_precision_ap.py

def calc_precision(rel):
    """
    return the precision from a series of hit or miss data
    """
    p = []
    hit = 0

    for i in xrange(len(rel)):
        if rel[i] > 0:
            hit += 1

        if hit > 0:
            p.append(hit / float(i + 1))
        else: 
            p.append(0)

    return p

def calc_delta_recall(recall):
    """
    return the recall delta (k - k-1)
    from a series of recall data
    """
    delta_r = []
    for i in xrange(len(recall)):
        if i <= 0:
            delta_r.append(recall[i])
        else:
            delta_r.append(recall[i] - recall[i - 1])
    return delta_r

def calc_recall(rel):
    """
    return the recall from a series of hit or miss data
    """
    r = []
    hit = 0
    total_hit = float(rel.count(1))
    for i in xrange(len(rel)):
        if rel[i] > 0:
            hit += 1

        r.append(hit / total_hit)
    return r

def ap(rel, use_delta=False):
    """
    Calculate average precision of a query
    use_delta papram switchs between: 
    using the delta of the recall or using the normal average formula
    https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision
    """
    ap = []
    p = calc_precision(rel)

    if use_delta:
        r = calc_recall(rel)
        dr = calc_delta_recall(r)

        for i in xrange(len(rel)):
            ap.append(p[i] * dr[i])

        return sum(ap)
    else:
        for i in xrange(len(rel)):
            if rel[i] > 0:
                ap.append(p[i])

        return sum(ap) / float(len(ap))

# queries mapped from 1 (hit) or 0 (miss)
a_rel = [1, 1, 1, 0, 1, 0, 0, 1, 1, 0]
b_rel = [0, 1, 1, 1, 1, 1, 1, 0, 0, 0]
c_rel = [1, 0, 1, 1, 1, 1, 0, 0, 0, 1]
d_rel = [1, 1, 0, 1, 0, 0, 0, 0, 0, 0]
e_rel = [1, 0, 0, 1, 0, 0, 0, 1, 0, 0]
f_rel = [0, 1, 1]

print ap(a_rel, use_delta=True)
print ap(b_rel, use_delta=True)