功能略有不同的重复数据消除代码

Question

我有两个非常相似的循环，这两个包含一个非常类似于第三个循环的内部循环（呃。。。:) ). 用代码说明，它看起来很接近：

# First function
def fmeasure_kfold1(array, nfolds):
    ret = []

    # Kfold1 and kfold2 both have this outer loop
    for train_index, test_index in KFold(len(array), nfolds):
        correlation = analyze(array[train_index])

        for build in array[test_index]:  # <- All functions have this loop

            # Retrieved tests is calculated inside the build loop in kfold1
            retrieved_tests = get_tests(set(build['modules']), correlation)

            relevant_tests = set(build['tests'])
            fval = calc_f(relevant_tests, retrieved_tests)
            if fval is not None:
                ret.append(fval)

    return ret

# Second function
def fmeasure_kfold2(array, nfolds):
    ret = []

    # Kfold1 and kfold2 both have this outer loop
    for train_index, test_index in KFold(len(array), nfolds):
        correlation = analyze(array[train_index])

        # Retrieved tests is calculated outside the build loop in kfold2
        retrieved_tests = _sum_tests(correlation)

        for build in array[test_index]:  # <- All functions have this loop

            relevant_tests = set(build['tests'])
            fval = calc_f(relevant_tests, retrieved_tests)
            if fval is not None:
                ret.append(fval)

    return ret

# Third function
def fmeasure_all(array):
    ret = []
    for build in array:  # <- All functions have this loop

        relevant = set(build['tests'])
        fval = calc_f2(relevant)  # <- Instead of calc_f, I call calc_f2
        if fval is not None:
            ret.append(fval)

    return ret

前两个函数只在方式和时间上不同，它们计算retrieved_tests。第三个函数与前两个函数的内部循环不同，它调用calc_f2，不使用retrieved_tests。你知道吗

实际上，代码更复杂，但是当重复的代码让我恼火的时候，我想我可以接受它。不过，最近我一直在对它进行修改，一次要在两到三个地方进行修改，真烦人。你知道吗

有没有合并重复代码的好方法？我能想到的唯一方法是引入类，它引入了很多样板文件，如果可能的话，我希望保持函数为纯函数。你知道吗

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编辑

这是calc_f和calc_f2的内容：

def calc_f(relevant, retrieved):
    """Calculate the F-measure given relevant and retrieved tests."""
    recall = len(relevant & retrieved)/len(relevant)
    prec = len(relevant & retrieved)/len(retrieved)
    fmeasure = f_measure(recall, prec)

    return (fmeasure, recall, prec)


def calc_f2(relevant, nbr_tests=1000):
    """Calculate the F-measure given relevant tests."""
    recall = 1
    prec = len(relevant) / nbr_tests
    fmeasure = f_measure(recall, prec)

    return (fmeasure, recall, prec)

f_measure计算精度和召回率的harmonic mean。你知道吗

基本上，calc_f2采用了很多快捷方式，因为不需要检索测试。你知道吗

Answer 1

典型的解决方案是识别算法的各个部分，并使用Template method design pattern在子类中实现不同的阶段。我完全不理解您的代码，但我假设会有makeGlobalRetrievedTests()和makeIndividualRetrievedTests()这样的方法？你知道吗

Answer 2

一种方法是将每个内部循环作为一个函数来编写，然后将外部循环作为一个单独的函数来接收其他循环作为参数。这与排序函数（接收用于比较两个元素的函数）中的操作非常接近。你知道吗

当然，困难的部分是找出所有函数之间究竟有什么共同点，这并不总是简单的。你知道吗

Answer 3

有一个公共函数，它需要一个额外的参数来控制计算retrieved_tests的位置，这也会起作用。你知道吗

例如

def fmeasure_kfold_generic(array, nfolds, mode):
    ret = []

    # Kfold1 and kfold2 both have this outer loop
    for train_index, test_index in KFold(len(array), nfolds):
        correlation = analyze(array[train_index])

        # Retrieved tests is calculated outside the build loop in kfold2
        if mode==2:
            retrieved_tests = _sum_tests(correlation)

        for build in array[test_index]:  # <- All functions have this loop
            # Retrieved tests is calculated inside the build loop in kfold1
            if mode==1:
                retrieved_tests = get_tests(set(build['modules']), correlation)

            relevant_tests = set(build['tests'])
            fval = calc_f(relevant_tests, retrieved_tests)
            if fval is not None:
                ret.append(fval)