这个功能可以优化速度吗？

Question

我正在写一段很长的代码，执行起来太长了。我在代码中使用了cProfile，发现下面的函数被调用了150次，每次调用需要1.3秒，仅此函数就需要200秒。功能是-

def makeGsList(sentences,org):
    gs_list1=[]
    gs_list2=[]
    for s in sentences:
        if s.startswith(tuple(StartWords)):
            s = s.lower()
            if org=='m':
                gs_list1 = [k for k in m_words if k in s]
            if org=='h':
                gs_list1 = [k for k in h_words if k in s]
            for gs_element in gs_list1:
                gs_list2.append(gs_element)
    gs_list3 = list(set(gs_list2))
    return gs_list3

代码应该包含一个句子列表和一个标志org。然后遍历每一行，检查它是否以列表中的任何单词开始StartWords，然后用小写字母表示。然后，根据org的值，它列出当前句子中同时出现在m_words或h_words中的所有单词。它不断地将这些单词附加到另一个列表gs_list2。最后，它生成一组gs_list2并返回它。你知道吗

有人能给我一些建议，如何优化这个函数以减少执行所需的时间吗？你知道吗

注意-单词h_words/m_words并非都是单个单词，其中许多是包含3-4个单词的短语。你知道吗

一些例子-

StartWords = ['!Series_title','!Series_summary','!Series_overall_design','!Sample_title','!Sample_source_name_ch1','!Sample_characteristics_ch1']

sentences = [u'!Series_title\t"Transcript profiles of DCs of PLOSL patients show abnormalities in pathways of actin bundling and immune response"\n', u'!Series_summary\t"This study was aimed to identify pathways associated with loss-of-function of the DAP12/TREM2 receptor complex and thus gain insight into pathogenesis of PLOSL (polycystic lipomembranous osteodysplasia with sclerosing leukoencephalopathy). Transcript profiles of PLOSL patients\' DCs showed differential expression of genes involved in actin bundling and immune response, but also for the stability of myelin and bone remodeling."\n', u'!Series_summary\t"Keywords: PLOSL patient samples vs. control samples"\n', u'!Series_overall_design\t"Transcript profiles of in vitro differentiated DCs of three controls and five PLOSL patients were analyzed."\n', u'!Series_type\t"Expression profiling by array"\n', u'!Sample_title\t"potilas_DC_A"\t"potilas_DC_B"\t"potilas_DC_C"\t"kontrolli_DC_A"\t"kontrolli_DC_C"\t"kontrolli_DC_D"\t"potilas_DC_E"\t"potilas_DC_D"\n',  u'!Sample_characteristics_ch1\t"in vitro differentiated DCs"\t"in vitro differentiated DCs"\t"in vitro differentiated DCs"\t"in vitro differentiated DCs"\t"in vitro differentiated DCs"\t"in vitro differentiated DCs"\t"in vitro differentiated DCs"\t"in vitro differentiated DCs"\n', u'!Sample_description\t"DAP12mut"\t"DAP12mut"\t"DAP12mut"\t"control"\t"control"\t"control"\t"TREM2mut"\t"TREM2mut"\n']

h_words = ['pp1665', 'glycerophosphodiester phosphodiesterase domain containing 5', 'gde2', 'PLOSL patients', 'actin bundling', 'glycerophosphodiester phosphodiesterase 2', 'glycerophosphodiester phosphodiesterase domain-containing protein 5']

mu的词是相似的。你知道吗

关于尺寸-

两个列表h_words和m_words的长度都在250000左右。列表中的每个元素平均有2个单词长。这个句子列表大约有10-20个句子，我提供了一个示例列表，让你知道每个句子可以有多大。你知道吗

Answer 1

1. 不要对m_words和k_words使用全局变量。你知道吗
2. 将if语句置于for循环之外。你知道吗
3. 一劳永逸地施放tuple(StartWords)。你知道吗
4. 使用程序创建的正则表达式而不是列表。你知道吗
5. 预先编译所有你能做的。你知道吗
6. 直接扩展列表，而不是通过它迭代到append()每个元素。你知道吗
7. 从一开始就使用set而不是list。你知道吗
8. 使用集合理解而不是显式的for循环。你知道吗

---

m_reg = re.compile("|".join(re.escape(w) for w in m_words))
h_reg = re.compile("|".join(re.escape(w) for w in h_words))

def make_gs_list(sentences, start_words, m_reg, h_reg, org):
    if org == 'm':
        reg = m_reg
    elif org == 'h':
        reg = h_reg

    matched = {w for s in sentences if s.startswith(start_words)
                 for w in reg.findall(s.lower())}

    return matched