我可以在python中调用Lambda表达式中的函数吗？

Question

我有一个函数，包括if，else条件和for循环。 我想在lambda表达式中编写这个函数。 我试过很多方法来创建这个lambda函数。 但我还是做不到。 这是我的另一个规则的功能。

negation ='no,not,never'.split(',')
list2 = 'miss,loss,gone,give up,lost'.split(',')

def f(sentence):
  s = sentence.split()
  l = [s.index(word) for word in s if word in list2]
# Will returns list of indices (of sentence) where word is in list2
   if len(l) > 0:
    for e in l:
        # Check previous word
        if s[e-1] not in negation:
            print 'sad'

我可以在lambda表达式中表达这个函数，因为我开发了一个基于规则的分类器，用于从快乐，悲伤，愤怒等句子中检测情绪。 以下是我的lambda函数。

rules = [(lambda x: word_tokenize(x)[-1] == '?', "neutral"),
         (lambda x: word_tokenize(x)[0] in question, "neutral"),
         (lambda x: any(word in list2 for word in [WordNetLemmatizer().lemmatize(word,'v') for word in word_tokenize(x)]), "sad"),
     (lambda x: any(word in list1 for word in [WordNetLemmatizer().lemmatize(word,'v') for word in word_tokenize(x)]), "happy")]

         print classify("I miss you", rules)

Answer 1

我没有把所有内容都填充到lambda表达式中，而是创建一个函数来完成你需要做的所有事情（从你的评论中，听起来你想要按照特定顺序将某些规则应用于句子）。 你总是可以在列表理解，映射，缩小等中使用该函数。由于我不确切知道你的规则是什么，这是我能给出的最好的例子：

a = ["This is not a sentence. That was false.", 
     "You cannot play volleyball. You can play baseball.", 
     "My uncle once ate an entire bag of corn chips! I am not lying!"]
def f(paragraph):
    sentences = paragraph.split(".")
    result = []
    for i in range(len(sentences)):
        //apply rules to sentences
        if "not" in sentences[i]:
            result.append("negative")
        else:
            result.append("positive")
    return result
my_result = [f(x) for x in a]

Answer 2

您的功能可以使用一些改进：

negation_words = {"no", "not", "never"}
sad_words = {"miss", "loss", "gone", "give", "lost"}

def count_occurrences(s, search_words, negation_words=negation_words):
    count = 0
    neg = False
    for word in s.lower().split():    # should also strip punctuation
        if word in search_words and not neg:
            count += 1
        neg = word in negation_words
    return count

print("\n".join(["sad"] * count_occurrences(s, sad_words)))