错误消息声称我正在将两个参数传递到一个只接受

Question

我正在关注一篇关于Python中自然语言处理的tutorial。我正在以一种我觉得更容易阅读的方式构建代码

class Datasets(object):
    labeled_training_data = pandas.read_csv(r"G:\Downloads\Kaggle\NLP\labeledTrainData.tsv\labeledTrainData.tsv", 
                        header=0, 
                        delimiter="\t", 
                        quoting=3)
    test_data = pandas.read_csv(r"G:\Downloads\Kaggle\NLP\testData.tsv\testData.tsv", 
                       header=0, 
                       delimiter="\t", 
                       quoting=3)
    unlabeled_training_data = pandas.read_csv(r"G:\Downloads\Kaggle\NLP\unlabeledTrainData.tsv\unlabeledTrainData.tsv", 
                                      header=0, 
                                      delimiter="\t", 
                                      quoting=3)

class TextCleaner(object):
    @classmethod
    def convert_to_word_list(html_text, remove_stopwords = False):
        cleaned_text = BeautifulSoup(html_text).get_text()
        cleaned_text = re.sub("[^a-zA-Z]", " ", cleaned_text)

        all_words = cleaned_text.lower().split()

        if remove_stopwords:
            stop_words = set(stopwords.words("english"))
            remaining_words = [word for word in all_words if not word in stop_words]

        return(all_words)

    @classmethod
    def __splitIntoSentences(text, tokenizer, remove_stopwords = False):
        sentences_in_text = tokenizer.tokenize(text.strip())

        non_empty_sentences = []

        for sentence in sentences_in_text:
            if len(sentence) > 0:
                non_empty_sentences.append(__convertToWordList(sentence, remove_stopwords))

        return non_empty_sentences

    @classmethod
    def get_all_sentences_from_training_sets(tokenizer):
        all_sentences = []

        print "Parsing sentences from the labeled training set..."
        for review in Datasets.labeled_training_data["review"]:
            all_sentences.append(TextCleaner.__splitIntoSentences(review, tokenizer))

        print "Parsing sentences from the unlabeled training set..."
        for review in Datasets.unlabeled_training_data["review"]:
            all_sentences.append(TextCleaner.__splitIntoSentences(review, tokenizer))

        print "There are %d sentences in our training sets." % len(all_sentences)

        return all_sentences

当我在REPL中运行这两行时，问题出现了：

tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
all_sentences = TextCleaner.get_all_sentences_from_training_sets(tokenizer)

我收到以下错误消息：

TypeError: getAllSentencesFromTrainingData() takes exactly 1 argument (2 given)

我已经在documentation中读到了关于nltk.data.load()的内容，但是对于导致错误消息的原因，我仍然一无所知

救命啊

Answer 1

classmethod的第一个参数应该是cls，它将由调用方法的特定类填充（编辑：由Python运行时，而不是由您）

不过，从pythonic的角度来看，这些函数实际上可能是模块级函数，因为它们没有以任何方式使用类的机制