Gensim：TypeError：doc2bow期望输入的是Unicode标记数组，而不是单个字符串

Question

我从一些python任务开始，使用gensim时遇到问题。我正在尝试从磁盘加载文件并进行处理（将它们分开并用lowercase（）对其进行处理）

我的代码如下：

dictionary_arr=[]
for file_path in glob.glob(os.path.join(path, '*.txt')):
    with open (file_path, "r") as myfile:
        text=myfile.read()
        for words in text.lower().split():
            dictionary_arr.append(words)
dictionary = corpora.Dictionary(dictionary_arr)

列表（dictionary_arr）包含所有文件中所有单词的列表，然后使用gensim
corpora.Dictionary处理该列表。但是我遇到了一个错误。

TypeError: doc2bow expects an array of unicode tokens on input, not a single string

我无法理解有什么问题，需要一些指导。

Answer 1

在dictionary.py中，初始化函数为：

def __init__(self, documents=None):
    self.token2id = {} # token -> tokenId
    self.id2token = {} # reverse mapping for token2id; only formed on request, to save memory
    self.dfs = {} # document frequencies: tokenId -> in how many documents this token appeared

    self.num_docs = 0 # number of documents processed
    self.num_pos = 0 # total number of corpus positions
    self.num_nnz = 0 # total number of non-zeroes in the BOW matrix

    if documents is not None:
        self.add_documents(documents)

函数add_documents从文档集合构建字典。每个文档都是令牌列表：

def add_documents(self, documents):

    for docno, document in enumerate(documents):
        if docno % 10000 == 0:
            logger.info("adding document #%i to %s" % (docno, self))
        _ = self.doc2bow(document, allow_update=True) # ignore the result, here we only care about updating token ids
    logger.info("built %s from %i documents (total %i corpus positions)" %
                 (self, self.num_docs, self.num_pos))

因此，如果以此方式初始化Dictionary，则必须传递文档，但不能传递单个文档。例如，

dic = corpora.Dictionary([a.split()])

还可以