Question

我有一个包含1,000个文件的目录。每个文件都有许多行，每行是一个ngram，从4到8个字节不等。我正在尝试解析所有文件以获取不同的ngrams作为标题行，然后对于每个文件，我想写一个具有该ngram序列频率的行发生在文件中。

以下代码通过收集标头来实现，但在尝试将标头写入csv文件时遇到内存错误。我在具有30GB RAM的Amazon EC2实例上运行它。任何人都可以提供我不知道的优化建议吗？

#Note: A combination of a list and a set is used to maintain order of metadata
#but still get performance since non-meta headers do not need to maintain order
header_list = []
header_set = set()
header_list.extend(META_LIST)
for ngram_dir in NGRAM_DIRS:
  ngram_files = os.listdir(ngram_dir)
  for ngram_file in ngram_files:      
      with open(ngram_dir+ngram_file, 'r') as file:
        for line in file:
          if not '.' in line and line.rstrip('\n') not in IGNORE_LIST:
            header_set.add(line.rstrip('\n'))

header_list.extend(header_set)#MEMORY ERROR OCCURRED HERE

outfile = open(MODEL_DIR+MODEL_FILE_NAME, 'w')
csvwriter = csv.writer(outfile)
csvwriter.writerow(header_list)

#Convert ngram representations to vector model of frequencies
for ngram_dir in NGRAM_DIRS:
  ngram_files = os.listdir(ngram_dir)
  for ngram_file in ngram_files:      
      with open(ngram_dir+ngram_file, 'r') as file:
        write_list = []
        linecount = 0
        header_dict = collections.OrderedDict.fromkeys(header_set, 0)
        while linecount < META_FIELDS: #META_FIELDS = 3
          line = file.readline()
          write_list.append(line.rstrip('\n'))
          linecount += 1 
        file_counter = collections.Counter(line.rstrip('\n') for line in file)
        header_dict.update(file_counter)
        for value in header_dict.itervalues():
          write_list.append(value)
        csvwriter.writerow(write_list)

outfile.close()

Answer 1

然后不要扩展该列表。使用来自itertools的链来链接列表并设置。

而不是：

header_list.extend(header_set)#MEMORY ERROR OCCURRED HERE

这样做（假设csvwriter.writerow接受任何迭代器）：

headers = itertools.chain(header_list, header_set)
...
csvwriter.writerow(headers)

这至少应该避免你目前看到的内存问题。

Pythonic方式处理2亿个元素数据集？

1 个答案: