[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:class LabeledIndividualSentence(object):\ndef __init__(self, sources):\nself.sources = sources\nflipped = {}\nfor key, value in sources.items():\nif value not in flipped:\nflipped[value] = [key]\nelse:\nraise Exception('Non-unique prefix encountered')\ndef __iter__(self):\nfor source, prefix in self.sources.items():\nwith utils.smart_open(source) as fin:\nfor item_no, line in enumerate(fin):\nline = line.lower()\ntokens = utils.to_unicode(line).split()\ntokens = [w for w in tokens if not w in stopset]\nyield LabeledSentence(tokens, [prefix + '_%s' % item_no])\ndef to_array(self):\nself.sentences = []\nfor source, prefix in self.sources.items():\nwith utils.smart_open(source) as fin:\nfor item_no, line in enumerate(fin):\nline = line.lower() # change case for better results\ntokens = utils.to_unicode(line).split() # LabeledSentence accepts only unicode tokens\ntokens = [w for w in tokens if not w in stopset] # remove stopwords\nself.sentences.append(LabeledSentence(tokens, [prefix + '_%s' % item_no]))\nreturn self.sentences\ndef getTag_words(self,words):\nreturn [s for s in self.to_array() if ' '.join(s.words)==words][0].tags[0]\ndef getWords_tag(self,tag):\nreturn ' '.join([s for s in self.to_array() if s.tags[0]==tag][0].words)"}, {'role': 'assistant', 'content': 'data_preprocessing'}]