108 lines
3.3 KiB
Python
108 lines
3.3 KiB
Python
import codecs
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import os
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import pickle
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import random
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import spacy
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TEMPLATE_CORPUS = 'austencorpus'
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CONTENT_CORPUS = 'lovecraftcorpus'
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print('Loading spaCy model... ', end='')
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nlp = spacy.load('en_core_web_lg')
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print('Done')
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def load_text_files(dirname):
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for (dirpath, dirnames, filenames) in os.walk(dirname):
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for filename in filenames:
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with codecs.open(os.path.join(dirpath, filename),
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encoding='utf-8') as f:
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yield f.read()
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def load_syntax(dirname):
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full_text = ''
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for text in load_text_files(dirname):
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full_text += text
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return nlp(full_text)
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def load_object_to_file(filename):
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with open(filename, 'rb') as f:
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return pickle.load(f)
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def save_object_to_file(filename, object):
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with open(filename, 'wb') as f:
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pickle.dump(object, f)
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def build_content_dict(content_syntax):
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content_dict = {}
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for word in content_syntax:
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if word.tag not in content_dict:
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content_dict[word.tag] = {}
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if word.dep not in content_dict[word.tag]:
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content_dict[word.tag][word.dep] = set()
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content_dict[word.tag][word.dep].add(word)
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return content_dict
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def find_closest_content_word(template_word, content_dict):
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closest = None
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closest_score = 0.0
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if template_word.tag in content_dict:
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if template_word.dep in content_dict[template_word.tag]:
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content_word_set = content_dict[template_word.tag][template_word.dep]
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else:
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random_dep = random.choice(list(content_dict[template_word.tag].keys()))
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content_word_set = content_dict[template_word.tag][random_dep]
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else:
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return None
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for content_word in content_word_set:
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if closest is None or template_word.similarity(content_word) > closest_score:
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closest = content_word
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closest_score = template_word.similarity(content_word)
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return closest
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if __name__ == '__main__':
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if os.path.exists('template_syntax.bin'):
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print('Loading parsed template corpus... ', end='')
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template_syntax = spacy.tokens.Doc(spacy.vocab.Vocab())
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template_syntax.from_disk('template_syntax.bin')
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print('Done')
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else:
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print('Parsing template corpus... ', end='')
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template_syntax = load_syntax(TEMPLATE_CORPUS)
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template_syntax.to_disk('template_syntax.bin')
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print('Done')
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if os.path.exists('content_syntax.bin'):
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print('Loading parsed content corpus... ', end='')
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content_syntax = spacy.tokens.Doc(spacy.vocab.Vocab())
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content_syntax.from_disk('content_syntax.bin')
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print('Done')
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else:
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print('Parsing content corpus... ', end='')
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content_syntax = load_syntax(CONTENT_CORPUS)
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content_syntax.to_disk('content_syntax.bin')
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print('Done')
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print('Building content_dict... ', end='')
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content_dict = build_content_dict(content_syntax)
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save_object_to_file('content_dict.bin', content_dict)
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print('Done')
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for template_word in template_syntax[0:100]:
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closest_word = find_closest_content_word(template_word, content_dict)
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if closest_word:
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print(closest_word.text_with_ws, end='')
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else:
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print('<NOMATCH> ', end='')
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import ipdb; ipdb.set_trace()
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