73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
#!/usr/bin/env python
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import asyncio
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import collections
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import os
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from operator import itemgetter
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import uvloop
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os.environ['MODE'] = 'DEV'
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from pprint import pprint
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from copy import deepcopy
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from soulbook.database.mongodb import MotorBase
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from soulbook.recommend.cosinesimilarity import CosineSimilarity
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from soulbook.fetcher.function import get_time
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async def get_user_tag():
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motor_db = MotorBase().get_db()
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user_tag_cursor = motor_db.user_tag.find({}, {'data': 1, 'user': 1, '_id': 0})
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result = {}
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user_book_dict = {}
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async for document in user_tag_cursor:
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if document['data']:
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user_book_dict[document['user'].replace('.', '·')] = document['data']
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result[document['user'].replace('.', '·')] = document['data']['user_tag']
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for key, value in result.items():
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if not value:
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continue
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print("\nUser:", key)
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print("User tags:", set(value))
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print("User books:", user_book_dict[key]["user_novels"])
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result_copy = deepcopy(result)
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del result_copy[key]
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cos = CosineSimilarity(value, result_copy)
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vector = cos.create_vector()
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resultDic = cos.calculate(vector)
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print("相似用户:")
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pprint(resultDic[:10])
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# pprint(type(resultList[1]))
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booksDic = collections.defaultdict(float)
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for userBooks in resultDic:
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for simuser, simrate in userBooks.items():
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# print(simuser, simrate)
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for book in user_book_dict[simuser]["user_novels"]:
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booksDic[book] += simrate
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# print(booksDic)
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# 推荐20本书，注意这里的推荐并没有去除用户收藏的书籍
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recommend = sorted(booksDic.items(), key=itemgetter(1), reverse=True)[0:20]
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print("书籍推荐：")
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pprint(recommend)
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recommend_novels = [book for book, simrate in recommend]
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await motor_db.user_recommend.update_one(
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{"user": key},
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{'$set': {'similar_user': resultDic, 'user_tag': result[key], 'recommend_novels': recommend_novels,
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"updated_at": get_time()}}, upsert=True)
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asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
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def get_user_tag_test():
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loop = asyncio.get_event_loop()
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task = asyncio.ensure_future(get_user_tag())
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loop.run_until_complete(task)
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return task.result()
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if __name__ == '__main__':
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get_user_tag_test()
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