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47 lines
1.6 KiB
47 lines
1.6 KiB
5 months ago
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import os
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import sys
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sys.path.append(os.getcwd())
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import tensorflow as tf
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from libreco.algorithms import PinSage
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from libreco.data import DataInfo
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import collections
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from datetime import datetime
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from config import subject_pinsage_model_path
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from config import logger,test_user_id
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def load_mmodel(model_path,model_name):
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tf.compat.v1.reset_default_graph()
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# load data_info
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data_info = DataInfo.load(path=model_path, model_name=model_name)
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model = PinSage.load(
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path=model_path, model_name=model_name, data_info=data_info,manual=True
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)
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return model
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def pinsage_recall(user_id, topk=20):
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"""
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通过pinsage推荐指定用户topk物品
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"""
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#加载预训练模型pinsage
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pinsage = load_mmodel(model_path=subject_pinsage_model_path,model_name="pinsage_model")
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start_time = datetime.now()
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logger.info(f"本次需要进行pinsage召回的用户ID: {user_id}")
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user_recall_items_dict = collections.defaultdict(dict)
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item_list = list(pinsage.recommend_user(user=user_id, n_rec=topk).values())[0].tolist()
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score = []
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for i in item_list:
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score.append(pinsage.predict(user=user_id, item=i)[0])
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user_recall_items_dict[user_id] = list(zip(item_list,tuple(score)))
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# 计算耗时毫秒
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end_time = datetime.utcnow()
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cost_time_millisecond = round(float((end_time - start_time).microseconds / 1000.0), 3)
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logger.info(f"本次推荐总耗时: {cost_time_millisecond} 毫秒")
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return user_recall_items_dict[user_id]
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if __name__ == '__main__':
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recall_results = pinsage_recall(user_id=test_user_id, topk=20)
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print(recall_results)
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