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62 lines
2.8 KiB
62 lines
2.8 KiB
5 months ago
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import os
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from config import samples_mode
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import time
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if __name__ == '__main__':
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# 训练召回模型
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# print("**"*10+"build_keywords"+"**"*10)
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# os.system('python ./matching/shixun/build_keywords.py')
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# print("**"*10+"faiss_word2vec"+"**"*10)
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# os.system('python ./matching/shixun/faiss_word2vec.py')
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# print("**"*10+"hnsw_faiss"+"**"*10)
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# os.system('python ./matching/shixun/hnsw_faiss.py')
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# print("**"*10+"item_embedding"+"**"*10)
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# os.system('python ./matching/shixun/item_embedding.py')
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# print("**"*10+"item_merge_emb"+"**"*10)
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# os.system('python ./matching/shixun/item_merge_emb.py')
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# print("**"*10+"Item2Vec"+"**"*10)
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# os.system('python ./matching/shixun/Item2Vec.py')
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# print("**"*10+"cold_start_recall"+"**"*10)
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# os.system('python ./matching/shixun/cold_start_recall.py')
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# print("**"*10+"item_embedding_recall"+"**"*10)
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# os.system('python ./matching/shixun/item_embedding_recall.py')
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# print("**"*10+"itemcf_recall"+"**"*10)
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# os.system('python ./matching/shixun/itemcf_recall.py')
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# print("**"*10+"youtubednn_recall_train"+"**"*10)
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# os.system('python ./matching/shixun/youtubednn_recall_train.py')
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# print("**"*10+"youtube_usercf_recall"+"**"*10)
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# os.system('python ./matching/shixun/youtube_usercf_recall.py')
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# print("**"*10+"dssm_recall_trainl"+"**"*10)
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# os.system('python ./matching/shixun/dssm_recall_train.py')
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# print("**"*10+"dssm_usercf_recall"+"**"*10)
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# os.system('python ./matching/shixun/dssm_usercf_recall.py')
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# print("**"*10+"fm_recall_train"+"**"*10)
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# os.system('python ./matching/shixun/fm_recall_train.py')
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# print("**"*10+"mind_recall_train"+"**"*10)
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# os.system('python ./matching/shixun/mind_recall_train.py')
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# print("**"*10+"pinsage_recall_train"+"**"*10)
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# os.system('python ./matching/shixun/pinsage_recall_train.py')
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# 全量数据计算每路离线召回结果耗时太长
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# 只在增量数据召回时合并多路召回的结果
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# if samples_mode:
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# print("**"*10+"multi_recall_combine"+"**"*10)
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# os.system('python ./matching/shixun/multi_recall_combine.py')
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# 排序特征工程
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# print("**"*10+"bert_embedding"+"**"*10)
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# os.system('python ./ranking/shixun/bert_embedding.py')
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# print("**"*10+"rank_features_engineering"+"**"*10)
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# os.system('python ./ranking/shixun/rank_features_engineering.py')
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time.sleep(60*60*4)
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# 训练排序模型
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print("**"*10+"xdeepfm_ranker_train"+"**"*10)
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os.system('python ./ranking/shixun/xdeepfm_ranker_train.py')
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print("**"*10+"difm_ranker_train"+"**"*10)
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os.system('python ./ranking/shixun/difm_ranker_train.py')
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print("**"*10+"bst_ranker_train"+"**"*10)
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os.system('python ./ranking/shixun/bst_ranker_train.py')
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