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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 pandas as pd
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import numpy as np
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from tqdm import tqdm
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import faiss
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import warnings
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import pickle
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import collections
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from config import logger
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from config import shixun_wordemb_i2i_sim_data
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from config import shixuns_embed_path
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from utils import get_file_size
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#只使用词向量进行相似度计算
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tqdm.pandas()
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warnings.filterwarnings('ignore')
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def embedding_i2i_sim(item_emb_df, topk):
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"""
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基于物品embedding计算相似性矩阵，返回topk个与其最相似的物品
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"""
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# 加载之前保存的
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if os.path.exists(shixun_wordemb_i2i_sim_data) and (get_file_size(shixun_wordemb_i2i_sim_data) > 1):
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item_sim_dict = pickle.load(open(shixun_wordemb_i2i_sim_data, 'rb'))
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return item_sim_dict
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# 物品索引与物品id的字典映射
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item_idx_2_rawid_dict = dict(zip(item_emb_df.index, item_emb_df['shixun_id']))
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# item_emb_cols = [x for x in item_emb_df.columns if 'emb' in x]
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item_emb_cols = [x for x in item_emb_df.columns if 'shixun_id' not in x]
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item_emb_np = np.ascontiguousarray(item_emb_df[item_emb_cols].values, dtype=np.float32)
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# 向量进行单位化
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item_emb_np = item_emb_np / np.linalg.norm(item_emb_np, axis=1, keepdims=True)
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# 建立faiss索引
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item_index = faiss.IndexFlatIP(item_emb_np.shape[1])
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item_index.add(item_emb_np)
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# 相似度查询，给每个索引位置上的向量返回topk个item以及相似度
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sim, idx = item_index.search(item_emb_np, topk) # 返回的是列表
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# 将向量检索的结果保存成原始id的对应关系
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item_sim_dict = collections.defaultdict(dict)
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for target_idx, sim_value_list, rele_idx_list in tqdm(zip(range(len(item_emb_np)), sim, idx)):
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target_raw_id = item_idx_2_rawid_dict[target_idx]
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# 从1开始是为了去掉物品本身, 所以最终获得的相似物品只有topk-1
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for rele_idx, sim_value in zip(rele_idx_list[1:], sim_value_list[1:]):
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rele_raw_id = item_idx_2_rawid_dict[rele_idx]
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item_sim_dict[target_raw_id][rele_raw_id] = item_sim_dict.get(target_raw_id, {}).get(rele_raw_id, 0) + sim_value
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# 保存embedding i2i相似度矩阵
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logger.info("保存word embedding i2i相似度矩阵")
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pickle.dump(item_sim_dict, open(shixun_wordemb_i2i_sim_data, 'wb'))
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return item_sim_dict
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if __name__ == '__main__':
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recall_item_num = 100
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logger.info('生成物品embedding相似度矩阵')
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item_emb_df = pd.read_csv(shixuns_embed_path, sep='\t', encoding='utf-8')
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emb_i2i_sim = embedding_i2i_sim(item_emb_df, topk=recall_item_num) |
