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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 datetime import datetime
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from config import logger
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from config import need_metric_recall
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from config import subject_emb_i2i_sim_data
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from config import subject_item_embedding_recall_dict
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from config import subjects_embed_path
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from config import samples_mode
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from config import test_user_id
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from matching.subject.recall_comm import get_all_select_df
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from matching.subject.recall_comm import get_item_info_df
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from matching.subject.recall_comm import get_user_item_time_dict
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from matching.subject.recall_comm import get_hist_and_last_select, get_recall_item_info_dict
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from matching.subject.recall_comm import get_item_topk_select
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from matching.subject.recall_comm import metrics_recall
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from utils import get_file_size
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tqdm.pandas()
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warnings.filterwarnings('ignore')
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def embdding_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(subject_emb_i2i_sim_data) and (get_file_size(subject_emb_i2i_sim_data) > 1):
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item_sim_dict = pickle.load(open(subject_emb_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['subject_id']))
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item_emb_cols = [x for x in item_emb_df.columns if 'subject_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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pickle.dump(item_sim_dict, open(subject_emb_i2i_sim_data, 'wb'))
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return item_sim_dict
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# 基于物品的召回i2i
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def item_based_recommend(user_id,
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user_item_time_dict,
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i2i_sim,
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sim_item_topk,
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recall_item_num,
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item_topk_select,
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item_info_dict,
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emb_i2i_sim):
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"""
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基于物品协同过滤的召回
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:param user_id: 用户id
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:param user_item_time_dict: 字典, 根据选择时间获取用户的选择物品序列 {user1: [(item1, time1), (item2, time2)..]...}
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:param i2i_sim: 字典，物品相似性矩阵
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:param sim_item_topk: 整数，选择与当前物品最相似的k个物品
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:param recall_item_num: 整数，最后召回的物品数量
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:param item_topk_select: 列表，选择次数最多的物品列表，用于召回补全
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:param item_info_dict: 字典，物品信息
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:param emb_i2i_sim: 物品embedding相似度矩阵
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:return: 召回的物品列表 [(item1, score1), (item2, score2)...]
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"""
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# 获取用户历史交互的物品
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user_hist_items = user_item_time_dict[user_id]
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user_hist_items_ = {user_id for user_id, _ in user_hist_items}
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filtered_item_name_list = []
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filtered_item_name_list.clear()
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item_rank = {}
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for loc, (hist_item, select_time) in enumerate(user_hist_items):
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# 过滤物品信息字典中没有的物品
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if hist_item not in item_info_dict:
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continue
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hist_item_created_time = item_info_dict[hist_item]['created_at_ts']
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for sim_item, wij in sorted(i2i_sim[hist_item].items(), key=lambda x: x[1], reverse=True)[:sim_item_topk]:
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# 过滤历史选择的物品
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if sim_item in user_hist_items_:
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continue
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# 过滤物品信息字典中没有的物品
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if sim_item not in item_info_dict:
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continue
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sim_item_created_time = item_info_dict[sim_item]['created_at_ts']
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sim_item_name = item_info_dict[sim_item]['subject_name']
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# 过滤物品名称重复的
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if sim_item_name in filtered_item_name_list:
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continue
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filtered_item_name_list.append(sim_item_name)
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# 物品创建时间差权重
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created_time_weight = np.exp(0.8 ** np.abs(hist_item_created_time - sim_item_created_time))
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# 相似物品和历史选择物品序列中历史物品所在的位置权重
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loc_weight = (0.9 ** (len(user_hist_items) - loc))
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content_weight = 1.0
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if emb_i2i_sim.get(hist_item, {}).get(sim_item, None) is not None:
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content_weight += emb_i2i_sim[hist_item][sim_item]
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if emb_i2i_sim.get(sim_item, {}).get(hist_item, None) is not None:
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content_weight += emb_i2i_sim[sim_item][hist_item]
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item_rank.setdefault(sim_item, 0)
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item_rank[sim_item] += created_time_weight * loc_weight * content_weight * wij
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# 不足的，用热门物品补全
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if len(item_rank) < recall_item_num:
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for index, item in enumerate(item_topk_select):
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# 填充的item应该不在原来的列表中
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if item in item_rank.items():
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continue
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# 随便给个负数就行
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item_rank[item] = - index - 100
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if len(item_rank) == recall_item_num:
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break
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item_rank = sorted(item_rank.items(), key=lambda x: x[1], reverse=True)[:recall_item_num]
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return item_rank
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def init_item_embedding_recall():
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"""
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初始化召回用到的一些数据
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"""
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global train_hist_select_df
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global user_item_time_dict
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global i2i_sim, sim_item_topk
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global recall_item_num
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global item_topk_select
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global item_info_dict
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global emb_i2i_sim
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global train_last_select_df
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logger.info("加载物品行为数据")
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all_select_df = get_all_select_df(offline=False)
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sim_item_topk = 120
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recall_item_num = 100
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logger.info('获取物品基本信息')
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item_info_df = get_item_info_df()
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logger.info('生成物品信息字典')
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item_info_dict = get_recall_item_info_dict(item_info_df)
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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(subjects_embed_path, sep='\t', encoding='utf-8')
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emb_i2i_sim = embdding_i2i_sim(item_emb_df, topk=recall_item_num)
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# 为了召回评估，提取最后一次选择作为召回评估
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# 如果不需要做召回评估直接使用全量的训练集进行召回
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if samples_mode and need_metric_recall:
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logger.info('获取物品行为数据历史和最后一次选择')
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train_hist_select_df, train_last_select_df = get_hist_and_last_select(all_select_df)
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else:
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train_hist_select_df = all_select_df
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logger.info('获取用户选择物品列表')
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user_item_time_dict = get_user_item_time_dict(train_hist_select_df)
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# 加载物品embedding相似度矩阵
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i2i_sim = pickle.load(open(subject_emb_i2i_sim_data, 'rb'))
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logger.info('获取选择次数最多的物品')
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item_topk_select = get_item_topk_select(train_hist_select_df, k=recall_item_num)
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def item_embedding_recall(user_id, topk):
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"""
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item embedding召回调用接口
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"""
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start_time = datetime.now()
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logger.info(f"本次需要item embedding召回的用户ID: {user_id}")
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recall_results = {}
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recall_results.clear()
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if user_id not in user_item_time_dict:
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return recall_results
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recall_results = item_based_recommend(user_id,
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user_item_time_dict,
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i2i_sim,
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topk + (topk // 3),
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topk,
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item_topk_select,
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item_info_dict,
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emb_i2i_sim)
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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"本次召回耗时: {cost_time_millisecond} 毫秒")
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return recall_results
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def item_embedding_recall_train():
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# 调用初始化召回用到的一些数据
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init_item_embedding_recall()
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# 只在采样模式下计算所有用户的召回数据并进行召回效果评估
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# 如果用全量数据计算所有用户的召回数据会非常耗时
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if samples_mode == True and need_metric_recall:
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# 定义召回物品的字典
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user_recall_items_dict = collections.defaultdict(dict)
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logger.info('生成item embedding所有用户的召回列表')
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for user_id in tqdm(train_hist_select_df['user_id'].unique()):
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user_recall_items_dict[user_id] = item_based_recommend(user_id,
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user_item_time_dict,
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i2i_sim,
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sim_item_topk,
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recall_item_num,
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item_topk_select,
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item_info_dict,
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emb_i2i_sim)
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logger.info('保存item embedding召回结果')
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pickle.dump(user_recall_items_dict, open(subject_item_embedding_recall_dict, 'wb'))
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logger.info('item embedding召回效果评估')
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metrics_recall(user_recall_items_dict, train_last_select_df, topk=recall_item_num)
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if __name__ == '__main__':
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item_embedding_recall_train()
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recall_results = item_embedding_recall(user_id=test_user_id, topk=100)
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print(recall_results) |
