BrandCultivation/recommend.py

from database.dao.mysql_dao import MySqlDao
from database.db.redis_db import RedisDatabaseHelper
import os
from models.item2vec.inference import Item2VecModel
from models.rank.data.config import CustConfig, ProductConfig, ShopConfig
from models.rank.data.utils import sample_data_clear
from models.rank.gbdt_lr_inference import GbdtLrModel


class Recommend:
    def __init__(self, city_uuid):
        self._redis = RedisDatabaseHelper().redis
        self._dao = MySqlDao()
        
        self._load_molde(city_uuid)
        
    def _load_molde(self, city_uuid):
        """加载推演模型"""
        self._city_uuid = city_uuid
        gbdtlr_model_path = os.path.join("./models/rank/weights", city_uuid, "gbdtlr_model.pkl")
        self._gbdtlr_model = GbdtLrModel(gbdtlr_model_path)
        self._item2vec_model = Item2VecModel(city_uuid)
        
    def _get_itemcf_recall(self, product_id):
        """协同召回"""
        key = f"fc:{self._city_uuid}:{product_id}"
        recall_list = self._redis.zrevrange(key, 0, -1, withscores=False)
        return recall_list
    
    def _get_hot_recall(self):
        """热度召回"""
        key = f"hot:{self._city_uuid}:sale_qty"
        recall_list = self._redis.zrevrange(key, 0, -1, withscores=False)
        return recall_list
    
    def _get_recal_cust(self, product_id, recall_count):
        """通过协同过滤和热度召回，召回待推荐商户列表"""
        itemcf_recall_list = self._get_itemcf_recall(product_id)
        hot_recall_list =  self._get_hot_recall()
        
        result = list(dict.fromkeys(itemcf_recall_list))
        # 如果结果不足，从hot_recall中补齐
        if len(result) < recall_count:
            hot_recall_set = set(hot_recall_list) - set(result)
            additional_items = [item for item in hot_recall_list if item in hot_recall_set]
            needed = recall_count - len(result)
            result.extend(additional_items[:needed])
            
        return result[:recall_count]
    
    def get_recommend_list_by_gbdtlr(self, product_id, recall_count=100, discovery_count=500):
        """根据gbdt_lr获取商户推荐列表"""
        # 获取召回的商户列表
        recall_cust_list = self._get_recal_cust(product_id, recall_count)
        print(len(recall_cust_list))
        # 获取卷烟数据
        product_data = self._dao.get_product_by_id(self._city_uuid, product_id)[ProductConfig.FEATURE_COLUMNS]
        product_data = sample_data_clear(product_data, ProductConfig)
        
        # 获取整合商户数据
        cust_data = self._dao.get_cust_by_ids(self._city_uuid, recall_cust_list)[CustConfig.FEATURE_COLUMNS]
        shop_data = self._dao.get_shop_by_ids(self._city_uuid, recall_cust_list)[ShopConfig.FEATURE_COLUMNS]
        cust_data = sample_data_clear(cust_data, CustConfig)
        shop_data = sample_data_clear(shop_data, ShopConfig)
        
        cust_feats = shop_data.set_index("cust_code")
        cust_data = cust_data.join(cust_feats, on="BB_RETAIL_CUSTOMER_CODE", how="inner")
        
        # 获取推理用的feats_map
        feats_map = self._gbdtlr_model.generate_feats_map(product_data, cust_data)
        print(len(cust_data))
        recommend_list = self._gbdtlr_model.get_recommend_list(feats_map, recall_cust_list)
        
        return recommend_list
    
if __name__ == "__main__":
    city_uuid = "00000000000000000000000011445301"
    product_id = '110110'
    recommend = Recommend(city_uuid)
    recommend_list = recommend.get_recommend_list_by_gbdtlr(product_id)