BrandCultivation/app.py

import argparse
from dao import load_order_data_from_mysql
from dao.redis_db import Redis
from models import HotRecallModel, UserItemScore, ItemCFModel, calculate_similarity_and_save_results
import os

def run_hot_recall(order_data):
    """运行热度召回算法"""
    hot_model = HotRecallModel(order_data)
    hot_model.calculate_all_hot_score()
    print("热度召回已完成！")

def run_itemcf(args):
    # """运行协同过滤算法"""
    # if os.path.exists(args.interst_score_path) and os.path.exists(args.similarity_matrix_path):
    #     os.remove(args.interst_score_path)
    #     os.remove(args.similarity_matrix_path)
    # # n_jobs = 4
    
    # # 计算user-score-item数据
    # cal_interest_scores_model = UserItemScore()
    # scores = cal_interest_scores_model.score(order_data)
    # scores.to_csv(args.interst_score_path, index=False, encoding="utf-8")
    # print("Interest Scores cal done!")
    
    # # 计算商户共现矩阵及相似度矩阵
    # calculate_similarity_and_save_results(order_data, args.similarity_matrix_path)
    # print("Shops similarity matrix cal done!")
    
    # 运行协同过滤召回
    itemcf_model = ItemCFModel()
    itemcf_model.train(args.interst_score_path, args.similarity_matrix_path, args.n, args.k, args.top_n, args.n_jobs)
    print("协同过滤已完成！")

def run_itemcf_inference(product_code):
        """
        从 Redis 中读取推荐结果，并返回 {shop_id: score} 的列表
        """
        redis_db = Redis()
        redis_key = f"fc:{product_code}"
        recommendations = redis_db.redis.zrange(redis_key, 0, -1, withscores=True, desc=True)
        
        # 将推荐结果转换为 {shop_id: score} 的字典列表
        result = [{shop_id: float(score)} for shop_id, score in recommendations]
        
        return result

def run():
    parser = argparse.ArgumentParser()
    
    # 运行方式
    parser.add_argument("--run_all", action='store_true')
    parser.add_argument("--run_hot", action='store_true')
    parser.add_argument("--run_itemcf", action='store_true')
    parser.add_argument("--run_itemcf_inference", action='store_true')
    
    # 协同过滤相关配置
    parser.add_argument("--interst_score_path", type=str, default="./models/recall/itemCF/matrix/score.csv")
    parser.add_argument("--similarity_matrix_path", type=str, default="./models/recall/itemCF/matrix/similarity.csv")
    parser.add_argument("--n", type=int, default=100)
    parser.add_argument("--k", type=int, default=10)
    parser.add_argument("--top_n", type=int, default=200, help='default n * k')
    parser.add_argument("--n_jobs", type=int, default=4)
    
    # 协同过滤推理配置
    parser.add_argument("--product_code", type=int, default=110111)
    
    args = parser.parse_args()
    
    if args.run_all:
        order_data = load_order_data_from_mysql()
        run_hot_recall(order_data)
        run_itemcf(order_data, args)
        
    elif args.run_hot:
        order_data = load_order_data_from_mysql()
        run_hot_recall(order_data)
        
    elif args.run_itemcf:
        # order_data = load_order_data_from_mysql()
        run_itemcf(args)
        
    elif args.run_itemcf_inference:
        recomments = run_itemcf_inference(args.product_code)
        print(recomments)
    
if __name__ == "__main__":
    run()