from database import MySqlDao
from models.rank.data.config import OrderConfig
import numpy as np
from sklearn.preprocessing import StandardScaler
class UserItemScore:
    def __init__(self, data):
        self._order_data = data.copy()
        self._load_data()
        
    def _load_data(self):
        """加载订单记录表"""
        self._order_data =self._order_data[OrderConfig.FEATURE_COLUMNS]
        
        # 数据清洗
        self._order_data["sale_qty"] = self._order_data["sale_qty"].fillna(0)
        self._order_data = self._order_data.groupby(["cust_code", "product_code"], as_index=False)["sale_qty"].mean()
        self._order_data = self._order_data[self._order_data["sale_qty"] != 0]
        
        # 归一化处理
        scaler = StandardScaler()
        normalized = scaler.fit_transform(self._order_data["sale_qty"].values.reshape(-1, 1))
        self._order_data["sale_qty"] = ((1 / (1 + np.exp(-normalized))) * 100).flatten()
    
        
    def generate_product_scores(self):
        self._order_data = self._order_data.rename(columns={'sale_qty': 'score'})
        self._order_data = self._order_data.sort_values(['product_code', 'score'], ascending=[True, False])
        self._score_data = self._order_data[['product_code', 'cust_code', 'score']]
        # self._score_data.to_csv(save_path, index=False, encoding="utf-8")
        return self._score_data
        
        
if __name__ == "__main__":
    save_path = "./data/itemcf/scores.csv"
    score_utils = UserItemScore("00000000000000000000000011445301") 
    score_utils.generate_product_scores(save_path)