BrandCultivation/models/rank/gbdt_lr_inference.py

import gc
import joblib
# from dao import Redis, get_product_by_id, get_custs_by_ids, load_cust_data_from_mysql
from database import RedisDatabaseHelper, MySqlDao
from models.rank.data import DataLoader
from models.rank.data import ProductConfig, CustConfig, ShopConfig, ImportanceFeaturesMap
from models.rank.data.utils import one_hot_embedding, sample_data_clear
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
import shap
from tqdm import tqdm
from utils import split_relation_subtable
import os
import tempfile

class GbdtLrModel:
    def __init__(self, model_path):
        self.load_model(model_path)
        self.redis = RedisDatabaseHelper().redis
        self._mysql_dao = MySqlDao()
        self._explanier = None
    
    def load_model(self, model_path):
        models = joblib.load(model_path)
        self.gbdt_model, self.lr_model, self.onehot_encoder = models["lgbm_model"], models["lr_model"], models["onehot_encoder"]
        
    def get_cust_and_product_data(self, city_uuid, product_id):
        """从商户数据库中获取指定城市所有商户的id"""
        self.product_data = self._mysql_dao.get_product_by_id(city_uuid, product_id)[ProductConfig.FEATURE_COLUMNS]
        self.custs_data = self._mysql_dao.load_cust_data(city_uuid)[CustConfig.FEATURE_COLUMNS]
    
    def generate_feats_map(self, product_data, cust_data):
        """组合卷烟、商户特征矩阵"""
        # 笛卡尔积联合
        cust_data["descartes"] = 1
        product_data["descartes"] = 1
        feats_map = pd.merge(cust_data, product_data, on="descartes").drop("descartes", axis=1)
        # recall_cust_list = feats_map["BB_RETAIL_CUSTOMER_CODE"].to_list()
        feats_map.drop('BB_RETAIL_CUSTOMER_CODE', axis=1, inplace=True)
        feats_map.drop('product_code', axis=1, inplace=True)
        
        # onehot编码
        onehot_feats = {**CustConfig.ONEHOT_CAT, **ProductConfig.ONEHOT_CAT, **ShopConfig.ONEHOT_CAT}
        onehot_columns = list(onehot_feats.keys())
        numeric_columns = feats_map.drop(onehot_columns, axis=1).columns
        feats_map = one_hot_embedding(feats_map, onehot_feats)
        
        # 数字特征归一化
        if len(numeric_columns) != 0:
            scaler = StandardScaler()
            feats_map[numeric_columns] = scaler.fit_transform(feats_map[numeric_columns])
            
        return feats_map
    
    def get_recommend_list(self, recommend_sample, recall_list):
        # gbdt_preds = self.gbdt_model.apply(recommend_sample)[:, :, 0]
        # gbdt_feats_encoded = self.onehot_encoder.transform(gbdt_preds)
        # scores = self.lr_model.predict_proba(gbdt_feats_encoded)[:, 1]
        
        gbdt_preds = self.gbdt_model.predict(recommend_sample, pred_leaf=True)
        gbdt_feats_encoded = self.onehot_encoder.transform(gbdt_preds)
        scores = self.lr_model.predict_proba(gbdt_feats_encoded)[:, 1]
        
        recommend_list = []
        for cust_id, score in zip(recall_list, scores):
            recommend_list.append({cust_id: float(score)})
            recommend_list.append({"cust_code": cust_id, "recommend_score": score})
            
        recommend_list = sorted(
            [item for item in recommend_list if "recommend_score" in item],
            key=lambda x: x["recommend_score"],
            reverse=True
        )
        return recommend_list
    
    def generate_feats_importance(self):
        """生成特征重要性"""
        # 获取GBDT模型的特征重要性
        feats_importance = self.gbdt_model.feature_importances_
        # 获取特征名称
        feats_names = self.gbdt_model.feature_name_
        importance_dict = dict(zip(feats_names, feats_importance))
        
        onehot_feats = {**CustConfig.ONEHOT_CAT, **ShopConfig.ONEHOT_CAT, **ProductConfig.ONEHOT_CAT}
        for feat, categories in onehot_feats.items():
            related_columns = [f"{feat}_{item}" for item in categories]
            
            if related_columns:
                # 合并类别重要性
                combined_importance = sum(importance_dict[col] for col in related_columns)
                # 删除onehot类别列
                for col in related_columns:
                    del importance_dict[col]
                # 添加合并后的重要性
                importance_dict[feat] = combined_importance
        
        # 排序
        sorted_importance = sorted(importance_dict.items(), key=lambda x: x[1], reverse=True)
        
        # 输出特征重要性
        cust_features_importance = []
        product_features_importance = []
        
        for feat, importance in sorted_importance:
            if feat in list(ImportanceFeaturesMap.CUSTOM_FEATURES_MAP.keys()):
                cust_features_importance.append({ImportanceFeaturesMap.CUSTOM_FEATURES_MAP[feat]: float(importance)})
            if feat in list(ImportanceFeaturesMap.SHOPING_FEATURES_MAP.keys()):
                cust_features_importance.append({ImportanceFeaturesMap.SHOPING_FEATURES_MAP[feat]: float(importance)})
            if feat in list(ImportanceFeaturesMap.PRODUCT_FEATRUES_MAP.keys()):
                product_features_importance.append({ImportanceFeaturesMap.PRODUCT_FEATRUES_MAP[feat]: float(importance)})
                
        return cust_features_importance, product_features_importance
    
    def generate_shap_interance(self, data):
        # 初始化SHAP解释器
        if self._explanier is None:
            self._explanier = shap.TreeExplainer(self.gbdt_model)
        
        # 获取数据基本信息
        n_samples = len(data)
        n_features = len(data.columns)
        batch_size = 500  # 可根据内存调整
        
        # 创建临时内存映射文件
        # temp_dir = tempfile.mkdtemp()
        temp_dir = "./data/tmp"
        temp_file = os.path.join(temp_dir, "shap_interaction_temp.dat")
        if os.path.exists(temp_dir):
            os.remove(temp_file)
        else:
            os.makedirs(temp_dir)
        
        try:
            # 预创建内存映射文件
            fp_shape = (n_samples, n_features, n_features)
            fp = np.memmap(temp_file, dtype=np.float32, 
                        mode='w+', 
                        shape=fp_shape)
            
            # 分批计算并存储SHAP交互值
            for i in tqdm(range(0, n_samples, batch_size), desc="计算SHAP交互值..."):
                batch_data = data.iloc[i:i+batch_size]
                batch_interaction = self._explanier.shap_interaction_values(batch_data)
                fp[i:i+len(batch_interaction)] = batch_interaction.astype(np.float32)
                fp.flush()  # 确保数据写入磁盘
            
            # 分批计算均值
            mean_interaction = np.zeros((n_features, n_features), dtype=np.float32)
            for i in tqdm(range(0, n_samples, batch_size), desc="计算均值..."):
                batch = fp[i:i+batch_size]  # 读取批数据并取绝对值
                mean_interaction += batch.sum(axis=0)  # 按批累加
            
            mean_interaction /= n_samples  # 计算最终均值
            
            # 构建交互矩阵DataFrame
            interaction_df = pd.DataFrame(
                mean_interaction,
                index=data.columns,
                columns=data.columns
            )
            
            # 分离卷烟和商户特征
            product_feats = [
                f"{feat}_{item}" 
                for feat, categories in ProductConfig.ONEHOT_CAT.items()
                for item in categories
            ]
            
            cust_feats = [
                f"{feat}_{item}"
                for feat, categories in {**CustConfig.ONEHOT_CAT, **ShopConfig.ONEHOT_CAT}.items()
                for item in categories
            ]
            
            # 提取交叉区块
            cross_matrix = interaction_df.loc[product_feats, cust_feats] 
            
            # 转换为长格式
            stacked = cross_matrix.stack().reset_index()
            stacked.columns = ['product_feat', 'cust_feat', 'relation']
            
            # 过滤掉零值或NaN的配对
            filtered = stacked[
                (stacked['relation'].abs() > 1e-6) &  # 排除极小值
                (~stacked['relation'].isna())         # 排除NaN
            ].copy()
            
            # 排序结果
            results = (
                filtered
                .sort_values('relation', ascending=False)
                .to_dict('records')
            )
            
            # 替换特征名称
            feats_name_map = {
                **ImportanceFeaturesMap.CUSTOM_FEATURES_MAP, 
                **ImportanceFeaturesMap.SHOPING_FEATURES_MAP, 
                **ImportanceFeaturesMap.PRODUCT_FEATRUES_MAP
            }
            
            for item in results:
                # 处理产品特征名
                product_f = item["product_feat"]
                product_infos = product_f.split("_")
                item["product_feat"] = f"{feats_name_map['_'.join(product_infos[:-1])]}({product_infos[-1]})"
                
                # 处理客户特征名
                cust_f = item["cust_feat"]
                cust_infos = cust_f.split("_")
                item["cust_feat"] = f"{feats_name_map['_'.join(cust_infos[:-1])]}({cust_infos[-1]})"
            
            # 返回最终结果
            return pd.DataFrame(results, columns=['product_feat', 'cust_feat', 'relation'])
        
        finally:
            # 清理临时文件
            try:
                del fp  # 必须先删除内存映射对象
                gc.collect()
                os.remove(temp_file)
                os.rmdir(temp_dir)
            except Exception as e:
                print(f"清理临时文件时出错: {e}")
    
if __name__ == "__main__":
    model_path = "./models/rank/weights/00000000000000000000000011445301/gbdtlr_model.pkl"
    city_uuid = "00000000000000000000000011445301"
    product_id = "110102"
    gbdt_sort = GbdtLrModel(model_path)
    # gbdt_sort.sort(city_uuid, product_id)
    
    # cust_features_importance,  product_features_importance = gbdt_sort.generate_feats_importance()

    # cust_df = pd.DataFrame([
    #     {"Features": list(item.keys())[0], "Importance": list(item.values())[0]}
    #     for item in cust_features_importance
    # ])
    # cust_df.to_csv("./data/cust_feats.csv", index=False)
    
    # product_df = pd.DataFrame([
    #     {"Features": list(item.keys())[0], "Importance": list(item.values())[0]}
    #     for item in product_features_importance
    # ])
    # product_df.to_csv("./data/product_feats.csv", index=False)
    data, _ = DataLoader("./data/gbdt/train_data.csv").split_dataset()
    data = data["data"].sample(n=300, replace=True, random_state=42)
    data.to_csv("./data/data.csv", index=False)
    # data = data["data"]
    result = gbdt_sort.generate_shap_interance(data)
    print("保存结果")
    result.to_csv("./data/feats_interaction.csv", index=False, encoding='utf-8-sig')
    split_relation_subtable(result, "./data")