""" 多店铺销售预测系统 - 数据处理工具函数 支持多店铺数据的加载、过滤和处理 """ import pandas as pd import numpy as np import os from datetime import datetime, timedelta from typing import Optional, List, Tuple, Dict, Any import logging # 配置日志 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) def load_multi_store_data(file_path: str = 'data/timeseries_training_data_sample_10s50p.parquet', store_id: Optional[str] = None, product_id: Optional[str] = None, start_date: Optional[str] = None, end_date: Optional[str] = None) -> pd.DataFrame: """ 加载多店铺销售数据，支持按店铺、产品、时间范围过滤 参数: file_path: 数据文件路径 store_id: 店铺ID，为None时返回所有店铺数据 product_id: 产品ID，为None时返回所有产品数据 start_date: 开始日期 (YYYY-MM-DD) end_date: 结束日期 (YYYY-MM-DD) 返回: DataFrame: 过滤后的销售数据 """ logger.info("\n[DEBUG-UTIL] ---> Entering load_multi_store_data function.") logger.info(f"[DEBUG-UTIL] Initial params: file_path={file_path}, store_id={store_id}, product_id={product_id}, start_date={start_date}, end_date={end_date}") try: # 构建数据文件的绝对路径，使其独立于当前工作目录 script_dir = os.path.dirname(os.path.abspath(__file__)) project_root = os.path.dirname(os.path.dirname(script_dir)) absolute_file_path = os.path.join(project_root, file_path) logger.info(f"[DEBUG-UTIL] Constructed absolute path to data file: {absolute_file_path}") if not os.path.exists(absolute_file_path): logger.error(f"[DEBUG-UTIL] Data file not found at absolute path: {absolute_file_path}") error_message = f"V3-FIX: 无法找到数据文件: {absolute_file_path}" raise FileNotFoundError(error_message) logger.info(f"[DEBUG-UTIL] Attempting to load file from: {absolute_file_path}") if absolute_file_path.endswith('.parquet'): df = pd.read_parquet(absolute_file_path) elif absolute_file_path.endswith('.csv'): df = pd.read_csv(absolute_file_path) elif absolute_file_path.endswith('.xlsx'): df = pd.read_excel(absolute_file_path) else: raise ValueError(f"不支持的文件格式: {absolute_file_path}") logger.info(f"[DEBUG-UTIL] Successfully loaded data file. Shape: {df.shape}") except Exception as e: logger.error(f"[DEBUG-UTIL] !!! An error occurred during file loading: {e}") import traceback logger.error(traceback.format_exc()) raise e logger.info(f"[DEBUG-UTIL] Initial DataFrame columns: {df.columns.tolist()}") # 确保date列是datetime类型 if 'date' in df.columns: df['date'] = pd.to_datetime(df['date']) logger.info("[DEBUG-UTIL] Converted 'date' column to datetime objects.") # 按店铺过滤 if store_id: df = df[df['store_id'] == store_id].copy() logger.info(f"[DEBUG-UTIL] Filtered by store_id='{store_id}'. Records remaining: {len(df)}") # 按产品过滤 if product_id: df = df[df['product_id'] == product_id].copy() logger.info(f"[DEBUG-UTIL] Filtered by product_id='{product_id}'. Records remaining: {len(df)}") # 按时间范围过滤 if start_date: start_date_dt = pd.to_datetime(start_date) df = df[df['date'] >= start_date_dt].copy() logger.info(f"[DEBUG-UTIL] Filtered by start_date>='{start_date}'. Records remaining: {len(df)}") if end_date: end_date_dt = pd.to_datetime(end_date) df = df[df['date'] <= end_date_dt].copy() logger.info(f"[DEBUG-UTIL] Filtered by end_date<='{end_date}'. Records remaining: {len(df)}") if len(df) == 0: logger.warning("[DEBUG-UTIL] Warning: DataFrame is empty after filtering.") # 标准化列名以匹配训练代码期望的格式 logger.info("[DEBUG-UTIL] Calling standardize_column_names...") df = standardize_column_names(df) logger.info(f"[DEBUG-UTIL] DataFrame columns after standardization: {df.columns.tolist()}") logger.info("[DEBUG-UTIL] <--- Exiting load_multi_store_data function.") return df def standardize_column_names(df: pd.DataFrame) -> pd.DataFrame: """ 标准化列名以匹配训练代码期望的格式 参数: df: 原始DataFrame 返回: DataFrame: 标准化列名后的DataFrame """ df = df.copy() # 列名映射：新列名 -> 原列名 column_mapping = { 'quantity_sold': 'sales_quantity', # 销售数量 'price': 'unit_price', # 单价 'weekday': 'day_of_week' # 星期几 } # 应用列名映射 for new_name, old_name in column_mapping.items(): if old_name in df.columns and new_name not in df.columns: df[new_name] = df[old_name] # 创建缺失的特征列 if 'date' in df.columns: df['date'] = pd.to_datetime(df['date']) # 创建数值型的weekday (0=Monday, 6=Sunday) if 'weekday' not in df.columns: df['weekday'] = df['date'].dt.dayofweek elif df['weekday'].dtype == 'object': # 如果weekday是字符串，转换为数值 weekday_map = { 'Monday': 0, 'Tuesday': 1, 'Wednesday': 2, 'Thursday': 3, 'Friday': 4, 'Saturday': 5, 'Sunday': 6 } df['weekday'] = df['weekday'].map(weekday_map).fillna(df['date'].dt.dayofweek) # 添加月份信息 if 'month' not in df.columns: df['month'] = df['date'].dt.month # 添加缺失的布尔特征列（如果不存在则设为默认值） default_features = { 'is_holiday': False, # 是否节假日 'is_weekend': None, # 是否周末（从weekday计算） 'is_promotion': False, # 是否促销 'temperature': 20.0 # 默认温度 } for feature, default_value in default_features.items(): if feature not in df.columns: if feature == 'is_weekend' and 'weekday' in df.columns: # 周末：周六(5)和周日(6) df['is_weekend'] = df['weekday'].isin([5, 6]) else: df[feature] = default_value # 确保数值类型正确 numeric_columns = ['sales', 'price', 'weekday', 'month', 'temperature'] for col in numeric_columns: if col in df.columns: df[col] = pd.to_numeric(df[col], errors='coerce') # 确保布尔类型正确 boolean_columns = ['is_holiday', 'is_weekend', 'is_promotion'] for col in boolean_columns: if col in df.columns: df[col] = df[col].astype(bool) print(f"数据标准化完成，可用特征列: {[col for col in ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature'] if col in df.columns]}") return df def get_available_stores(file_path: str = 'data/timeseries_training_data_sample_10s50p.parquet') -> List[Dict[str, Any]]: """ 获取可用的店铺列表 参数: file_path: 数据文件路径 返回: List[Dict]: 店铺信息列表 """ try: df = load_multi_store_data(file_path) # 获取唯一店铺信息 stores = df[['store_id', 'store_name', 'store_location', 'store_type']].drop_duplicates() return stores.to_dict('records') except Exception as e: print(f"获取店铺列表失败: {e}") return [] def get_available_products(file_path: str = 'data/timeseries_training_data_sample_10s50p.parquet', store_id: Optional[str] = None) -> List[Dict[str, Any]]: """ 获取可用的产品列表 参数: file_path: 数据文件路径 store_id: 店铺ID，为None时返回所有产品 返回: List[Dict]: 产品信息列表 """ try: df = load_multi_store_data(file_path, store_id=store_id) # 获取唯一产品信息 product_columns = ['product_id', 'product_name'] if 'product_category' in df.columns: product_columns.append('product_category') if 'unit_price' in df.columns: product_columns.append('unit_price') products = df[product_columns].drop_duplicates() return products.to_dict('records') except Exception as e: print(f"获取产品列表失败: {e}") return [] def get_store_product_sales_data(store_id: str, product_id: str, file_path: str = 'data/timeseries_training_data_sample_10s50p.parquet') -> pd.DataFrame: """ 获取特定店铺和产品的销售数据，用于模型训练 参数: file_path: 数据文件路径 store_id: 店铺ID product_id: 产品ID 返回: DataFrame: 处理后的销售数据，包含模型需要的特征 """ # 加载数据 df = load_multi_store_data(file_path, store_id=store_id, product_id=product_id) if len(df) == 0: raise ValueError(f"没有找到店铺 {store_id} 产品 {product_id} 的销售数据") # 确保数据按日期排序 df = df.sort_values('date').copy() # 数据标准化已在load_multi_store_data中完成 # 验证必要的列是否存在 required_columns = ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature'] missing_columns = [col for col in required_columns if col not in df.columns] if missing_columns: print(f"警告: 数据标准化后仍缺少列 {missing_columns}") raise ValueError(f"无法获取完整的特征数据，缺少列: {missing_columns}") return df def aggregate_multi_store_data(product_id: str, aggregation_method: str = 'sum', file_path: str = 'data/timeseries_training_data_sample_10s50p.parquet') -> pd.DataFrame: """ 聚合多个店铺的销售数据，用于全局模型训练 参数: file_path: 数据文件路径 product_id: 产品ID aggregation_method: 聚合方法 ('sum', 'mean', 'median') 返回: DataFrame: 聚合后的销售数据 """ # 加载所有店铺的产品数据 df = load_multi_store_data(file_path, product_id=product_id) if len(df) == 0: raise ValueError(f"没有找到产品 {product_id} 的销售数据") # 按日期聚合（使用标准化后的列名） agg_dict = {} if aggregation_method == 'sum': agg_dict = { 'sales': 'sum', # 标准化后的销量列 'sales_amount': 'sum', 'price': 'mean' # 标准化后的价格列，取平均值 } elif aggregation_method == 'mean': agg_dict = { 'sales': 'mean', 'sales_amount': 'mean', 'price': 'mean' } elif aggregation_method == 'median': agg_dict = { 'sales': 'median', 'sales_amount': 'median', 'price': 'median' } # 确保列名存在 available_cols = df.columns.tolist() agg_dict = {k: v for k, v in agg_dict.items() if k in available_cols} # 聚合数据 aggregated_df = df.groupby('date').agg(agg_dict).reset_index() # 获取产品信息（取第一个店铺的信息） product_info = df[['product_id', 'product_name', 'product_category']].iloc[0] for col, val in product_info.items(): aggregated_df[col] = val # 添加店铺信息标识为全局 aggregated_df['store_id'] = 'GLOBAL' aggregated_df['store_name'] = f'全部店铺-{aggregation_method.upper()}' aggregated_df['store_location'] = '全局聚合' aggregated_df['store_type'] = 'global' # 对聚合后的数据进行标准化（添加缺失的特征列） aggregated_df = aggregated_df.sort_values('date').copy() aggregated_df = standardize_column_names(aggregated_df) return aggregated_df def get_sales_statistics(file_path: str = 'data/timeseries_training_data_sample_10s50p.parquet', store_id: Optional[str] = None, product_id: Optional[str] = None) -> Dict[str, Any]: """ 获取销售数据统计信息 参数: file_path: 数据文件路径 store_id: 店铺ID product_id: 产品ID 返回: Dict: 统计信息 """ try: df = load_multi_store_data(file_path, store_id=store_id, product_id=product_id) if len(df) == 0: return {'error': '没有数据'} stats = { 'total_records': len(df), 'date_range': { 'start': df['date'].min().strftime('%Y-%m-%d'), 'end': df['date'].max().strftime('%Y-%m-%d') }, 'stores': df['store_id'].nunique(), 'products': df['product_id'].nunique(), 'total_sales_amount': float(df['sales_amount'].sum()) if 'sales_amount' in df.columns else 0, 'total_quantity': int(df['quantity_sold'].sum()) if 'quantity_sold' in df.columns else 0, 'avg_daily_sales': float(df.groupby('date')['quantity_sold'].sum().mean()) if 'quantity_sold' in df.columns else 0 } return stats except Exception as e: return {'error': str(e)} # 向后兼容的函数 def load_data(file_path='data/timeseries_training_data_sample_10s50p.parquet', store_id=None): """ 向后兼容的数据加载函数 """ return load_multi_store_data(file_path, store_id=store_id)