365 lines
12 KiB
Python
365 lines
12 KiB
Python
"""
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多店铺销售预测系统 - 数据处理工具函数
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支持多店铺数据的加载、过滤和处理
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"""
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import pandas as pd
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import numpy as np
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import os
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from datetime import datetime, timedelta
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from typing import Optional, List, Tuple, Dict, Any
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def load_multi_store_data(file_path: str = 'pharmacy_sales_multi_store.csv',
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store_id: Optional[str] = None,
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product_id: Optional[str] = None,
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start_date: Optional[str] = None,
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end_date: Optional[str] = None) -> pd.DataFrame:
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"""
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加载多店铺销售数据，支持按店铺、产品、时间范围过滤
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参数:
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file_path: 数据文件路径
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store_id: 店铺ID，为None时返回所有店铺数据
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product_id: 产品ID，为None时返回所有产品数据
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start_date: 开始日期 (YYYY-MM-DD)
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end_date: 结束日期 (YYYY-MM-DD)
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返回:
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DataFrame: 过滤后的销售数据
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"""
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# 尝试多个可能的文件路径
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possible_paths = [
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file_path,
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f'../{file_path}',
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f'server/{file_path}',
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'pharmacy_sales_multi_store.csv',
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'../pharmacy_sales_multi_store.csv',
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'pharmacy_sales.xlsx', # 后向兼容原始文件
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'../pharmacy_sales.xlsx'
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]
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df = None
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for path in possible_paths:
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try:
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if path.endswith('.csv'):
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df = pd.read_csv(path)
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elif path.endswith('.xlsx'):
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df = pd.read_excel(path)
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# 为原始Excel文件添加默认店铺信息
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if 'store_id' not in df.columns:
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df['store_id'] = 'S001'
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df['store_name'] = '默认店铺'
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df['store_location'] = '未知位置'
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df['store_type'] = 'standard'
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if df is not None:
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print(f"成功加载数据文件: {path}")
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break
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except Exception as e:
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continue
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if df is None:
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raise FileNotFoundError(f"无法找到数据文件，尝试的路径: {possible_paths}")
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# 确保date列是datetime类型
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if 'date' in df.columns:
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df['date'] = pd.to_datetime(df['date'])
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# 按店铺过滤
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if store_id:
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df = df[df['store_id'] == store_id].copy()
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print(f"按店铺过滤: {store_id}, 剩余记录数: {len(df)}")
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# 按产品过滤
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if product_id:
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df = df[df['product_id'] == product_id].copy()
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print(f"按产品过滤: {product_id}, 剩余记录数: {len(df)}")
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# 按时间范围过滤
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if start_date:
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start_date = pd.to_datetime(start_date)
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df = df[df['date'] >= start_date].copy()
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print(f"开始日期过滤: {start_date}, 剩余记录数: {len(df)}")
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if end_date:
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end_date = pd.to_datetime(end_date)
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df = df[df['date'] <= end_date].copy()
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print(f"结束日期过滤: {end_date}, 剩余记录数: {len(df)}")
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if len(df) == 0:
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print("警告: 过滤后没有数据")
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# 标准化列名以匹配训练代码期望的格式
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df = standardize_column_names(df)
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return df
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def standardize_column_names(df: pd.DataFrame) -> pd.DataFrame:
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"""
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标准化列名以匹配训练代码期望的格式
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参数:
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df: 原始DataFrame
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返回:
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DataFrame: 标准化列名后的DataFrame
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"""
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df = df.copy()
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# 列名映射：新列名 -> 原列名
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column_mapping = {
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'sales': 'quantity_sold', # 销售数量
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'price': 'unit_price', # 单价
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'weekday': 'day_of_week' # 星期几
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}
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# 应用列名映射
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for new_name, old_name in column_mapping.items():
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if old_name in df.columns and new_name not in df.columns:
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df[new_name] = df[old_name]
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# 创建缺失的特征列
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if 'date' in df.columns:
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df['date'] = pd.to_datetime(df['date'])
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# 创建数值型的weekday (0=Monday, 6=Sunday)
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if 'weekday' not in df.columns:
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df['weekday'] = df['date'].dt.dayofweek
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elif df['weekday'].dtype == 'object':
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# 如果weekday是字符串，转换为数值
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weekday_map = {
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'Monday': 0, 'Tuesday': 1, 'Wednesday': 2, 'Thursday': 3,
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'Friday': 4, 'Saturday': 5, 'Sunday': 6
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}
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df['weekday'] = df['weekday'].map(weekday_map).fillna(df['date'].dt.dayofweek)
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# 添加月份信息
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if 'month' not in df.columns:
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df['month'] = df['date'].dt.month
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# 添加缺失的布尔特征列（如果不存在则设为默认值）
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default_features = {
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'is_holiday': False, # 是否节假日
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'is_weekend': None, # 是否周末（从weekday计算）
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'is_promotion': False, # 是否促销
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'temperature': 20.0 # 默认温度
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}
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for feature, default_value in default_features.items():
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if feature not in df.columns:
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if feature == 'is_weekend' and 'weekday' in df.columns:
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# 周末：周六(5)和周日(6)
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df['is_weekend'] = df['weekday'].isin([5, 6])
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else:
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df[feature] = default_value
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# 确保数值类型正确
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numeric_columns = ['sales', 'price', 'weekday', 'month', 'temperature']
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for col in numeric_columns:
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if col in df.columns:
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df[col] = pd.to_numeric(df[col], errors='coerce')
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# 确保布尔类型正确
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boolean_columns = ['is_holiday', 'is_weekend', 'is_promotion']
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for col in boolean_columns:
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if col in df.columns:
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df[col] = df[col].astype(bool)
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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]}")
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return df
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def get_available_stores(file_path: str = 'pharmacy_sales_multi_store.csv') -> List[Dict[str, Any]]:
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"""
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获取可用的店铺列表
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参数:
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file_path: 数据文件路径
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返回:
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List[Dict]: 店铺信息列表
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"""
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try:
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df = load_multi_store_data(file_path)
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# 获取唯一店铺信息
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stores = df[['store_id', 'store_name', 'store_location', 'store_type']].drop_duplicates()
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return stores.to_dict('records')
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except Exception as e:
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print(f"获取店铺列表失败: {e}")
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return []
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def get_available_products(file_path: str = 'pharmacy_sales_multi_store.csv',
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store_id: Optional[str] = None) -> List[Dict[str, Any]]:
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"""
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获取可用的产品列表
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参数:
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file_path: 数据文件路径
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store_id: 店铺ID，为None时返回所有产品
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返回:
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List[Dict]: 产品信息列表
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"""
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try:
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df = load_multi_store_data(file_path, store_id=store_id)
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# 获取唯一产品信息
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product_columns = ['product_id', 'product_name']
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if 'product_category' in df.columns:
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product_columns.append('product_category')
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if 'unit_price' in df.columns:
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product_columns.append('unit_price')
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products = df[product_columns].drop_duplicates()
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return products.to_dict('records')
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except Exception as e:
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print(f"获取产品列表失败: {e}")
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return []
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def get_store_product_sales_data(store_id: str,
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product_id: str,
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file_path: str = 'pharmacy_sales_multi_store.csv') -> pd.DataFrame:
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"""
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获取特定店铺和产品的销售数据，用于模型训练
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参数:
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file_path: 数据文件路径
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store_id: 店铺ID
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product_id: 产品ID
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返回:
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DataFrame: 处理后的销售数据，包含模型需要的特征
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"""
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# 加载数据
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df = load_multi_store_data(file_path, store_id=store_id, product_id=product_id)
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if len(df) == 0:
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raise ValueError(f"没有找到店铺 {store_id} 产品 {product_id} 的销售数据")
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# 确保数据按日期排序
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df = df.sort_values('date').copy()
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# 数据标准化已在load_multi_store_data中完成
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# 验证必要的列是否存在
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required_columns = ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
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missing_columns = [col for col in required_columns if col not in df.columns]
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if missing_columns:
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print(f"警告: 数据标准化后仍缺少列 {missing_columns}")
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raise ValueError(f"无法获取完整的特征数据，缺少列: {missing_columns}")
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return df
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def aggregate_multi_store_data(product_id: str,
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aggregation_method: str = 'sum',
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file_path: str = 'pharmacy_sales_multi_store.csv') -> pd.DataFrame:
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"""
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聚合多个店铺的销售数据，用于全局模型训练
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参数:
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file_path: 数据文件路径
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product_id: 产品ID
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aggregation_method: 聚合方法 ('sum', 'mean', 'median')
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返回:
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DataFrame: 聚合后的销售数据
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"""
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# 加载所有店铺的产品数据
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df = load_multi_store_data(file_path, product_id=product_id)
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if len(df) == 0:
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raise ValueError(f"没有找到产品 {product_id} 的销售数据")
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# 按日期聚合（使用标准化后的列名）
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agg_dict = {}
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if aggregation_method == 'sum':
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agg_dict = {
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'sales': 'sum', # 标准化后的销量列
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'sales_amount': 'sum',
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'price': 'mean' # 标准化后的价格列，取平均值
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}
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elif aggregation_method == 'mean':
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agg_dict = {
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'sales': 'mean',
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'sales_amount': 'mean',
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'price': 'mean'
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}
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elif aggregation_method == 'median':
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agg_dict = {
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'sales': 'median',
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'sales_amount': 'median',
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'price': 'median'
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}
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# 确保列名存在
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available_cols = df.columns.tolist()
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agg_dict = {k: v for k, v in agg_dict.items() if k in available_cols}
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# 聚合数据
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aggregated_df = df.groupby('date').agg(agg_dict).reset_index()
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# 获取产品信息（取第一个店铺的信息）
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product_info = df[['product_id', 'product_name', 'product_category']].iloc[0]
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for col, val in product_info.items():
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aggregated_df[col] = val
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# 添加店铺信息标识为全局
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aggregated_df['store_id'] = 'GLOBAL'
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aggregated_df['store_name'] = f'全部店铺-{aggregation_method.upper()}'
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aggregated_df['store_location'] = '全局聚合'
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aggregated_df['store_type'] = 'global'
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# 对聚合后的数据进行标准化（添加缺失的特征列）
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aggregated_df = aggregated_df.sort_values('date').copy()
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aggregated_df = standardize_column_names(aggregated_df)
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return aggregated_df
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def get_sales_statistics(file_path: str = 'pharmacy_sales_multi_store.csv',
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store_id: Optional[str] = None,
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product_id: Optional[str] = None) -> Dict[str, Any]:
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"""
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获取销售数据统计信息
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参数:
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file_path: 数据文件路径
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store_id: 店铺ID
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product_id: 产品ID
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返回:
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Dict: 统计信息
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"""
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try:
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df = load_multi_store_data(file_path, store_id=store_id, product_id=product_id)
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if len(df) == 0:
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return {'error': '没有数据'}
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stats = {
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'total_records': len(df),
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'date_range': {
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'start': df['date'].min().strftime('%Y-%m-%d'),
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'end': df['date'].max().strftime('%Y-%m-%d')
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},
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'stores': df['store_id'].nunique(),
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'products': df['product_id'].nunique(),
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'total_sales_amount': float(df['sales_amount'].sum()) if 'sales_amount' in df.columns else 0,
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'total_quantity': int(df['quantity_sold'].sum()) if 'quantity_sold' in df.columns else 0,
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'avg_daily_sales': float(df.groupby('date')['quantity_sold'].sum().mean()) if 'quantity_sold' in df.columns else 0
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}
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return stats
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except Exception as e:
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return {'error': str(e)}
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# 向后兼容的函数
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def load_data(file_path='pharmacy_sales.xlsx', store_id=None):
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"""
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向后兼容的数据加载函数
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"""
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return load_multi_store_data(file_path, store_id=store_id) |