ShopTRAINING/server/utils/data_utils.py

"""
药店销售预测系统 - 数据处理工具函数
"""

import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split

class PharmacyDataset(Dataset):
    """
    药店销售数据集类，用于PyTorch数据加载
    """
    def __init__(self, data_X, data_Y):
        self.data_X = data_X
        self.data_Y = data_Y
        
    def __getitem__(self, index):
        return self.data_X[index], self.data_Y[index]
    
    def __len__(self):
        return len(self.data_X)

def create_dataset(datasetX, datasetY, look_back=1, predict_steps=1):
    """
    将时间序列数据转换为监督学习问题的格式
    
    参数:
    datasetX: 输入特征数据
    datasetY: 目标变量数据
    look_back: 使用过去多少天的数据作为输入
    predict_steps: 预测未来多少天的数据
    
    返回:
    dataX: 输入特征，形状为 (样本数, 时间步, 特征数)
    dataY: 目标变量，形状为 (样本数, 预测步数)
    """
    dataX, dataY = [], []
    for i in range(len(datasetX) - look_back - predict_steps + 1):
        x = datasetX[i:(i + look_back)]
        dataX.append(x)
        y = datasetY[(i + look_back):(i + look_back + predict_steps)]
        dataY.append(y.flatten())
    return np.array(dataX), np.array(dataY)

def prepare_data(product_data, sequence_length=30, forecast_horizon=7):
    """
    准备训练和验证数据
    
    参数:
    product_data: 产品销售数据DataFrame
    sequence_length: 输入序列长度
    forecast_horizon: 预测天数
    
    返回:
    X, y: 全部特征和目标
    X_train, X_val: 训练和验证特征
    y_train, y_val: 训练和验证目标
    scaler_X, scaler_y: 特征和目标的归一化器
    """
    # 创建特征和目标变量
    features = ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
    
    # 预处理数据
    X_raw = product_data[features].values
    y_raw = product_data[['sales']].values  # 保持为二维数组
    
    # 归一化数据
    scaler_X = MinMaxScaler(feature_range=(0, 1))
    scaler_y = MinMaxScaler(feature_range=(0, 1))
    
    X_scaled = scaler_X.fit_transform(X_raw)
    y_scaled = scaler_y.fit_transform(y_raw)
    
    # 创建时间序列数据
    X, y = create_dataset(X_scaled, y_scaled, sequence_length, forecast_horizon)
    
    # 划分训练集和验证集（80% 训练，20% 验证）
    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=False)
    
    return X, y, X_train, X_val, y_train, y_val, scaler_X, scaler_y

def prepare_sequences(X, y, batch_size=32):
    """
    将数据转换为DataLoader对象，用于批量训练
    
    参数:
    X: 输入特征
    y: 目标变量
    batch_size: 批次大小
    
    返回:
    DataLoader对象
    """
    # 转换为PyTorch张量
    X_tensor = torch.tensor(X, dtype=torch.float32)
    y_tensor = torch.tensor(y, dtype=torch.float32)
    
    # 创建数据集
    dataset = PharmacyDataset(X_tensor, y_tensor)
    
    # 创建数据加载器
    data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
    
    return data_loader