106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
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"""
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药店销售预测系统 - 数据处理工具函数
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"""
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import numpy as np
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import torch
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from torch.utils.data import Dataset, DataLoader
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.model_selection import train_test_split
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class PharmacyDataset(Dataset):
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"""
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药店销售数据集类，用于PyTorch数据加载
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"""
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def __init__(self, data_X, data_Y):
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self.data_X = data_X
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self.data_Y = data_Y
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def __getitem__(self, index):
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return self.data_X[index], self.data_Y[index]
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def __len__(self):
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return len(self.data_X)
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def create_dataset(datasetX, datasetY, look_back=1, predict_steps=1):
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"""
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将时间序列数据转换为监督学习问题的格式
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参数:
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datasetX: 输入特征数据
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datasetY: 目标变量数据
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look_back: 使用过去多少天的数据作为输入
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predict_steps: 预测未来多少天的数据
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返回:
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dataX: 输入特征，形状为 (样本数, 时间步, 特征数)
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dataY: 目标变量，形状为 (样本数, 预测步数)
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"""
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dataX, dataY = [], []
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for i in range(len(datasetX) - look_back - predict_steps + 1):
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x = datasetX[i:(i + look_back)]
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dataX.append(x)
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y = datasetY[(i + look_back):(i + look_back + predict_steps)]
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dataY.append(y)
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return np.array(dataX), np.array(dataY)
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def prepare_data(product_data, sequence_length=30, forecast_horizon=7):
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"""
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准备训练和验证数据
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参数:
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product_data: 产品销售数据DataFrame
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sequence_length: 输入序列长度
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forecast_horizon: 预测天数
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返回:
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X, y: 全部特征和目标
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X_train, X_val: 训练和验证特征
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y_train, y_val: 训练和验证目标
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scaler_X, scaler_y: 特征和目标的归一化器
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"""
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# 创建特征和目标变量
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features = ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
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# 预处理数据
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X_raw = product_data[features].values
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y_raw = product_data[['sales']].values # 保持为二维数组
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# 归一化数据
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scaler_X = MinMaxScaler(feature_range=(0, 1))
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scaler_y = MinMaxScaler(feature_range=(0, 1))
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X_scaled = scaler_X.fit_transform(X_raw)
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y_scaled = scaler_y.fit_transform(y_raw)
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# 创建时间序列数据
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X, y = create_dataset(X_scaled, y_scaled, sequence_length, forecast_horizon)
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# 划分训练集和验证集（80% 训练，20% 验证）
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X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=False)
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return X, y, X_train, X_val, y_train, y_val, scaler_X, scaler_y
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def prepare_sequences(X, y, batch_size=32):
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"""
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将数据转换为DataLoader对象，用于批量训练
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参数:
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X: 输入特征
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y: 目标变量
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batch_size: 批次大小
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返回:
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DataLoader对象
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"""
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# 转换为PyTorch张量
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X_tensor = torch.tensor(X, dtype=torch.float32)
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y_tensor = torch.tensor(y, dtype=torch.float32)
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# 创建数据集
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dataset = PharmacyDataset(X_tensor, y_tensor)
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# 创建数据加载器
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data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
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return data_loader
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