c0fe213b70
1. 修复前端图表日期排序问题: - 改进 PredictionView.vue 和 HistoryView.vue 中的图表渲染逻辑 - 确保历史数据和预测数据按照正确的日期顺序显示 2. 修复后端API处理: - 解决 optimized_kan 模型类型的路径映射问题 - 添加 JSON 序列化器处理 Pandas Timestamp 对象 - 改进预测数据与历史数据的衔接处理 3. 优化图表样式和用户体验
127 lines
3.8 KiB
Python
127 lines
3.8 KiB
Python
import torch
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import numpy as np
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from torch.utils.data import Dataset, DataLoader
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from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import train_test_split
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# 定义数据集类
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class PharmacyDataset(Dataset):
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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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# 定义用于时间序列预测的数据处理函数
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def create_dataset(datasetX, datasetY, look_back=1, predict_steps=1):
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dataX, dataY = [], []
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for i in range(0, len(datasetX) - look_back - predict_steps + 1):
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x = datasetX[i:(i + look_back), :]
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if predict_steps == 1:
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y = datasetY[i + look_back]
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else:
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y = datasetY[i + look_back:i + look_back + predict_steps, 0] # 仅取销量列
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dataX.append(x)
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dataY.append(y)
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return np.array(dataX), np.array(dataY)
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# 评估函数
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def evaluate_model(y_true, y_pred):
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mse = mean_squared_error(y_true, y_pred)
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rmse = np.sqrt(mse)
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mae = mean_absolute_error(y_true, y_pred)
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r2 = r2_score(y_true, y_pred)
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# MAPE计算时避免除以0
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mask = y_true != 0
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y_true_masked = y_true[mask]
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y_pred_masked = y_pred[mask]
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mape = np.mean(np.abs((y_true_masked - y_pred_masked) / y_true_masked)) * 100
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return {
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'MSE': mse,
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'RMSE': rmse,
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'MAE': mae,
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'R²': r2,
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'MAPE(%)': mape
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}
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# 为优化版KAN模型添加的数据准备函数
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def prepare_data(product_data, sequence_length=30, forecast_horizon=7, test_size=0.2, random_state=42):
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"""
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准备时间序列数据，用于训练和评估模型
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参数:
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product_data: 单个产品的数据
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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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test_size: 测试集比例
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random_state: 随机种子
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返回:
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X, y, X_train, X_val, y_train, y_val, scaler_X, scaler_y
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"""
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# 提取特征
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features = ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
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# 确保数据按日期排序
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product_data = product_data.sort_values('date')
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# 提取特征和目标变量
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X_data = product_data[features].values
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y_data = product_data[['sales']].values
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# 标准化数据
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scaler_X = StandardScaler()
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scaler_y = StandardScaler()
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X_scaled = scaler_X.fit_transform(X_data)
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y_scaled = scaler_y.fit_transform(y_data)
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# 创建序列数据
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X, y = [], []
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for i in range(len(X_scaled) - sequence_length - forecast_horizon + 1):
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X.append(X_scaled[i:i+sequence_length])
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y.append(y_scaled[i+sequence_length:i+sequence_length+forecast_horizon])
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X = np.array(X)
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y = np.array(y)
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# 如果y是3D的，压缩为2D (batch_size, forecast_horizon)
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if y.ndim == 3:
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y = y.reshape(y.shape[0], y.shape[1])
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# 分割训练集和验证集
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X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=test_size, random_state=random_state)
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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 |