184 lines
9.8 KiB
Python
184 lines
9.8 KiB
Python
"""
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药店销售预测系统 - 模型预测函数
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"""
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import os
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import torch
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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import matplotlib.pyplot as plt
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from sklearn.preprocessing import MinMaxScaler
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import sklearn.preprocessing._data # 添加这一行以支持MinMaxScaler的反序列化
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from typing import Optional
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from models.transformer_model import TimeSeriesTransformer
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from models.slstm_model import sLSTM as ScalarLSTM
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from models.mlstm_model import MLSTMTransformer as MatrixLSTM
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from models.kan_model import KANForecaster
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from models.tcn_model import TCNForecaster
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from models.optimized_kan_forecaster import OptimizedKANForecaster
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from analysis.trend_analysis import analyze_prediction_result
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from utils.visualization import plot_prediction_results
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from utils.multi_store_data_utils import get_store_product_sales_data, aggregate_multi_store_data
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from core.config import DEVICE, get_model_file_path, DEFAULT_DATA_PATH
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def load_model_and_predict(model_path: str, product_id: str, model_type: str, store_id: Optional[str] = None, future_days: int = 7, start_date: Optional[str] = None, analyze_result: bool = False, version: Optional[str] = None, training_mode: str = 'product', history_lookback_days: int = 30):
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"""
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加载已训练的模型并进行预测 (v3版 - 支持自动回归)
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参数:
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... (同上, 新增 history_lookback_days)
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history_lookback_days: 用于图表展示的历史数据天数
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返回:
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预测结果和分析
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"""
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try:
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print(f"v3版预测函数启动，模型路径: {model_path}, 预测天数: {future_days}, 历史回看: {history_lookback_days}")
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if not os.path.exists(model_path):
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print(f"模型文件 {model_path} 不存在")
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return None
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# 加载销售数据
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from utils.multi_store_data_utils import aggregate_multi_store_data
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if training_mode == 'store' and store_id:
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product_df = aggregate_multi_store_data(store_id=store_id, aggregation_method='sum', file_path=DEFAULT_DATA_PATH)
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product_name = product_df['store_name'].iloc[0] if not product_df.empty else f"店铺{store_id}"
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elif training_mode == 'global':
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product_df = aggregate_multi_store_data(aggregation_method='sum', file_path=DEFAULT_DATA_PATH)
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product_name = "全局销售数据"
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else:
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product_df = aggregate_multi_store_data(product_id=product_id, aggregation_method='sum', file_path=DEFAULT_DATA_PATH)
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product_name = product_df['product_name'].iloc[0] if not product_df.empty else product_id
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if product_df.empty:
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print(f"产品 {product_id} 或店铺 {store_id} 没有销售数据")
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return None
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# 加载模型和配置
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try:
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torch.serialization.add_safe_globals([sklearn.preprocessing._data.MinMaxScaler])
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except Exception: pass
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checkpoint = torch.load(model_path, map_location=DEVICE, weights_only=False)
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if 'config' not in checkpoint or 'scaler_X' not in checkpoint or 'scaler_y' not in checkpoint:
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print("模型文件不完整，缺少config或scaler")
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return None
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config = checkpoint['config']
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scaler_X = checkpoint['scaler_X']
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scaler_y = checkpoint['scaler_y']
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# 创建模型实例
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# (此处省略了与原版本相同的模型创建代码，以保持简洁)
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if model_type == 'transformer':
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model = TimeSeriesTransformer(num_features=config['input_dim'], d_model=config['hidden_size'], nhead=config['num_heads'], num_encoder_layers=config['num_layers'], dim_feedforward=config['hidden_size'] * 2, dropout=config['dropout'], output_sequence_length=config['output_dim'], seq_length=config['sequence_length'], batch_size=32).to(DEVICE)
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elif model_type == 'mlstm':
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model = MatrixLSTM(num_features=config['input_dim'], hidden_size=config['hidden_size'], mlstm_layers=config['mlstm_layers'], embed_dim=config.get('embed_dim', 32), dense_dim=config.get('dense_dim', 32), num_heads=config.get('num_heads', 4), dropout_rate=config['dropout_rate'], num_blocks=config.get('num_blocks', 3), output_sequence_length=config['output_dim']).to(DEVICE)
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elif model_type == 'kan':
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model = KANForecaster(input_features=config['input_dim'], hidden_sizes=[config['hidden_size'], config['hidden_size']*2, config['hidden_size']], output_sequence_length=config['output_dim']).to(DEVICE)
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elif model_type == 'optimized_kan':
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model = OptimizedKANForecaster(input_features=config['input_dim'], hidden_sizes=[config['hidden_size'], config['hidden_size']*2, config['hidden_size']], output_sequence_length=config['output_dim']).to(DEVICE)
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elif model_type == 'tcn':
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model = TCNForecaster(num_features=config['input_dim'], output_sequence_length=config['output_dim'], num_channels=[config['hidden_size']] * config['num_layers'], kernel_size=config['kernel_size'], dropout=config['dropout']).to(DEVICE)
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else:
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print(f"不支持的模型类型: {model_type}"); return None
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model.load_state_dict(checkpoint['model_state_dict'])
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model.eval()
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# --- 核心逻辑修改：自动回归预测 ---
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features = ['sales', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
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sequence_length = config['sequence_length']
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# 确定预测的起始点
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if start_date:
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start_date_dt = pd.to_datetime(start_date)
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# 获取预测开始日期前的 `sequence_length` 天数据作为初始输入
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prediction_input_df = product_df[product_df['date'] < start_date_dt].tail(sequence_length)
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else:
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# 如果未指定开始日期，则从数据的最后一天开始预测
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prediction_input_df = product_df.tail(sequence_length)
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start_date_dt = product_df['date'].iloc[-1] + timedelta(days=1)
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if len(prediction_input_df) < sequence_length:
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print(f"错误: 预测所需的历史数据不足。需要 {sequence_length} 天, 但只有 {len(prediction_input_df)} 天。")
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return None
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# 准备用于图表展示的历史数据
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history_for_chart_df = product_df[product_df['date'] < start_date_dt].tail(history_lookback_days)
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# 自动回归预测循环
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all_predictions = []
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current_sequence_df = prediction_input_df.copy()
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print(f"开始自动回归预测，共 {future_days} 天...")
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for i in range(future_days):
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# 准备当前序列的输入张量
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X_current_scaled = scaler_X.transform(current_sequence_df[features].values)
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X_input = torch.tensor(X_current_scaled.reshape(1, sequence_length, -1), dtype=torch.float32).to(DEVICE)
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# 模型进行一次预测（可能预测出多个点，但我们只用第一个）
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with torch.no_grad():
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y_pred_scaled = model(X_input).cpu().numpy()
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# 提取下一个时间点的预测值
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next_step_pred_scaled = y_pred_scaled[0, 0].reshape(1, -1)
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next_step_pred_unscaled = scaler_y.inverse_transform(next_step_pred_scaled)[0][0]
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next_step_pred_unscaled = float(max(0, next_step_pred_unscaled)) # 确保销量不为负，并转换为标准float
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# 获取新预测的日期
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next_date = current_sequence_df['date'].iloc[-1] + timedelta(days=1)
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all_predictions.append({'date': next_date, 'predicted_sales': next_step_pred_unscaled})
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# 构建新的一行数据，用于更新输入序列
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new_row = {
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'date': next_date,
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'sales': next_step_pred_unscaled,
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'weekday': next_date.weekday(),
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'month': next_date.month,
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'is_holiday': 0,
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'is_weekend': 1 if next_date.weekday() >= 5 else 0,
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'is_promotion': 0,
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'temperature': current_sequence_df['temperature'].iloc[-1] # 沿用最后一天的温度
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}
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# 更新序列：移除最旧的一行，添加最新预测的一行
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new_row_df = pd.DataFrame([new_row])
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current_sequence_df = pd.concat([current_sequence_df.iloc[1:], new_row_df], ignore_index=True)
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predictions_df = pd.DataFrame(all_predictions)
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print(f"自动回归预测完成，生成 {len(predictions_df)} 条预测数据。")
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# 分析与可视化
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analysis = None
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if analyze_result:
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try:
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y_pred_for_analysis = predictions_df['predicted_sales'].values
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# 使用初始输入序列的特征进行分析
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initial_features_for_analysis = prediction_input_df[features].values
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analysis = analyze_prediction_result(product_id, model_type, y_pred_for_analysis, initial_features_for_analysis)
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except Exception as e:
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print(f"分析预测结果失败: {str(e)}")
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# 在返回前，将DataFrame转换为前端期望的JSON数组格式
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history_data_json = history_for_chart_df.to_dict('records') if not history_for_chart_df.empty else []
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prediction_data_json = predictions_df.to_dict('records') if not predictions_df.empty else []
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return {
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'product_id': product_id,
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'product_name': product_name,
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'model_type': model_type,
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'predictions': prediction_data_json, # 兼容旧字段，使用已转换的json
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'prediction_data': prediction_data_json,
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'history_data': history_data_json,
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'analysis': analysis
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}
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except Exception as e:
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print(f"预测过程中出现未捕获的异常: {str(e)}")
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import traceback
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traceback.print_exc()
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return None |