391 lines
13 KiB
Python
391 lines
13 KiB
Python
"""
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药店销售预测系统 - TCN模型训练函数
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"""
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import os
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import time
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import pandas as pd
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import DataLoader
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from sklearn.preprocessing import MinMaxScaler
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import matplotlib.pyplot as plt
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from tqdm import tqdm
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from models.tcn_model import TCNForecaster
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from utils.data_utils import prepare_data, PharmacyDataset, prepare_sequences
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from utils.visualization import plot_loss_curve
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from analysis.metrics import evaluate_model
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from core.config import DEVICE, DEFAULT_MODEL_DIR, LOOK_BACK, FORECAST_HORIZON
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from utils.training_progress import progress_manager
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from utils.model_manager import model_manager
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def train_product_model_with_tcn(
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model_identifier: str,
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training_df: pd.DataFrame,
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feature_list: list,
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training_mode: str,
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epochs: int = 50,
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sequence_length: int = LOOK_BACK,
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forecast_horizon: int = FORECAST_HORIZON,
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model_dir: str = DEFAULT_MODEL_DIR,
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product_id: str = None,
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store_id: str = None,
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aggregation_method: str = None,
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version: str = None,
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socketio=None,
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task_id: str = None,
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progress_callback=None,
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**kwargs
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):
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"""
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使用TCN模型训练产品销售预测模型 (新数据管道版)
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"""
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def emit_progress(message, progress=None, metrics=None):
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"""发送训练进度到前端"""
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if socketio and task_id:
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data = {
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'task_id': task_id,
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'message': message,
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'timestamp': time.time()
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}
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if progress is not None:
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data['progress'] = progress
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if metrics is not None:
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data['metrics'] = metrics
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socketio.emit('training_progress', data, namespace='/training')
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emit_progress(f"开始训练 TCN 模型")
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min_required_samples = sequence_length + forecast_horizon
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if len(training_df) < min_required_samples:
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error_msg = f"训练数据不足: 需要 {min_required_samples} 条记录, 但只有 {len(training_df)} 条。"
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emit_progress(error_msg)
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raise ValueError(error_msg)
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product_name = training_df['product_name'].iloc[0] if 'product_name' in training_df.columns else model_identifier
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emit_progress(f"开始为 '{product_name}' (标识: {model_identifier}) 训练TCN模型")
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# --- 新数据管道核心改造 ---
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emit_progress("数据预处理中...")
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# 1. 使用标准化的 prepare_data 函数处理数据
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_, _, trainX, testX, trainY, testY, scaler_X, scaler_y = prepare_data(
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training_df=training_df,
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feature_list=feature_list,
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target_column='net_sales_quantity',
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sequence_length=sequence_length,
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forecast_horizon=forecast_horizon
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)
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# 2. 使用标准化的 prepare_sequences 函数创建 DataLoader
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batch_size = 32
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train_loader = prepare_sequences(trainX, trainY, batch_size)
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test_loader = prepare_sequences(testX, testY, batch_size)
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total_batches = len(train_loader)
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total_samples = len(trainX)
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if hasattr(progress_manager, 'total_batches_per_epoch'):
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progress_manager.total_batches_per_epoch = total_batches
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progress_manager.batch_size = batch_size
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progress_manager.total_samples = total_samples
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input_dim = trainX.shape[2]
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output_dim = forecast_horizon
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hidden_size = 64
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num_layers = 3
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kernel_size = 3
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dropout_rate = 0.2
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model = TCNForecaster(
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num_features=input_dim,
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output_sequence_length=output_dim,
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num_channels=[hidden_size] * num_layers,
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kernel_size=kernel_size,
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dropout=dropout_rate
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)
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# TODO: Implement continue_training logic with the new model_manager
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model = model.to(DEVICE)
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criterion = nn.MSELoss()
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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emit_progress("开始模型训练...")
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train_losses = []
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test_losses = []
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start_time = time.time()
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# 版本锁定
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current_version = model_manager.peek_next_version(
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model_type='tcn',
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product_id=product_id,
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store_id=store_id,
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training_mode=training_mode,
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aggregation_method=aggregation_method
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)
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print(f"🔒 本次训练版本锁定为: {current_version}")
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checkpoint_interval = max(1, epochs // 10)
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best_loss = float('inf')
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best_model_path = None
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progress_manager.set_stage("model_training", 0)
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emit_progress(f"开始训练 - 总epoch: {epochs}, 检查点间隔: {checkpoint_interval}")
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for epoch in range(epochs):
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progress_manager.start_epoch(epoch)
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model.train()
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epoch_loss = 0
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for batch_idx, (X_batch, y_batch) in enumerate(train_loader):
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X_batch, y_batch = X_batch.to(DEVICE), y_batch.to(DEVICE)
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if y_batch.dim() == 2:
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y_batch = y_batch.unsqueeze(-1)
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outputs = model(X_batch)
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loss = criterion(outputs, y_batch)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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epoch_loss += loss.item()
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if batch_idx % 10 == 0 or batch_idx == len(train_loader) - 1:
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current_lr = optimizer.param_groups[0]['lr']
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progress_manager.update_batch(batch_idx, loss.item(), current_lr)
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train_loss = epoch_loss / len(train_loader)
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train_losses.append(train_loss)
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progress_manager.set_stage("validation", 0)
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model.eval()
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test_loss = 0
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with torch.no_grad():
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for batch_idx, (X_batch, y_batch) in enumerate(test_loader):
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X_batch, y_batch = X_batch.to(DEVICE), y_batch.to(DEVICE)
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if y_batch.dim() == 2:
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y_batch = y_batch.unsqueeze(-1)
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outputs = model(X_batch)
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loss = criterion(outputs, y_batch)
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test_loss += loss.item()
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if batch_idx % 5 == 0 or batch_idx == len(test_loader) - 1:
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val_progress = (batch_idx / len(test_loader)) * 100
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progress_manager.set_stage("validation", val_progress)
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test_loss = test_loss / len(test_loader)
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test_losses.append(test_loss)
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progress_manager.finish_epoch(train_loss, test_loss)
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if (epoch + 1) % 5 == 0 or epoch == epochs - 1:
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progress = ((epoch + 1) / epochs) * 100
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current_metrics = {
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'train_loss': train_loss,
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'test_loss': test_loss,
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'epoch': epoch + 1,
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'total_epochs': epochs
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}
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emit_progress(f"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}",
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progress=progress, metrics=current_metrics)
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if (epoch + 1) % checkpoint_interval == 0 or epoch == epochs - 1:
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checkpoint_data = {
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'epoch': epoch + 1,
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'model_state_dict': model.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'train_loss': train_loss,
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'test_loss': test_loss,
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'train_losses': train_losses,
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'test_losses': test_losses,
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'scaler_X': scaler_X,
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'scaler_y': scaler_y,
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'config': {
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'input_dim': input_dim,
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'output_dim': output_dim,
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'hidden_size': hidden_size,
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'num_layers': num_layers,
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'num_channels': [hidden_size] * num_layers,
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'dropout': dropout_rate,
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'kernel_size': kernel_size,
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'sequence_length': sequence_length,
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'forecast_horizon': forecast_horizon,
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'model_type': 'tcn'
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},
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'training_info': {
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'product_id': product_id,
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'product_name': product_name,
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'training_mode': training_mode,
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'store_id': store_id,
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'aggregation_method': aggregation_method,
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'timestamp': time.time()
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}
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}
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if test_loss < best_loss:
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best_loss = test_loss
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best_model_path, _ = model_manager.save_model(
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model_data=checkpoint_data,
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product_id=product_id,
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model_type='tcn',
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store_id=store_id,
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training_mode=training_mode,
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aggregation_method=aggregation_method,
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product_name=product_name,
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version=f"{current_version}_best"
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)
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emit_progress(f"💾 保存最佳模型检查点 (epoch {epoch+1}, test_loss: {test_loss:.4f})")
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if (epoch + 1) % 10 == 0:
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print(f"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, Test Loss: {test_loss:.4f}")
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training_time = time.time() - start_time
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progress_manager.set_stage("model_saving", 0)
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emit_progress("训练完成，正在保存模型...")
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model.eval()
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with torch.no_grad():
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all_test_X = []
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all_test_Y = []
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for X_batch, y_batch in test_loader:
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all_test_X.append(X_batch)
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all_test_Y.append(y_batch)
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testX_tensor = torch.cat(all_test_X, dim=0)
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testY_tensor = torch.cat(all_test_Y, dim=0)
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test_pred = model(testX_tensor.to(DEVICE))
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test_pred = test_pred.squeeze(-1).cpu().numpy()
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# 反归一化需要reshape
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test_pred_inv = scaler_y.inverse_transform(test_pred)
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test_true_inv = scaler_y.inverse_transform(testY_tensor.cpu().numpy())
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metrics = evaluate_model(test_true_inv, test_pred_inv)
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metrics['training_time'] = training_time
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print("\n模型评估指标:")
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print(f"MSE: {metrics['mse']:.4f}")
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print(f"RMSE: {metrics['rmse']:.4f}")
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print(f"MAE: {metrics['mae']:.4f}")
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print(f"R²: {metrics['r2']:.4f}")
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print(f"MAPE: {metrics['mape']:.2f}%")
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print(f"训练时间: {training_time:.2f}秒")
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final_model_data = {
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'epoch': epochs,
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'model_state_dict': model.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'train_loss': train_losses[-1],
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'test_loss': test_losses[-1],
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'train_losses': train_losses,
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'test_losses': test_losses,
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'scaler_X': scaler_X,
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'scaler_y': scaler_y,
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'config': {
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'input_dim': input_dim,
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'output_dim': output_dim,
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'hidden_size': hidden_size,
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'num_layers': num_layers,
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'num_channels': [hidden_size] * num_layers,
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'dropout': dropout_rate,
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'kernel_size': kernel_size,
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'sequence_length': sequence_length,
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'forecast_horizon': forecast_horizon,
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'model_type': 'tcn'
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},
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'metrics': metrics,
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'metrics': metrics,
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'training_info': {
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'product_id': product_id,
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'product_name': product_name,
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'training_mode': training_mode,
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'store_id': store_id,
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'aggregation_method': aggregation_method,
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'timestamp': time.time(),
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'training_completed': True
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}
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}
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progress_manager.set_stage("model_saving", 50)
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final_model_path, final_version = model_manager.save_model(
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model_data=final_model_data,
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product_id=product_id,
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model_type='tcn',
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store_id=store_id,
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training_mode=training_mode,
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aggregation_method=aggregation_method,
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product_name=product_name,
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version=current_version
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)
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progress_manager.set_stage("model_saving", 100)
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final_metrics = {
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'mse': metrics['mse'],
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'rmse': metrics['rmse'],
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'mae': metrics['mae'],
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'r2': metrics['r2'],
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'mape': metrics['mape'],
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'training_time': training_time,
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'final_epoch': epochs,
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'version': final_version
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}
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emit_progress(f"模型训练完成！版本 {final_version} 已保存", progress=100, metrics=final_metrics)
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# 准备 scope 和 identifier 以生成标准化的文件名
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scope = training_mode
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if scope == 'product':
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identifier = model_identifier
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elif scope == 'store':
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identifier = store_id
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elif scope == 'global':
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identifier = aggregation_method
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else:
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identifier = product_name # 后备方案
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# 绘制带有版本号的损失曲线图
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loss_curve_path = plot_loss_curve(
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train_losses=train_losses,
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val_losses=test_losses,
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model_type='tcn',
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scope=scope,
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identifier=identifier,
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version=current_version, # 使用锁定的版本
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model_dir=model_dir
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)
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print(f"📈 带版本号的损失曲线已保存: {loss_curve_path}")
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# 更新模型数据中的损失图路径
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final_model_data['loss_curve_path'] = loss_curve_path
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artifacts = {
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"versioned_model": final_model_path,
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"loss_curve_plot": loss_curve_path,
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"best_model": best_model_path,
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"version": final_version
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}
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return metrics, artifacts
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# --- 将此训练器注册到系统中 ---
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from models.model_registry import register_trainer
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register_trainer('tcn', train_product_model_with_tcn) |