413 lines
15 KiB
Python
413 lines
15 KiB
Python
"""
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药店销售预测系统 - Transformer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 sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
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from models.transformer_model import TimeSeriesTransformer
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from utils.data_utils import create_dataset, PharmacyDataset
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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 utils.visualization import plot_loss_curve
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from analysis.metrics import evaluate_model
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from core.config import (
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DEVICE, DEFAULT_MODEL_DIR, LOOK_BACK, FORECAST_HORIZON
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)
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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_transformer(
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product_id,
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model_identifier,
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product_df=None,
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store_id=None,
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training_mode='product',
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aggregation_method='sum',
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epochs=50,
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sequence_length=LOOK_BACK,
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forecast_horizon=FORECAST_HORIZON,
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model_dir=DEFAULT_MODEL_DIR,
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version=None,
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socketio=None,
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task_id=None,
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continue_training=False,
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patience=10,
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learning_rate=0.001,
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clip_norm=1.0
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):
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"""
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使用Transformer模型训练产品销售预测模型
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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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print(f"[{time.strftime('%H:%M:%S')}] {message}", flush=True)
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import sys
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sys.stdout.flush()
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sys.stderr.flush()
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emit_progress("开始Transformer模型训练...")
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try:
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from utils.training_progress import progress_manager
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except ImportError:
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class DummyProgressManager:
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def set_stage(self, *args, **kwargs): pass
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def start_training(self, *args, **kwargs): pass
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def start_epoch(self, *args, **kwargs): pass
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def update_batch(self, *args, **kwargs): pass
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def finish_epoch(self, *args, **kwargs): pass
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def finish_training(self, *args, **kwargs): pass
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progress_manager = DummyProgressManager()
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if product_df is None:
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from utils.multi_store_data_utils import aggregate_multi_store_data
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product_df = aggregate_multi_store_data(
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product_id=product_id,
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aggregation_method=aggregation_method
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)
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training_scope = f"全局聚合({aggregation_method})"
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else:
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training_scope = "所有店铺"
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if product_df.empty:
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raise ValueError(f"产品 {product_id} 没有可用的销售数据")
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min_required_samples = sequence_length + forecast_horizon
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if len(product_df) < min_required_samples:
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error_msg = (
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f"❌ 训练数据不足错误\n"
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f"当前配置需要: {min_required_samples} 天数据 (LOOK_BACK={sequence_length} + FORECAST_HORIZON={forecast_horizon})\n"
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f"实际数据量: {len(product_df)} 天\n"
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f"产品ID: {product_id}, 训练模式: {training_mode}\n"
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)
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print(error_msg)
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raise ValueError(error_msg)
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product_df = product_df.sort_values('date')
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product_name = product_df['product_name'].iloc[0]
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print(f"[Transformer] 训练产品 '{product_name}' (ID: {product_id}) 的销售预测模型", flush=True)
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print(f"[Device] 使用设备: {DEVICE}", flush=True)
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print(f"[Model] 模型将保存到目录: {model_dir}", flush=True)
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features = ['sales', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
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progress_manager.set_stage("data_preprocessing", 0)
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emit_progress("数据预处理中...")
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X = product_df[features].values
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y = product_df[['sales']].values
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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)
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y_scaled = scaler_y.fit_transform(y)
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progress_manager.set_stage("data_preprocessing", 40)
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train_size = int(len(X_scaled) * 0.8)
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X_train, X_test = X_scaled[:train_size], X_scaled[train_size:]
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y_train, y_test = y_scaled[:train_size], y_scaled[train_size:]
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trainX, trainY = create_dataset(X_train, y_train, sequence_length, forecast_horizon)
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testX, testY = create_dataset(X_test, y_test, sequence_length, forecast_horizon)
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progress_manager.set_stage("data_preprocessing", 70)
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trainX_tensor = torch.Tensor(trainX)
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trainY_tensor = torch.Tensor(trainY)
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testX_tensor = torch.Tensor(testX)
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testY_tensor = torch.Tensor(testY)
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train_dataset = PharmacyDataset(trainX_tensor, trainY_tensor)
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test_dataset = PharmacyDataset(testX_tensor, testY_tensor)
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batch_size = 32
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train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
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total_batches = len(train_loader)
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total_samples = len(train_dataset)
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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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progress_manager.set_stage("data_preprocessing", 100)
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emit_progress("数据预处理完成，开始模型训练...")
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input_dim = X_train.shape[1]
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output_dim = forecast_horizon
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hidden_size = 64
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num_heads = 4
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dropout_rate = 0.1
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num_layers = 3
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model = TimeSeriesTransformer(
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num_features=input_dim,
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d_model=hidden_size,
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nhead=num_heads,
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num_encoder_layers=num_layers,
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dim_feedforward=hidden_size * 2,
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dropout=dropout_rate,
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output_sequence_length=output_dim,
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seq_length=sequence_length,
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batch_size=batch_size
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)
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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=learning_rate)
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scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=patience // 2, factor=0.5)
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train_losses = []
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test_losses = []
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start_time = time.time()
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checkpoint_interval = max(1, epochs // 10)
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best_loss = float('inf')
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epochs_no_improve = 0
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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}, 耐心值: {patience}")
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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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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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if clip_norm:
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torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=clip_norm)
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optimizer.step()
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epoch_loss += loss.item()
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if batch_idx % 5 == 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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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 % 3 == 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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scheduler.step(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_heads': num_heads,
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'dropout': dropout_rate,
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'num_layers': num_layers,
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'sequence_length': sequence_length,
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'forecast_horizon': forecast_horizon,
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'model_type': 'transformer'
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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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model_manager.save_model(
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model_data=checkpoint_data,
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product_id=model_identifier, # 修正：使用唯一的标识符
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model_type='transformer',
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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='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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epochs_no_improve = 0
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else:
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epochs_no_improve += 1
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if (epoch + 1) % 10 == 0:
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print(f"📊 Epoch {epoch+1}/{epochs}, 训练损失: {train_loss:.4f}, 测试损失: {test_loss:.4f}", flush=True)
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if epochs_no_improve >= patience:
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emit_progress(f"连续 {patience} 个epoch测试损失未改善，提前停止训练。")
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break
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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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loss_curve_path = plot_loss_curve(
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train_losses,
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test_losses,
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product_name,
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'Transformer',
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model_dir=model_dir
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)
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print(f"📈 损失曲线已保存到: {loss_curve_path}", flush=True)
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model.eval()
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with torch.no_grad():
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test_pred = model(testX_tensor.to(DEVICE)).cpu().numpy()
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test_true = testY
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test_pred_inv = scaler_y.inverse_transform(test_pred)
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test_true_inv = scaler_y.inverse_transform(test_true)
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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(f"\n📊 模型评估指标:", flush=True)
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print(f" MSE: {metrics['mse']:.4f}", flush=True)
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print(f" RMSE: {metrics['rmse']:.4f}", flush=True)
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print(f" MAE: {metrics['mae']:.4f}", flush=True)
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print(f" R²: {metrics['r2']:.4f}", flush=True)
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print(f" MAPE: {metrics['mape']:.2f}%", flush=True)
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print(f" ⏱️ 训练时间: {training_time:.2f}秒", flush=True)
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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_heads': num_heads,
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'dropout': dropout_rate,
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'num_layers': num_layers,
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'sequence_length': sequence_length,
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'forecast_horizon': forecast_horizon,
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'model_type': 'transformer'
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},
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'metrics': metrics,
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'loss_curve_path': loss_curve_path,
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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=model_identifier, # 修正：使用唯一的标识符
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model_type='transformer',
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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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)
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progress_manager.set_stage("model_saving", 100)
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emit_progress(f"模型已保存到 {final_model_path}")
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print(f"💾 模型已保存到 {final_model_path}", flush=True)
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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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return model, final_metrics, epochs
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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('transformer', train_product_model_with_transformer) |