199 lines
6.9 KiB
Python
199 lines
6.9 KiB
Python
"""
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药店销售预测系统 - XGBoost 模型训练器 (插件式)
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"""
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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 xgboost as xgb
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from sklearn.preprocessing import MinMaxScaler
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from xgboost.callback import EarlyStopping
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# 导入核心工具
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from utils.data_utils import prepare_tabular_data
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from analysis.metrics import evaluate_model
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from utils.model_manager import model_manager
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from models.model_registry import register_trainer
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from utils.visualization import plot_loss_curve # 导入绘图函数
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def train_product_model_with_xgboost(
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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 = 500, # XGBoost通常需要更多轮次
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sequence_length: int = 1, # 对于非序列模型，此参数意义不大，但为兼容性保留
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forecast_horizon: int = 1,
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model_dir: str = 'saved_models',
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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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**kwargs
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):
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"""
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使用 XGBoost 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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print(f"🚀 XGBoost训练器启动: model_identifier='{model_identifier}'")
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# --- 1. 数据准备和验证 ---
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if training_df.empty:
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raise ValueError(f"用于训练的数据为空")
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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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# --- 2. 数据预处理和适配 ---
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print(f"[XGBoost] 开始数据预处理，使用 {len(feature_list)} 个预选特征...")
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# 使用标准化的表格数据准备函数
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trainX, testX, trainY, testY, scaler_X, scaler_y = prepare_tabular_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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)
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# 转换为 XGBoost 核心 DMatrix 格式
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dtrain = xgb.DMatrix(trainX, label=trainY)
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dtest = xgb.DMatrix(testX, label=testY)
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# --- 3. 模型训练 (使用核心 xgb.train API) ---
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xgb_params = {
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'learning_rate': kwargs.get('learning_rate', 0.08),
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'subsample': kwargs.get('subsample', 0.75),
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'colsample_bytree': kwargs.get('colsample_bytree', 1),
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'max_depth': kwargs.get('max_depth', 7),
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'gamma': kwargs.get('gamma', 0),
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'objective': 'reg:squarederror',
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'eval_metric': 'rmse', # eval_metric 在这里是原生支持的
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'n_jobs': -1
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}
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n_estimators = kwargs.get('n_estimators', 500)
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print("开始训练XGBoost模型 (使用核心xgb.train API)...")
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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='xgboost',
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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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start_time = time.time()
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evals_result = {}
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model = xgb.train(
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params=xgb_params,
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dtrain=dtrain,
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num_boost_round=n_estimators,
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evals=[(dtrain, 'train'), (dtest, 'test')],
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early_stopping_rounds=50, # early_stopping_rounds 在这里是原生支持的
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evals_result=evals_result,
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verbose_eval=False
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)
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training_time = time.time() - start_time
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print(f"XGBoost模型训练完成，耗时: {training_time:.2f}秒")
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# --- 4. 模型评估与可视化 ---
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# 使用 model.best_iteration 获取最佳轮次的预测结果
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test_pred = model.predict(dtest, iteration_range=(0, model.best_iteration))
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# 反归一化时需要将1D数组变回2D
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test_pred_inv = scaler_y.inverse_transform(test_pred.reshape(-1, 1))
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test_true_inv = scaler_y.inverse_transform(testY.reshape(-1, 1))
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metrics = evaluate_model(test_true_inv.flatten(), test_pred_inv.flatten())
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metrics['training_time'] = training_time
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metrics['best_iteration'] = model.best_iteration
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print("\n模型评估指标:")
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print(f"MSE: {metrics['mse']:.4f}, RMSE: {metrics['rmse']:.4f}, MAE: {metrics['mae']:.4f}, R²: {metrics['r2']:.4f}, MAPE: {metrics['mape']:.2f}%")
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# 提取损失并绘制曲线
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train_losses = evals_result['train']['rmse']
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test_losses = evals_result['test']['rmse']
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# --- 5. 保存工件 ---
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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 = product_id
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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='xgboost',
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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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# 准备要保存的最终模型数据
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model_data = {
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'model_state_dict': model, # 直接保存模型对象
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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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'sequence_length': sequence_length,
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'forecast_horizon': forecast_horizon,
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'model_type': 'xgboost',
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'features': feature_list,
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'xgb_params': xgb_params
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},
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'metrics': metrics,
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'loss_history': evals_result,
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'loss_curve_path': loss_curve_path # 直接包含路径
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}
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# 保存最终版本模型
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final_model_path, final_version = model_manager.save_model(
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model_data=model_data,
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product_id=product_id,
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model_type='xgboost',
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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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print(f"✅ XGBoost最终模型已通过统一管理器保存，版本: {final_version}")
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# XGBoost的 `best_model` 就是它自己，因为 `xgb.train` 内部处理了早停。
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# 我们创建一个指向最终模型的 "best" 版本文件，以保持接口一致性。
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best_model_path, _ = model_manager.save_model(
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model_data=model_data,
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product_id=product_id,
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model_type='xgboost',
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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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print(f"✅ XGBoost最佳模型引用已创建，版本: {current_version}_best")
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# 组装返回的工件
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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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register_trainer('xgboost', train_product_model_with_xgboost) |