237 lines
8.1 KiB
Python
237 lines
8.1 KiB
Python
from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.feature_selection import SelectFromModel
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from sklearn.preprocessing import OrdinalEncoder
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from sklearn.impute import SimpleImputer
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from sklearn.model_selection import train_test_split
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import pandas as pd
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from sklearn.linear_model import Lasso
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from xgboost import XGBRegressor
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import statsmodels.api as sm
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import plotly.express as px
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import plotly.graph_objects as go
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import xgboost as xgb
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from xgboost import plot_importance, plot_tree
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from sklearn.metrics import mean_absolute_error
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from statsmodels.tools.eval_measures import mse, rmse
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from sklearn.model_selection import GridSearchCV
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import warnings
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import pickle
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from sklearn.metrics import mean_squared_error
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from sklearn.metrics import r2_score
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import numpy as np
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from matplotlib import pyplot as plt
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import os
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from matplotlib import rcParams
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rcParams['font.sans-serif'] = ['SimHei'] # Windows系统自带的中文字体
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rcParams['axes.unicode_minus'] = False # 解决负号显示问题
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pd.set_option('display.max_rows', 40)
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pd.set_option('display.max_columns', 40)
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class xgboostmodels():
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def __init__(self, df, steps=3):
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self.df = df
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self.steps = steps
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self.features = []
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def optimize_Model(self, df):
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# 生成滞后特征（示例使用3期滞后）
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steps = self.steps
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for i in range(1, steps+1):
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df[f'lag_{i}'] = df['y'].shift(i)
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# 生成多步预测目标（示例预测未来3步）
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forecast_horizon = steps
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for i in range(forecast_horizon):
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df[f'target_{i+1}'] = df['y'].shift(-(i+1))
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df = df.dropna() # 删除包含NaN的行
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# 更新特征和目标变量
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features = [col for col in df.columns if col.startswith('lag')]
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targets = [col for col in df.columns if col.startswith('target')]
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X = df[features]
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y = df[targets]
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# 按时间顺序分割数据集（时间序列不能随机分割）
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split = int(0.8 * len(df))
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X_train, x_test = X.iloc[:split], X.iloc[split:]
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y_train, y_test = y.iloc[:split], y.iloc[split:]
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# 修改模型为多输出回归
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from sklearn.multioutput import MultiOutputRegressor
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base_model = XGBRegressor(random_state=0)
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estimator = MultiOutputRegressor(base_model) # 保持多输出包装器
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# 模型参数网格需要调整为适用于多输出回归器
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parameters = {
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'estimator__max_depth': range(2, 11, 2), # 注意参数前缀
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'estimator__n_estimators': range(50, 101, 10),
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'estimator__learning_rate': [0.01, 0.03, 0.1, 0.3, 0.5, 1]
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}
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grid_search_XGB = GridSearchCV(
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estimator=estimator,
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param_grid=parameters,
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cv=3,
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verbose=True,
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scoring='neg_mean_squared_error' # 使用更适合时序的评估指标
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)
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grid_search_XGB.fit(X_train, y_train)
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# 优化后的结果展示
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print("最佳参数组合:")
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best_params = {}
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for param, value in grid_search_XGB.best_params_.items():
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best_params[param.split('__')[1]] = value
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print(f"{param}: {value}")
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best_params_df = pd.DataFrame(
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[best_params], index=["最佳参数组合"])
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y_pred = grid_search_XGB.predict(x_test)
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# 多步预测评估指标
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evaluation_results = []
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for step in range(forecast_horizon):
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mse = mean_squared_error(y_test.iloc[:, step], y_pred[:, step])
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rmse = np.sqrt(mse)
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r2 = r2_score(y_test.iloc[:, step], y_pred[:, step])
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evaluation_results.append({
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'预测步长': step+1,
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'均方根误差(RMSE)': rmse,
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'R²分数': r2
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})
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# 生成多步预测结果对比表
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results_df = pd.DataFrame(evaluation_results).set_index('预测步长')
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print("\n多步预测性能评估:")
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print(results_df)
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# 保存完整模型（包含多输出包装器）
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Pkl_Filename = "report\日度价格预测_最佳模型.pkl"
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with open(Pkl_Filename, 'wb') as file:
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pickle.dump(grid_search_XGB.best_estimator_, file)
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return results_df, best_params_df
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def forecast_price(self, df):
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steps = self.steps
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import joblib
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Best_model_DalyLGPrice = joblib.load(
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"report\日度价格预测_最佳模型.pkl")
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df = df.copy()
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df.set_index('ds', inplace=True)
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# 生成滞后特征（与训练时保持一致）
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for i in range(1, steps+1):
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df[f'lag_{i}'] = df['y'].shift(i)
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# 获取特征列（保持与训练时相同的特征顺序）
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features = [f'lag_{i}' for i in range(1, steps+1)]
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# 初始化预测序列（保留最后3个已知值作为初始输入）
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current_values = df[features][-1:].values # 初始形状 (1,3)
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predictions = Best_model_DalyLGPrice.predict(current_values)[0]
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# 生成带日期的预测结果
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last_date = df.index[-1]
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date_range = pd.date_range(
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start=last_date + pd.Timedelta(days=1),
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periods=steps
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)
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# 保存预测结果到DataFrame
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forecast_series = pd.Series(
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predictions, index=date_range.strftime('%Y-%m-%d'), name='预测值')
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# 拼接原始数据和预测结果
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df = pd.concat([df[['y']], forecast_series], axis=0)
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# 绘制预测结果
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plt.figure(figsize=(12, 6))
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n_points = min(len(df), 30) # 自动适配数据量
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plt.plot(df.index[-n_points:], df['y'].iloc[-n_points:],
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label=f'原始数据(近{n_points}天)', marker='o', markersize=4)
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plt.plot(forecast_series.index, forecast_series.values,
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label=f'预测值 (未来{steps}天)', linestyle='--', marker='x')
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plt.axvline(x=last_date, color='red', linestyle=':',
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label='当前日期分界线', alpha=0.7)
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plt.title('价格预测趋势')
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plt.xlabel('日期')
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plt.ylabel('价格')
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plt.legend()
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plt.grid(True)
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# 自动旋转日期标签
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plt.gcf().autofmt_xdate()
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# 确保保存目录存在
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os.makedirs("report", exist_ok=True)
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plt.savefig("report/价格预测.png", dpi=300, bbox_inches='tight')
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plt.close()
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return forecast_series
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def generate_xgboost_report(self, model_results, best_params_df, forecast_series,
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save_path="report/xgboost_report.md"):
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"""
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生成XGBoost模型报告
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参数:
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model_results: 模型对比结果DataFrame
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evaluation_df: 多步评估结果DataFrame
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forecast_series: 预测结果Series
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save_path: 报告保存路径
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"""
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# 构建Markdown内容
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md_content = f"""
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# XGBoost价格预测分析报告 <span style="color:gray; font-size:0.8em">{pd.Timestamp.now().strftime('%Y-%m-%d')}</span>
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## 一、模型实现
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### 1. 特征工程
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- 使用{len(model_results)}期历史滞后特征
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- 预测未来{len(model_results)}个时间步长
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- 数据集分割比例：80% 训练集 / 20% 测试集
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### 2. 模型配置
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{best_params_df.to_markdown()}
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## 二、性能评估
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### 多步预测误差分析
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{model_results.to_markdown()}
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## 三、预测结果
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### 未来{len(forecast_series)}日价格预测
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{forecast_series.to_markdown()}
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"""
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# 保存报告
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os.makedirs(os.path.dirname(save_path), exist_ok=True)
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with open(save_path, 'w', encoding='utf-8') as f:
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f.write(md_content)
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print(f"报告已生成至：{os.path.abspath(save_path)}")
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if __name__ == '__main__':
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df = pd.read_csv(
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"D:\code\PriceForecast-svn\yuanyoudataset\指标数据.csv")
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df['ds'] = pd.to_datetime(df['ds'])
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xg = xgboostmodels(df=df, steps=5)
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model_results, best_params_df = xg.optimize_Model(df=df)
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forecast_series = xg.forecast_price(df)
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xg.generate_xgboost_report(
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model_results=model_results,
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best_params_df=best_params_df,
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forecast_series=forecast_series
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)
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