Traceback (most recent call last): File "C:\Users\17680\PyCharmMiscProject\过拟合修改5.py", line 102, in <module> plot_learning_curve(model, X_train, y_train, cv=cv, n_jobs=-1) ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\17680\PyCharmMiscProject\过拟合修改5.py", line 73, in plot_learning_curve estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_scores, ^^^^^^^^^^^^ UnboundLocalError: cannot access local variable 'train_scores' where it is not associated with a value

根据你提供的错误信息，问题出在 `plot_learning_curve` 函数中。具体来说，`train_scores` 变量在调用 `learning_curve` 函数之前没有被正确定义。 ### 问题原因 在 `plot_learning_curve` 函数中，`train_sizes, train_scores, test_scores = learning_curve(...)` 这一行代码的返回值应该是三个变量：`train_sizes`、`train_scores` 和 `test_scores`。然而，在后续代码中，`train_sizes` 被错误地赋值为 `train_scores`，导致了 `UnboundLocalError` 错误。 ### 解决方法 我们需要修正 `plot_learning_curve` 函数中的变量名冲突问题，并确保 `learning_curve` 的返回值被正确分配给对应的变量。 以下是修复后的完整代码： ```python import pandas as pd import numpy as np import xgboost as xgb import shap import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split, learning_curve, cross_val_score from sklearn.metrics import mean_squared_error, r2_score from sklearn.preprocessing import StandardScaler from sklearn.model_selection import ShuffleSplit # 设置中文字体 plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False # 读取数据 data = pd.read_csv('D:/数据/数据8.csv') # 数据预处理 X = data.drop(['station_id', '空间活力'], axis=1) y = data['空间活力'] # 标准化特征 scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42) # 使用最佳参数创建XGBoost模型，并添加正则化和早停机制 model = xgb.XGBRegressor( colsample_bytree=0.7, gamma=0.2, learning_rate=0.01, # 减小学习率 max_depth=5, n_estimators=500, # 增加迭代次数 subsample=0.7, reg_alpha=0.1, # 添加L1正则化 reg_lambda=1.0, # 添加L2正则化 early_stopping_rounds=10, # 添加早停机制 random_state=42 ) # 训练模型（使用验证集） eval_set = [(X_test, y_test)] model.fit(X_train, y_train, eval_set=eval_set, verbose=False) # 在测试集上进行预测 y_pred = model.predict(X_test) # 评估模型性能 mse = mean_squared_error(y_test, y_pred) rmse = np.sqrt(mse) r2 = r2_score(y_test, y_pred) print(f"模型性能评估:") print(f"MSE: {mse:.4f}") print(f"RMSE: {rmse:.4f}") print(f"R² 分数: {r2:.4f}") # 定义函数绘制学习曲线 def plot_learning_curve(estimator, X, y, cv=None, n_jobs=None, train_sizes=np.linspace(0.1, 1.0, 5)): """ 绘制学习曲线：训练集和验证集上的性能随训练样本量的变化 """ fig, ax = plt.subplots(figsize=(10, 6)) ax.set_title('学习曲线') ax.set_xlabel("训练样本数量") ax.set_ylabel("分数 (负MSE)") # 计算学习曲线数据 train_sizes, train_scores, test_scores = learning_curve( estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, scoring='neg_mean_squared_error', shuffle=True, random_state=42 ) # 计算平均和标准差 train_scores_mean = -np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) test_scores_mean = -np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) # 绘制学习曲线 ax.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="r") ax.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="g") ax.plot(train_sizes, train_scores_mean, 'o-', color="r", label="训练集分数") ax.plot(train_sizes, test_scores_mean, 'o-', color="g", label="验证集分数") ax.legend(loc="best") plt.grid(True) plt.tight_layout() plt.savefig('learning_curve.png') plt.show() return fig # 绘制学习曲线 cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=42) plot_learning_curve(model, X_train, y_train, cv=cv, n_jobs=-1) # 创建SHAP解释器并计算SHAP值 explainer = shap.TreeExplainer(model) shap_values = explainer.shap_values(X_test) # 可视化特征重要性 shap.summary_plot(shap_values, X_test, feature_names=X.columns, plot_type="bar") plt.title('特征重要性 (SHAP值绝对值的平均)') plt.tight_layout() plt.savefig('shap_feature_importance.png') plt.show() # 可视化SHAP汇总图 shap.summary_plot(shap_values, X_test, feature_names=X.columns) plt.tight_layout() plt.savefig('shap_summary_plot.png') plt.show() # 可视化单个预测的SHAP值 shap.force_plot(explainer.expected_value, shap_values[0], X_test[0], feature_names=X.columns) plt.savefig('shap_force_plot_single.png', bbox_inches='tight') plt.close() # 可视化所有预测的SHAP值分布 shap.force_plot(explainer.expected_value, shap_values, X_test, feature_names=X.columns) plt.savefig('shap_force_plot_all.png', bbox_inches='tight') plt.close() # 计算并显示每个特征的平均SHAP值 feature_importance = pd.DataFrame({ 'Feature': X.columns, 'SHAP Importance': np.abs(shap_values).mean(axis=0) }).sort_values('SHAP Importance', ascending=False) print("\n特征重要性排序:") print(feature_importance) # 保存模型 model.save_model('xgb_shap_model.model') print("\n模型已保存为: xgb_shap_model.model") ``` ### 解释 1. **变量名冲突**： - 在原始代码中，`train_sizes, train_scores, test_scores = learning_curve(...)` 的返回值被错误地分配给了 `train_scores`，导致后续代码无法正确访问 `train_scores`。 - 修复后，`train_scores` 和 `test_scores` 正确接收了 `learning_curve` 的输出。 2. **学习曲线功能**： - 学习曲线展示了模型在不同训练样本数量下的表现，帮助我们判断模型是否过拟合或欠拟合。 --- ###