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代碼實現

數據讀取

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn import metrics import scipy.stats as stats plt.rcParams['font.family'] = 'Times New Roman' plt.rcParams['axes.unicode_minus'] = False df_train = pd.read_excel('GBDT_train.xlsx') df_test = pd.read_excel('GBDT_test.xlsx')

從 Excel 文件中分別加載訓練數據 (GBDT_train.xlsx) 和測試數據 (GBDT_test.xlsx) 到數據框 (df_train 和 df_test) 中，里面包含真實值以及預測值。

模型性能計算

from sklearn import metrics # 真實 y_train = df_train['Experimental value'] y_test = df_test['Experimental value'] # 預測 y_pred_train = df_train['Predicted value'] y_pred_test = df_test['Predicted value'] y_pred_train_list = y_pred_train.tolist() y_pred_test_list = y_pred_test.tolist() # 計算訓練集的指標 mse_train = metrics.mean_squared_error(y_train, y_pred_train_list) rmse_train = np.sqrt(mse_train) mae_train = metrics.mean_absolute_error(y_train, y_pred_train_list) r2_train = metrics.r2_score(y_train, y_pred_train_list) # 計算測試集的指標 mse_test = metrics.mean_squared_error(y_test, y_pred_test_list) rmse_test = np.sqrt(mse_test) mae_test = metrics.mean_absolute_error(y_test, y_pred_test_list) r2_test = metrics.r2_score(y_test, y_pred_test_list) print("訓練集評價指標:") print("均方誤差 (MSE):", mse_train) print("均方根誤差 (RMSE):", rmse_train) print("平均絕對誤差 (MAE):", mae_train) print("擬合優度 (R-squared):", r2_train) print("\n測試集評價指標:") print("均方誤差 (MSE):", mse_test) print("均方根誤差 (RMSE):", rmse_test) print("平均絕對誤差 (MAE):", mae_test) print("擬合優度 (R-squared):", r2_test)

從訓練集和測試集的數據中提取真實值 (Experimental value) 和預測值 (Predicted value)，計算模型在訓練集和測試集上的回歸性能指標。

文章一可視化

# 創建一個包含訓練集和測試集真實值與預測值的數據框 data_train = pd.DataFrame({ 'True': y_train, 'Predicted': y_pred_train, 'Data Set': 'Train' }) data_test = pd.DataFrame({ 'True': y_test, 'Predicted': y_pred_test, 'Data Set': 'Test' }) data = pd.concat([data_train, data_test]) # 自定義調色板 palette = {'Train': '#b4d4e1', 'Test': '#f4ba8a'} # 創建 JointGrid 對象 plt.figure(figsize=(8, 6), dpi=1200) g = sns.JointGrid(data=data, x="True", y="Predicted", hue="Data Set", height=10, palette=palette) # 繪制中心的散點圖 g.plot_joint(sns.scatterplot, alpha=0.5) # 添加訓練集的回歸線 sns.regplot(data=data_train, x="True", y="Predicted", scatter=False, ax=g.ax_joint, color='#b4d4e1', label='Train Regression Line') # 添加測試集的回歸線 sns.regplot(data=data_test, x="True", y="Predicted", scatter=False, ax=g.ax_joint, color='#f4ba8a', label='Test Regression Line') # 添加邊緣的柱狀圖 g.plot_marginals(sns.histplot, kde=False, element='bars', multiple='stack', alpha=0.5) # 添加擬合優度文本在右下角 ax = g.ax_joint ax.text(0.95, 0.1, f'Train $R^2$ = {r2_train:.3f}', transform=ax.transAxes, fontsize=12, verticalalignment='bottom', horizontalalignment='right', bbox=dict(boxstyle="round,pad=0.3", edgecolor="black", facecolor="white")) ax.text(0.95, 0.05, f'Test $R^2$ = {r2_test:.3f}', transform=ax.transAxes, fontsize=12, verticalalignment='bottom', horizontalalignment='right', bbox=dict(boxstyle="round,pad=0.3", edgecolor="black", facecolor="white")) # 在左上角添加模型名稱文本 ax.text(0.75, 0.99, 'Model = GBDT', transform=ax.transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left', bbox=dict(boxstyle="round,pad=0.3", edgecolor="black", facecolor="white")) # 添加中心線 ax.plot([data['True'].min(), data['True'].max()], [data['True'].min(), data['True'].max()], c="black", alpha=0.5, linestyle='--', label='x=y') ax.legend() plt.savefig("TrueFalse.pdf", format='pdf', bbox_inches='tight') plt.show()

文章二基礎可視化

plt.figure(figsize=(8, 6), dpi=1200) plt.scatter(y_test, y_pred_test, color='coral', label="Predicted N?O concentration", alpha=0.2) # 預測值散點圖 plt.plot(y_test, y_test, color='grey', alpha=0.6, label="1:1 Line") # 1:1灰色虛線 # 擬合線 z = np.polyfit(y_test, y_pred_test, 1) p = np.poly1d(z) plt.plot(y_test, p(y_test), color='blue', alpha=0.6, label=f"Line of Best Fit\n$R^2$ = {r2_test:.2f},MAE = {mae_test:.2f}") plt.title("GBDT Regression") plt.xlabel("Observed Values") plt.ylabel("Predicted Values") plt.legend(loc="upper left") plt.savefig('1.pdf', format='pdf', bbox_inches='tight') plt.show()