update

plot.py

@@ -0,0 +1,61 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+
+# 数据准备：不同模型的 MSE 和 R² 值
+data = {
+    'Model': [
+        '1D-Linear Regression', '1D-Stochastic Gradient Descent', '1D-K-Nearest Neighbors',
+        '1D-Decision Tree', '1D-Random Forest', '1D-XGBoost', '1D-Multi-layer Perceptron',
+        '2D-Linear Regression', '2D-Stochastic Gradient Descent', '2D-K-Nearest Neighbors',
+        '2D-Decision Tree', '2D-Random Forest', '2D-XGBoost', '2D-Multi-layer Perceptron',
+        '3D-Stochastic Gradient Descent', '3D-K-Nearest Neighbors', '3D-Decision Tree',
+        '3D-Random Forest', '3D-XGBoost', '3D-Multi-layer Perceptron', 'UniMol QSAR'
+    ],
+    'MSE': [
+        32.3949, 230009980374197965960989638656.0, 30.2081,
+        27.7150, 26.5204, 27.7147, 143.3505,
+        30.1093, 33.7336, 48.8179,
+        30.2360, 28.7916, 30.2351, 30.1715,
+        64.5768, 38.6921, 30.2360,
+        30.8310, 30.2362, 29.9844, 59.7204
+    ],
+    'R2': [
+        0.6525, -2467672699617844819673481216.0, 0.6759,
+        0.7027, 0.7155, 0.7027, -0.5379,
+        0.6770, 0.6381, 0.4763,
+        0.6756, 0.6911, 0.6756, 0.6763,
+        0.3072, 0.5849, 0.6756,
+        0.6692, 0.6756, 0.6783, 0.3593
+    ]
+}
+
+# 转换为 DataFrame
+df = pd.DataFrame(data)
+
+# 过滤掉负值
+df_filtered = df[(df['MSE'] >= 0) & (df['R2'] >= 0)]
+
+# 设置画布
+plt.figure(figsize=(14, 10))
+
+# 绘制 MSE 比较的条形图
+plt.subplot(2, 1, 1)
+sns.barplot(x='MSE', y='Model', data=df_filtered, palette='viridis')
+plt.title('Comparison of MSE across QSAR Models (Filtered)')
+plt.xlabel('Mean Squared Error')
+plt.ylabel('QSAR Model')
+
+# 绘制 R² 比较的条形图
+plt.subplot(2, 1, 2)
+sns.barplot(x='R2', y='Model', data=df_filtered, palette='plasma')
+plt.title('Comparison of R² across QSAR Models (Filtered)')
+plt.xlabel('R-squared')
+plt.ylabel('QSAR Model')
+
+# 调整布局以避免重叠
+plt.tight_layout()
+
+# 显示图表
+# plt.show()
+plt.savefig('qsar_r2_scores.png', dpi=300)