feat: performance

experiments/performance_comparison.py

@@ -0,0 +1,172 @@
+import plotly.graph_objects as go
+import pandas as pd
+import numpy as np
+
+# 生成符合学术规范的模拟数据
+np.random.seed(42)  # 确保可重复性
+methods = ['RSM', 'PSRN', 'NGGP', 'PySR', 'BMS', 'uDSR', 'AIF', 
+          'DGSR', 'E2E', 'SymINDy', 'physo', 'TPSR', 'SPL', 
+          'DEAP', 'SINDy', 'NSRS', 'gplearn', 'SNIP', 'KAN', 'EQL']
+
+# 生成0-1之间的RMSE数据（保持原图分布模式）
+rmse_values = np.clip(np.abs(np.random.normal(0.3, 0.15, len(methods))), 0.05, 0.95)
+uncertainty = np.random.uniform(0.02, 0.08, len(methods))
+param_sizes = np.array([1e3, 1e4, 5e4, 1e5, 5e5, 1e6, 2e6])  # 定义标准参数规模
+
+# 构建分类系统（基于方法原理）
+method_categories = {
+    'Symbolic': ['RSM', 'PySR', 'SymINDy', 'gplearn', 'EQL'],
+    'Neural': ['NGGP', 'DGSR', 'E2E', 'KAN'],
+    'Evolutionary': ['PSRN', 'DEAP', 'NSRS'],
+    'Physics-based': ['physo', 'TPSR', 'SPL'],
+    'Hybrid': ['BMS', 'uDSR', 'AIF', 'SINDy', 'SNIP']
+}
+
+# 创建数据框架
+df = pd.DataFrame({
+    'Method': methods,
+    'RMSE': rmse_values,
+    'Uncertainty': uncertainty,
+    'ParamSize': np.random.choice(param_sizes, len(methods))
+}).sort_values('RMSE', ascending=True)
+
+# 添加分类信息
+df['Category'] = df['Method'].apply(lambda x: next((k for k,v in method_categories.items() if x in v), 'Other'))
+
+# 颜色映射系统（学术级调色板）
+category_colors = {
+    'Symbolic': '#1f77b4',
+    'Neural': '#ff7f0e',
+    'Evolutionary': '#2ca02c',
+    'Physics-based': '#d62728',
+    'Hybrid': '#9467bd'
+}
+
+# 创建基础图表
+fig = go.Figure()
+
+# 动态尺寸计算系统
+size_min = np.log(df['ParamSize'].min())
+size_max = np.log(df['ParamSize'].max())
+sizes = 15 + 25 * (np.log(df['ParamSize']) - size_min) / (size_max - size_min)  # 动态尺寸范围
+
+# 添加主数据轨迹
+for category in df['Category'].unique():
+    df_sub = df[df['Category'] == category]
+    fig.add_trace(go.Scatter(
+        x=df_sub['RMSE'],
+        y=df_sub['Method'],
+        mode='markers',
+        name=category,
+        marker=dict(
+            size=sizes[df_sub.index],
+            color=category_colors[category],
+            opacity=0.9,
+            line=dict(width=1, color='black')
+        ),
+        error_x=dict(
+            type='data',
+            array=df_sub['Uncertainty'],
+            color='rgba(40,40,40,0.6)',
+            thickness=1.2,
+            width=10
+        ),
+        hoverinfo='text',
+        hovertext=df_sub.apply(lambda r: f"{r['Method']}<br>RMSE: {r['RMSE']:.3f} ± {r['Uncertainty']:.3f}<br>Params: {r['ParamSize']:,.0f}", axis=1)
+    ))
+
+# 动态轴范围计算
+data_min = (df['RMSE'] - df['Uncertainty']).min()
+x_min = max(data_min - 0.05, 0)  # 保证最小值不低于0
+x_max = 1.0
+
+# 专业级布局配置
+fig.update_layout(
+    title='Methods RMSE Comparison with Parameter Scale',
+    xaxis=dict(
+        title='Root Mean Square Error (RMSE) → Lower is better',
+        range=[x_min, x_max],
+        tickvals=np.arange(0, 1.1, 0.1),
+        gridcolor='#F0F0F0',
+        zeroline=False,
+        showspikes=True
+    ),
+    yaxis=dict(
+        categoryorder='array',
+        categoryarray=df['Method'].tolist(),
+        tickfont=dict(size=12),
+        showticklabels=False  # 禁用默认标签
+    ),
+    plot_bgcolor='white',
+    width=1100,
+    height=700,
+    margin=dict(l=180, r=50, t=80, b=40),
+    legend=dict(
+        title='Method Categories',
+        orientation='v',
+        yanchor="top",
+        y=0.98,
+        xanchor="left",
+        x=1.02
+    )
+)
+
+# 添加自定义y轴标签（分类着色）
+y_positions = np.linspace(0.03, 0.97, len(methods))  # 动态计算标签位置
+for idx, method in enumerate(df['Method']):
+    category = df[df['Method'] == method]['Category'].values[0]
+    fig.add_annotation(
+        x=0.01,
+        y=y_positions[idx],
+        xref='paper',
+        yref='paper',
+        text=method,
+        showarrow=False,
+        font=dict(
+            size=12,
+            color=category_colors[category]
+        ),
+        xanchor='right'
+    )
+
+# 添加专业级尺寸图例系统
+size_legend_values = [1e3, 1e4, 1e5, 1e6]  # 定义标准参数规模
+size_legend_sizes = 15 + 25 * (np.log(size_legend_values) - size_min) / (size_max - size_min)
+
+fig.add_trace(go.Scatter(
+    x=[0.82, 0.85, 0.88, 0.91],
+    y=[0.15, 0.20, 0.25, 0.30],
+    mode='markers',
+    marker=dict(
+        size=size_legend_sizes,
+        color='#444444',
+        opacity=0.8
+    ),
+    showlegend=False
+))
+
+# 添加尺寸图例标注
+size_labels = ['1K', '10K', '100K', '1M']
+for i, (x, y, label) in enumerate(zip([0.95]*4, [0.15,0.20,0.25,0.30], size_labels)):
+    fig.add_annotation(
+        x=x,
+        y=y,
+        xref="paper",
+        yref="paper",
+        text=label,
+        showarrow=False,
+        font=dict(size=10),
+        xanchor='left'
+    )
+
+# 添加最终标注
+fig.add_annotation(
+    x=0.98, y=0.02,
+    xref='paper', yref='paper',
+    text='叶璨铭 | Parameters (log scale)',
+    showarrow=False,
+    font=dict(size=10, color='#666666'),
+    bgcolor='white'
+)
+
+fig.show()

experiments/test.py

@@ -1,69 +0,0 @@
-import plotly.graph_objects as go
-
-# 示例数据（请替换为实际数据）
-methods = ['RSRM', 'PSRN', 'NGGP', 'PySR', 'BMS', 'uDSR', 'AIF', 
-          'DGSR', 'E2E', 'SymINDy', 'PhySO', 'TPSR', 'SPL', 
-          'DEAP', 'SINDy', 'NSRS', 'gplearn', 'SNIP', 'KAN', 'EQL']
-recovery_rates = [85, 78, 92, 88, 76, 83, 95, 81, 89, 77, 84, 86, 80, 
-                79, 82, 87, 75, 88, 90, 84]  # 恢复率百分比
-errors = [3, 4, 2, 3, 5, 2, 1, 3, 2, 4, 3, 2, 3, 4, 2, 3, 5, 2, 3, 2]  # 误差范围
-
-# 创建图形对象
-fig = go.Figure()
-
-# 添加带误差线的数据点
-fig.add_trace(go.Scatter(
-    x=recovery_rates,
-    y=methods,
-    mode='markers',
-    error_x=dict(
-        type='data',
-        array=errors,
-        visible=True,
-        color='#FF5733',
-        thickness=2,
-        width=10
-    ),
-    marker=dict(
-        size=12,
-        color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
-              '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',
-              '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',
-              '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5'],
-        opacity=0.8
-    )
-))
-
-# 设置布局
-fig.update_layout(
-    title='不同方法的恢复率比较',
-    xaxis=dict(
-        title='恢复率 (%)',
-        range=[0, 100],
-        dtick=20,
-        title_standoff=25
-    ),
-    yaxis=dict(
-        title='Methods',
-        title_font=dict(size=14),
-        tickfont=dict(size=12),
-        autorange="reversed"  # 使第一个方法显示在最上方
-    ),
-    hovermode='closest',
-    width=1000,
-    height=600,
-    showlegend=False
-)
-
-# 添加注释（可选）
-fig.add_annotation(
-    x=0,
-    y=0.95,
-    xref='paper',
-    yref='paper',
-    text='知乎 @x66ccff',
-    showarrow=False,
-    font=dict(size=10)
-)
-
-fig.show()