update notebook labels

Author: seofernando25

notebooks/experiment_analysis.ipynb

@@ -95,17 +95,13 @@
         "for ax, model in zip(axes, models):\n",
         "    optimizers = sorted(hpo_grouped[model].keys())\n",
         "    data_to_plot = [hpo_grouped[model][opt] for opt in optimizers]\n",
-        "    all_values = [val for sublist in data_to_plot for val in sublist]\n",
         "    \n",
-        "    if all_values:\n",
-        "        y_min, y_max = np.percentile(all_values, [2, 98])\n",
-        "        ax.set_ylim(y_min - (y_max - y_min) * 0.1, y_max + (y_max - y_min) * 0.1)\n",
         "    \n",
         "    bp = ax.boxplot(data_to_plot, tick_labels=optimizers, patch_artist=True)\n",
         "    for patch in bp['boxes']:\n",
         "        patch.set_facecolor('lightblue')\n",
         "    \n",
-        "    ax.set_title(f'{model} - Final Fitness (HPO)', fontweight='bold')\n",
+        "    ax.set_title(f'{model}', fontweight='bold')\n",
         "    ax.set_xlabel('Optimizer')\n",
         "    ax.set_ylabel('Composite Fitness')\n",
         "    ax.grid(True, alpha=0.3)\n",
@@ -180,7 +176,7 @@
         "               f'{val:.4f}', ha='center', va='bottom', fontsize=9)\n",
         "    \n",
         "    ax.set_ylim(0, 1)\n",
-        "    ax.set_title(f'{model} - Test Performance', fontweight='bold')\n",
+        "    ax.set_title(f'{model}', fontweight='bold')\n",
         "    ax.set_xlabel('Optimizer')\n",
         "    ax.set_ylabel('Composite Fitness')\n",
         "    ax.grid(True, alpha=0.3, axis='y')\n",
@@ -252,8 +248,8 @@
         "        ax.fill_between(generations, mean_curve - std_curve, mean_curve + std_curve, \n",
         "                       color=color, alpha=0.2)\n",
         "    \n",
-        "    ax.set_title(f'{model} - Convergence', fontweight='bold')\n",
-        "    ax.set_xlabel('Generation')\n",
+        "    ax.set_title(f'{model}', fontweight='bold')\n",
+        "    ax.set_xlabel('Evaluation Count ($n$)')\n",
         "    ax.set_ylabel('Best Fitness')\n",
         "    ax.legend()\n",
         "    ax.grid(True, alpha=0.3)\n",