Add current fitness overlay visualization

Author: seofernando25

notebooks/experiment_analysis.ipynb

@@ -27,6 +27,16 @@
         "FINAL_TRAINING_DIR = REPO_ROOT / '.cache' / 'final_training'\n",
         "COLORS = {'RS': '#1f77b4', 'GA-STANDARD': '#ff7f0e', 'GA-MEMETIC': '#d62728', 'PSO': '#2ca02c'}\n",
         "\n",
+        "\n",
+        "def _exp_smooth(values: np.ndarray, smoothing_slider: float) -> np.ndarray:\n",
+        "    smoothed = np.empty_like(values, dtype=np.float32)\n",
+        "    last = values[0]\n",
+        "    for idx, point in enumerate(values):\n",
+        "        last = smoothing_slider * last + (1.0 - smoothing_slider) * point\n",
+        "        smoothed[idx] = last\n",
+        "    return smoothed\n",
+        "\n",
+        "\n",
         "def parse_experiment_name(exp_name):\n",
         "    if '-' not in exp_name:\n",
         "        return None, None\n",
@@ -258,6 +268,109 @@
         "plt.show()\n"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Current Fitness Overlays\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "SMOOTHING_SLIDER_CURRENT_FITNESS = 0.5\n",
+        "current_overlay = defaultdict(lambda: defaultdict(list))\n",
+        "\n",
+        "for exp_dir in sorted(EXPERIMENT_DIR.iterdir()):\n",
+        "    if not exp_dir.is_dir():\n",
+        "        continue\n",
+        "    model, optimizer = parse_experiment_name(exp_dir.name)\n",
+        "    if not optimizer:\n",
+        "        continue\n",
+        "    for run_dir in sorted(exp_dir.iterdir()):\n",
+        "        conv_path = run_dir / 'convergence.json'\n",
+        "        with open(conv_path) as f:\n",
+        "            conv = json.load(f)\n",
+        "        evaluations = conv.get('evaluations')\n",
+        "        current_fitness = conv.get('current_fitness')\n",
+        "        if evaluations and current_fitness:\n",
+        "            current_overlay[model][optimizer].append((evaluations, current_fitness))\n",
+        "\n",
+        "total_runs = sum(len(v) for model_data in current_overlay.values() for v in model_data.values())\n",
+        "print(f\"Loaded current-fitness traces for {total_runs} runs\\n\")\n",
+        "\n",
+        "models = sorted(current_overlay.keys())\n",
+        "# Prefer consistent optimizer ordering; fallback to sorted discovered optimizers\n",
+        "preferred_order = [\"RS\", \"GA-STANDARD\", \"GA-MEMETIC\", \"PSO\"]\n",
+        "observed_opts = sorted({opt for model_data in current_overlay.values() for opt in model_data.keys()})\n",
+        "optimizer_order = [opt for opt in preferred_order if opt in observed_opts]\n",
+        "optimizer_order += [opt for opt in observed_opts if opt not in optimizer_order]\n",
+        "\n",
+        "fig, axes = plt.subplots(len(optimizer_order), len(models), figsize=(6 * len(models), 3 * len(optimizer_order)))\n",
+        "if len(optimizer_order) == 1 and len(models) == 1:\n",
+        "    axes = [[axes]]\n",
+        "elif len(optimizer_order) == 1:\n",
+        "    axes = [axes]\n",
+        "elif len(models) == 1:\n",
+        "    axes = [[ax] for ax in axes]\n",
+        "\n",
+        "for row_idx, optimizer in enumerate(optimizer_order):\n",
+        "    for col_idx, model in enumerate(models):\n",
+        "        ax = axes[row_idx][col_idx]\n",
+        "        runs = current_overlay[model].get(optimizer, [])\n",
+        "        if not runs:\n",
+        "            ax.text(0.5, 0.5, 'N/A', ha='center', va='center')\n",
+        "            ax.set_xticks([])\n",
+        "            ax.set_yticks([])\n",
+        "            if row_idx == 0:\n",
+        "                ax.set_title(model, fontweight='bold')\n",
+        "            if col_idx == 0:\n",
+        "                ax.set_ylabel(optimizer)\n",
+        "            continue\n",
+        "\n",
+        "        base_eval = np.array(runs[0][0])\n",
+        "        aligned = []\n",
+        "        for evals, curr in runs:\n",
+        "            evals_arr = np.array(evals)\n",
+        "            curr_arr = np.array(curr)\n",
+        "            if len(evals_arr) != len(base_eval) or not np.allclose(evals_arr, base_eval):\n",
+        "                curr_arr = np.interp(base_eval, evals_arr, curr_arr)\n",
+        "            aligned.append(curr_arr)\n",
+        "        aligned = np.array(aligned)\n",
+        "        mean_curve = aligned.mean(axis=0)\n",
+        "        std_curve = aligned.std(axis=0)\n",
+        "        mean_smoothed = _exp_smooth(mean_curve, SMOOTHING_SLIDER_CURRENT_FITNESS)\n",
+        "        std_smoothed = _exp_smooth(std_curve, SMOOTHING_SLIDER_CURRENT_FITNESS)\n",
+        "        color = COLORS.get(optimizer, '#888888')\n",
+        "        ax.fill_between(base_eval, mean_smoothed - std_smoothed, mean_smoothed + std_smoothed,\n",
+        "                        color=color, alpha=0.2)\n",
+        "        ax.plot(base_eval, mean_smoothed, label=optimizer, color=color, linewidth=2)\n",
+        "\n",
+        "        # set per-model y-limits\n",
+        "        model_key = model.lower()\n",
+        "        if model_key == 'cnn':\n",
+        "            ax.set_ylim(0.40, 0.85)\n",
+        "        elif model_key == 'dt':\n",
+        "            ax.set_ylim(0.275, 0.36)\n",
+        "        elif model_key == 'knn':\n",
+        "            ax.set_ylim(0.225, 0.475)\n",
+        "\n",
+        "        ax.grid(True, alpha=0.3)\n",
+        "        if row_idx == len(optimizer_order) - 1:\n",
+        "            ax.set_xlabel('Evaluation ($n$)')\n",
+        "        if col_idx == 0:\n",
+        "            ax.set_ylabel(f'{optimizer}\\nCurrent Fitness')\n",
+        "        if row_idx == 0:\n",
+        "            ax.set_title(f'{model}', fontweight='bold')\n",
+        "        ax.legend(fontsize=8, loc='best')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},